intelligent automation – smart manufacturing · pdf filetwi with support from industry....

INTELLIGENT AUTOMATION –SMART MANUFACTURING

Dr Raphael Grech

Technical Specialist

Robott-Net – Tecnalia San Sebastián

15th -16th March 2017

2

A BIT ABOUT MYSELF...

Robotics & AutomationElectrical & ElectronicsSystems & ControlComputer Vision

IndustryAcademia

Dr Raphael GrechTechnical SpecialistDigital Engineering Group

Direct: +442476647395Mobile: +447973972323

Email: [email protected]

3

The MTC

4

MANUFACTURING TECHNOLOGY CENTRE

FOUNDED IN 2010Independent RTOCompany limited by guarantee (profit re-invested in MTC)

Purpose built facility - to allow industry & academia to perform industrial scale projects

FOUNDED BY LEADING RESEARCH ORGANISATIONS:University of Birmingham

Loughborough University

University of Nottingham

TWI

With support from industry

6

TECHNOLOGY READINESS LEVEL

7

THIS TALK

Industry LandscapeHandling Data - The Age of SmartIntelligent MachinesCollaborative RobotsSensing and PerceptionSafety

8

INTELLIGENT AUTOMATION - DEFINITIONS

Automation: the use or introduction of automatic equipment in a manufacturing or other process or facility.

Robot: a machine capable of carrying out a complex series of actions automatically, especially one programmable by a computer.

Intelligent / Autonomous: able to vary its state or action in response to varying situations and past experience

http://www2.deloitte.com/content/dam/Deloitte/lu/Images/promo_images/lu-intelligent-automation-business-world-1x1.jpg

9

Industry Landscape

10

EVOLUTION OF THE CONSUMER LANDSCAPE

Global market placeGreater customisationShorter lead timesSupply chains

flexibleresponsive to consumer pull

Automation is the only way to retain a competitive advantage in the face of these new demands

11

MANUFACTURING AUTOMATION PROCESS

evolving at a faster pace than ever (http://www.ifr.org/industrial-robots/statistics/)

need for greater automationless repetitive heavily constrained tasks more human like processes high adaptabilityflexibilityfast reactive responserobot human interaction

http://www.jeffbullas.com/wp-content/uploads/2015/06/10-Top-Digital-Marketing-Automation-Tools.jpg

12

INDUSTRY EVOLUTION

http://www.allaboutlean.com/industry-4-0/industry-4-0-2/

13

CHALLENGES WITH EXISTING MANUFACTURING AUTOMATION PROCESSES

Designed for high volume streamlined productsUnsuitable for handling small batches or bespoke designsHigh cost and effort due to in tool changeoverDowntimeComplexity in parts handling, logistics flowAmount of human interaction

14

Handling Data -The Age of Smart

15

SMART TECHNOLOGY

Electronic device or systemConnected to the InternetUsed interactivelyIntelligent to some extent

http://memolition.com/wp-content/uploads/2015/06/the-takeover-is-here-the-rise-of-smart-technology-93063.png

16

INTERNET OF THINGS (IoT)

Network of physical objects

Collect and exchange dataEmbedded

electronics, software & sensors

Network connectivityhttp://news.mit.edu/sites/mit.edu.newsoffice/files/images/2016/internet-of-things_0.png

17

SMART FACTORIES / WAREHOUSES / ENVIRONMENTS

Information and communication technologyHigher level of automation and digitisationSelf-optimisationSelf-configurationArtificial intelligenceSuperior cost effectivenessBetter quality

Industry 4.0

https://www.unbelievable-machine.com/wp-content/uploads/2015/05/i40_2-bitkom.jpg

18

http://sites.tcs.com/blogs/digital-reimagination/wp-content/uploads/Industry-4.0.png

19

INDUSTRY 4.0

Complete transparency of product lifecycles from anywhere in the world in real-timeGetting machinery up and running as quickly and as cheaply as possible after a breakdownCompiling due diligence on robotics or end-of-line process efficienciesAutomation equipment being capable of carrying out multiple tasks

greater variabilitysmaller batch sizes

20http://www.industries-4.com/wp-content/uploads/2014/12/Industry-4.0.png

21

INDUSTRY 4.0

Combination of industry and the current Internet of Things (IoT) technology.

Move towards digitisation (Digital Engineering)internet of thingsbig data powerful analyticscommunications infrastructure

22

CHALLENGES

Robot safetyRemote monitoringConnected factories Value adding robotics Predictive maintenance Collaborative equipment

23

Intelligent Machines

24

INTELLIGENT MACHINES

How can we do it?...advanced robotic algorithmsintelligent sensing techniques environment perception

‘Think’ and act autonomously in a safe manner

25

DETERMINISTIC VS STOCHASTIC

Deterministic: Algorithm, model, procedure, process, etc., whose resulting behavior is entirely determined by its initial state and inputs, and which is not random or stochastic. Processes or projects having only one outcome are said to be deterministic their outcome is 'pre-determined.' A deterministic algorithm, for example, if given the same input information will always produce the same output information.

(http://www.businessdictionary.com/definition/deterministic.html)

Stochastic: having a random probability distribution or pattern that may be analysed statistically but may not be predicted precisely.

(http://www.oxforddictionaries.com/definition/english/stochastic)

26

SCI-FI…THE ULTIMATE MACHINE

Singularity / Turing Test

A ‘living’ machineemotion survival instinct

Fear of intelligent machines

http://www.tinymixtapes.com/sites/default/files/1504/film-ex-machina.jpg

27

FEAR OF INTELLIGENT MACHINES – TROLLEY PROBLEM

Is this something we should really worry about?Dilemma - no unanimous consensus on best action

http://www.skeptic.com/eskeptic/2015/images/15-04-08/trolley-problem.jpg

28

SOME QUESTIONS...

Which industrial applications are likely to be the early adopters for autonomous technology?

What are the key barriers to autonomous technology uptake from an industrial perspective?

Where should the research community be focusing effort to maximise potential future and benefits for industrial applications and benefits?

29

ROBOTS IN MANUFACTURING TODAY

https://static01.nyt.com/images/2012/08/19/business/JP-ROBOT-1/JP-ROBOT-1-superJumbo.jpg

30

BREAKING THE BOUNDARIES FROM CURRENT INDUSTRIAL CONSTRAINTS

Industrial RobotsAble to perform repetitive tasks but they’re not intelligent

LogisticsCan we move tools around rather than just the manufactured product?

http://www04.abb.com/global/seitp/seitp202.nsf/e308f3e92d9a8fc5c1257c9f00349c99/5c32783f3b598c29c125759800301eef/$FILE/ABB+robot+IRB+6400.jpg

http://www.kuka.com/res/media/geschaeftsberichte/gb_2008/sites/all/themes/kuka/images/start/en/slide4.jpg

31

BREAKING THE BOUNDARIES FROM CURRENT INDUSTRIAL CONSTRAINTS

Consumer robots - Dyson360EyeDriverless cars – Google carTesla autopilotRobot kitchen

http://www.dyson360eye.com/img/share.jpg

http://www.google.com/selfdrivingcar/images/home-where.jpghttps://cdn3.vox-cdn.com/thumbor/LBhxgwBkkQzOF-uLtgGkbpIXfSo=/0x0:1920x1080/1600x900/cdn0.vox-

cdn.com/uploads/chorus_image/image/49681973/VRG_VUP_285_tesla-autopilot-mishaps_SITE.00_00_43_13.Still003.0.0.jpg

http://www.thetimes.co.uk/tto/multimedia/archive/00887/db009d16-e216-11e4-_887880b.jpg

32

Collaborative Robots

33

HUMAN FRIENDLY ROBOTS

https://namethegarels.files.wordpress.com/2009/04/i-am-a-friendly-robot.jpg?w=500

34

A NEW GENERATION OF ROBOTS

http://www.universal-robots.com/

http://new.abb.com/products/robotics/yumi

http://www.kuka-robotics.com/en/products/industrial_robots/sensitiv/lbr_iiwa_7_r800/

http://www.rethinkrobotics.com/baxter/

http://www.intelligent-automation.org.uk/about-us/overview

35

PERCEIVED ADVANTAGES

Human Robot Collaboration / Assisted ManufacturingProductivityFlexibilityLow Running Costs

36

CURRENT CHALLENGES

Collaboration vs RiskComplicated Safety Cases

ScalingEverything we’ve seen so far is small…

Economic CostsPurchase & Ownership

Achieving ProductivityMaking the most of humans & robots working together

Application Design Ergonomics, ease of use, etc.

37

WORKING SIDE BY SIDE


38


http://b-i.forbesimg.com/jenniferhicks/files/2013/08/UR5_VW_GlowPlug_Source_FotogesignGasparini_3.jpg

39


http://www.automotivemanufacturingsolutions.com/wp-content/uploads/2014/02/MEDIA_130628-Web-2.jpg

40

ROBOT HUMAN INTERACTION

Robot Assisting HumansABB – YuMiKuka – iiwa

CurrentlyForce / Torque controlRedundant DOFComplacencySoft(er) materials


http://robotics.naist.jp/wiki/?plugin=ref&page=%E7%A0%94%E7%A9%B6%E8%A8%AD%E5%82%99%2FKUKA&src=LBRiiwa.png

41

SMART WAREHOUSE

OcadoAmazonBoston Dynamics - Atlas Robot

http://uk.businessinsider.com/ocado-secondhands-project-online-grocer-aims-to-create-an-army-of-humanoid-robots-with-artificial-intelligence-2015-6

http://cdn.slashgear.com/wp-content/uploads/2016/02/new-atlas-800x420.jpg

42

HUMAN ROBOT COLLABORATION - CHALLENGES

challenge to pre-empt human action

needs better sensing - more real-time data processing

Humans tend to think that another human being will have a better judgment than a machine in case of an emergency

43

Sensing and Perception

44

HUMAN VISION IN AN INDUSTRIAL CONTEXT… SOME CHALLENGES

SubjectiveLimited spectrum densityObservations limited to visible spectrumUnable to monitor very slow or very fast moving objectsHumans cannot carry out accurate visual measurementsCannot store what they see for traceability Fatigue due to highly repetitive task

Hence why we get machines to help us do the job.

http://goddidit.org/wp-content/uploads/2012/02/human-eye-825x510.jpg

45

Picture Credits:http://www.dpaonthenet.net/global/showimage/Article/53711/http://www.vision-systems.com/content/dam/VSD/print-articles/2013/05/fea1-1305vsd.jpghttp://www.vision-systems.com/content/dam/VSD/print-articles/2014/11/1412VSD_ProdFocus_Fig1b.jpghttp://lmi3d.com/sites/default/files/Gocator-2880-log-scan2.jpg

VISION IN MANUFACTURING AT PRESENT

46

MOST COMMON VISION APPLICATIONS IN INDUSTRY

Manufacturing IndustryVisual InspectionMetrologyObject Recognition

Vision in other areasMedical ImagingPeople TrackingEye TrackingGesture RecognitionAutomated Driver Assistance3D Scanning and 3D ReconstructionAugmented Reality

http://www.automation.com/images/article/omron/MVWP3.jpg

http://3.imimg.com/data3/EM/CH/MY-12149695/electronic-eye-cctv-camera-500x500.jpg

47

INSPECTION AND METROLOGY… MACHINE OR HUMAN?

Humans do more than just visual inspection

How can we improve vision sensing and perception to fuse with other sensors and actuators?

http://img.hexus.net/v2/features/ecs_mainboard_factory_china_2005/images/mainboard/maskingforcheck_big.jpg

http://www.imechinstitute.com/wp-content/uploads/2015/08/visualinspection1-300x225.jpg

48

THE CHALLENGES

Vision in manufacturing is still very structuredControlled lightingCalibrated camerasHigh quality lensesNot Flexible

Human vision is highly adaptive to its surroundingsCan we replicate that for machine vision?

Real time sensor fusion and coordination with actuators for better task understanding

49

ENVIRONMENT EXPLORATION AND INTERACTION

Humans are very good at being placed in an unknown scene and start interacting with it.

Machines still struggle to do this – especially in an industrial context.

