emmc-csa d4.7 m30-vfinal-pu-web · secondary results include the gained expe rience in using the...

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in parts, except with prior written consent of the EMMC-CSA consortium.

EMMC-CSA European Materials Modelling Council

Report on Translation case studies

describing the gained experience (obstacles, etc.)

TABLE OF CONTENT

1. EXECUTIVE SUMMARY ........................................................................................................................... 2

1.1 Description of the content and objectives ............................................................................................... 2

1.2 Major outcome ........................................................................................................................................ 3

2. PROGRESS REPORT (MAIN ACTIVITIES) ................................................................................................... 3

2.1 Collection of the Translation case studies .............................................................................................. 3

2.2 Translation case studies ......................................................................................................................... 4

2.3 Analysis of results .................................................................................................................................. 9

3. CONCLUSIONS .................................................................................................................................... 11

4. REFERENCES ...................................................................................................................................... 12

5. ANNEX ................................................................................................................................................ 14

The EMMC-CSA project has received funding from the European Union‘s Horizon 2020 research and innovation programme

under Grant Agreement No 723867

EMMC-CSA – GA N°723867 Report on Translation case studies describing the gained experience (obstacles, etc.)

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1. Executive summary

1.1 Description of the content and objectives

This report describes the activities carried out within task collecting Translation case studies (by compiling the EMMC Translation case template and using the EMMC Translators guide), analysing the progress and results and describing the gained experience. Efforts were especially spent to find cases of translation carried out by or for Small Medium Enterprises (SMEs1). In order to boost the utilization of simulation tools by the industry/end-users, it is crucial to show the economic benefits that can be created. Successful use cases of materials modelling in industry, including SMEs, are one way of displaying the potential of material models as a tool for solving problems in the production process or related to the product quality. The suggested (modelling) solution can be either research-based (requiring a long-term project) or more "operations"-oriented (shorter time span/duration or simpler tools). The need for use cases has been expressed recurrently by stakeholders during EMMC workshops (including the recent Workshop on Industrial views and needs for translation in Eindhoven, December 2018; International Workshop in Vienna, April 2017 and February 2019; Expert Meeting on Training requirements for translators in Hamburg, March 2019) as well as in the last EMMC and Marketplace surveys. In the context of EMMC, translation is the process of translating industrial problems into questions to be solved using modelling and simulations tools for, e.g., creating industrial innovation. The role of the translator is to advice the end-user on the defining viable possibilities to integrate modelling to solve the industrial issue, propose modelling workflows as related to the manufacturing problem and available data, time and resources (knowledge/competencies, SW tools, people) as well as limitations. Translator is the multi-professional specialist who can estimate the modelling project effort and possible outcome. The translators must listen to the needs of the end-user and suggest, to their knowledge, optimal workflows and tools, being neutral and confidential, and connect the end-user with the executor of the modelling. In order to suggest feasible workflows, translators need to have a good technical understanding of the modelling tools together with knowledge of the industrial context and of the time and budget constraints. The EMMC has developed tools such as the Translation case template2, the Translators' guide3, the Review of Material Modelling4 (RoMM) and the standardization of modelling simulation by MOdelling DAta5 (MODA) that can support the job of the translator and facilitate the communication between the translator and the end-user.

1 For a complete definition of the term SME, according to the Euopean Commission, see the following link: https://ec.europa.eu/growth/smes/business-friendly-environment/sme-definition_en

2 https://emmc.info/emmc-translation-case-template/

3 https://emmc.info/wp-content/uploads/2018/01/Translators-Guide.pdf

4 https://emmc.info/version-6-of-the-romm-is-now-available/

5 https://emmc.info/moda-workflow-templates/


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1.2 Major outcome

The major outcome of this activity has been to produce 17 use cases of translation that display the use of materials modelling in industry, also SMEs, and highlight the benefits. The report contains two parts:

- 17 translation case studies, 10 of which refer to SMEs (Annex 1). - A report analysing the process of collection and compilation of the case studies, as well as an analysis of

the results. Secondary results include the gained experience in using the EMMC tools (translation case template, translators guide, RoMM, MODA) for supporting the figure and the job of the translator. This may translate into further suggestion for future use of these tools, for e.g. training session of translators.

2. Progress report (main activities)

2.1 Collection of the Translation case studies

Selection and establishment of use cases

The translators that retrieved the translation case and compiled the template were found via personal professional contacts obtained during previous or current collaboration by SINTEF, ACCESS, DPI, HGZ, POLITO, MD and with the help of the involved WP partners, as well as by the involving translators to present their professional expertise at the EMMC events.

The cases were selected to give a good representation of all types of translators: Software Owners, Academic groups, Research Institutes, Consultancy, Manufacturing (internal translators in the company). A good representation of the clients (large and small-medium enterprises) was also sought for.

Progress: obstacles and feedback

Efforts and time were spent to get the translators acquainted with the EMMC concepts and terminology (e.g. translation, etc.), and explain the task for finding a good case and filling out the template: this slowed down the process of collecting translation cases. During the communication it was also done some work to connect the concept of translation with concrete tasks and competencies to create a link between theory and practice.

Even if several EMMC stakeholders have made explicit that seeing successful cases will increase the attractiveness of materials models for companies, the disclosure of customer (by the translator) is still the major obstacle in finding (and making public) cases. In particular, the confidentiality of details of the cases (such as the industrial and technical problem of the company, the details of the model, the use of the model and the benefits) hindered the collection process. In some of the collected cases the translator could not disclose the name of the client. When the use case referred to a modelling case already published (in scientific or popular science articles) or that was carried out more than 20 years ago, it was easier to obtain information.

The translators (outside the CSA) that kindly collaborated with the EMMC Translation Team and provided us with translation cases have expressed some difficulties in filling in the template:

- The EMMC terminology is not yet spread. - Quantifying the benefits was difficult: in many cases the translator is not able to quantify the return on

investment of the company, not even an overall value because he/she does not know these details before/during the translation, or he/she is not informed by the client on this aspect after the translation. In some cases, after the work, the customer communicates to be satisfied with the solution or that he/she will employ the recommendations indicated by the result of the modelling and simulation work. In some cases, after the translation and before the modelling job is commissioned, it is the client that estimates if the material modelling work is worth the investment.

- The translation process has some steps, which are described in the Translation guide, and is usually iterative. Translators should obtain high communication skills and contact the industrial client not only once during the modelling project. It is difficult to document and evaluate the real translation efforts.


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2.2 Translation case studies

The Translation Case Template include several sections with information about translator and client, industrial/business case, translation to modelling solution, evaluation of the translation case and client's benefits. Translator profile (type, size) A total of 17 cases was collected with translators from:

Research institute (7) Academic group (1) Software owner company (4) Manufacturing industry (1) Other (Consultancy, etc.), (4)

Six cases belong to two partners of the CSA project. Client (business, size) The clients' business areas span very diverse industrial segments. Ten cases refer to SMEs. Relationship between client and translator In most cases the translator had previously worked with the client. Some additional peculiarities are discussed here. In the case provided by the academic group (University of Bergamo), the client is a provider of consultancy service: they cooperated to establish a new design approach supported by modelling and analytical calculations. The final clients are energy companies and need the design of current cathodic protection. In this case the engineering consultancy company acts as a bridge between the needs of the end-user and the expertise available in academia. The academic group has supported the engineering company in building knowledge of modelling and modelling tools. The case provided by Nanolayers Research Computing refers to a consortium of large and small companies and academic research partners as "clients", in a EU funded project: in this respect it is a special case of translation where time was spent also to create common understanding of the problem and a common language. It may be looked at as a case of multiple and parallel translation examples. The initiation of the project in several use cases was driven by the supplier company (of goods, i.e. manufacturer; or of services, i.e. consultant) and its wish to deliver a better product to the end-user. The case by Continental is a case of internal translation. Industrial/business case The industrial problems of the use cases refer often to a need of better understanding the production process and its effect on the final product. The wished outcome is recommendation on how to increase the productivity (process optimization) and/or increase the (resulting) product quality. The duration of the projects of the cases varies largely from 8 weeks to 2 years. If the task relies on existing and consolidated models, shorter time is needed. Longer project duration corresponds to cases where research is needed for assessing the use of the model for the application and for further development. A summary of the content of the Translation use cases is reported in Table 2.

Table 1 – List of collected translation case studies.6


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Translator Customer Company name

Country Type of translator

Number of employees

Company name

Main business area

Size

TelTek7 (now SINTEF)

Norway research institute

30 BASF Chemical See note8

TelTek7 (now SINTEF)

Norway research institute

30 Undisclosed Paints and coatings

Large

Nanolayers Research Computing9

UK SW consultant and developer

4 Consortium of SMEs and academic res.inst.s

Electronic manufacturers

SMEs

Access e.V.10 Germany SW owner, consultant and developer

50 Undisclosed Power engineering

Large

Access e.V. Germany SW owner, consultant and developer

50 Undisclosed Steel Large

Access e.V. Germany SW owner, consultant and developer

50 Undisclosed Material supplier and semi-finished products

Large

DYNAmore GmbH11

Germany SW consultant company and SW co-developer and distributor

85 Generic automotive, aeronautical industry, process engineering

from OEM12 to SME

University of Bergamo13

Italy Academic group

8 Cescor SrL Engineering services for the energy sector

SME

SINTEF14 Norway Research institute

2000 Undisclosed Tool manufacturer

SME

SINTEF Norway Research institute

2000 Undisclosed Material producer

SME

SINTEF Norway Research institute

2000 Raufoss Technology

Aluminium forming

SME

7 http://www.tel-tek.no/eng/ Note: TelTek was a technical-industrial R&D institute at the time when the translation work of the use case was carried out; now it is part of SINTEF, which is a large research institute.

8 When the translation was carried out, the client was a small company with another owner company than today. Currently it is owned by BASF.

9 http://www.nanolayers.com/index.php

10 http://www.access.rwth-aachen.de/

11 https://www.dynamore.de/en

12 OEM stands for Original Equipment Manufacturer.

13 https://en.unibg.it/

14 https://www.sintef.no/en/


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IPC15 France Technology Transfer Institute

100 MOPLA produce plastic injection parts, as well as molds and prototypes

SME

IK4-Tekniker16 Spain Research Organization (TTI)

278 SAPA PLACENCIA

Defense and Energy

SME

Nanomatch17 Germany SW company 3 Undisclosed develops and manufactures organic electronic materials and device

Large

Property18 Vectors

USA Consulting company

1 Generic large semiconductor company

Large

Property Vectors

USA Consulting company

1 Undisclosed Defense contractor

SME

Continental Tires19

Germany Internal 243000 Continental Tires

Manufacturing Large

Table 2 – Industrial/business cases and proposed material modelling solutions.6

Translator Customer Translation case Company name

Company name

Main business area

Industrial/business case Modelling tool/solution

TelTek (now SINTEF)

BASF Chemical Large variation of product quality between batches

Chemometrics

TelTek (now SINTEF)

Undisclosed Paints and coatings

Production variations (product quality, produced scrap, energy usage)

Chemometrics methods and multivariate data analysis

Nanolayers Research Computing

Consortium of SMEs and academic res.inst.s

Electronic manufacturers

Design of electronic devices by Combination of electronic and atomistic models

Access e.V. Undisclosed Power engineering

Insufficient properties of welded components (stray grains formation)

Continuum models: phase-field coupled to thermodynamics

Access e.V. Undisclosed Steel Different risk of hot-tearing for similar alloys

Continuum models: phase-field coupled to thermodynamics

15 https://ct-ipc.com/

16 https://www.tekniker.es/en

17 http://www.nanomatch.com/

18 http://www.propertyvectors.com/

19 https://www.continental-tires.com/car


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Access e.V. Undisclosed Material supplier and semi-finished products

