Principles for modelling of manufacturing

sequences

MATS WERKE

DOCTORAL THESIS

STOCKHOLM, SWEDEN 2015

I

Abstract

The manufacturing sequence influence, to a large extent, component properties like fatigue

life, shape accuracy and manufacturability. By simulating the manufacturing sequence, using

numerical or empirical models, and extracting important accumulated data, like residual

stress, hardness and shape, the possibilities of early analysis of a design concept and the

associated manufacturing sequence will increase. An established methodology has the

potential of reducing physical testing and the time and costs of product design and process

planning.

This thesis proposes an algorithm to be used for setting up a framework of interconnected

process step models. With support from the algorithm, it is possible to extract a virtual

simulation sequence from a physical manufacturing sequence. Thereby, you can replicate the

aggregated effects of process steps on part key features and manufacturing features. The

algorithm will serve as a tool in process planning when establishing virtual manufacturing

sequences. The virtual sequences should be used for virtual prediction of component

properties, optimization of process parameters and evaluation of the effects of replacing,

removing or adding process steps to a manufacturing sequence

The algorithm is based on stepwise upstream selection of process steps, definition of

interconnected models and selection of interconnected datasets using breadth first search. The

algorithm completes existing procedures for data mapping and exchange of data between

models into an overall approach for establishing virtual manufacturing sequences. Other

scientific contributions are methods for modelling of deep rolling and blasting, a model

material for validation of rolling and forging simulation and principles for integration of

process simulation with CAD/CAM.

II

Acknowledgements

This work was accomplished with support from a number of people. First, I would like to

address my gratitude to my supervisor Professor Mihai Nicolescu and my co-advisor,

Professor Torsten Kjellberg for discussions and consultations.

Further, I would like to thank all the researchers, who have contributed to this work by means

of discussions, co-authoring of papers and reports and active work in the case studies. I

especially would like to thank Dr Mats Bagge for his contributions in the development of the

upstream metamodelling algorithm in Paper V, Lars Johanson, Swerea IVF for development

of the C6t2 utility, Dr Sven Haglund, Swerea KIMAB, for fatigue discussions and finite

element analyses etc in the steering arm case in Paper II, Dr Mikael Hedlind, for his

contribution concerning the ISO 10303 standard in Paper VI, Thorbjörn Hansén, Swerea

MEFOS, for the collaborative work in Paper IV. I also would like to thank Mikael Ström,

Swerea IVF, for inspiring discussions concerning research methodology.

My gratitude also goes to representatives of Scania, Volvo Trucks, Bharat Forge and Parker

Hannifin for contribution with time and resources in various research projects by means of

simulation, forging tests, material data development, residual stress measurements, fatigue

testing, co-authoring of papers and reports, discussions and knowledge contribution.

The work was made possible by financial aid from Vinnova, Sweden´s Innovation Agency,

which is gratefully acknowledged.

Finally I would like to thank my family Birgitta, Fredrik and Sofia for supporting me in all

thinkable ways and for putting up with my writing during countless weekends and vacations.

III

Content 1 Introduction ......................................................................................................................... 1

2 Appended papers ................................................................................................................. 3

3 Contributions to appended papers ....................................................................................... 4

4 Referred publications .......................................................................................................... 5

5 Definitions and abbreviations ............................................................................................. 6

6 Motivation ........................................................................................................................... 7

7 Research methodology ...................................................................................................... 10

8 Challenges ......................................................................................................................... 12

8.1 Selection of process steps to be included in the virtual MS ...................................... 12

8.2 Definition of interconnected models and datasets ..................................................... 12

8.3 Map output data on proceeding models ..................................................................... 13

8.4 Exchange of data between process step models ........................................................ 14

8.5 Validation .................................................................................................................. 14

8.6 Implementation of sequential simulation in product development ........................... 15

9 Research questions ............................................................................................................ 16

10 Summary of appended papers ...................................................................................... 17

Paper I .................................................................................................................................. 17

Paper II ................................................................................................................................. 19

Paper III ................................................................................................................................ 20

Paper IV ................................................................................................................................ 21

Paper V ................................................................................................................................. 22

Paper VI ................................................................................................................................ 24

Paper VII .............................................................................................................................. 25

11 Synthesis of research .................................................................................................... 27

12 Research results ............................................................................................................ 28

13 Scientific contributions and future research ................................................................. 29

14 References .................................................................................................................... 30

15 Appendix ...................................................................................................................... 33

1

1 Introduction

The development of the manufacturing industry relies on research into manufacturing

processes and materials and the development of new products. Global competition forces

manufacturing companies to continuously optimise process sequence performance and

efficiency in order to assure product quality and competitiveness. Manufacturing sequence

(MS) optimization refers to at least two manufacturing processes in consideration of one or

more target criteria. The objective of process chain design is a target-oriented arrangement of

adequate processes depending on certain component characteristics as defined by the design

specifications, to enable highly efficient manufacturing. The purpose of modelling and

simulation of manufacturing sequences is to improve efficiency in product design and process

planning. This will lead to the economic manufacturing of high-quality products with the

greatest possible productivity.

This thesis introduces a systematic representation methodology to be used in order to set up a

set of interconnected numerical or empirical process step models for simulation of aggregated

features from the MS. This metamodelling methodology includes procedures for selection of

process steps to be included in the virtual sequence, definition of interconnected models and

datasets, data mapping and data exchange between models. Furthermore, procedures for

implementation of MS simulation in practice and validation of plastic deformation are also

presented, see Figure 1.

Fig. 1 Procedures for metamodelling of manufacturing sequences.

The research motivation is described in chapter 6 whereas the research methodology is

described in chapter 7. The challenges are clarified in chapter 8 and the research questions in

chapter 9 are based on the identified gaps between existing and desired situations in chapter 8.

