aalto industrial internet campus

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Lista Lista Lista Lista Lista Aalto Industrial Internet Campus Jari Juhanko COO, AIIC Aalto University Innovation and Encounters backed up by Research and Education

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Aalto Industrial Internet Campus

Jari Juhanko

COO, AIIC

Aalto UniversityInnovation and

Encounters backed up by Research and

Education

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Bio

TkT Jari Juhanko, Aalto-yliopistoCOO, Aalto Industrial Internet Campus, http://[email protected], +358 50 556 3564

Koneenrakentaja, mekatroonikko

Suunnittelu, valmistus ja kunnossapito

Industrial Internet realisti

Professori 2008…2013, tutkimuspäällikkö, tutkija

Roll Dynamics Engineering Oy, CEO, 1999 – 2001

Rollresearch Intl Oy, hallitus 2001 – …

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Bio

TkT Jari Juhanko, Aalto UniversityCOO, Aalto Industrial Internet Campus, http://[email protected], +358 50 556 3564

Engineering design, mechatronics

Design, manufacturing and maintenance

Industrial Internet realist

Professor 2008…2013, research manager, researcher

Roll Dynamics Engineering Oy, CEO, 1999 – 2001

Rollresearch Intl Oy, board 2001 – …

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From products to system of systems

(Porter and Heppelman 2015)

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AIIC Innovation ecosystem

Aalto University in collaboration with industrial partners offer physical and digital infra for industrial internet innovation and development

Student projects and assignments require companies• to open interfaces, build APIs and act as active instructors

• to redesign the product and system architecture for collaborative R&D&I

• to study new technologies and platform economy in real pilot installation

Research projects and professional education• utilize pilot installations for testing new technologies including platform features, data governance models

and IPR security

• enable collaboration workshops with core partners and future prospects

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Research Platform for Manufacturing

Smart crane with

extensive PLM models

and IoT interfaces

IoT instrumentation for

research apparatus for

studying magnetic

bearingsExtension of NetLeap

4G/LTE/5G

infrastructure

Mindsphere IoT platform connecting

machines and physical infrastructure to

the digital world

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Aalto Industrial Internet Campus - Thematic Scope

Building and

construction

Manufacturing

industry

Process

industry

Data platform

5G for industrial internet

Business, strategy

R&D, innovation

Internet of

energy

Intelligent

mobility

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Ecosystem convergence

Competence ecosystem

Businessecosystem

Policies

Platforms

Services

Data management

Analytics

Cloud

Networking

Connectivity

Intelligence

Sensing & actuating

Assets

C CTech partner 1

Tech partner 2

Tech partner n

Service partner

Policy partner

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Digital value in the era of Industrial Internet?

Today, ”available data” cannot be utilized in daily operations

Data integrity, classification and governed distribution for differentstakeholders and different purposes are missing

• E.g. whose business is data analytics? What is the value of analytics and how therevenue is shared in the ecosystem?

• How a 3rd party company can join the ecosystem business and its digital platform?

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IT/OT Convergence

Study the challenges and implications of the foreseen convergence of 5G communications, distributed cloud computing, and CPS-oriented control systems towards a unified industrial data and computation platform

Material flow, logistics

Production equipment

On-line control & diagnostic units

Control nodes (I4.0)

Data cloud

ERP, business apps

5G

Cloud

EcosystemIndustrial

data platformFederation layer

Unified industrial data &

computation platform

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Winning recipe

open research, development &

innovation ecosystem

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Digital twin

Juuso Autiosalo

Doctoral Candidate & Project Manager

Aalto University

Working title of doctoral thesis:

Digital twin for industrial products

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Contents for the rest of the presentation

Introduction to digital twin concept

• 4 slides

Building blocks of digital twin

• 12 slides

Current activities at Aalto University and how to proceed?

• 3 slides

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Introduction to digital twin concept

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Digital twin: why, what, and how? *

Why?

• “Citius, altius, fortius”: “Faster, Higher, Stronger”

What?

• Merging the digital and physical

How?

• No standard realization exists

* Contents of this slide inspired by my supervisor Kari Tammi

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Introduction to digital twin concept

There is lots of different definitions, and none of them seems to be totally wrong.

There is many different types of Digital Twins

Perhaps the most used definition:

“Digital Twin (DT)—the Digital Twin is a set of virtual information constructs that fully

describes a potential or actual physical manufactured product from the micro atomic

level to the macro geometrical level. At its optimum, any information that could be

obtained from inspecting a physical manufactured product can be obtained from its

Digital Twin.” *

* Grieves, M. and Vickers, J. (2017) ‘Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in ComplexSystems’, in Kahlen, F.-J., Flumerfelt, S., and Alves, A. (eds) Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Cham: Springer International Publishing, pp. 85–113. doi: 10.1007/978-3-319-38756-7_4.

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Introduction to digital twin concept

Physical TwinDigital TwinKnowledge

Data

A short definition from one year ago, has become a bit outdated: “Digital Twin is the cyber part of a Cyber Physical System.” *

Currently, my insight is that digital twin is a cloud object that gathers together all the information that is available from a single product.