Insufficient properties of semi-finished parts (stray grains formation)

Continuum models: phase-field coupled to thermodynamics databases, models for fluid flow, analytical models

DYNAmore GmbH

Generic automotive, aeronautical industry, process engineering

Need for a more predictable model to describe the deformation and fracture behaviour of polymer based unreinforced materials

Implementation of new constitutive model

University of Bergamo

Cescor SrL Engineering services for the energy sector

Need for modelling tool to support the design of cathodic protection for above ground tanks for storage of liquid hydrocarbon

Modelling by commercial multiphysics software

SINTEF Undisclosed Tool manufacturer

Need to understand the source of surface defects on galvanized goods as well as the efficiency of the applied surface oxide removing flow device

Computational flow modelling by a 3D CFD code

SINTEF Undisclosed Material producer

Product degradation problem (during production of magnetite severe clogging happened in a hopper)

Computational flow modelling by a 3D CFD code

SINTEF Raufoss Technology

Aluminium forming

Need for a more advanced hardening and damage model for misuse incidents of components

Development and implementation of hardening and fracture model and calibration of the model for different materials

IPC

MOPLA produce plastic injection parts, as well as molds and prototypes

A molding tool used to produce body actuator parts came to its end of life. The objectives were to develop a more efficient version of the tool regarding productivity and quality using laser melting technology

In-house modelling methodology to help designing cooling system for injection molding

IK4-Tekniker SAPA PLACENCIA

Defense and Energy

Measure and model the tensional and microstructural state of gears at the different manufacturing steps (forging, normalizing, quenching and tempering), as well as to perform a dimensional control of them, in order to determine the origin of distortions of parts, which are one of the major causes of rejection

A continuum model based on FEM simulations of heat treatment processes on forged gears


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Nanomatch Undisclosed develops and manufactures organic electronic materials and device

Lack of a single model/module that computes properties of complex multilayer devices, solely based on information on the chemical compounds that are used in the devices

Multiscale approach

Property Vectors

Undisclosed Large semiconductor company

Help identify a new or optimized material that can survive the varied processing steps and be manufactured at a very low defect level

Atomic-scale materials and chemistry modeling

Property Vectors

Undisclosed Defense contractor

Looking to set out to meet a request for a new device that is more portable, lasts longer, and requires less collateral to keep at the optimum temperature that is higher than the operating temperature for current devices for a wide variety of defense applications

Plane wave density functional theory

Continental Tires

End user of Continental Tires

Manufacturing Understanding the effect of polymer-filler interface characteristics on the energy dissipation in the material to improve tire performances

Viscoelastic and mechanical properties of polymer nanocomposites, discrete models

Modelling solution Types of models: A variety of models were used in the collected cases, depending on the industrial problem at hand, the time allocated for designing the solution and delivering the results, and the industrial expertise of the translator. Material modelling competence: In most cases involving SMEs as client, the competence of materials modelling of the client is not very extensive. The modelling workflow and solution in all cases involve a tight cooperation between the translator, the modeller and the experts (of the production process, usually) in the client company. Clients' benefits See section 2.3.

Evaluation of the translation case In most cases the modelling results helped the customers solving their problem and the suggested recommendations were implemented by the client. From these cases it is hard to conclude upon the follow up of the modelling work: does the modelling work leads to more projects and modelling? Small companies often need to efficiently take on to the next problem so they might change their focus quickly.


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When groups with different expertise are involved, such as in large projects with several partners, time is needed for agreeing on a common terminology and for understanding each other expertise. Time is also needed to interpret the results that must be transferred between groups. In cases where modelling activities are used for solving SMEs problems, bottlenecks may be limited resources from the client ("people are busy"), limited expertise of the client in materials modelling, time allocation for establishment of agreements and contracts.

2.3 Analysis of results

The objective of this activity is to prepare translation cases that can show the added value of using materials modelling by the industry, especially by SMEs. A good translation is a very important step: it contributes to the successful use of materials models by the industry. Analysing how translation is carried out may highlight aspects that the translators should focus on when discussing with a client. The following analysis is based on the collected translation cases and feedback and discussions during EMMC meetings with all stakeholders. Added value of materials modelling for the client: benefits and impact Goldbeck and Court (2016) analysed potential measures of added value of using materials modelling and the identified key performance indicators (KPI) are summarized in Table 3.

Table 3 – Qualitative benefits (Goldbeck G. and Court C., 2016).

Key performance indicator More efficient and targeted exploration Deeper understanding Broader exploration R&D strategy development Source of property data Trouble shooting Performance optimisation Intellectual property protection Value chain benefits Improve communication and collaboration between R&D and production Upscaling and market introduction as well as marketing benefits

Further, typical benefit categories as identified by a study organized by The Minerals, Metals and Materials Society on implementing ICME (Integrated Computational Materials Engineering) in the Aerospace, Automotive, and Maritime Industries (2013) include • Decreased testing requirements • Reduced risk, time, and iterations for the materials and process development • Elimination or reduction of costly traditional product iterations • Guide patent development and even reverse engineer product patents for existing products.


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Figure 1 Tentative evaluation of KPI relevant for the collected case studies.

For each case the most relevant KPI (one or several) from the list provided in Table 3, was assigned in a qualitative manner (by either the person that filled in the case or by the author of this report). Because of this, it is retained most interesting to present the aggregated results, see Figure 1, and have a generic discussion about them. However, it is possible to say that in most translation cases the modelling work brought forward a solution to a well-defined problem and better understanding of the raw material-process-product relations (benefit). It thus supported the product development process towards an improved quality of the final product (impact). It allowed to measure, at least in terms of trends, the effect of the variables involved in the production process on the final product. More accuracy of models leads to better control of material or process parameters and allows to control their effect on the final product. General impact of the modelling work is cost savings. In the case provided by Dynamore, societal gain is also a benefit: the improved material models help achieving better predictions and generate increased safety for people (see Dynamore case). In general, it was difficult for the translator to quantify the benefits that the material modelling work created for the client. One reason is that the translator and modellers are not usually involved in further evaluation of the use of the models, after the activity is completed, due to confidentiality of economical data. In some cases, e.g. the case by Nanomatch, the results from the modelling may impact market share and sales over time so it is hard to make an estimate. However, a quantitative analysis of the involved costs and the benefits brought by the use of material models, and a calculation of the ROI (Return On Investment) were provided in the case by IPC. IPC used an economical argument, in addition to the technical one, to convince the customer of using modelling for design optimization (see use case for more details).


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The translation case template is a tool for supporting translators in communicating with the client as well as for increasing the visibility and credibility of the translators. The translator can use the translation case documentation as a reference for their expertise and share it with other translators or potential clients. Moreover, the translation case template could be used to document (keep track on) the translator’s own activity and external relations. From the feedback received on the translation case template and its use it emerges that:

The template is well organized, but more work needs to be done to spread the terminology among the translators (translation and the steps of translation as presented by EMMC in the translators' guide). Collecting and arranging the information of a translation case into the layout/framework of the template was an exercise for the translators that made them think of the translation process in a new way: it helped structure the steps and give them a name.

All the steps of the translation process as suggested by the EMMC are present in the translation process, but sometimes the order is different, and sometimes some tasks are carried out jointly.

Translators wish for focused workshop with hands-on sessions where they build (translation) cases using the template. This can help the translators to be more aware of the parts involved in the translation process, e.g. without being too unbalanced towards sales such as for SW owners or towards doing basic research like for academic groups.

Translators still need to increase their knowledge on the economic impact of the modelling activities as related to business decisions. For the time being the translators' job is more focused on finding the proper modelling solution to propose.

By discussion with the translators it emerges that clients (i.e. companies that use translator services) are still searching for help for specific problems and usually prefer to contact somebody they know (for their professional expertise) and that is geographically located close to them. In one of the collected use cases (SINTEF/Raufoss Technology), the MODA of the modelling work was also provided. MODA is a tool that allows to describe the modelling workflow and distinguish between the components of a modelling case: the issue to be solved and the employed physics equations, numerical solver, and post-processing. An example of the workflow of the modelling work done in this case is depicted in Figure 2.

Figure 2 Modelling workflow for the use case SINTEF/Raufoss Technology.

3. Conclusions The gained experience from the collection of translation cases is summarized.

The figure of the translator is recognised by all stakeholders of material modelling but often it is called differently (business developer, project manager, etc.). Giving it a common name and identifying the steps of translation helps defining the skills and competencies this figure should have.


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By knowing the competencies necessary for the translator, universities can shape courses so that this figure can be more easily built. However, the translation education covers multi-focused and multi-disciplinary training, which could be realized for graduate/PhD students with special programs, including the business and industrial problem understanding as well as the modelling and experimental techniques.

As regards training of translators, it has been discussed that material modelling translators, nowadays, have a strong background in materials modelling and then learn on the job (e.g. talking to clients for external translation and talking to colleagues in different departments for internal translation).

Many useful tools have been produced by the EMMC (translation case template, RoMM, MODA) that can create common terminology and ease communication between the translator and clients, research partners, or between work departments: more work needs to be done to spread them and their use in Europe.

SMEs' use of material modelling is focused to solve practical problems; therefore, models need to be mature enough as well as the tools. The level of competence in material modelling in the SMEs varies a lot (since they often have not got a specialized person focused only on this narrow competence area) hinders the knowledge on the potential of material models. Cooperation between SMEs and research partners should be facilitated by e.g. limiting the risk of the company in trying new tools. Trust in material modelling from the companies may be built by letting the researcher/modeller/developer learn about the company's business: this will give him/her an insight on the constraints dictated by the (daily) management of production.

Cooperation between industry and research environment still depends on trust built on historical cooperation and knowledge of published scientific work. It may be helpful investigating the following topics for finding ways to facilitate the contact between companies (closer to operations) and model developers (closer to research tools) and for increasing the use of material models in industry: o schemes used in different countries to financially support research and innovation for companies

and SMEs: how they work and what is the outcome; o forms of aggregation (e.g. clusters) for generation of research and for facilitating contact between

industry and research partners (academia, research institutes, etc.);

Ways to facilitate cooperation between industry and research partners may be: Internship of researchers into companies (to learn about how companies work and what

problems they work with daily); New arenas for interaction (funding schemes that require both industrial and research partners

to join the project, for example)

The Translation case template could include the list of KPIs to facilitate a more quantitative evaluation of the materials modelling's benefits and impact for the client.

4. References G. Goldbeck and C. Court "The economic impact of materials modelling" EMMC 2016 Integrated Computational Materials Engineering (ICME): Implementing ICME in the Aerospace, Automotive, and Maritime Industries. A study organized by The minerals, Metals & Materials Society, Warendale, PA 15086 (2013).