Chapter 10 summarizes the enclosed publications and the research is synthesised in chapter

11. Chapter 12 concludes the research and the scientific contributions are described in chapter

13 together with suggestions for future research.

An algorithm for metamodelling of manufacturing sequences is proposed. The algorithm is

based on stepwise upstream selection of process steps, definition of interconnected models

and selection of interconnected datasets. The algorithm supports framework modelling and is

2

applicable for both numerical and empirical models. With support from the algorithm, it is

possible to extract a virtual simulation sequence from a physical manufacturing sequence.

Thereby, you can replicate the aggregated effects of process steps on part key features and

manufacturing features. The algorithm will serve as a tool in process planning when

establishing virtual manufacturing sequences. The virtual sequences should be used for

optimization of process parameters and evaluation of the effects of replacing, removing, or

adding process steps to a manufacturing sequence. The algorithm will also serve as a tool in

strength and shape analysis of components.

Other scientific contributions are modelling methods for deep rolling and blasting, a new

model material for validation of plastic deformation during rolling and forging and an

approach for implementation of MS simulation in practice.

The research is based on experiences from case studies of forged components using a steering

knuckle, steering arms, a bevel gear pinion, a crown wheel and a crankshaft and a casted

cylinder block.

3

2 Appended papers

Paper I Werke M, Moshfegh R, Johanesson B, Svenningstorp J, Johansson P, Stinstra E

(2005) Statistical methods for extracting and evaluating manufacturing

parameters of significant importance to component robustness, Proceeding of

QMOD conference, Palermo

Paper II Werke M, Kristoffersen H, Haglund S, Svensson L Nord A (2008) Predicting

residual stresses and hardness of a critical component using a combination of

numerical and empirical methods, Article in Research Steel International special

edition, Krakow

Paper III Werke M (2008) Methods and models for shot peening simulation, Proceedings of

SPS08, Stockholm

Paper IV Hansen T, Werke M (2012) Modelling and model validation of the deformation of

macro inclusions during hot deformation, Proceedings of 8th International

Conference on Clean Steel, Budapest

Paper V Werke M, Bagge M, Nicolescu M, Lindberg B (2015) Process modelling using

upstream analysis of manufacturing sequences, Article in International Journal of

Advanced Manufacturing Technology, DOI 10.1007/s00170-015-7076-4

Paper VI Werke M, Hedlind M, Nicolescu M (2014) Geometric distortion analysis using

CAD/CAM based manufacturing simulation, Proceedings to SPS14, Gothenburg

Paper VII Werke M, Nicolescu M, Semere D, (2015) Variation analysis of manufacturing

sequences, to be published

4

3 Contributions to appended papers

Paper I M Werke contributed to the development of the overall methodology and the deep

rolling model. E Stinstra contributed with statistical methods, P Johansson

contributed with the VMEA methodology.

Paper II M Werke contributed to the modelling methodology, using a combination of

numerical and empirical models and to the fatigue analysis. S Haglund

contributed with the methods for measurement of the surface roughness, fatigue

analysis, and load analysis of the test case using FEM.

Paper III M Werke contributed to the complete paper including the literature survey.

Paper IV M Werke contributed to the analysis of plastic deformation of the steering arm

and crankshaft. T Hansen contributed to the development of the model material

and P Ottosson, Swerea IVF, contributed to the simulation of the deformation of

inclusions during rolling.

Paper V M Werke contributed to the upstream metamodelling methodology. M Bagge

contributed with the development of the algorithm.

Paper VI M Werke contributed to the methodology and global modelling and simulation of

machining. M Hedlind contributed with knowledge concerning STEP.

Paper VII M Werke contributed to the overall MS simulation methodology. M Nicolescu and

D Semere contributed to the Monte Carlo simulations and FEA.

5

4 Referred publications

The author has also contributed to other publications with major influence on the research.

The most important publications are enlisted below

[10] Werke M, Kristoffersen H, Andersson J, Johansson K, Thoors H (2009) Virtual

verification of the shape accuracy of a powertrain component. Research report

Prosim project

[13] Werke M (2009) Simulation of manufacturing sequences for verification of

product properties. Licentiate thesis, KTH.

[18] Werke M (2003) Internet based distribution of simulation results, EVEN

conference, Trinity College, R. I. Campbell, N. O. Balc, Loughborough, pp 4 – 13

[22] Werke M, Gotte A, Sibeck L (2014) Castforging for production of components

with tailored geometry and strength, Research report, Vinnova

[28] Werke M, Ström M (2013) Konceptkonstruktion – Framtid och

forskningsmetoder. Uppdragsrapport Vinnova

[33] Wärmefjord K, Söderberg R, Ottosson P, Werke M, Lorin S, Lindkvist L,

Wandebäck F (2013) Prediction of geometrical variation of forged and stamped

parts for assembly variation simulation, Proceedings of International Deep

Drawing Research Group Conference IDDRG2013, Zurich

[34] Werke M (2009) Blasting influence of residual stresses and hardness – An

investigation with support from measurements, Swerea IVF project report 09/09

6

5 Definitions and abbreviations

CAD/CAM: Computer aided design/Computer aided manufacturing

CFD: Computer fluid dynamics

DACE: Design and analysis of computer experiments

DoE: Design of experiments

DRM: Design research methodology

DS: Descriptive study

FE/FEM/FEA: Finite element/Finite element method/Finite element analysis

FMEA: Failure mode and effect analysis

LHD: Latin hypercube design

Metamodel: A sequence or framework of interconnected process step models.

MS: Manufacturing sequence

Validation: Judgement of the validity of a model for prediction of required data, using

measurements.

