26.9.2018 VTT – beyond the obvious 1

Human-Automation Interaction in Digitalized Manufacturing Industry

Digital Factory, Berlin 12th September, 2018

Riikka VirkkunenResearch Manager, Digitalizing IndustriesTechnical Research Centre of Finland Ltd

Human-Automation Interaction in Digitalized Manufacturing Industry

Opportunities and challenges

Technologies changing industrial work

Examples of empowering worker and work community

Summary

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Opportunities: pysical-virtual-human

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Opportunities: value of IoT

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[The Internet of things: Mapping the value beyond the hype (2015) mckinsey global institute]

Value of IoT in factoriesoperations and

equipment optimisation

Opportunities: potential of Artificial Intelligence

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2,6

2,1 2,5

1,7 1,6 1,4 1,4

1,7

0,8

1,6 1,7

1,0

4,6

4,1

3,0

3,6 3,2

3,0 3,0 2,9 2,7 2,7

2,5

1,8

Baseline AI steady state

Real gross value added (GVA) (%, growth)Source: Accenture and Frontier Economics

Annual growth rates of gross value added (close approx. of GDP) by 2035,

comparing baseline growth by 2035 to an artificial intelligence scenario where AI has been absorbed into the economy

Opportunities: VTT Lighthouses

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Challenges

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Automation creates

unemployment

Technology not safe and secure

enough

’I do not want or I can not use

it’

New jobs will emerge

Develop safe, and secure solutions

Engage user and workers

8

Changing industrial work

9

Merging of physical and cyber

All levels affected but maybe not at same pace

Working with robots

From hands on to remote, e.g.

welding to control

maintenence to user support

steering crane to fleet manager

Knowledge intensive

Technologies changing industrial work

Artificial Intelligence

Robotics & human-robot interaction, exosceletons,

wearables

Remote handling solutions

Enabling technologies

(connectivity, sensor technology, big data, blockchain etc)

Advanced visualisation (AR/VR/MR)

Knowledge sharing tools

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AR maintenance(S-STEP, Dynavis)

Next examples are based on:

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DIMECC programs www.dimecc.com

European collaboration in Factory2Fit

VTT experience with more than 150 VR/AR use cases:

eeedo

Several

industrial

cases,

e.g.

Rauta-

ruukki

1980 1991 1994

The first

kinematics

DHM e.g.

Workplace

design

2000 20041996 1998 2003 2006

First digital

human model

in CAD-

system

The first VR

environment

in projects

e.g. Tele-safe,

ELINA

1st vehicular

machine

simulator

(VIRSU,

TERMA)

VR in manual

task design

(VIRVA, EU-

VIRTUE)

The first

kinetics DHM

(SIMTU,

TÄRSKY)

Easy-to-use

DHMs (Osku,

Kone-Masina)

The design

simulator for

cabins

(HumanICT)

Maintenance

work design

and training

in VR (VIRVO)

2009

Manual work

System

lifecycle &

VR&AR

(ManuVAR)

Maintenance

work design

and training

in VR (VIRVO)

Maintenance

work design

and training

in VR (VIRVO)

Maintenance

work design

and training

in VR (VIRVO)

2007

Augmented

assembly

(AugAsse)

2010

System

engineering

in PLM

(SE-PLM)

2012

VR & MR to

increase

human

comfort (VRHyperSpace)

2014

VR/MR/AR

continuum

(TellMe, UIW)

2015

ESA –

AR for AIT,

AIV and Orbit

Operations

Delta Cygni Labs, VTT spin-off 2013 www.deltacygnilabs.com

Empowering and participatory adaptation of factory automation to fit for workers

12www.factory2fit.eu

Approach:

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Co-design of solutions in industrial

showcases with workers

Approach:

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Co-design of solutions in industrial

showcases with workers

Augmented reality

Augmented reality assisted

maintenance, assembly and training

Shows 3D step-by-step instructions

over the target

Content creation pipeline

AR Work flow

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Developing work practises

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Tech solutions and workpractices developedsimultaneously

Wish list frommaintenance personnel

example

Knowledge sharing ’AR-SoMe’

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• Knowledge sharing solutions for field personnel

• Post messages, images, comments, timelines, likes..

• Attach messages to real world

• Mobile application to visualize social media

• Feedback from personnel and customers to official documents and practices

https://www.youtube.com/watch?v=-soabHbUzwM

https://link.springer.com/chapter/10.1007/978-3-319-60492-3_14#citeas

Engaging the work community

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?Video

Documentation

Help

Contact #1

Contact #2

Worker model

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A set of characteristics which complementarily describe all aspects of a worker in a modern factory exploiting Industry 4.0 data sources, allowing adaptation of processes & work-related elements to each worker’s profile.

Emotional

Physical

Skills

Preferences

Demographic information, Trade & Hierarchy

Quantified worker

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Feedback of own well-being and performance supports competence development and work satisfaction

“Worker feedback dashboard”

STERA’S BUSINESS UNIT MANAGER, TERO MARJAMÄKI:

“ONCE WE CAN INCREASE PEOPLE’S JOB SATISFACTION,

THE WORK EFFICIENCY WILL ALSO IMPROVE” .

“THIS APP PROVIDES FEEDBACK TO EMPLOYEES ONLY,

AND THAT IS THE WAY IT SHOULD BE. ‘’

‘’IN THE FUTURE,

WE SHOULD HAVE A THIRD PARTY INVOLVED,

PROVIDING THE EMPLOYER WITH COMPILED AND FILTERED

FEEDBACK, WHICH WOULD HELP US IDENTIFY

PROBLEM AREAS AND THINGS THAT NEED CHANGES.”

https://www.assemblymag.com/ext/resources/Issues/2018/July/lines/asb0718lines3.jpg?1530631217

https://www.shponline.co.uk/new-dashboard-app-to-give-workers-wellbeing-feedback/

Heikkilä, P., Honka, A., Kaasinen, E.: Quantified Factory Worker: Designing a Worker Feedback Dashboard. NordiCHI 2018 conference, 1-3 Oct 2018, Oslo, Norway.

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Summary

Industrial work will change and

benefit from technology but

Technology alone is not enough

Develop work practises

Empower, engage, motivate people

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Riikka.Virkkunen@vtt.fi+358 50 520 2381@VirkkunenRiikka

#IndustrialRenewalVTT