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Contributing partners ACCESS, HZG, DPI, POLITO, GCL, MDS

EC-Grant Agreement 723867 Project acronym EMMC-CSA

Project title European Materials Modelling Council - Network to capitalize on strong European position in materials modelling and to allow industry to reap the benefits

Instrument CSA Programme HORIZON 2020 Client European Commission Start date of project 01 September 2016 Duration 36 months

Consortium TU WIEN Technische Universität Wien Austria

FRAUNHOFER Fraunhofer Gesellschaft Germany

GCL Goldbeck Consulting Limited United Kingdom

POLITO Politecnico di Torino Italy

UU Uppsala Universitet Sweden

DOW Dow Benelux B.V. Netherlands

EPFL Ecole Polytechnique Federale de Lausanne Switzerland

DPI Dutch Polymer Institute Netherlands

SINTEF Stiftelsen SINTEF Norway

ACCESS e.V. ACCESS e.V. Germany

HZG Helmholtz-Zentrum Geesthacht Zentrum für Material- und Küstenforschung GMBH

Germany

MDS Materials Design S.A.R.L France

QW QuantumWise A/S Denmark

GRANTA Granta Design LTD United Kingdom

UOY University of York United Kingdom

SINTEF SINTEF AS Norway

UNIBO ALMA MATER STUDIORUM – UNIVERSITA DI BOLOGNA Italy

SYNOPSYS Synopsys Denmark ApS Denmark

Coordinator – Administrative information Project coordinator name Nadja ADAMOVIC Project coordinator organization name TU WIEN

Address TU WIEN | E366 ISAS | Gusshausstr. 27-29 | 1040 Vienna | Austria

Phone Numbers +43 (0)699-1-923-4300 Email [email protected] Project web-sites & other access points https://emmc.info/

Authors Micol Pezzotta, Jesper Friis (SINTEF)


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5. Annex Annex 1: Case studies

EMMC Translation Case: TelTek/SINTEF

Introduction

Translator Profile • What type of Translator is your institution: TTI (Technology Transfer Institute), Academic

group, Software Company, Manufacturing Industry, Other (Consultancy, etc.).

TelTek is a technical-industrial R&D institute and now is part of SINTEF, which is a large research institute.

• What is you field of expertise: specify type of material, type of models according to RoMM

(please see Review of Materials Modelling), type of property/phenomenon, other?

At the time the methodology was developed TelTek was organized into four groups specialized respectively in Powder technology, Gas technology, Technology centre and Innovation. From 2018 TelTek is a part of SINTEF. The Smart Production method belongs to the Innovation group's field of expertise and focus on product and production optimization. Elements from several fields were employed, i. e., industrial economics, process chemistry, process technology, analytical chemistry, instrumentation, multivariate data analysis, simulation and modelling. The method helps reducing undesired variation in a process and product's quality; such undesired variation is not defined a priori but it is chosen case by case. Non-optimal production in this context means production that does not exploit in an optimal way raw material and energy, implying therefore non-optimal cost efficiency.

Client • Who is the client? Is the client a large company, SME or a consortium thereof?

The client is a large company and the product under study is a binding agent/adhesive.

• Which value chain segment (e.g. material producer, convertor, end-user) it is positioned in?

It is a convertor.

• Did you have existing collaboration with the client?

No.

Industrial/Business Case

• Describe briefly the industrial problem.

The production of binding agent is partly an esterification process which takes place at about 250 °C where alcohol, an acid and vegetable oil becomes a kind of polyester, i. e., a binding agent. The reaction occurs in 12 ton sized reactors and the yearly production capacity is about 15000 ton binding agent. Most companies in the processing industry face costly and undesired variations in their production. Examples of production variations are variations in the quality of the product or the capacity of the production, in the profit, in the scrap quantity, or in the energy usage in the production process. Usually the industry methodologies such as "Toyota Production Systems" and "Lean" production do not consider the reasons for undesired variations, especially if they are complex. By using model-based analyses of the

https://publications.europa.eu/en/publication-detail/-/publication/ec1455c3-d7ca-11e6-ad7c-01aa75ed71a1/language-en/format-PDF/source-search


product and production process competencies the origins of the variations can be found and understoodi.

• Indicate involved budget or preferred time to solution (duration).

This was a small project performed around 2010. The "Smart Production" project was started later (2011-2014).

• Indicate what was the expected outcome of the translation process.

The translation process' aim is to build a tool for measuring real time, on the production line, the value of parameters and use this input for predicting the quality of the final product. The method helps identifying the chemical process' variables which mostly affect the production: by this it is possible to optimize the production process and obtain a product of much more standardized/homogeneous properties.

Translation to modelling solution

• What type of model(s) did you propose and use? Explain arguments and criteria used to propose and choose a specific modelling approach and modelling executor for the specific industrial problem.

o Include inventory and data quality assessment. Was it necessary to realize dedicated experiments prior to simulation? Describe the required validation steps.

o Were model accuracy and necessary investments discussed? If so – please describe.

o Who made the final choice for the model and for the modelling executor? Based on which criteria?

o Explain the involvement of the client in the case.

"Smart production" is a generic strategy with 8 steps to define the problem and the improvement potential, identify the involved variables and setup the model-based simulations for the optimization process. For the process industry and in particular the client, chemometrics methods and multivariate data analysis were used.

Customer specifications and properties of final product are needed data for this method and are usually easily available in a (process) company. The choice of variables affecting the most the final product quality, was a result of discussion with experts in the production in the client company and multivariate analysis. At the end of the analysis it was suggested to implement Near-infrared spectroscopy (NIR) instrumentation for a quicker (online) evaluation of the parameters at the beginning of the production process.

Client’s benefits from the modelling

• How did the client use the modelling results? • What were the benefits for the client of using modelling? Give a semi quantitative or

qualitative estimate (For more information on the types of client benefits and how to


estimate quantitative impacts, please see the attached document or Economic Impact of Materials Modelling).

The model shall reduce the undesired variations in the industrial production process and improve the quality of the products manufactured. The production time have been decreased and the final quality of the product (binding agent) can be controlled and is more predictable. The model allowed the client company to improve the quality of the product (i. e., more homogeneous and predictable), to reach more predictable production times, and to increase by 15 % the production capacity.

Figure 1 Distribution of one of the chosen quality parameters before (top) and after (bottom) the application of the smart production analysis. The range of desired specifications is indicated by the red linesii.

Evaluation of the translation case

• Indicate eventual bottlenecks encountered in the translation process or any suggestion for improvement of the process.

No particular bottlenecks were encountered.

i Hugo Ryvik "Vil produsere smartere" Industrien, Mars 2010. In Norwegian. ii F. Brakstad, R, Arneberg, N.H. Myhre, T.-G. Abrahamsen "Smart produksjon-en praktisk tilnærming til prosessoptimalisering" Kjemi, 2014. In Norwegian.

https://mafiadoc.com/the-economic-impact-of-molecular-modelling-of-wordpresscom_598ea2f51723ddcf69a3e933.html



Introduction


group, Software Company, Manufacturing Industry, Other (Consultancy, etc.).

TelTek is a technical-industrial R&D institute and now is part of SINTEF, which is a large research institute.

• What is you field of expertise: specify type of material, type of models according to RoMM


At the time the methodology was developed TelTek was organized into four groups specialized respectively in Powder technology, Gas technology, Technology centre and Innovation. From 2018 TelTek is a part of SINTEF. Multivariate methods are employed: these use tools from mathematics, statistics, and information theory for obtaining hidden information from large amount of data.


The client is a company that produces fish oil in Omega 3-capsules. The company was owned by Hydro (www.hydro.com) at the time this method (multivariate analysis) was introduced and now is owned by BASF.

• Which value chain segment (e.g. material producer, convertor, end-user) it is positioned

in?

The BASF owned company is a convertor.

• Did you have existing collaboration with the client?

No.

Figure 1 The company produces omega 3-capsules from sardine oili.





The company's plant in Sandefjord (Norway) has produced concentrate with omega 3-fatty acids by refining fish oil for more than 20 years. After the company obtained a patent on the preparation Omacor, the demand worldwide was overwhelming. The variation of the product quality was large between batches. The variation was associated to the production process, as the production came from the same raw material, i. e., sardine oil. Measurements of process data and results from analyses were not able to reveal the reasons for such variations.

• Indicate involved budget or preferred time to solution (duration). Budget 150 kNOK, time, 8 weeks duration. • Indicate what was the expected outcome of the translation process. The quality of the product could be stabilized and the effect of the quality of raw materials on the final product could be understood.


• What type of model(s) did you propose and use? Explain arguments and criteria used to propose and choose a specific modelling approach and modelling executor for the specific industrial problem.




o Explain the involvement of the client in the case. Hydro, the owner of the company at that time, had researchers dedicated to chemometrics and had used successfully multivariate methods to improve the production in other plants. Tel-Tek with chemometrics expertise was contacted and the methods proved soon to be very helpful. New equipment was not needed, apart from software. The data used by the method were already collected by the company but in different places in the production process and different laboratories. Historical data were collected from different sources (papers in folders, logging books, and production files) and input into spreadsheets. The purity of the product was defined as the result of the analysis. The combination of data never associated before and the analysis of the result from each step of the process separately helped understanding the effect of each process step on the quality of the final product. A quantitative model was created which allowed the process operator to input the properties of the raw materials and simulate the process step for calculating the final product quality. This way it was possible to steer the raw materials in the process for obtaining the desired quality of the final product. By feeding more information on the raw material of several batches the model improves its predictive capabilities. Additionally, more efficient and precise methods of analysis of the fish oil were introduced: a Fourier-transform infrared spectroscopy (FTIR) instrument was bought by the company. This method gives the molecular structure which in turns give much quantitative information and it is a quick



• How did the client use the modelling results? • What were the benefits for the client of using modelling? Give a semi quantitative or

qualitative estimate (For more information on the types of client benefits and how to estimate quantitative impacts, please see the attached document or Economic Impact of Materials Modelling).

The use of this model allowed first process predictability and then process optimization which lead to increase of productivity. The company was bought in 2003 by Ferd for 450 millions NOK which was earned back just slightly after one year.

Figure 2 Quality of the products by the company before (left) and after(right) the optimization of the process by using multivariate analysis. The chart illustrates the product purity level versus time. The red line indicates the target level of qualityi.

i M. Valestrand "Gullegget klekket ved hjelp av kjemometri" Prosessindustrien, September 2005.



EMMC Translation Case: Nanolayers Res. Comp.

Introduction

Translator Profile Materials Modelling Consultancy and Scientific Data Management Expertise: Amorphous Materials, Nanotribology, Machine Learning in Modelling, Virtual Atomic Force Microscopy Type: Atomistic and Electronic modelling using a range of simulation techniques.

Client The clients were a consortium of large companies and SMEs in addition to academic research institutes. We aimed to solve problems at the manufacturing level. In particular, we aimed to produce more reliable devices.


The production of electronic devices relies heavily on technology computer aided design (TCAD). These tools are usually packaged into software that are built upon mathematical models of electrical phenomena. Over the past decades, the models used were based on empirical or phenomenological models. However, electronics manufacturers, due to the decreasing physical dimensions of devices crossing over into the realm where quantum effects become important, have recently reached a junction where these models are no longer satisfactory. They therefore require solutions that incorporate highly accurate physical models to describe electronic phenomena at the nanoscale to be able to design and produce reliable devices The main source of funding came from the European Union to support the entire consortium. As such, the time to develop a coherent solution between all the groups was over the duration of the 5 year project. The expected outcome, which due to hard work and excellent collaboration between the groups was achieved, was the development of models which were fundamentally supported by highly accurate atomistic simulations. The parameters extracted from these simulations were then used in models that were then implemented into higher level TCAD models. Therefore, the electrical engineers designing new devices had much more accurate models describing the plethora of processes that occur while also having a much better understanding of the chemical processes that occur within the device and lead to reliability issues.


Our solution used a combination of electronic and atomistic models. As the problem was defined by the reliability issues that occur at the nanoscale due to the failure of mesoscopic models, a more highly spatially resolved solution was required. The main simulation techniques were molecular dynamics and density functional theory, each of which fulfilled the requirements for the models we used.

EMMC Translation Case: Nanolayers Res. Comp.