OEM: Original equipment manufacturer

PS: Prescriptive study

RC: Research clarification

STEP: Standard for exchange of product model data

UNV: Ideas universal file format

VMEA: Variation mode and effect analysis

VRML: Virtual reality modelling language

VTF: View tech file format

XRD: X-ray diffraction

7

6 Motivation

Forged components like shafts, gears, knuckles and steering arms are characterized by high

strength and robust properties. The components are available in many applications such as

final drives and gearboxes in vehicles, see Figure 2, handheld tools, agricultural and forestry

machines, but also in power plants, process and mining, aviation and marine and defence

equipment. These types of components, whose function is to transfer force and torque, are

manufactured in steel and in traditional process steps such as forging, machining, heat

treatment and mechanical surface treatment.

Fig. 2 Transmission components in final drive and gearbox.

A process step is here defined as an activity that creates a shape (e.g. forging, casting, powder

methods and rapid prototyping), modifies a shape (e.g. machining, heat treatment), joins parts

(e.g. adhesives, welding, fasteners) and finalises a product (e.g. polishing, coating, painting

and mechanical surface treatment), see Ashby [1].

Component properties like strength, geometric distortions and manufacturability are not solely

dependent on single process steps but are sequentially aggregated through the MS. This is

illustrated in the example in Figure 3 where fatigue strength is improved through a sequence

of process steps.

Fig. 3 Possible improvement of fatigue- and tensile strength through a MS [2].

8

In fact the complete chain, including the melting, solidification and forming processes

influences the final product features. This is schematically illustrated with an example where

a bevel gear pinion is manufactured through several process steps, see Figure 4 and Paper V.

Fig. 4 Manufacturing sequence for a bevel gear pinion.

The melting and solidification processes introduce crystallographic structure, voids,

inclusions and segregations, i.e. irregularities, in the material. Those material properties are

then modified during the proceeding rolling and forging processes where voids are closed,

inclusions deformed and recrystallization occurs. Anisotropy will evolve and influence fatigue

strength, see Temmel [3], and geometric distortions, see Brinksmeier [4]. The heating and

cooling processes will introduce residual stresses and the material removal during the

proceeding turning and gear cutting processes will release residual stresses and introduce

residual deformations, see Paper VI. Thus, in order to predict properties the complete

sequence should be taken into consideration and a vision is to establish a sequence of process

step models and simulate the MS. A process step model is here defined as “a representation

of a process step intended to enhance our ability to understand, predict, or control its

behaviour”, whereas simulation is defined as “the imitation of the behaviour of the process”

see Kaizer [5].

Today process simulation is used in practice in order to optimise individual process steps like

casting, rolling, forging, heat treatment, machining and mechanical surface treatment. FEM is

the most established method, see Gur et al. [6] and foundries use the simulation technique in

order to study how variations in metal fluid flow and cooling conditions influence the

solidification process as well as residual stresses and shape after casting, see Vijayaram et al.

[7]. Milling and forging shops utilise FE simulation in order to study how the rolling machine

or forging press kinematics, the tool and the temperature of the workpiece influence the shape

of the workpiece and the material filling in the tools, see Hartley et al. [8]. FE simulation of

machining, like drilling, turning and milling, may be used in order to study how the cutting

tool geometry, machining parameters and fixture design influence the workpiece shape,

temperature and residual stresses, see Mackerle [9]. Empirical modelling is also possible e.g.

Werke et al. suggest in [10] a polynomial model based on residual stress measurements where

9

the residual stresses are predicted based on feed rate and cutting depth. With heat treatment

simulation it is possible to study how variations in temperature, cooling time and cooling rate

influence the residual stresses, hardness and phase distribution, see Mackerle [11]. FE

simulation of mechanical surface treatment like e.g. deep rolling and shot peening may be

used in order to study how variations in process parameters and material influence surface

residual stresses and deformations, see Paper III.

The common denominator using FEM is the discretization of continuum problems into finite

elements that are connected with nodes and thus the model data are always expressed in terms

of nodal and elemental data. These types of data also constitute the output results from the

simulations and it is thereby possible to use output data from one process step simulation as

input data to the proceeding process steps, see Figure 5.

Fig. 5 Process step simulation data flow, including process history.

This fact introduces possibilities to connect process step simulations and predict aggregated

effects from the MS on the final or intermediate component features. Sequential simulation

has a potential to improve load- and shape analysis of components, improve optimisation of

processes through the MS and evaluation of the effects of replacing, removing or adding

process steps to a MS, see Figure 6. The motivation to develop and implement a methodology

is thereby high and will reduce development time and costs of product design and process

planning.

Fig. 6 Evaluation of alternative manufacturing routes using MS simulation.

10

7 Research methodology

The research is based on Design Research Methodology (DRM), which is described by

Blessing et al. in [12]. The methodology consists of Research Clarification (RC), Descriptive

Study (DS) and Prescriptive Study (PS), see Figure 7.

Fig. 7 Design Research Methodology according to Blessing [14].

The research is clarified based on the challenges with MS simulations according to chapter 8.

The gap between the existing and desired situation for each challenge is identified and the

research questions in chapter 9 are extracted from the gap analysis.

In the descriptive study empirical data is assembled and analysed in order to derive a better

understanding of the problem. The knowledge development is performed with support from

case studies developed in the research projects according to table 1. The results are presented

in the enclosed publications I – VII and in the licentiate thesis [13]. The descriptive study is

summarized in chapter 10.

In the prescriptive phase, which is presented in chapter 11, the developed knowledge is

synthesized into principles and algorithms for MS simulation. The results are evaluated in

descriptive study II where conclusions are drawn and potential future research is identified.

The descriptive study II is presented in chapter 12 and 13.

11

Table 1: Summary of projects, case studies and partners involved in the research.

Phase Publication Case study Projects Industrial partners Research organisations

1 I Steering

knuckle

IMS-EURobust

(EU)

Volvo Trucks Swerea IVF, Chalmers

1 II Steering

arm,

Pinion

ProSim (Vinnova) Volvo Trucks,

Scania, Bharat

Forge, Parker

Hannifin

Swerea IVF, Swerea

KIMAB

1 III Steering

arm Blasting (Vinnova)

Bharat Forge Swerea IVF

1 [13] Pinion ModArt (Vinnova) Scania, Sandvik

Coromant

Swerea IVF, KTH/IIP

2 IV Crankshaft Clean Steel

(Swerea)

Bharat Forge Swerea MEFOS, Swerea

IVF

2 V Pinion,

Steering

arm

Upstream

simulation

(Vinnova)

Volvo Trucks, GKN Swerea IVF

2 V Pinion,

Crown

wheel

ReVer (Vinnova) Scania Swerea IVF, Chalmers

KTH/IIP, Swerea KIMAB

2 VI Crown

wheel

FBOP (Vinnova) Scania KTH/IIP, Swerea IVF

2 VII Cylinder

block

Scania KTH/IIP, Swerea IVF

12

8 Challenges

The modelling and simulation of complete manufacturing sequences is a challenge which may

lead to unrealistic and time consuming modelling efforts and extensive computational

requirements. This is due to the often complex material transformations through several

consecutive process steps. Simplifications are therefore required and a systematic approach is

needed. Since the simulation of MS is limited in practice there is also the need for methods

that facilitate industrial adaption of sequential simulation. The challenges are discussed

below.

8.1 Selection of process steps to be included in the virtual MS

In order to establish a simplified virtual sequence there is a need to reduce the number of

process steps to be included in the virtual sequence, i.e. a need for a method for selecting the

first, intermediate and final process steps to be included in the virtual sequence

The selection of the first process step may be a difficult task due to the complex material

transformations that, for example, a transmission component is exposed to through a long

sequence of process steps. In order to avoid this problem, one possibility is to include the

complete physical sequence in the virtual chain. This may however lead to unnecessarily long

virtual sequences. The difficulties concerning selection of intermediate process steps may

vary. If, for instance an intermediate process step is painting it is obvious that it will not

influence e.g. the final distortions and it should be excluded from the virtual sequence. On the

other hand, e.g. a heat treatment should be included. The final process step may be the last

process that influences the part key features such as strength, geometry and dimensional

tolerances. The final process step may also be an intermediate process after which

manufacturing features, such as: machinability (e.g. hardness and grain size), hardenability

(e.g. phase composition) and forgeability (e.g. crack tendencies during plastic forming) are of

interest for the study.

The selection of process steps may be performed with support from experiments and e.g.

Brinksmeyer et al. [4] use physical experiments according to Design of Experiments (DoE) in

order to analyze the influence on geometric distortions from different process steps like

casting, forming, cutting and case hardening on discs. The experimental approach is however

time- and cost consuming and there is a need for more efficient guidance when selecting

process steps. No systematic approach for selecting process steps has been found in literature.

This thesis discusses 3 major approaches, namely selection of the most important single

process step, selection of a subset of process steps downstream through the sequence and

selection of process steps upstream through the sequence.

8.2 Definition of interconnected models and datasets

The description of generic methods for definition of interconnected models and selection of

interconnected datasets is limited in literature. Govik et al. describe in [14] a special case for

simulation of forming, assembly and spotwelding of stamped parts where simplifications of

interconnected models and simulations are described. The model simplifications are here

based on knowledge and no systematic method is used. Denkena et al. propose a methodology

13

where the interdependencies between processes and interconnected datasets are stored in

“Technological interfaces”. The authors demonstrate the use of technological interfaces in

conjunction to the manufacturing of helical car-gearings in [15] and in conjunction to the

manufacturing of pinion shafts in [16], including precision forging, hardening, hard turning

and grinding. The publications describe how to store datasets but do not present how to select

the datasets to be included in the technological interfaces. This thesis discusses two major

approaches namely downstream modelling and upstream modelling of the MS.

8.3 Map output data on proceeding models

Different process steps require various, mesh topologies and element types depending on the

physical phenomena that the models are intended to reflect, see examples in Figure 8.

Fig. 8 Different models and mesh topologies for different processes.

In the case hardening model the mesh has high density in surface regions in order to reflect

various carbon contents during decarburisation, see lic. thesis [13]. In deep rolling or

shotpeening simulation it may be sufficient with a local model of a small substrate of the

material near the surface, see Paper I and Paper III. For process steps that lead to large

plastic deformations, like e.g. forging, a complete global model that reflects the global

geometry is required. Further, for these types of process steps there is a need for automatic

remeshing during the execution in order to solve problems with large element distortions.

This may lead to a high density mesh in areas exposed to large plastic deformation and lower

density in regions with lower deformations. The mesh may here differ from the mesh that is

created with support from a CAD model to be used in a structural load analysis, see Paper II.

Thus when simulating a sequence of process steps the mesh and geometry requirement may

vary between the process step models concerning model geometry and mesh topology. The

requirement of the element type may also vary for different process steps concerning element

shape (brick, tetrahedron, 2D or 3D etc) and number of integrations points. Mapping

methodologies exists and e.g. Afazov et al. [17] present a computer based algorithm for

mapping of data from an output mesh onto a new input mesh. The algorithm is however

14

limited to mapping of data between models developed in Abaqus, Ansys, MSC Marc, Deform

and Morfeo.