* Autiosalo, J. (2018) ‘Platform for industrial internet and digital twin focused education, research, and innovation: Ilmatar the overhead crane’, in 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). Singapore: IEEE, pp. 241–244. doi: 10.1109/WF-IoT.2018.8355217.

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Physical twin vs digital twin

Physical TwinDigital TwinKnowledge

Data

1. Can be inspected through a user interface from anywhere around the world

2. Laws of physics must be simulated3. Can have artificial intelligence that can be

used to control physical twin4. Must be updated to match the physical twin

1. Can be inspected with all senses on location

2. Obeys laws of physics3. Doesn’t have

intelligence4. Is constantly changing

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Building blocks of digital twin

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Building blocks of digital twin

Datalink

Database

Identifier

Simulation model

Artificial intelligence

Physical product

Security

Userinterface

More?

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Simulation model

• Countless different alternatives: CAE, 3D or process

• Perhaps the most common example of Digital Twin!

Specific use cases:

• Digital Twin Shop-floor

• Virtual prototyping

Tao, F., & Zhang, M. (2017). Digital Twin Shop-floor: A New Shop-floor Paradigm towards Smart Manufacturing. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2756069

Michael, S., & Juergen, R. (2016). From Simulation to Experimentable Digital Twins: Simulation-based development and operation of complex technical systems. In 2016 IEEE International Symposium on Systems Engineering (ISSE). https://doi.org/10.1109/SysEng.2016.7753162

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User interface

- Could be just a web site

- Different views and permissions for different users

- 3D model with AR would be nice!

Digital Twin

SalesDesign

Manufacturing

Maintenance

OutsidersOperatorhttps://blogs.windows.com/devices/2017/09/20/ford-brings-microsoft-hololens-to-design-studio-drives-speed-creativity-and-collaboration/

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Database

In many cases, just showing the data

may provide enough value.

There can be also multiple databases.

Possible contents include: Static 3D

model, Measurement data (with error

limits) Metadata/environment data,

Location, Historical data (PLM aspect)https://www.applause.com/blog/digital-twins-iot-faq/

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Identifier

- Globally unique address- Existing examples include

MAC, IP address, URL, DOI- Technologies to access the

identifier include RFID, QR code

- Can be used to access the Digital Twin

- Should be available at the ”physical twin”

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Artificial intelligence

Makes DT an active or even autonomous object.

• For reference: compare the potential of a regular car to an autonomous car.

• AI enhanced DT actively takes care of the physical product.

Use case examples:

• Analyse the condition of physical twin

• Reschedule maintenance if needed

• Trigger alarm or even stop the machine

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Security

Safe and consistent operation are minimum requirements for adopting any new

system, and DTs are no exception.

Security by design must be implemented throughout any DT

Furthermore, specific security features of DT include:

• User access management

• Update management service for the physical product

• AI based scanning of network environment

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Computation

Computation can take place in various locations:

• locally at the device

• at the edge in a gateway device

• in the cloud at some server hardware

Digital twin should orchestrate where the computation takes place depending on the time scale and computational intensity.

• Remote computing enables use of low-cost IoT devices, while local computation lowers the need for communication bandwidth

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Linking the blocks

Database

Identifier

Simulation model

Artificial intelligence

Physical product

Security

Userinterface

More?

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Linking the blocks

Datalink

Database

Identifier

Simulation model

Artificial intelligence

Physical product

Security

Userinterface

More?

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Data link

ERP

Enterprise Resource

Planning

Digital Twin

data link

IoT

equipment

PDM

Product Data

Management

CMMS

Computerized Maintenance

Management System

CRM

Customer Relationship

Management

CAE

Computer Aided

Engineering

SCM

Supply Chain

Management

ICS

Industrial Control

System

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Data link

Probably the most difficult issue because of

the multiple requirements

• APIs for IoT/M2M and connectivity

• Connection to physical twin

• Links to external databases

Basic idea proposed in:Hribernik, K. A. et al. (2006) ‘The product avatar as a product-

instance-centric information management concept’, International

Journal of Product Lifecycle Management, 1(4), p. 367. doi:

10.1504/IJPLM.2006.011055.

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Current activities at Aalto University and how to proceed?

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DigiTwin project

The focus of the research project is in coupling the crane and its digital twin for the whole life cycle.

Physical twin

Knowledge

Data

Project builds demonstrators to prove the industrial value of digital twin concept.

Digital Twin

SalesDesign

Manufacturing

Maintenance

OutsidersOperator

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Physical world

Digital twin

Rotor Gear Motor

Simulation model Machine learning

Machine and design data

Meas. dataMetadata

Combined and verified virtual data

BEN

EFIT

Misc data

Enterprise dataWorker ID, Process, Sales data, Weather

etc.

Digital twin

Twinrotor project – combining physical models and operational data

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How to proceed?

We have started building a collaboration

network around the digital twin concept.

You are welcome to hear more at

DigiTwin Demo Day on 18th January

2019!

digitwin.fiMore info and registration to the event: