Automotive Manufacturing 2016 Vision Based Error Proofing ...

Originating Author: Frank Maslar August 17, 2016

Collaborative Robots & Automation Technologies Automotive Manufacturing 2016

Vision Based Error Proofing Techniques

FrankMaslarVisionSystemsTechnicalSpecialistFordMotorCompanyPowertrainManufacturingAdvancedManufacturingEngineeringEmail:[email protected]

Originating Author: Frank Maslar August 17, 2016

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Vision Based Error Proofing Techniques

Session Outline •  Automotive powertrain manufacturing environment overview •  Traceability •  Error proofing •  Technology development opportunities

Originating Author: Frank Maslar August 17, 2016

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Automotive Powertrain Manufacturing Environment

•  Powertrain: Engine, transmission and drive axle components •  Repetitive – similar parts are always processed

–  Lines can handle part type variations –  Generally metal parts with differences in surface finish

•  Location variation less than 5mm in all directions •  Parts stay in station 20-30 seconds

–  Typical vision processing time can be up to 5 seconds

Originating Author: Frank Maslar August 17, 2016

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Automotive Powertrain Manufacturing Environment

•  Lines run 24/7 –  Ease of recovery from “vision failures” is a requirement –  Downtime is expensive

•  Vision is included in the standard (required) process –  Bypass buttons have been removed –  6 σ performance ( 3 defects/million) performance is required

Originating Author: Frank Maslar August 17, 2016

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Vision Applications in Assembly

Average 100 industrial cameras in each line •  Standard system is PC based using 5MP GigE cameras •  50 cameras read Data Matrix codes

–  Standard data matrix code 20 rows X 20 columns –  Used to uniquely identify each component

•  Complete part traceability and assembly birth history •  Make sure the right part is used in the assembly

•  50 cameras verify proper assembly –  Parts fully assembled –  Manage complexity using color if parts cannot be marked with the data matrix

code

Originating Author: Frank Maslar August 17, 2016

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Assembly Verification Classes

•  Process verification / error proofing –  Fully seated –  Gears ground

•  Complexity management –  Data matrix –  Color –  Shape or part features

•  RTV dispense verification –  Bead size repeatable to 0.030mm –  Bead location repeatable to 0.010mm

•  Robot guidance

Originating Author: Frank Maslar August 17, 2016 7

Vision Screen

4cameras3differentsta<ons3differentapplica<ons-  RTVverifica<on-  ErrorProofing-  Traceability

Originating Author: Frank Maslar August 17, 2016 8

Data Matrix reading applications

DataMatrixcodesareusedfortraceabilityandcomplexitymanagement

Lasermarkspreferred

Originating Author: Frank Maslar August 17, 2016 9

RTV Verification – engine front cover

•  Partsizeapproximately24”square•  16MegaPixelcamera•  2,918measurementwindows

•  Beadwidth•  Beadloca<on

•  1secondtotalverifica<on<me

Originating Author: Frank Maslar August 17, 2016 10

Color Error Proofing Spark Plugs

SparkPlugInstalla<on•  4differentplugs•  144pertray

Redcirclesindicatewrongplugintray

Originating Author: Frank Maslar August 17, 2016 11

Error Proofing Piston Ring Assembly

Missingrings

GoodAssembly

Originating Author: Frank Maslar August 17, 2016 12

Process Verification Transmission Final Check

Originating Author: Frank Maslar August 17, 2016 13

Assembly Verification Shaft Seal

Seal Missing Seals present Steel shaft surface

•  The shaft is rotating in front of the 3D laser scanner •  The vision system builds an image of the complete part

by stitching subsequent images together. •  Height is translated to brightness – low is dark, high is

bright •  Scan lines are stitched together to build an image

Originating Author: Frank Maslar August 17, 2016 14

Current Technology – Flexible Inspection

•  StandardvisioncameramountedonUniversalRobo<csUR10robot

•  Robotmovesthecameratodifferentinspec<onposi<ons

•  Visionsystemusedforassemblyverifica<on•  Commercialsystemsbecomingavailable

Originating Author: Frank Maslar August 17, 2016

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Future Technology Opportunity 3D Data Visualization

Most 3D cameras represent 3D data as traditional 2D images where distance is converted to gray scale •  3D data is displayed in 2D •  2D image processing tools are used to analyze the data •  Issues:

–  Some 3D details are lost in the conversion –  Traditional 2D image processing tools do not take advantage of the true

3D data

Originating Author: Frank Maslar August 17, 2016

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Future Technology Opportunity 3D Data Visualization

The need is to develop systems that work in 3D space •  Data should be visualized as a solid model •  Visualization tools should be able to manipulate the data like a solid model

including zoom, pan and rotate •  3D image processing tools need to be developed to analyze the 3D data

–  Edge tools and 3D distance measuring tools –  Blob tools which characterize the object in 3D –  3D Pattern matching tools

Originating Author: Frank Maslar August 17, 2016

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Future Technology Opportunity Vision Data Sensor Fusion

Vision data can come from a variety of sources •  Traditional 2D gray scale images •  2D color images •  3D information

–  Multi-camera or stereo –  3D point cloud

•  Laser type scanner •  Area camera with structured light

•  Each data type is typically processed in its native environment using tools developed for this environment

Originating Author: Frank Maslar August 17, 2016

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Future Technology Opportunity Vision Data Sensor Fusion

The need is to combine these data types into a unified workspace •  New image analysis tools are required to analyze the data in this unified

workspace •  Tool Requirements:

–  Intuitively easy to use –  Seamlessly work with all the data types

Originating Author: Frank Maslar August 17, 2016

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Vision Based Error Proofing Techniques

Questions ??