8.4 Exchange of data between process step models

It is possible to exchange data between a majority of the most common simulation softwares

in native ASCII-formats or in generic formats according to standards like VRML-, VTF- and

UNV- formats or STEP. VRML is an open standard for visualisation of 3D models on the

Internet. It is possible to exchange the surface mesh and display various results with colour

representations, see Werke [18], but the possibilities for exchange of other types of simulation

data is limited. The VTF format was originally developed by the company Ceetron (former

ViewTech) and is a standard file format for post-processor applications like Glview Inova and

Glview Express (used by e.g. the simulation software Forge). The UNV format was originally

developed by the Structural Dynamics Research Corporation (SDRC) in order to facilitate

data transfer between CAD, computer aided test (CAT) and CAE. These representations are

generic but the implementations in practice are limited. Another possibility is to utilize the

generic ISO 10303 (STEP) standard for data communication. STEP includes all aspects

concerning data transfer between different engineering disciplines including CAD, CAM and

CAE. STEP contains several applications protocols (AP:s) dedicated to specific engineering

tasks. AP 209 (Composite and metallic structural analysis and related design) is especially

suitable for FEA and has possibilities to communicate both CAD models and FE models, see

[19]. One disadvantage is that the standard is limited to linear FEA.

Another option is to reformat output data into input data to the next simulation code using

dedicated reformatting codes. Afazov presents in [20] a data translation and mapping software

with the ability to communicate data between simulation software like Abaqus, Ansys,

MSC.Marc, Deform and Morfeo. A similar system is presented by Åström [21] and Werke et

al. describe in [22] how to reformat output mesh and porosity data from casting simulation in

ProCast to input data for forging simulation in Deform. The data exchange is performed using

a tailored data translation software. The purpose is here to simulate castforging which is an

innovative manufacturing method where pre-casted components are forged into final shape in

order to increase the ductility and fatigue strength. One disadvantage with the described

approaches is however that CAD/CAM data is not included in the data exchange.

8.5 Validation

Modelling and simulation of process steps are connected with risks for errors. The reasons for

simulation errors may originate from insufficient accuracy in material- and process data,

inconsistency in material models and mesh topology. The assembly of several process step

models into a sequence of models will also imply risks for inaccuracies connected with

mapping- and data exchange due to inconsistencies between different material models. Since

errors from one process step simulation will aggregate as input to proceeding process step

simulations it is important to validate the virtual sequence. Validation is here defined as “The

process of determining the degree to which a model is an accurate representation of the real

world from the perspective of the intended uses of the model”, see Obercampf et al. [23].

Validation provides evidence of how accurately the computational model simulates the real

15

world for system responses of interest. This is performed by the assessment of the model

accuracy by comparison with experimental data and the decision of model accuracy for the

intended use.

The possibilities to validate macrostructural models, with support from measurements, are

well described in literature. Obercampf et al. [24] propose a generic validation methodology

that decomposes and simplifies complex physical phenomena into four levels; system level,

subsystem level, benchmark level and unit problem level and where each level is validated

using measurements. This is demonstrated by Ahlberg [25] through the validation of welding

and heat treatment simulation in a sequence. The description of how to validate

microstructural models is however limited in literature. Milesi et al. [26] demonstrate

possibilities to simulate the deformation of inclusions during forging using microstructural

models. The paper does however not explain how to validate the simulations.

8.6 Implementation of sequential simulation in product

development

Virtual methods invoking CAD models and tools for finite element analysis of component

strength and stiffness provide well established support for efficient product development and

are compulsory in a majority of the mechanical design offices when designing and optimising

components. It is a well-known fact that the manufacturing processes have a large influence

on product properties like fatigue strength, shape accuracy and surface quality. The influence

of product properties induced by the manufacturing processes is not yet included in the virtual

product development methodology and the design analysis often starts with virgin material

data. This means that the engineering development includes several loops of prototyping and

physical testing in order to ensure that the required properties are reached. This uncertainty

also results in a conservative dimensioning philosophy, unnecessarily heavy and over-

dimensioned components and thereby unnecessarily high safety factors. A virtual design

philosophy invoking the effects on component properties from the manufacturing sequence

may have good potential to increase efficiency in the product development process and

increase the quality of the component and manufacturing processes. Product development, i.e.

“Total design” according to Pugh [27], includes conceptual and detail design activities, see

Figure 9.

Fig. 9 Illustration of product development according to Pugh [29].

In the conceptual design phase the design engineer makes drafts of various concepts of the

component that are evaluated against each other, see Werke et al. [28]. FEA is then

conducted, based on the CAD models, where the stresses and deformations due to the applied

loads are predicted. If the stress- or deformation levels are too high the designs are modified

and new feasibility tests are performed using FEA. One or several concepts are selected for

further development in the detail design phase. In this phase the weak spots in the design are

16

eliminated, the concept is refined and the final decision concerning geometry is made. A

virtual design philosophy invoking the effects on component properties from the

manufacturing sequence may have good potential to increase the accuracy in the design and

thereby minimize the need for costly prototypes and physical tests, see Figure 10.

Conceptual

designSpec.

CAD

modelFEA

Design OK?

Poc. plan OK?Detail

designTestPrototype Test OK? Manufacture

Process

planExisting situation

Desired situation

Yes

No

Yes

No

Conceptual

designSpec.

CAD

modelFEA

Design OK?

Poc. plan OK?

Detail

designManufacture

Process

plan

Yes

No

MS

simulation

Fig. 10 Illustration of existing (top) and desired (bottom) situation in virtual product

development.

Process planning activities may also be supported with MS simulation for procedures like

selection of process steps, to be included in the MS and optimisation of process parameters.

The challenge is to integrate sequential simulation with established CAD tools in order to

improve efficiency in both geometrical design and process planning.

Cunningham [29] describes a module within the Deform simulation software to be used by

process planners in order to select and optimise process steps. The module consists of

workpiece and process attributes. The workpiece object attributes include geometry, which

can be supplied from a 2D CAD-file, material data and mesh density. The process attributes

include process parameters, tooling and fixtures etc. and by assembling these objects a

sequence of operations can be built up and simulated, beginning with workpieces in their

initial state which are then subjected to successive processes, each of which determine the

new attributes of the workpiece. However, no systematic approach concerning how to

integrate simulation of MS with CAD has been found.

