26.9.2018 VTT – beyond the obvious 1 Human-Automation Interaction in Digitalized Manufacturing Industry Digital Factory, Berlin 12 th September, 2018 Riikka Virkkunen Research Manager, Digitalizing Industries Technical Research Centre of Finland Ltd
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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[email protected]+358 50 520 2381@VirkkunenRiikka
#IndustrialRenewalVTT