As this project was highly oriented toward solving a manufacturing issue, a large range of experimental data was collected and used to calibrate the resulting model. In fact, a number of partners modified their experimental techniques to provide specialized data to us. Validation of the model required us to make sure that all the extracted parameters could explain and were consistent with all the experimental results that were obtained. The final choice of accepting the models were down to the whole consortium, with a heavy emphasis on accepting the model based on its agreement with experiment.


The models were used to research different manufacturing processes and were used throughout higher level simulations during the design of the devices. These models allowed some of the partners, whose role it was to design circuits, to be able to simulate more reliable devices during the design phase. The clients therefore benefited from a more accurate simulation, due to the more accurate parameters they received. They are then able to produce more reliable devices for their clients.


The consortium constituted different research and development groups, some of whom worked on materials or device simulation and the rest working on device manufacture and measurement. Understandably, it took a few months of hours long meetings to make sure that everybody spoke the same technical language. Regardless of whether the groups worked on similar simulation techniques, there was time needed to make sure that each of the partners understood one another. Furthermore, transferring results was and remains an issue. Each partner establishes their own unique way of producing and analysing data, which requires translation upon transferal from one group to the next.

EMMC Translation Case: Access

Introduction

Translator Profile • Software developer, Software owner, consultant, research center • Field of expertise: metals and alloys, application of continuum models (microstructure),

thermodynamic type models to describe microstructure evolution during solidification and heat treatmenst

Client • A large company in the area of power engineering • End user of products (superalloy components) • Relations existing before translation case: Yes



Formation of stray grains during brazing/welding of superalloy components resulting in insufficient properties of welded component


Contractual work. Preferred time to solution: as soon as possible. Budget: confidential


(i) Better understanding of the process. (ii) Explanation for the formation of stray grains during the brazing process (iii) proposals how to mitigate stray grain formation


• What type of model(s) did you use propose and use?

Continuum models: phase-field coupled to thermodynamic databases

• Explain arguments and criteria used to propose and choose a specific modelling approach and modelling executor for the specific industrial problem.

Welding takes place on the scale of the microstructure ruling out any e/a/m approach. Phase-field models coupled to thermodynamics identified as method of choice to model microstructure evolution


Process data (e.g. brazing cycle) and alloy compositions of both components and brazing material were readily available from production data. No need for additional experiments. Validation via comparison with existing experimental microstructures (LOM, SEM)



No.


The company. Probably because they expected progress in understanding in any case and possibly also a solution to the problem


The entire simulation scenario was intensively discussed with the client before he placed the order.



No real bottlenecks were encountered. Translator and modeller had a common understanding of the problem.


• How did the client use the modelling results?

Based on an improved understanding the client could improve the brazing alloy and prevent the formation of stray grains

• What were the benefits for the client of using modelling?

Solution to an actual problem.

Availability of a methodology to tackle similar problems in future.

Further – as specified by the customer in a presentation – (http://web.access.rwth-aachen.de/THERMOCALC/proceedings/proceedings2010/piegert.pdf)

speed up of investigations and decrease time to market

reduction of experimental testing

“long term trials” within hours/days

definition of process windows even of unknown systems possible

calculation of stable and metastable „real“ systems

versatile: any type of calculation which is related to thermodynamics and/or kinetics can be thought of


influence of distinct parameters on the system can be studied separately

Publication demonstrating technical expertise of the customer:

B. Böttger, M. Apel, B.Laux, S. Piegert: Detached Melt Nucleation during Diffusion Brazing of a Technical Ni-based Superalloy: A Phase-Field Study: 2015 IOP Conf. Ser.: Mater. Sci. Eng. 84 012031, http://dx.doi.org/10.1088/1757-899X/84/1/012031

http://dx.doi.org/10.1088/1757-899X/84/1/012031


Introduction



Client • A large steel company • Material producer for semifinished products (steel slabs) • Yes



3 almost identical steel grades with identical process conditions; risk of hot tearing much bigger for one of these alloys; more than 200k€ losses per damage case




(i) Better understanding of the process. (ii) Explanation for the largely different behaviour of the three almost identical steel grades under the same processing condition. (iii) proposals how to mitigate the hot tearing risk



Continuum models: phase-field coupled to thermodynamic databases


Hot tearing phenomena considered as taking place on the scale of the microstructure ruling out any e/a/m approach. Phase-field models coupled to thermodynamics identified as method of choice to model microstructure evolution


Process data (e.g. cooling curves) and alloy compositions were readily available from production data. No need for additional experiments. Validation via comparison with experimental microstructures (LOM, SEM)



In the end not the model accuracy was relevant, but the exact specification of the alloy composition.


The steel company, probably because they expected progress in understanding in any case and possibly also a solution to the problem





No real bottlenecks encountered. Translator and modeller had a common understanding of the problem.



Based on an improved understanding he could improve the stability of his process and prevent damage


Solution to an actual , severe an costly problem.



B. Böttger, M. Apel, B. Santillana, and D.G. Eskin: Relationship Between Solidification Microstructure and Hot Cracking Susceptibility for Continuous Casting of Low-Carbon and High-Strength Low-Alloyed Steels: A Phase-Field Study Metallurgical and Materials Transactions A 44 5 (2013) 3765. DOI: 10.1007/s11661-013-1732-9


Introduction



Client • A large company (materials supplier) • Semifinished products (superalloy) • Relations existing before translation case: Yes



Formation of stray grains during electroslag remelting (ESR] purification step of superalloy billets resulting in insufficient properties of the semifinished part




(i) Better understanding of the process. (ii) Explanation for the formation of stray grains during ESR (iii) proposals how to mitigate stray grain formation



Continuum models: phase-field coupled to thermodynamic databases, models for fluid flow, analytical models


Stray grain formation takes place on the scale of the microstructure and is influenced by fluid flow ruling out any e/a/m approach. Phase-field models coupled to thermodynamics and fluid flow were identified as method of choice to model microstructure evolution during ESR


Process data (e.g. parameters of the ESR process) and superalloy composition were readily available from production data. No need for additional experiments. No dedicated validation took place



No.


The company. Probably because they expected progress in understanding in any case and possibly also a solution to the problem





No real bottlenecks were encountered. Translator and modeller had a common understanding of the problem.



Based on an improved understanding he could improve the process and at least reduce the formation of stray grains in the billets


Better understanding of an actual problem.



B. Böttger, G. J. Schmitz, F-J. Wahlers, J. Klöwer, J. Tewes, B. Gehrmann

New Freckle Criterion for Technical Remelting Processes, High Temperatures-High Pressures 42 2 (2013) 115-136

EMMC Translation Case: DYNAmore

Introduction

Translator Profile • DYNAmore GmbH (www.dynamore.de) is a software company. We are co-developing and

distributing the LSTC software suite (LS-DYNA, LS-OPT, LS-TASC) and related software tools (Oasis Primer, FEM-Zip etc.) in Europe (except UK). A new service is to test material and calibrate constitutive models for the application within LS-DYNA.

• I am responsible for method development and process simulation. Most applications are related to engineering impact in the field of structural mechanics. Hence loading is dominated by higher loading velocities, higher strain rates and the prediction of fracture in large scale models (up to 20 Mio finite elements) is crucial. Materials under investigation are modern steel grades (AHSS, UHSS), aluminium, unreinforced and reinforced polymers (glass, carbon, etc.). Properties to be modelled are elasticity, plasticity, strain-rate dependence, volumetric and deviatoric splitting (EOS) and fracture for 3D and shell submodels as well as beam elements; models could be isotropic or anisotropic. Typical element length scales range from 0.1mm to 10mm – of course depending on the application.

Client • Clients are mostly from automotive (crashworthiness, stiffness but also quasi-static load

cases), aeronautical industry (fan blade out, bird impact), process engineering (sheet metal forming, bending, hot forming). Client size ranges from OEM to SME (engineering consultants).

• All value-chain segments are supported. • Did you have existing collaboration with the client? Yes, long year collaboration with

automotive OEMs and tier II suppliers as well as engineering SMEs.


• Typically new constitutive models need to be implemented into or enhanced within the source code of LS-DYNA. Clients would typically request properties or features that DYNAmore is implementing. Validation and calibration is done together. Dissemination by presentations at respective conferences.

• New development typically takes 6-18 PM depending on material complexity. Calibration may take another 3-9 PM.


• The following is just one case in which we helped the client to solve the engineering problem.

• The client wanted a more predictable model to describe the deformation and fracture behaviour of polymer based unreinforced materials. Investigations showed that the models that are typically applied in crashworthiness (and pedestrian safety) investigations did not incorporate the necessary properties (pressure dependent yield, complex yield locus, damage and fracture).

• A model was proposed and further developed with the client. Implementation was done at DYNAmore, testing, early bug-fixing and a first calibration for a certain polymer took 1-2


years. The model is in the meantime established in the community and is taken as reference in many models.

• However, testing to gain data for calibration is much more complex that the state of the art and hence the model is typically only applied if (more limited) standard methods show a certain shortcoming in predictivness.

• The investment was of course discussed. The project help the client (OEM) to become more predictive but the costs were seen are to high for every day engineering application.

Figure 1 Finite element model of a longitudinal aluminium member subjected to impact loading


• The modelling results help the client to be more predictive in pedestrian safety investigations and occupant safety simulations. Typically the models need to be predictive up to large strains when damage and fracture plays a significant role.

• The benefit for the client is hard to estimate. It is believed that many of the design criteria cannot be met any more without the help of predictive simulation models. Hence sharpening the predictiveness of simulation tools is of importance. In many cases this can be done by more complex and better calibrated constitutive models. However, these improvements are only a small but important part if the whole model setup. So the investment of let’s say 100TEUR for a newly and better calibrated model or a couple of 100TEUR for a complete new constitutive model may seem high. But the regular application within the product development process may still save much more money.


• In our opinion the major bottleneck of simulation is the improvement of the model quality which is directly related to the calibration quality (and sometimes complexity) of the constitutive models used. However, these models are become more and more complex while the typical design engineer is not able to understand the differences any more. It is a question of how do we educated people at university and how can we continuously educate them during their professional live such that the application of better models (more complex, more expensive in computing time, more expensive in calibration etc.) find better acceptance.


Contact:

DYNAmore GmbH Dr. André Haufe Industriestr. 2 D-70565 Stuttgart

fon +49 (0)711 459600 17 fax +49 (0)711 459600 29 email [email protected] web http://www.dynamore.de

mailto:[email protected]

EMMC Translation Case UniBg

Figure 1 Effect of soil resistivity on potential distribution on a zone protected by 12 anode strips (Bazzoni et al., 2008).

Introduction


group, Software Company, Manufacturing Industry, Other (Consultancy, etc.). Academic group.

• What is you field of expertise: specify type of material, type of models according to RoMM (please see Review of Materials Modelling), type of property/phenomenon, other?

Material science and technology, specifically corrosion and cathodic protection of metallic materials, durability of reinforced concrete.


The client is CESCOR Srl1, a company offering Corrosion Engineering, Cathodic Protection and Integrity Management services for energy sector (oil&gas, offshore wind and othe renewables), civil, infrastructures and other industrial areas. Cescor is a SME of about 30 persons, based in Milan, Italy. A subsidiary company, Cescor UK Ltd has been established in 2016 in Chiswick, London.

• Which value chain segment (e.g. material producer, convertor, end-user) it is positioned? Convertor.

• Did you have existing collaboration with the client? Cescor and our Academic group have a long-term relationship since more than ten years. Currently cooperation is not formal or based on written agreements or contracts.