9 Research questions

Based on the previous discussion it is concluded that there are gaps between the existing and

desired situation concerning principles for metamodelling, i.e. principles for selection of

process steps, definition of interconnected models and selection of datasets. Other gaps have

been identified concerning validation of inclusion models. It is also concluded that the use of

sequential simulation is limited and in order to implement MS modelling and simulation in

practice it is beneficial to adapt modelling and simulation methodology to existing tools for

product development and process. The gaps between the existing and the desired situation are

summarized in Figure 11. Thus, in order to establish a complete MS modelling and simulation

17

methodology the existing principles for dataexchange and mapping should be complemented

with metamodelling principles and validation techniques.

Fig. 11 Gaps between existing and desired situations concerning modelling of MS.

The research questions are formulated as:

1. “How to define a simplified metamodel?”

2. “How to validate modelling of inclusions and segregations?”

3. “How to integrate MS simulation with CAD?”

10 Summary of appended papers

Paper I

A method for simulation on how the MS influence component robustness is proposed. The

method is a combination of engineering knowledge, statistical methods and simulation.

The first step is to select the most important process step using FMEA, see Lundberg [30].

The second step is to select the most important process parameters from the selected process

step using VMEA. VMEA is a method for analysis of variations and their effect on properties,

see Johansson et al. [31]. As a third step a process model is defined and variations in material

and process parameters are simulated. The strategy is illustrated in Figure 12.

18

Fig. 12 Single process step selection and modelling method.

The method is demonstrated on a component, a forged steering knuckle, which is an

important component in the steering mechanism of a truck. The main steps in the

manufacturing sequence are described in Figure 13.

Fig. 13 Steering knuckle with manufacturing sequence.

The deep rolling process is selected as the most important process step using FMEA. Deep

rolling is used in order to strengthen the component, for example in zones exposed to high

loads and the steering knuckle is exposed to deep rolling in a critical fillet radius. A spherical

ball is pressed against the surface to be treated, see Figure 14. The process introduces

compressive residual stresses, cold deformation and reduces microcracks.

Fig. 14 Deep rolling process.

With support from VMEA the most influential material- and process parameters on fatigue

strength are selected, i.e. spindle velocity, feed rate and hydraulic pressure, Young’s modulus,

yield strength and tensile strength. Based on this information a FE model of deep rolling is

developed together with a plan for computer simulation experiments with several test points

between the low and high values. A computer based test plan is developed with support from

Latin Hypercube Design, see Stehouwer et al. [32]. Computer based test plans according to

19

DoE are also possible to use, see Wärmefjord et al. [33]. The effect on variations in surface

residual stresses from variations in process- and material parameters is predicted and the

component robustness is evaluated.

Paper II

A method for prediction of the influence from the MS on fatigue life is proposed. It is based

on downstream modelling and simulation of a subset of the MS. The method is demonstrated

on a micro alloyed steering arm, an essential component in the steering mechanism of a truck.

The main steps in the manufacturing sequence are described in Figure 15.

Fig. 15 Downstream simulation of steering arm.

The subset is selected, based on knowledge and the modelling starts with a mesh of the input

bar. Hot shearing, hot rolling, roughing blow, finishing blow and controlled cooling are

simulated in a sequence using a forging simulation code. The output mesh, describing the

geometry after the final forging step, including nodal temperatures from the forging

simulations, is used as input to a cooling simulation using thermo-metallurgical-mechanical

simulation. During blasting the components are bombarded with small steel balls in order to

remove slag from the surface. Other effects are increased compressive residual stresses and

cold work deformation, factors that increase fatigue strength. Based on a literature study in

Paper III it is concluded that the existing FE models for shot peening do not manage to

describe the complex blasting process where decarburized parts are tumbling against each

other at the same time as they are exposed to blasting. An approach with a combination of

empirical and numerical models for blasting is therefore proposed. The hardness and residual

stresses are predicted using measurements and a new method for analysing the influence of

the blasted surface texture on the stress concentration factor using a combination of

measurements and FEA is proposed, see Figure 16.

Fig. 16 Surface topology measurement with a confocal microscope and surface stress

concentration factor from blasting grooves analysis using a FE model.

20

The paper concludes that the blasting should be modelled using a combination of empirical

and numerical models. The knowledge is further developed by Werke [34]. Here

measurements of residual stresses and hardness before and after blasting during various

blasting times are systematically stored in a database. It is concluded that the surface

conditions that decarburisation effects on stresses and strains should be involved in the

empirical models.

Paper III

A literature survey is presented where 10 shot peening models are evaluated. Shot peening

improves fatigue strength with support from bombardment of the surface with small steel

balls, see Figure 17. Shot peening increases the surface strength through the introduction of

compressive stresses, increased hardness through cold work deformation and reduction of

microcracks.

Fig. 17 Shot peening process and schematic illustration of residual stress curve.

The results from a shot peening simulation may give indications of the residual stress- and

plastic strain profile below the surface as well as indentation depth. Significant for the

presented FE models is the fact that the studied geometric target volume represents a local

substrate below the surface. The state of the art concerning FE models for shot peening is still

on a basic level with one ball shot or a pattern of impacts against a substrate of an ideal

material. The investigated numerical models may be used effectively in qualitative studies of

how e.g. variations in shot velocity, hardness, shape and size of shot influence surface

residual stresses. However, the described models are not sufficient to describe the effects from

the machine, like the tumbling of pieces against each other at the same time as the shot

peening. Further, the physical surface conditions may be far from ideal including e.g.

decarburisation effects induced from previous forging and heat treatment processes. Thus in

order to perform quantitative studies concerning the effect of shot peening or blasting on

residual stresses and hardness a parametric model based on measurements is an alternative

approach, see Figure 18.

21

Fig. 18 Parametric shot peening model approach.