Robots need to be considered as an aid to humans not as a replacement

http://cdn.phys.org/newman/csz/news/800/2014/neuralnetwor.jpg

50

WHAT’S ON THE HORISON – BIOLOGICALLY INSPIRED OPTICS

https://mrl.illinois.edu/news/nature-stretchable-silicon-camera-next-step-artificial-retina

http://www.vision-systems.com/content/dam/VSD/print-articles/2015/01/1501VSDprodFocF2.jpg

https://techaeris.files.wordpress.com/2013/11/varioptic-a316s-ois-liquid-lens1.jpg

51

SELECTIVE ATTENTION

Can this be done reliably and in real time by a machine? http://www.tobiipro.com/

http://uxmag.com/sites/default/files/uploads/west-eye-tracker/eye-tracking-results.jpg

image.slidesharecdn.com/modev2015romanobergstromstrohl2015323final-150330133545-conversion-gate01/95/eye-tracking-the-ux-of-mobile-what-you-need-to-know-25-638.jpg?cb=1427740729

52

BIOLOGICALLY INSPIRED VISION

http://ilab.usc.edu/publications/doc/Siagian_Itti09tro.pdf

53

SEMANTICS AND UNDERSTANDING

Same question…Can this be done reliably and in real time by a machine?

http://www.cse.buffalo.edu/~jcorso/r/career/files/rep-figure-CAREER.png

54

THE OPPORTUNITIES

Human Robot CollaborationPerception of the environment

Machine Learning / Deep Learning

The closer machines can see as humans, the higher the likelihood that robots will be able to interact with humans

55

HAND – EYE COORDINATION

Are we able to teach a robot to do this just by demonstration?

No. We need to code pattern recognition, segmentation, salient object extraction…

http://www.earlyyearscareers.com/eyc/wp-content/uploads/2016/02/DSC_0274.jpg

56

CHALLENGES – 3D BIN PICKING

http://users.isr.ist.utl.pt/~jrodrigues/home/images/website_images/bin_image2.jpg

http://www.easyfairs.com/uploads/tx_ef/BinPicking-1ad399.JPG

57

MORE CHALLENGES

What if we introduce an ‘off-the-shelf robot system’ which can learn by observation and start doing work?

Is Artificial intelligence robust enough to be considered safe?

Humans are still much more adaptable than machines

http://fullcircle.asu.edu/wp-content/uploads/2015/12/DSCF6893w.jpg

https://img.rt.com/files/news/21/64/b0/00/000_dv1430909.jpg

http://www.21stcentech.com/wp-content/uploads/2014/07/robot-jobs-f.png

58

Safety

59

APPLICATION PROTECTION LEVELS

Ris

kR

educ

tion

Mea

sure

sLevel 6 Perception-based real-time

adjustment to environment

Level 5 Personal protectiveequipment

Level 4 Software-based collision detection,manual back-drivability

Level 3 Power and speed limitation

Level 2 Injury-avoiding mechanicaldesign and soft padding

Level 1 Low payload and low robot inertia

Robot System – Mechanical Hazards

Impa

ct

Cla

mpi

ng

Oth

er A

pplic

atio

n S

peci

fic

https://www.roboticsbusinessreview.com/wp-content/uploads/2016/05/Industrial_HRC_-_ERF2014.pdf

60

SAFETY FUNCTIONS OF INDUSTRAIAL ROBOTS

E-StopsProtective StopsOperating Modes

Automatic/Manual High Speed/ManualPendant Controls

‘Dead Man’ HandleStart/RestartHold to Run

Limit SwitchesMuting Functions

ALL GOVERNED BY ISO 10218 http://img.directindustry.com/images_di/photo-g/15481-8373310.jpg

61

TYPES OF COLLABORATIVE OPERATION (According to ISO 10218-1)

ISO10218-1 Clause

Type of CollaborativeOperation

Main Means of Risk Reduction

Pictogram(ISO 10218-1)

5.10.2Safety-rated monitored stop(Example: manual loading station)

No robot motion when operator is in collaborative work space

5.10.3Hand Guiding(Example: operation as assist device)

Robot motion onlythrough direct input of operator

5.10.4

Speed and separation monitoring(Example: replenishing parts containers)

Robot motion only when separation distance above minimum separation distance

5.10.5

Power and force limiting by inherent design or control(Example: ABB YuMi, Kukaiiwa, Universal Robot URx)

In contact events, robot can only impart limited static and dynamics forces


62

COLLABORATIVE ROBOTS

Quick Quiz

Which of these is a ‘collaborative’ robot?

a) The industrial robot?b) The force/torque limited robot?c) Both?d) Neither?