1 https://www.cescor.it/en/about-us/company.html


https://www.cescor.it/en/about-us/company.html




Above ground tanks for storage of liquid hydrocarbon are often erected with secondary containment membrane installed below the tank bottom to prevent the soil contamination in case of leakage. The design of impressed current cathodic protection in presence of the plastic membrane is based on distributed anodes installed in the space between the tank bottom and the membrane; among available anodes, the most commonly used are the titanium grid or ribbon activated with noble metal oxides. The configuration of the grid or ribbon anode system confined in the closed space between bottom and membrane creates specific issues concerning the electrochemical reactions occurring at anode and cathode, the ohmic drops in the anode system and the potential and current distribution at the cathode2.

The design of tank bottom with membrane shows some specific issues, not met in traditional CP in soil: - the geometry of the system is confined in the flat region between anode and cathode, with quite limited distance between the two; - the ohmic drops in both anodes and current distributors, typically made of titanium as base metal, limit the potential distribution between anode and cathode; - the limited water availability (at least in case of dry sand as electrolyte) within a nominally closed system as the region between the tank bottom and the membrane is, can affect the electrochemical reactions occurring at anode and cathode. Mistakes in design are frequent, including excessive spacing between the anode strips and/or between the current distributors or insufficient number of feeding connections between positive cables and titanium current distributors (both factors having a significant impact on the economics of the CP system). The negative effects can range from uneven distribution of the protection conditions, up to enhanced corrosion phenomena which can lead to re-bottoming of the tank in very short time.


The approximate duration of the project was 1 year and half, including publication of results.


Cescor was expecting to define a new approach in design of cathodic protection for above ground storage tanks, assisted by modelling as well as traditional analytical calculations and to develop internal capabilities for modelling with FEM.

Modelling has been identified as an essential tool for prediction of overprotection and underprotection conditions, for optimization of protection systems, for sensitivity analysis based on main influencing parameters replacing choices only based on cathodic protection engineer’s experience.

2 B. Bazzoni, S. Lorenzi, P. Marcassoli, T. Pastore "Current and potential distribution modelling for cathodic protection of tank bottoms" NACE International Corrosion Conference and Expo 2008 Paper No.08059 (2008).



• What type of model(s) did you use propose and use? Explain arguments and criteria used to propose and choose a specific modelling approach and modelling executor for the specific industrial problem.


o Were model accuracy and necessary investments discussed? If so – please describe. o Who made the final choice for the model and for the modelling executor? Based on

which criteria? o Explain the involvement of the client in the case.

Modelling was performed by Comsol Multiphysics software. Boundary conditions were defined based on electrochemistry of cathodic surfaces to be protected, derived from consolidated knowledge from corrosion science.

All input data were derived from typical applications taken form Cescor’s database of projects and from the experience of cathodic protection engineers.

Validation of models is expected to be carried out through field measurements with reference electrodes placed below tank bottoms, especially with portable electrodes in slotted pipes.

All the choices of model input and parameters were mutually agreed between Cescor and our Group.

Figure 2 Geometry for 3D FE model (Bazzoni et al., 2008).




Results of the FEM simulations and of the analytical model have been used to develop a number of relationships easily applicable for designing. In particular simple formulas have been provided for calculation of the minimum and maximum current densities at the cathode and of the maximum allowed feeding voltage to avoid overprotection, both suitable to assess the maximum convenient spacing between anodes and between conductors.

In summary, Cescor is now improving design for this application also through formulas derived from modelling results and has also developed autonomous capabilities for modelling other industrial cases of cathodic protection.

• What were the benefits for the client of using modelling? Give a semi quantitative or qualitative estimate (For more information on the types of client benefits and how to estimate quantitative impacts, please see the attached document or Economic Impact of Materials Modelling).

The use of modelling in the design of protection systems for tank bottom has a direct benefit in optimization of number of anodic and distribution elements, through definition of optimized spacing with following benefit for material procurement, since a smaller amount of components are necessary. This lead to consequent cost savings for the final client, estimated in the order of -10÷-30% savings.

The monitoring is also optimized since the modelling results provide a reference for interpretation of a limited number of field measurements and available data.



The cooperation with Cescor was efficient and proactive, no bottlenecks were found.

With Cescor or other similar companies, more in general, for shorter deadline projects and activities, finalization of preliminary commercial process with offer and contract may represent a critical and time-consuming step in a relation Academic-Industrial.



EMMC Translation Case: SINTEF

Introduction

Translator Profile • What type of Translator is your institution:

o Non profit Research Institute • What is you field of expertise: specify type of material, type of models according to RoMM

(please see Review of Materials Modelling), type of property/phenomenon, other? o The model was a Continuum model. o The flow and oxide skin behaviour in hot dip galvanizing process was simulated

and optimized


o The client is a SME • Which value chain segment (e.g. material producer, convertor, end-user) it is positioned?

o Convertor • Did you have existing collaboration with the client?

o No


• Describe briefly the industrial problem. o The problem was that oxide skins form on the surface of a hot dip galvanizing

bath. A special technology was developed to remove the oxide skin. However, the process was challenging and not very efficient in some cases. Inclusions were formed and would attach to the newly galvanized surfaces. It was a need to understand the source of the surface defects on galvanized goods as well as the efficiency of the applied surface oxide removing flow device.

o The SME was delivering the furnace to clients and the work was related to technology transfer to support the delivery of the furnaces

• Indicate involved budget or preferred time to solution (duration). o The budget corresponded to two full work weeks, time to completion was 4

weeks.



• Indicate what was the expected outcome of the translation process. o The work should end with concrete recommendations to improve the process.



o The customer of the SME was visited and the inclusion related challenges was seen in operation. This was critical in order to device a modelling strategy.

o By the help of computational flow modelling and process understanding the process was simulated in a 3D CFD code.

o The domain knowledge of the executor was critical for providing a good answer to the SME.

o No process data was provide by the SME, only qualitative information. Physical properties for the fluids and solid were provided. The SME had no resources to obtain detailed experimental data from the process. Accordingly, no model validation was performed. The SME took the recommendations, based on the CFD study, as input to their further developments.

o Model accuracy was not discussed. The SME did at the time not have qualified people to take part in such discussions (at that time they had none with master degree or higher).

o Possible investments would be on the SME Client side. This was not discussed. o One single person ran the project, but in collaboration with the QA responsible.

The modelling executor was decided by the PM and QA responsible, based on combined modelling skills and domain knowledge.

o The client (SME) invited to a visit to the clients vendor. The trip was arranged by the client. The client was responsive to answer questions from us. After the report was finished a meeting with the client was arranged to discuss the results.


• How did the client use the modelling results? o The recommendations from the report was considered as very useful by the client.

This would be useful in advising the client's clients about oxide skimming solutions.

• What were the benefits for the client of using modelling? Give a semi quantitative or qualitative estimate

o We had no feedback from the client after the project was finished. No quantitative estimate can be given. However, the work would strengthen the knowledge that is necessary for successful sales and would be a significant advantage for the customers.




o The main bottleneck was that the client had very limited resourced to do development. The work was done in direct contact with the CEO as he was the only with technical overview. He was also the sales manager.


Introduction

Translator Profile • What type of Translator is your institution:

Non-profit Research Institute • What is you field of expertise: specify type of material, type of models according to RoMM


The model was a Continuum model. The severe clogging problem of iron oxide in a hopper was simulated and actions to resolve the problem was proposed


The client is a SME. • Which value chain segment (e.g. material producer, convertor, end-user) it is positioned?

Material producer • Did you have existing collaboration with the client?

No.



The problem was that during production of magnetite severe clogging happened in a hopper. As a result, the product would build up on specific parts of the hopper wall. This was a flow problem, but even more a product degradation problem. Due to exothermic reactions the sticking powder would over-heat and the product would be significantly degraded. The working remedy was to apply a powerful canon that would shoot bullets into the outside of the hopper wall, at regular intervals. This created shock waves that would loosen the build-up at the hopper wall and the material would drop into the underlaying silo where cooling was available.

The SME was producing magnetite powder to customers that would use this in their production of magnets for their customers


The budget corresponded to one full work week (42 h), but time to completion was 3 weeks.


The work should end with concrete recommendations to improve the process.





The contact with the customer was based on telephone contact and exchange of information by telefax. There was no site visit included as this would increase the budget. However, the given information was sufficient to device a modelling strategy.

By the help of computational flow modelling and process understanding the process was simulated in a 3D CFD code.

The domain knowledge of the executor was critical for providing a good answer to the SME.

Telefaxed drawing of the hopper, transport channels and surrounding system was provided by the SME. The SME also provide info about the powder properties and surface materials of the hopper. Based on the simulation results a rather unconventional strategy was proposed to solve the problem. The proposed solution was also simulated. The SME implemented the recommendation. This included building an internal device into the hopper. The result was very satisfactory, and the cannon could be scrapped.

Model accuracy was not discussed. The SME did at the time not have qualified people nor time to take part in such discussions.

One single person ran the project, but in collaboration with the QA responsible. The modelling executor was decided by the PM and QA responsible, based on combined modelling skills and domain knowledge.



The main result from the simulation was a recommendation for an action, not modelling results alone. The client took the recommendation, did a smaller modification of the equipment and solved their process problem. The modification included installation of an internal device that redistributed the flow of gas and powder.

• What were the benefits for the client of using modelling? Give a semi quantitative or qualitative estimate

We did not receive other feedback apart from the indication that the problem was happily solved on the client's side; no more interaction followed this short project with the SME.




In this case there was no bottleneck. The recommendation was immediately taken to action.

It would have good to hear more from the SME about other problems that SINTEF could help to resolve. The PM for this project has no knowledge if this has happened. Possibly the PM in the SME might have left the company and the contact may have been broken.


Introduction

Figure 1 Example of component produced by Raufoss Technology AS and picture of production line.


group, Software Company, Manufacturing Industry, Other (Consultancy, etc.). SINTEF AS is one of Europe’s largest independent research organisations (www.sintef.com).

• What is your field of expertise: specify type of material, type of models according to RoMM (please see Review of Materials Modelling), type of property/phenomenon, other?

SINTEF AS is a multidisciplinary research organisation with expertise in the fields of technology, natural sciences, medicine and social sciences. In this particular translation case, competences from material and structural mechanics were used. The material of concern was an aluminium alloy from the 6xxx series. In this translation case, a continuum model was used to describe the elasto-plastic response of the material.


Raufoss Technology AS is an SME. It is an internationally recognised aluminium forming company, and delivers advanced forged products, such as wheel suspensions, to some of the world's leading automobile manufacturers.

• Which value chain segment (e.g. material producer, convertor, end-user) it is positioned? Raufoss Technology AS is a convertor: it transforms cast ingots/rod/extrusions into advanced forged/3D cold formed products.

• Did you have existing collaboration with the client? Yes. Raufoss Technology AS has been and is among industrial partners in many projects where SINTEF AS was/is research partner. SINTEF also runs regularly direct projects with Raufoss Technology AS.

http://www.sintef.com/





Raufoss Technology AS performs misuse tests on components and performs the corresponding simulations. In the simulations, the material is assumed to have isotropic behaviour – its behaviour is independent on the direction – and combined with a strain-based fracture model. This damage model was used to predict the response of cylindrical specimens under uniaxial tension and provided good results. However, this model is not appropriate for the conditions arising at misuse incidents since it does not reproduce the experimental results obtained from tests. Therefore, there was a need for a more advanced hardening and damage model.


The budget was 480 kNOK for the first project (4 months in 2015) and 100 kNOK for the second one (few months in 2016).


The expected outcome of the translation process was twofold: i) to develop and implement a hardening and fracture model for the client and ii) calibrate the model for different materials.