Paper IV

A new physical model material for validation of plastic deformation after rolling and forging

simulation is proposed. The model material consists of a combination of two steel grades with

slightly different composition. With support from Hot Isostatic Pressing (HIP) the model

material is shaped as bars with quadratic shaped cross section, see Figure 19.

Fig. 19 Model material shaped as a round bar.

The usability is demonstrated on rolled bars, a forged crankshaft and a forged steering arm.

The model material is here complemented with engrafted inclusions, in order to demonstrate

how to validate deformations of inclusions, and a centre round steel wire (4 mm) material in

order to validate the simulation of deformations on the kernel material. The model material is

rolled and forged and the output workpieces are visually inspected and compared to

simulations. The agreements between the position of the wire in the model material and

simulation results are good, see Figure 20.

22

Fig. 20 Comparison between simulation and etched measurements of plastic deformations.

Paper V

A new method for metamodelling of the MS is proposed. The method is based on upstream

selection of process steps, definition of interconnected models and selection of datasets. The

paper compares the advantages and disadvantages of upstream and downstream

metamodelling, see Figure 21.

Fig. 21 Comparison of downstream (left) and upstream (right) metamodelling.

In the downstream approach the first task is to analyse the first process step. If it is concluded

that that it will influence final results a model is defined. The output data, which may be used

as input data to proceeding models, are then used as guidelines for proceeding process step

selection and model definition and the approach is repeated stepwise forward until the

interconnected models for the final process step are defined.

23

In the upstream approach the user starts with analysing the last process step and defines a

model of this process step. The model input data that carries a history from previous processes

is then detected and defines the required output data from previous process step models. If the

previous process step influences the required output data, it is included in the virtual chain

and a model is defined, otherwise it is excluded. The process step selection and modelling

actions are repeated stepwise upstream. If no historical input data are needed for a defined

model the first process step in the metamodel is defined and the complete virtual sequence is

established.

Thus the upstream approach has better possibilities to reduce the number of process steps to

be included. Further, in the upstream approach the definition of models is guided by the

requested key features, which are stepwise extracted to specifications of output data needed

for previous models upstream through the sequence. This guidance from key features is

missing in the downstream approach.

When there is a need for several models to represent one process step the sequence of models

will be expanded into a framework of interconnected models. Two search algorithms, to be

used in order to navigate in the framework modelling procedure, are evaluated namely depth

first search and breadth first search, see Cormen et al. [35] and Figure 22. When using depth

first search, one branch is modelled to the end before modelling a new branch. When using

breadth first search, each process step is analysed and modelled completely before moving

stepwise upstream or downstream to the next process steps.

Fig. 22 Depth first search (top) and breadth first search (bottom) navigation.

In a depth first search scenario, all the previous process steps in a branch are analysed and

modelled before jumping back and modelling a new branch. This fragmentation in modelling

is avoided using breadth first search and the paper concludes that breadth first search is more

24

efficient as compared to depth first search. Based on those conclusions an algorithm for

metamodeling of manufacturing sequences is proposed. The algorithm is based on stepwise

upstream selection of process steps and definition of interconnected models using breadth first

search. The algorithm is enclosed in the paper in Scilab code and is demonstrated with

support from two test cases: a bevel gear pinion and steering arm, see Figure 23.

Fig. 23 Crown wheel (left), bevel gear pinion (middle) and steering arm (right) for heavy

trucks.

Paper VI

A CAD/CAM and FEM based methodology is proposed to be used in order to control

geometric distortions after machining. The method is demonstrated in the process planning of

a crown wheel according to the above Figure 23.

The component is designed in a CAD system and intermediate machining workpiece models

are developed. The CAD model of the component is used in order to create CAD models of

the forging dies and the billet to be used for process simulation of forging and cooling in a

sequence. The output from the cooling simulation is used as input to the proceeding

machining simulation. The models of the fixtures, machining features and the input raw piece

for machining are assembled in the CAD/CAM system and translated to machining

simulation. Several steps of virtual machining are then performed where the output residual

deformations and residual stresses from one machining setup are used as input to the

proceeding machining operation. The process parameters, material data and component

geometry are adjusted until the level of residual deformations are accepted. The data

exchange between CAD/CAM and simulations is performed using the STEP AP209. The

activities are illustrated in Figure 24.

25

Fig. 24 CAD/Cam based methodology.

Paper VII

In order to eliminate the sources of variations, at the process planning stage, it is important to

consider these variations in the developed models.

This paper investigates the dimensional variation propagation and the interactions among

machining operations in a process sequence. The main reason of variation propagation in

machining processes is that the previously machined features are used as the datum in the

subsequent machining operations. The method presented in this paper simulates the MS and

evaluate the effect on the geometry of machined part in terms of set-up and machining

deviations. The computational model estimates the dimensional characteristics of the

machined component by combining Monte Carlo technique with FEA. The first set-up

variation considered is caused by the orientation of the workpiece on the machine tool table.

The second set-up variation is due to elastic deformation of the fixture. Therefore,

dimensional variations are introduced at each process step by the setup and machine errors.

The deflection after face milling of the head face and the milling of the cylinder bores on a

cylinder block are simulated, see Figure 25.

26

Fig. 25 MS simulation concept.

Further, a method for prediction of the effects from variations in surface roughness on stress

variations is presented, using a combination of Monte Carlo simulation and FEA. A

population of 5000 different surfaces was used to simulate the contact line between a cylinder

and an engine piston. The simulation was performed by superimposing several random

sinusoidal curves of different wavelengths and phases. The wavelength, amplitude, and phase

of the sinusoids used to create this population were generated from normal and uniform

distributions. The contact stress was then computed by FE method, see Fig. 26.

Fig. 26 The simulation of the surface roughness on the contact line between cylinder and

piston (left) and the stress distribution on the contact line (right).