The correct answer is…c) Both


http://www.kuka-systems.com/NR/rdonlyres/24B67A72-17EB-4725-B04D-7265CF61539D/0/KUKA_Panelisation_web.pdf

63

COLLABORATIVE ROBOTS

So this poses the question…

Does this mean that robots no longer need guarding?

Yes and No…

It all depends on the process the automation is carrying out and the risk assessment…

http://tumblr.rifftrax.com/image/44239510630

64

SAFETY STANDARDS FOR ROBOTS

C-LevelISO11161 – Integrated Manufacturing Systems ISO10218-1 - Robot ISO 10218-2 – Robot

System/Cell

B-Level

EN ISO 13849-1:2008 IEC 62061:2005

A-Level

IEC 61508 – Functional Safety ISO 12100 – Risk Assessment

European Machinery Directive2006/42/EC

Other C-level machinery standards that may be needed

65

BIOMECHANICAL LIMIT CRITERIA

ISO/TS 15066 – Clause 5.44 “Power & Force Limiting”Free Impact/transient contact

Contact event is short (<50ms)Human body part can recoil

Accessible Parameters in Design or ControlEffective mass (robot pose, payload)Speed (relative)

Pain Threshold

Highest loading level accepted in design

Minor Injury Threshold

Highest loading level accepted in risk assessment

in case of single failure

Constrained Impact/Quasi-Static ContactContact duration is “extended”Human body part cannot recoil & is trapped

Accessible Parameters in Design or ControlForce (joint torques, pose)

Pain Threshold

Highest loading level accepted in design

Minor Injury Threshold

Highest loading level accepted in risk assessment

in case of single failure


66

QUASI-STATIC CONTACT - SEVERITY

Collaborative Operation ??Collaborative

Operation?

Thre

shol

d fo

r tou

ch

sens

atio

n

Thre

shol

d fo

r pai

n se

nsat

ion

Thre

shol

d fo

r low

-leve

l in

jury

Thre

shol

d fo

r sev

er

reve

rsib

le in

jury

Thre

shol

d fo

r non

-re

vers

ible

inju

ry

Pressure Forces


67

SCALING

All the robots that are currently marketed as ‘collaborative’ are small

This generally makes them safer

But industry needs bigger payloads and working ranges

So how do we go from this…http://www.kuka-industries.com/NR/rdonlyres/0E84B75F-E4F2-403E-B342-

8C88027F10C3/0/KUKAflexFellow_gro%C3%9F.jpg

68

SCALING

All the robots that are currently marketed as ‘collaborative’ are small

This generally makes them safer

But industry needs bigger payloads and working ranges

So how do we go from this……to this?

MTC

69

ECONOMIC MOTIVATION

Mass customisationIncreasing product variantsShorter product lifetimes

Competition from low cost economies

Product flexibility

Robot Zone

HardAutomation

Units per model

No.

of m

odel

s an

d sh

ort l

ife s

pan

ManualMass Customisation

MassProduction


70

PRODUCTIVITY

No of Variants

Flexibility Lot Size Productivity

Manual Assembly High High Low Low

Hybrid Assembly Medium Medium Medium Medium

AutomaticAssembly Low Low High High

Adapted from: Lotter et al.

71

DISCLAIMER:The data contained in this document contains proprietary information. It may not be copied or communicated to a third party, or used for any purpose other than that for which it was supplied, without the MTC’s prior written consent. © MTC

Contact Details:

Dr Raphael GrechTechnical SpecialistDigital Engineering Group

Direct: +442476647395Mobile: +447973972323

Email: [email protected]

Questions?

intelligent automation – smart manufacturing · pdf filetwi with support from industry....

Documents