• What type of model(s) did you use, propose and use? Explain arguments and criteria used to propose and choose a specific modelling approach and modelling executor for the specific industrial problem.


o Were model accuracy and necessary investments discussed? If so – please describe. o Who made the final choice for the model and for the modelling executor? Based on

which criteria? o Explain the involvement of the client in the case.

The components produced present a non-homogeneous distribution of the material properties due to the manufacturing process. However, quantifying and describing the distribution of the alloy properties would be a difficult task and would demand for process analyses that were beyond the scope of the work. It was therefore decided to test only one type of material, i.e. extruded, such that product/design effects were avoided, resulting in a material model being alloy related and less process dependent. It was believed that upper and lower bounds of the component response in misuse conditions would be obtained by calibration of the isotropic hardening and damage model using specimens in different directions. Another approach was to use an anisotropic hardening model. The material model developed and implemented for the client was a combined isotropic/anisotropic hardening and fracture model. The main motivations were that the models should be simple, easy to understand and efficient. Therefore, conventional models were chosen and implemented.


Three yield criteria were implemented: one isotropic and two anisotropic. The hardening model was an extended Voce equation. The (ductile) fracture model was the Cockcroft-Latham criterion. The client was involved in all choices and decisions.



The model is currently being used at Raufoss Technology AS to predict the behaviour of the components in relevant in-service loading situations and thereby support their design.

• What were the benefits for the client of using modelling? Give a semi quantitative or qualitative estimate (For more information on the types of client benefits and how to estimate quantitative impacts, please see the attached document or Economic Impact of Materials Modelling).

The model implemented provides means to assess the occurrence of fracture in forged components more accurately than models used until now and thereby provides better support to the design of some of our products. This may lead to weight savings and economic benefits.



There were no particular bottlenecks in the translation process.



MODA V3. Released on emmc.info on 30 January 2017

MODA for advanced hardening and damage model Simulated in project AHDM

OVERVIEW of the SIMULATION

1 USER CASE

Raufoss Technology AS is an SME. It is an internationally recognised aluminium forming company that delivers advanced forged products, such as wheel suspensions, to some of the world's leading automobile manufacturers. It performs misuse tests on components and performs the corresponding simulations. In the simulations, the material is assumed to have isotropic behaviour – its behaviour is independent on the direction – and combined with a strain-based fracture model. Although providing good results in some cases, this damage model is not appropriate since it does not reproduce the experimental results obtained from misuse tests. Therefore, there was a need for a more advanced hardening model, i.e. anisotropic, and damage model.

2 CHAIN OF MODELS

MODEL 1

Continuum material mechanics describing anisotropic hardening and damage for ductile materials.

MODEL 2 None

DATA-BASED MODEL

None

3

PUBLICATION PEER-

REVIEWING THE DATA

None

4 ACCESS CONDITIONS

The model was developed and implemented as a user-defined material for the non-linear finite element commercial software Abaqus. The user-defined material code is a proprietary code. The data used to calibrate the model are also proprietary.

5 WORKFLOW AND ITS RATIONALE

Aluminium alloys exhibit very often plastic anisotropy due to the thermo-mechanical processes used to produce them (like extrusion or rolling); it is therefore important to use appropriate yield functions to capture such behaviour, e.g. the models from Hershey – for initially isotropic materials – and Barlat 9 parameters – for initially anisotropic materials. The latter is chosen for its combination of low number of parameters (i.e. relatively easy to calibrate), CPU efficiency and reasonable accuracy. Aluminium alloys are ductile and exhibit ductile fracture. It is then expected that a ductile fracture model would be appropriate to describe such behaviour. For simplicity and efficiency an energetic-based model is selected.


Workflow picture

MODA

Physics-based Model

MODEL 1: Continuum model

1 ASPECT OF THE USER CASE/SYSTEM TO BE SIMULATED

1.1

ASPECT OF THE USER CASE TO BE

SIMULATED

Misuse test on car components. The simulation often translates an experimental misuse test. The component or structure is typically given an initial velocity and will impact a wall or solid (non-deformable) feature.

1.2 MATERIAL 6xxx aluminium alloys.

1.3 GEOMETRY

Bulk material described either with solid or shell elements depending on the complexity of the components to be modelled.

1.4 TIME LAPSE From milliseconds to seconds.

1.5

MANUFACTURING PROCESS OR IN-SERVICE CONDITIONS

The boundary conditions are representative of in-service conditions and are taken from the experimental test. In this case it is the initial velocity.

1.6 PUBLICATION ON THIS DATA

There is no publication since the data and model are proprietary.

2 GENERIC PHYSICS OF THE MODEL EQUATION

2.0 MODEL TYPE

AND NAME Solid mechanics.

2.1 MODEL ENTITY

Finite volumes.

2.2

MODEL PHYSICS/

CHEMISTRY EQUATION

Equation Mechanical equilibrium equation (conservation of momentum).


PE Physical quantities

Forces and displacements.

2.3 MATERIALS RELATIONS

Relation 1. Hooke's law 2. Anisotropic yield functions: von Mises, Hershey, Barlat

9 parameters 3. Voce hardening model 4. Cockcroft-Latham fracture model

Physical quantities/ descriptors for each MR

Physics quantities: stress, elastic strain, accumulated

plastic strain Constants:

1. Young's modulus, Poisson coefficient 2. Yield stress, anisotropy coefficients 3. Hardening parameters 4. Fracture parameter

2.4 SIMULATED

INPUT None

3 SOLVER AND COMPUTATIONAL TRANSLATION OF THE SPECIFICATIONS

3.1 NUMERICAL SOLVER

Implicit or explicit finite element solver

3.2 SOFTWARE TOOL Abaqus

3.3 TIME STEP

The time step ranges typically from 10-6 to 10-3 s using an explicit solver, or up to a second when using an implicit solver.

3.4 COMPUTATIONAL REPRESENTATION

PHYSICS EQUATION, MATERIAL

RELATIONS, MATERIAL

All material relations have been implemented through a user-defined material subroutine for both explicit and implicit solvers.

3.5 COMPUTATIONAL

BOUNDARY CONDITIONS

The boundary conditions are prescribed displacements (e.g. initial velocity) translated from the physical test.

3.6 ADDITIONAL

SOLVER PARAMETERS

None

4 POST PROCESSING

4.1 THE PROCESSED

OUTPUT The outputs are stress and strain fields in the component to visualize the heterogeneity of the distribution of the properties. Damage (or


accumulated plastic work) is output to assess whether the component has reached fracture or not. Resulting forces and displacements at relevant locations may also be output to compare with experimental results.

4.2 METHODOLOGIES

4.3

MARGIN OF ERROR

The errors are related to the accuracy of the model, the calibration of the model and the representation of the physical problem i.e. finite element discretization (simplification of geometry, boundary conditions …)

EMMCTranslationCaseTemplate

Introduction

Translator Profile

IPCistheFrenchTechnologyTransferInstitutededicatedtoplasticsandcompositesmaterials.As

such,IPCisinchargeofprovidingtheplasticsandcompositesindustryclusterwithinnovativeand

highaddedvaluefacilitiesandmanufacturingpilotlines,cuttingedgeexpertiseandservices.IPC

coversthefullindustryvaluechainwithitskeyfieldsofexpertiseincludingdesignandsimulation

of parts and processes; advanced injection molding and tooling; thermoplastics materials;

compositesmaterials;3D-MID(MoldedInterconnectDevices).

Mainmarketsaddressedtodayencompassautomotive,aeronautics,health,packaging,connectors,

houseappliances,horology.Developingnewvaluechainsisakeystrategicissuewithfocuse.g.on

micro/nanostructuredplasticparts,microsystemsonplastics;smartcomposites;multi-materials

additivemanufacturing.

https://ct-ipc.com/

Client

MOPLA isan Italiancompany founded in1975andheadedby theSavoia family.Theyproduce

plastic injection parts, as well as molds and prototypes. MOPLA business mainly deals with

productswithhighqualitytechnicaldetails.Therefore,MOPLAhasagreatandspecificexperience

inhandlingproductionwiththatdegreeofdetails.Suchexperiencehasbroughttothedevelopment

–togetherwithalocalSoftwareHouse–ofanSPCcontrolsoftwarewhichisnowavailableonthe

market.Thissoftwarehasconstantlybeenusedsince1982tocertifythequalityoftheparts.

http://www.mopla.it/default.htm

IPC and MOPLA was partners into the FP7 MOLD4PRODE project.


Injectionmoldingisthemostwidelyusedpolymerprocessingmethodtomanufactureplasticparts.

This cyclic process can be defined in four essential stages: cavity filling, melt packing, part

solidificationandthenejection.Alargepartofthetotaltimeoftheprocessisdedicatedtothepart

cooling.Thatiswhycoolingsystemhavetobedesignwithcarefulness.

Manydefects suchaswarpage, shrinkage, thermal residual stresses, sinkmarksoriginates ina

flaweddesignoftheregulationsystemorunbalancedcooling.Fromaneconomicalpointofview,

an efficient process can particularly be achieved through fast heat removal. Quality and

productivity are often two conflicting objectives that optimization procedures can solve.

Optimizationhasbeenusedtodetermineoptimalcoolingconditionswhichminimizeundesired

effects.


MOPLAneedsaredirectlyrelatedtothiscontext.Amoldingtoolusetoproducebodyactuatorparts

came to its end of life. So the objectives were to develop amore efficient version of the tool

regardingproductivityandqualityusinglasermeltingtechnology.

Figure1.Bodyactuator

Twoqualitycriterionaredefined.TheplanaritydefinedonFigure2insuresthepositioningofthe

part.Thetargetdefinesintheproductspecificationisadefectof0.2mm.Thesecondcriterionis

theperpendicularityof the shortest cylinderaxiswith thebottomof thepart.Perpendicularity

defectinfluencesnoiseandenergyconsumption.Ithastobeunderthevalueof0.15mm.

Figure2-Partqualitycriteria

Total MOPLA investment was related to the mold insert design including modelling and the

manufacturingovercostofusinglasermeltingtomanufacturedsomepartsofthenewmold.That

representsanamountof€10,000.

Thetranslationprocesshelpsin

o designinganewcoolingsystemininjectionmoldingmold

o identifyingthebestlocationforcoolingchannelProviding

o understandingheattransferinmetalduringin-servicebehavior


Translationisbasedonanin-housemodellingmethodologytohelpdesigningcoolingsystemfor

injectionmoulding.ThemethodologyisillustratedinFigure3.


Figure3-ModellingWorkflow

Modelling strategy

Theworkflowthathasbeendevelopedisbasedonthemodellingofheatflowintheplasticpartand

theinjectionmold.Heatflowmodelhasbeenusedfirstlytoestimatethecoolingtimeofthepart

and,secondly,tocalculatethetemperaturefieldsinthemoldandinthepart.Themainassumption

ofthiscontinuummodellaysinthefactthatthephasechangekineticsofthepolymerisconsidered

independentofthermalhistoryofthematerial.Then,modellingissimplifiedandkineticscoupling

is avoided. The problem remains nonlinear due to the dependence of the specific heat to the

temperature.Tomodelanon-perfectcontactbetweenthemouldandthepolymerandtheresulting

temperaturejump,athermalcontactresistanceisintroducedattheinterface.Thecyclicaspectof

theinjectionmouldingprocessistakenintoaccountthroughperiodicconditions.