27

11 Synthesis of research

A flowchart which illustrates how the knowledge and modelling principles evolve through the

publications is presented in Figure 27.

Fig. 27 Relations between the appended publications.

a. In Paper I a method for single process step selection and modelling is proposed using

FMEA and VMEA. The approach has limitations and in order to derive an overview

of how the manufacturing sequence influence key features several process steps must

be included in the virtual MS. The methodology is therefore expanded into a

modelling and simulation of a subset of the process steps. A downstream modelling

approach is presented in Paper II.

b. The obtained knowledge concerning deep rolling and shot peening simulation,

presented in Paper I and Paper III, lead to the methodology for blasting simulation in

Paper II using a combination of numerical and empirical models, i.e. a framework

modelling approach.

c. The experiences from Paper II concerning downstream modelling, framework

modelling and the utilisation of both numerical and empirical models lead to the

suggestion of stepwise upstream framework modelling in Paper V and the proposed

algorithm in Scilab code.

28

d. A new model material presented in Paper IV, to be used as reference data when

validating modelling and simulation of inclusions and segregations.

e. In the licentiate thesis [13] different possibilities to communicate data between

models are discussed and an algorithm for translation and mapping of data, C6t2, for

tailored data exchange is presented. The C6t2 utility is written in C++ and runs at a

command prompt or from a script file. The utility is a complement to previous

described work in [17], [20] and [21]. During the development of the C6t2 algorithm

several pitfalls concerning mapping emerged. Several issues varied in the input and

output formats like node and integration point ordering, distribution of integration

points, results described in either a local or global coordinate system etc. The utility

also has the ability to transform a sheet metal forming mesh into a welding mesh with

solid elements and map output data from the shell elements to the solid elements, see

Figure 28.

Fig. 28 Data transfer functionality in C6t2 (left). Transformation of shell elements to

solid elements and mapping of stresses (Von Mises) on the new solid mesh (right).

f. In the licentiate thesis [13] STEP AP209 is identified as a suitable standard for

communication of data such as mesh, stresses and deformations in connection to

product development and process planning in practice, using CAD/CAM. This

knowledge was used in the development of the CAD/CAM based method for virtual

process planning that is proposed in Paper VI.

g. The simulation of variations in one process step in Paper I is expanded to simulation

of variations through a sequence of process steps in Paper VII.

12 Research results

The research results are summarized below:

“How to define a simplified metamodel?”

The stepwise upstream metamodelling approach is more effective, as compared to the

downstream approach in the efforts to establish a simplified metamodel. It is also concluded

that the breadth first search procedure is advantageous, as compared to the depth first search,

when establishing a framework of models stepwise upstream.

“How to validate modelling of inclusions and segregations?”

A new model material for validation of plastic deformations on both macro- and microscale

during rolling and forging is proposed.

29

“How to integrate MS simulation with CAD?”

STEP AP209 is sufficient in process simulation for communication of datasets such as mesh,

stresses, strains and deformations in connection to communication of CAD models. It has

been shown that the standard may be sufficient for limited MS applications such as prediction

of global machining distortions due to clamping.

13 Scientific contributions and future research

Metamodelling algorithm: A new approach with an algorithm for upstream metamodelling of

manufacturing sequences is proposed. The algorithm is feasible for the establishment of a

framework of models that contains both numerical and empirical models. The algorithm

completes existing methods and algorithms for mapping of data and exchange of data into an

overall set of principles for modelling manufacturing sequences. The upstream metamodelling

approach may be used in conjunction with methods proposed by Bagge [36] where the

process planner defines processes to be included in the sequence by asking: “What is the

required final outcome of the sequence?”, after which upstream selection of process steps is

conducted as shown in Figure 29.

Fig. 29 Process planning methodology as compared to manufacturing sequence.

Validation of models for inclusions: The new model material for validation of deformations in

microstructure modelling is a scientific contribution.

Mechanical surface treatment: Scientific contributions are a new model for deep rolling,

taking the surface roughness prior to deep rolling into consideration and blasting simulation,

using a combination of numerical and empirical models. Blasting is however a complex

process where the workpieces are tumbled against each other and at the same time exposed to

blasting. The surface decarburisation during forging and cooling will also have a major effect

on the surface residual stresses before and after blasting. Those effects are not fully

investigated, and this is recommended for future research.

Implementation in product development: The scientific contribution is the proposed

methodology for adaption of MS simulation in practice using CAD/CAM and STEP. The

development of STEP for comprehensive exchange of nonlinear simulation data together with

CAD is a subject for future research.

30

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33

15 Appendix

Paper I

Statistical methods for extracting and evaluating manufacturing parameters of

significant importance to component robustness

Werke M, Moshfegh R, Johanesson B, Svenningstorp J, Johansson P, Stinstra E

Presented at QMOD conference, Palermo 2005

Paper II

Predicting residual stresses and hardness of a critical component using a combination of

numerical and empirical methods

Werke M, Kristoffersen H, Haglund S, Svensson L Nord A (2008)

Article in Research Steel International special edition, Krakow 2008

Paper III

Methods and models for shot peening simulation

Werke M

Presented at SPS8 conference, Stockholm 2008

Paper IV

Modelling and model validation of the deformation of macro inclusions during hot

deformation

Hansen T, Werke M

Presented at 8th

International Conference on Clean Steel, Budapest 2012

Paper V

Process modelling using upstream analysis of manufacturing sequences

Werke M, Bagge M, Nicolescu M, Lindberg B

Article in International Journal of Advanced Manufacturing Technology, 2015

Paper VI

Geometric distortion analysis using CAD/CAM based manufacturing simulation

Werke M, Hedlind M, Nicolescu M

Presented at SPS14 conference, Gothenburg 2014

Paper VII

Variation analysis of manufacturing sequences

Werke M, Nicolescu M, Semere D

To be published