Figure4-Heatflowresults

Thecoolingchannelpositionisdeducedfromabi-objectiveoptimizationstrategytodeterminethe

distributionofcoolanttemperatureintothemold.Theoptimizationstrategywasachievedthanks

to the minimization of the cost function by means of a gradient method. The cost function

represents aweighted combinationbetween the cooling efficiency and the fact that thepart is

uniformlycooled.Indeed,itissupposedthatauniformtemperaturealongthepartsurfacehighly

reduceddeflectionthroughwarpageandshrinkage

Figure5–Comparisonincoolingchanneldesign:formermoldwithdrilledcoolingchannelsand

newmoldwithconformalcoolingchannels


Validation

Tovalidateefficiencyofthenewversionofthemoldcoolingchannels,simulationofinjection

moldingprocesshavebeenperformedinordertocomparetheeffectofeachcoolingchannel

designregardingcycletimeandqualitycriterion.

Table1-Numericalvalidationcomparedtoproductiondataandfinaloutcome


Direct benefit:

Directbenefitisrelatedtothereductionofthecycletimethathaveimpactontheproductcost.

Annual product quantity to be produced 400000

Life time of the mold 4year

Gain on the product cost 0.03€

Direct Benefit 48 000

Indirect benefit

Optimisationoftheregulationchannelsenablesareductioninthethermalinertiaofthemold.Then

production set up has been improved and sped up. Benefit is related to a better tuning and a

decreaseinthenumberofsetup.

Another indirect benefit relies on the enhancement of the product quality but it has not been

quantified.

Cost of a set up 3000€

reduction of number of set up 3

Indirect Benefit 9000 €


Total ROI

Direct benefit 48000€

Indirect Benefit 9000€

Investment 10000€

ROI 4,7


Themainchallengeencounteredinthetranslationprocessistoincludeamodellingstepinsidea

productdesignworkflowwithoutdelayinthelogisticchain.Thatiswhymodellingwasadaptedto

timetomarketneedsintermofdeadlineandbudget.

TheovercostgeneratedbymodellingneededtobeexplainedclearlytotheclientandaROIneeded

tobeestimatedbeforethework.

EMMC Translation Case

Introduction

Translator Profile

What type of Translator is your institution: TTI (Technology Transfer Institute), Academic group, Software Company, Manufacturing Industry, Other (Consultancy, etc.).

IK4 – TEKNIKER (http://www.tekniker.es/en) is a private research organization which main

mission is to enhance the positioning and competitiveness of our clients through technology

transfer. Hence, IK4 - TEKNIKER plays the role of TTI in the framework of EMMC.

What is you field of expertise: specify type of material, type of models according to RoMM (please see Review of Materials Modelling), type of property/phenomenon, other?

IK4-TEKNIKER is a research organization which main activity is focused on manufacturing. The organization covers 4 Research areas: Advanced Manufacturing, Surface Engineering, ICTs and

Product Engineering (for more information go to: https://www.slideshare.net/teknikerik4 ). The

type of materials considered during the development of research projects are Metals and polymers,

as well as functional coatings. Regarding the type of models, both continuum and discrete models

are used as a tool for getting better knowledge about the experimental results obtained in the

characterization stage. The type of properties of phenomenon we studied are, mainly, surface

integrity, surface functionality, material response to different advanced manufacturing processes,

service life assessment and benchmark testing.

Client

The client who leads the industrial case is SAPA PLACENCIA:

http://sapaplacencia.com/sapa_index.php?lang=2 ). This company is a SME which main

application sector is Defence (Antiaircraft artillery) and Energy (Mobility and power generation

and power management). The Company and IK4 - TEKNIKER belonged to the same manufacturing

cluster promoted by the Basque Government.


Describe briefly the industrial problem.

The Company contacted to IK4 - TEKNIKER to measure and model the tensional and

microstructural state of gears at the different manufacturing steps (forging, mormalizing,

quenching and tempering), as well as to perform a dimensional control of them, in order to

determine the origin of distortions of parts, which are one of the major causes of rejected

components.

Indicate involved budget or preferred time to solution (duration).

The preferred time to solution was 1 year. The major limitation on the schedule was the constraints

linked to the access to Neutron facilities to measure the residual stresses on the bulk of the gears.

Indicate what was the expected outcome of the translation process.

The main expected outcome is to evaluate the use of simulation tools to gain more insight about the

effect of the different production stages (manufacturing and thermal treatments) on material

http://www.tekniker.es/en


https://www.slideshare.net/teknikerik4

http://sapaplacencia.com/sapa_index.php?lang=2


microstructure, final dimensional accuracy and physical properties of gears, prior to perform the

sequence of treatments on the industrial component.


The type of model proposed was a continuum based on FEM simulations of heat treatment

processes on forged gears, considering the commercial software SYSWELD which provided the

required information connected to the effect of those treatments on the residual stresses and

hardness of the component. These properties explain to a large extent the unpredicted distortions

often observed after those manufacturing stages. The SYSWELD software assumed homogeneus

material microstructure , however, inhomogeneities (banded structures, for example) can be

included into the model to reproduce local changes in hardness and residual stresses on the

component. The client was not familiar with the use of modelling/simulation to understand the

material response to different manufacturing processed used on a daily basis. Hence, the client

relied on IK4 - TEKNIKER to execute the modelling and characterization, based on the previous

collaboration between both entities to solve different manufacturing problems. Additionally, IK4 -

TEKNIKER has a great expertise on elucidating the diagnosis of mechanisms and the causes of faults

such as fractures, residual stresses, abnormal wear or corrosion, which occur during the

manufacturing stages and during the service life of elements and components

(http://www.tekniker.es/en/materials-andsurfaces-performance ). This way, correlation between

modelling results and experimental observations can be monitored in a very straightforward way.

Additionally, by considering the last generation laboratory characterization equipment, IK4 -

TEKNIKER can properly determine the value of material parameters required to be included in the

modeling platform.

A snapshot describing the problem, solution proposed and results is indicated as follows:

http://www.tekniker.es/en/materials-andsurfaces-performance



The client was satisfied with the results. These results were published in a research journal (V. García Navas et al. Materials Science and Engineering). But, it is worth mentioning that further 3D

- modelling including the contribution of banded structures (microstructural heterogeneities: such

as ferrite – perlite bands and decarburization), material grain sizes and orientation is mandatory

to predict the experimental residual stresses and hardness values observed. This way, the

industrial application requires the contribution of discrete models to be combined with continuum

models. The main benefit for the client was to establish a methodology based on modelling and

material characterization to be applied before the thermal treatment stages on their forged parts.

Hence, a 20-30% reduction (semi quantitative) in the time consumed in trial-and-error assays was

reached by the consideration of the tools explored in this translation case.

EMMC Translation Case: Nanomatch GmbH

Introduction

Translator Profile Nanomatch is a Software Company (SME) located in Germany. We provide predictive & parameter-

free simulation tools for virtual materials design and computer based device optimization in

organic electronics, implemented in a seamless workflow using electronic, atomistic, and

continuum models. Additionally, we developed a generic workflow platform for the efficient

translation of complex multiscale simulation approaches with modules from many sources into

market ready tools for industry.

Client The client is a large, globally active enterprise consisting of several subsidiaries, and develops and

manufactures organic electronic materials and devices. Our software and services are mainly used in

the enterprise’s internal basic research unit. We have an ongoing collaboration with the client.


The vast number of stable chemical organic compounds and the variety of system parameters

(number of layers, layer thicknesses, doping concentrations, etc.) makes the identification of

perfect material combinations and device parameters via experimental trail & error a time-

consuming and costly endeavour. Furthermore, many processes and properties on the electronic

scale cannot be observed in experiment and inhibit a targeted approach to device design.

In contrast to other branches such as the automotive or aerospace industry where computer aided

design is a major pillar in R&D, development of novel materials and devices in organic electronics

(OE), namely organic photovoltaic (OPV) devices or organic light emitting diode (OLED)

applications (such as displays), relies strongly on time consuming and costly experimental trial &

error approaches. One of the fundamental reasons therefore is the lack of a single model/module

that computes properties of complex multilayer devices, solely based on information on the

chemical compounds that are used in the devices. This is overcome by combining multiple models

on the electronic, molecular and device scale into a seamless, predictive multi-scale workflow. As

the computation of properties and processes in organic electronic layers and devices is still subject

to academic research, this also includes modules of academic state-of-the-art modules.

To establish modelling in the client’s R&D, the client allocated a lower six-digit sum for software

and consultation, plus personnel costs (presumably 1-2 FTE). The expected outcome of the

translation process was the large-scale in-house application of state-of-the-art models by the client,

in order to allow simulations on systems that include proprietary materials (which wouldn’t have

been possible if simulations were conducted as a service by us). While we maintain an ongoing

collaboration with the client to develop custom-tailored solutions including the latest methods, the

first translation period was 12 months.


Established solutions for OE, such as Drift Diffusion or Finite Element solvers, rely on parametrised

models, limiting computer aided design in two ways: First, these models need to be calibrated using

experimental data and hence impede full virtual design. Second, no information on the electronic


scale that is crucial for a fundamental understanding of the impact of specific microscopic processes

and effects on device properties is provided by these models. This understanding, however, is

essential for the targeted design of compounds and devices.

We therefore used the multiscale approach, where information on the electronic scale (quantum

mechanical computations of molecules) is transferred via the atomistic scale (molecular layers) to

the device scale in four simulation steps. This parameter-free approach eliminates the necessity of

experiments prior to simulation, as no experimental input/calibration is needed. This approach is

illustrated in Fig. 1. As there was no module for step 4 at the beginning of the collaboration, only

modules for steps 1-3 were provided to the client. Starting on January 1st 2018, this last step, as

well as improved modules for steps 1-3, are provided in the ongoing collaboration.

Figure 1: Multiscale workflow for predictive OLED or OPV simulations.

The simulation outcome can be validated at several stages: After step 2, properties of molecular

layers, such as density or radial distribution function, can be compared to experimental data. Step

3 provides information on the electronic structure, namely orbital energy levels, that is also

accessible via experiment. Finally, charge carrier simulations provide macroscopic device

properties such as I-V curves, that can be easily measured in experiment. Possible validation steps

and expected levels of accuracy were discussed with the client. However, to protect ongoing

research and IP, no information on the performed validation steps or their outcome was disclosed

to us by the client.

We recently entered negotiations with the client for the development of specific features to

increase model accuracy. For the implementation of the module extensions, the client will invest a

sum in the lower six-digit range over the course of 12 months. The decision on models, their exact

features and necessary extensions is therefore subject to ongoing research collaborations. We

expect this to continue over the next years, as it is essential to maintain a competitive edge over competing approaches by staying at the state-of-the-art level of academic research. New features

are then developed by Nanomatch using compounds and systems in the public domain, before

providing stable releases to the client for application to proprietary systems.



While the client did not disclose any simulation results to us, we are aware that the simulations

were conducted to develop a fundamental understanding of processes and effects in OE layers and

devices. This understanding will help to improve device performance, thereby contributing to

innovation on a long-term scale and helping to secure and improve the client’s position in the

market. By incorporating modelling at this early stage in the product design process, the client is

able to avoid dead ends and limit the efforts spent on experimental trial&error approaches, saving

time and money and, most importantly, allowing shorter time to market. A quantitative estimate

for the client’s benefits, such as ROI or cost savings, is hard to achieve both due to the limited

information and the time scale at which an impact on market share and sales can be observed.


As indicated above, there are several bottlenecks in the translation process, that we also

encountered with other clients:

• Modelling accuracy and applicability: The applicability of the individual methods

implemented in the first version acquired by the client was demonstrated in various

scientific publications. However, it is necessary to constantly incorporate state-of-the-art

approaches into our software and to develop custom-tailored extensions in collaborations

with out clients to achieve acceptable accuracy for specific use cases.

• Understanding the client’s needs: While the client’s general interest was clear from the

start, the specific application domain was not disclosed to us. During several occasions,

we therefore could not provide an optimal solution that only became obvious at a later

stage. We hope this will be limited in the near future due to a higher level of trust resulting

from the long-term collaboration.

• Modelling execution: As experts in OE modelling, the most efficient way to conduct

simulations as well as analyse and understand the results is to execute simulations

ourselves and provide the results to the client in form of reports. Due to sensitive IP, this

is not possible and lead to a large amount of support. In the future, we will overcome this

barrier by providing an extended set of webinars for specific use cases. This will both

increase scalability of our products and minimize training periods for the clients.

• Conquering the complexity of multiscale simulations conducted on remote resources (in-

house high-performance computing (HPC) architectures or cloud resources): To obtain

results in a reasonable computation time, most of our models require execution on

scalable resources (~100 cores in parallel). Furthermore, results between individual

modules need to be converted and copied by hand before starting each simulation step,

limiting the scalability and thereby the impact of modelling. This demands a certain level

of specialized expertise in HPC handling, operating systems, command line and scripting.

While the client in this use case already provided this know-how from the start, this is a

limiting factor for other potential customers. We therefore developed the workflow

platform SimStack (www.simstack.eu) that automatically handles the information

transfer and execution on remote computational resources as well as the integration of

any modules into seamless workflows.

http://www.simstack.eu/


Introduction

Translator Profile ● I work as an independent consultant with my own private consulting company

Property Vectors focused on atomic-scale materials and chemistry modeling in a wide variety of application areas and HPC consulting. I previously worked as a Senior Research Scientist in the semiconductor industry effectively as an internal consultant to the experimental research and development teams.

● My primary experience in materials modeling is focused on atomistic models with a particular focus on Plane Wave and Local Orbital Density Functional Theory. I also have a significant amount of experience in classical Molecular Dynamics and less in Monte Carlo in the atomistic regime. Above the atomistic regime I have limited experience in Finite Element Modeling as well as a number of electrical resistance, device, and circuit models that are typical in the semiconductor field and well as three-dimensional process modeling. I have worked on a wide variety of mechanical (elastic constants, cohesive energy, stress vs. strain, CTE, and more) and electrical properties (static [resistivity, charge density, polarizability, dielectric constant, and Schottky Barrier] and dynamic [current density, IV])

Client ● In the past my “clients” were experimental co-workers in semiconductors working to

research, qualify, and perfect new materials, chemistries, and then integrates structures in logic devices. My most recent customer in consulting is a defence contractor bidding with the government building a new Infrared camera that operates at a higher temperature through use of new materials technology. The defence contractor qualifies as a small business of less than 200 people.

● Both the large semiconductor company and the defence contractor qualify as manufacturing companies that perform their research and development in-house.

● I had a prior relationship with the defence contractor as they had offered me a full-time job in the past. In the case of the semiconductor company, I worked there for 15 years after starting as an intern after completing my Master’s at Stanford and being recommended for the internship by my thesis advisor.


● In semiconductors, the business case is that the industry is predicated on Moore’s Law and shrinking of the dimensions of a logic semiconductor chip to deliver an increase in computing power over time. These devices are the foundation that have helped drive the computing revolution of the past decades. This drive to reduce dimensions leads to changes in the current and power required in the chip and also in the thermal stresses. This leads to changing material property requirements that either require changing the structure or makeup of the original material or finding an entirely new material that meets the new property requirements. The crux of the problem in semiconductors however is usually that although you may be able to find a new material that meets the property requirements, it is extremely challenging to integrate it into the traditional semiconductor flow. This flow requires many different steps, chemistries, temperatures, and other materials that the new material will come into contact. In addition, the allowed defect percentages required for


optimum performance are orders of magnitude lower than what can be simulated in DFT as manufacturing needs to be done at a very high-quality level. In the case of the defence contractor, they are looking to set out to meet a request for a new device that is more portable, lasts longer, and requires less collateral to keep at the optimum temperature that is higher than the operating temperature for current devices for a wide variety of defence applications. The requirements on the defects levels are significantly lessened in the case of the defence application as the acceptable signal-to-noise ratio is much higher than in the case of semiconductors.

● The total budget to develop the next generation of logic processing chip is on the order of 10’s of billions of dollars. The portion dedicated exclusively to materials-type of research is significantly smaller portion of that budget, likely on the order of 1/10th of the total R&D cost. There are extremely high capital costs for all source materials and machines that are part of the manufacturing process and the price pressure for an optimum price of the final chip is strong. Due to this, many different logic processing chips must be manufactured side-by-side to an extremely high level of quality. If we associate the budgets of large TCAD departments as being reflective on the allocation of funds towards materials related problems it amounts to at least 10-100’s of millions of dollars per year. That does not account for the experimental budgets dedicated to materials. In the case of the defence contract the complete budget for the entire defence contract is on the order of a few million dollars. Of this on the order of a few 100k dollars is dedicated to the search for the right materials with modelling to meet the needs if the cost of engineers and computing is taken into account.

● In semiconductors the expected outcome of the translation process is to help identify a new or optimized material that can survive the varied processing steps and be manufactured at a very low defect level. In the defence contractor, the expected outcome is much more easily attainable goal of providing insight for the experimentalists in their search for a new material to meet their needs. In addition, the level of quality and difficulty of integration into a product is much lower. Finally, part of the role of translation with both cases is to educate the experimentally focused client about the power and limitations of the techniques and how despite their inherent errors they can be used to guide research and development at reduced cost and timeline.

Translation to modelling solution ● I will focus on the defence contractor alone in this case as the range of models used in

semiconductors was too wide for the space allotted. I primarily used plane wave density functional theory to demonstrate the effectiveness to predict properties of interest to the company (band gap, defect states, etc.). The focus in this first stage was to generate data to help in the justification for a follow-on defence contract submission proposal. At the same time, I was training one internal engineer on these methods. While I focused on generating results and content for the defence contract proposal the client’s engineer was also familiarizing himself with DFT tools and methodologies under my guidance. For that reason, I started with very general and basic DFT models as I was walking the engineer through the basics of DFT at the time and did not want to overload him. Throughout the discussion I explained to him the problems of convergence testing, importance of thickness of slab models being tested, and impacts of surface orientation and reconstruction. At this stage the level of accuracy was not very critical and 10% was acceptable as part of the role was just to identify a trend to show that these properties in principal could be modulated. Unlike in semiconductors at this stage, choosing a recommendation that was likely to


manufacturable and integratable was not a concern. In the future for follow-on studies I suggested explore Non-Equilibrium Green Function Methods to estimate transport as well as potentially Time-Dependent DFT methods. I recommended that in the future device-level models may be required and we would need to identify what property inputs would be needed as data for those parameterized models from the DFT results. At this stage, no experiments were performed and previously published literature was used as a validation check. Most of the primary final decisions on structures and models used were made by me as the primary engineer I was working on was not extremely knowledgeable in this area. His advice and input mostly focused on making sure we were working on the right materials set.

Client’s benefits from the modelling ● The client used the model results to show competency to the reviewer of the grant

proposal in both in-person presentations and the actual writing of the grant proposal. I was not present at the presentation, but the overall impression from the client was that the funding agency representatives were happy with the results. The final decision on the funding grant is pending. In addition, I also made a series of predictions of the total computing and human resources that would be required for a long-term plan of the grant moving onto the more advanced techniques and device models I mentioned above. I also surveyed available HPC cloud computing options to supplement their sub-par academic based supercomputer that was used for the initial calculations and estimated the costs and benefits of various engines.

● For the defence contractor, the monetary benefits have not yet been realized. For the semiconductor company, it is extremely difficult to quantify except to say that my total compensation averaged approximately $200k for a decade.

Evaluation of the translation case ● Bottlenecks included bringing the less experienced engineer up-to-speed on the

techniques and their tricks and difficulty of use. In addition, the lack of sufficiently reliable computing resources also was a significant impediment to the efficiency of the process. In the case of the semiconductor client, a large portion of the difficulty was related to gaining the confidence of skeptical experimental clients in the methods and their efficacy. It is always also a challenge to get high quality and timely characterization data to build our structural models that are representative of reality. Often times the real structures also would require a model size that was too computationally expensive and smaller models would need to be used instead.


Introduction

Translator Profile

What type of Translator is your institution: TTI (Technology Transfer Institute), Academic group, Software Company, Manufacturing Industry, Other (Consultancy, etc.).

Manufacturing Industry

What is your field of expertise: specify type of material, type of models according to RoMM (please see Review of Materials Modelling), type of property/phenomenon, other?

My name is Ali Karimi. I am mainly interested in the viscoelastic and mechanical

properties of polymer nanocomposites, and I am using the discrete models to understand

the molecular mechanisms of different macroscopic properties of the material.

Client

Who is the client? Is the client a large company, SME or a consortium thereof?

This project was carried out in collaboration with Continental Tires. Continental is

founded in Hannover, Germany, in 1871, and is among the leading automotive suppliers

worldwide and currently has approximately 218,000 employees in 55 countries.

Which value chain segment (e.g. matjerial producer, convertor, end-user) it is positioned?

The client is the end-user and the outcome of the material modelling is used to improve

tire performances.

Did you have existing collaboration with the client?

I am working as an internal translator.


Describe briefly the industrial problem.

Upon deformation of a polymer composite consisting of chemically or physically

connected network entropy of the system changes, which leads to short and long-range

restoring forces. The most active dissipation mechanisms upon deformation are

viscoelastic dissipations in the polymer matrix and energy lost due to filler-filler and

filler-polymer interactions [Hager et al. Macromolecules 2015, 48, 9039 – Hentschke, Constitutive

Models for Rubber VIII 2013, 1, 299 – Meyer et al. Scientific Reports 2017, 7, 11275 – Meyer et al.

Macromolecules 2017, 50, 6679]. While the former is sensitive to monomer friction and

polymer network defects, the latter depends on filler microstructure that includes the

dispersion, distribution and geometrical features of fillers in the matrix. In this work, we

were mainly interested in understanding the effect of polymer-filler interface

characteristics on the energy dissipation in the material.

Indicate involved budget or preferred time to solution (duration).

The project was carried out during a period of 3 years.



Indicate what was the expected outcome of the translation process.

The main expectation was to gain a molecular understanding of the macroscopic

properties of the material.


What type of model(s) did you use propose and use? Explain arguments and criteria used to propose and choose a specific modelling approach and modelling executor for the

specific industrial problem.

Based on the main objective of the project, which was to gain a molecular understanding

of the process, using the discrete models was proposed. I selected an academic group

with which we have been previously collaborating, mainly due to the trust, which has

been build up in time, and the great interest that the group has shown to our questions.

However, if it were a new topic, I would search for the best/most suitable partner in the

field.

A direct comparison between the outcome of the models and experimental data is

challenging due to the difference in the time and length scales. In order to extend the

simulation results to the macroscopic length scales and be able to compare the results to

experimental data, several theoretical models were needed to be developed. More details

on the modelling approaches and comparison to the experimental results can be found

here: Meyer et al. Scientific Reports 2017, 7, 11275 – Meyer et al. Macromolecules 2017, 50, 6679.

The following figure illustrates how the modelling has been used to describe the change

in the ratio of the loss modulus to the storage modulus (tan δ) as a function of

temperature by adding nanoparticles to a polymer matrix.



How did the client use the modelling results?

What were the benefits for the client of using modelling? Give a semi quantitative or qualitative estimate (For more information on the types of client benefits and how to

estimate quantitative impacts, please see the attached document or Economic Impact of

Materials Modelling).

The outcome the work has provided a deeper understanding of the material at the

molecular level. Such know-how helps the compounders to make better-informed

decisions on designing the new materials and support the experts during

troubleshooting.



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