Quarterly Review 031609

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NSF Engineering Research Center (ERC) for Reconfigurable Manufacturing Systems (RMS) Reconfigurable Manufacturing Systems (RMS) ERC Big Three Quarterly Review Meeting ERC Big Three Quarterly Review Meeting March 16, 2009 The University of Michigan, College of Engineering

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Transcript of Quarterly Review 031609

Page 1: Quarterly Review 031609

NSF Engineering Research Center (ERC) forReconfigurable Manufacturing Systems (RMS)Reconfigurable Manufacturing Systems (RMS)

ERC – Big Three Quarterly Review MeetingERC Big Three Quarterly Review Meeting

March 16, 2009,

The University of Michigan, College of Engineering

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Project review HistoryProj Project Reviewed

TA Proj# Project Title

j12/13/07 03/05/08 06/06/08 09/12/08 12/05/08 03/16/09 05/18/09

1Data Analysis and Causal Identification for Gear Noise Reduction in Transmission Systems

2Cyclic Waveform Signal Analysis for Monitoring and Control of Powertrain Manufacturing Systems

Iand Control of Powertrain Manufacturing Systems

3Throughput Analysis of Mfg Systems with Closed Loop MHS

4Computer Aided Simulation Model Verification, Testing and Optimization

1Hardware-in-the-Loop Simulation for Verification and Validation of Logic Control

2Manufacturing Network Time Synchronization Best Practices (NIST Funded)Reducing Unscheduled Downtime Through

II 3Reducing Unscheduled Downtime Through Automated Event-Based Control

4 Development, Application and Transfer of a Network ROI Cost Calculator (on hold after 12/07)

5 Wireless Network Analysis and Testing6 The Reconfigurable Factory Testbed (RFT)

III

1 Cylinder Bore Inspection

2 Pore Detection in Small Diameter Bores

3 In-line Vale Seat Inspection

Y. Koren Overview #2

III 3 In line Vale Seat Inspection

4 Thread Measurement

5 Camshaft / Crankshaft Polishing Testing

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Meeting Agenda

2:00 Introduction

2:05 – 2:40 Reducing unscheduled downtime through automated event-based control James Moyne

C li f i l l i f it i d t l2:40 – 3:10 Cyclic waveform signal analysis for monitoring and control of powertrain manufacturing systems Judy Jin

3:10 – 3:30 Computer aided simulation model verification, testing and optimization Sam Yangp

3:30 – 3:40 Break

3:40 – 4:05 In-line valve seat inspection / PKM introduction Reuven Katz/Hagay B.

4:05 – 4:25 Thread Measurement Reuven Katz/Hongwei Z4:05 4:25 Thread Measurement Reuven Katz/Hongwei Z

4:25 – 4:35 New proposal: run-out measurement of crankshaft sprocket Reuven Katz

Discussions:

4:35 – 5:00

Discussions: • Acceleration of tech implementation and transfer • Third-party vendors involvement format• Wrap-up and next steps

All

5 00 Adj

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Y. Koren Overview #3

5:00 Adjourn

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Engineering Research Center forReconfigurable Manufacturing Systemsg g y

Reducing Unscheduled Downtime Through Automated Event-based Control

Dr. James Moyne—UMProf. Dawn Tilbury –UM

Jeff Dobski—Global EngineStudent Lead: David Linz

Supporting Students: Garima Garg Edwin Teng Deepak SharmaSupporting Students: Garima Garg, Edwin Teng, Deepak Sharma

QRM, March 16th, 2009

Indicates New Result

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 1

The University of Michigan, Ann Arbor

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Outline• Introduction

– The idea, objectives and approach

• Project History

C t F• Current Focus– Addressing changing conditions– Interim results

• Next Steps– Short termShort term– Longer term project planning– Summary and discussion

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 2

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The Basic Idea:Reducing Costs Due to MaintenanceThe overall cost per part goes

Cost

The overall cost per part goes way up due to the higher per

unit cost of unscheduled downtime This is the

UnScheduledUnSched ledIdealized Cap

downtimeopportunity for fault

predictionThe benefit of lower scheduled downtime is nearly wiped out by the

Scheduled Downtime

UnScheduled Downtime

Scheduled

UnScheduled Downtime

Scheduled D ti

UnScheduled Downtime

Idealized Cap.(no downtime)

nearly wiped out by the unscheduled downtime (due

to longer times for diagnosis) and f

Production

Scheduled Downtime

Production

Downtime

Production

ProductivityLow due to conservative approach to maintenance

caused by high cost of

diagnosis), and…With fault prediction in place unscheduled downs are

reduced (turned into scheduled)

OptimizedPractice

AggressiveMaintenance Strategy

CurrentPractice

ProductionProduction y gunscheduled downsMore aggressive approach

to maintenance improves productivity slightly,

scheduled)

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 3

PracticeMaintenance StrategyPracticeproductivity slightly, however…

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The Basic Idea:Common Approaches To Scheduling PM• Manufacturers Estimates: Usually fixed times listed in the manual

Problems: Tend to be overly conservative, do not reflect factory conditions

E ti ti MTTF• Estimating MTTF: Observing failures and adjusting time to failure estimates; support part-count maintenance as necessary

Problems: Does not account for variance in machine downtimes

• Reliability Studies: Conducting independent statistical studies to model reliability

Problems: Expensive, requires factory downtime to conduct testProblems: Expensive, requires factory downtime to conduct test

• Failure Prediction through Data Corelation – Accounts for Variance

O ti i ff ti– Optimizes effectiveness – Reduces Costs– Can be implemented with event data

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 4

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Project Objectives and Approach• Short-Term: Help Global Engine identify gaps in plant-floor systems

– Better utilization of systems– Improve data quality and data usability of these systemImprove data quality and data usability of these system

• Mid-Term: Predict and reduce unscheduled downtime– Provide solutions for auto correlation of data sets

• Long-Term: Schedule preventive maintenance through ECA rule based control

• Continuous: Provide and help implement best practices in data• Continuous: Provide and help implement best practices in data management for improvement in maintenance management and downtime prediction

– Improving maintenance data quality– Maintenance pooling– Matching practice to specification– Adjust project focus as necessary to adopt to the impact of changing economic conditions

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 5

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ECA Control SystemOptimize

Approach: Closing the Loop at Global Engine

Diagnostics Equipment/Tool Control

MaintenanceManagement

AutomaticShutdown

OptimizeMaintenanceScheduling

Shutdown

BENEFITSData Engineers

Operations with Top Fault Count

0

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OP300_1 OP110_2 OP60_3 OP160_3 OP280_3

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Excel• Reduced

Unscheduled Downtime

• ReducedTODAY

StoresEngineers

MachineAdjustment

p

OPC

•••

Reduced Scrap

• Reduced MTTR

TODAY

Production Machines

PLCs

Production Machines

CNC’s Production Machines

PLCs

Production Machines

CNC’s

••• • Improved Productivity

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 6

Machines Machines Machines Machines

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Outline• Introduction

– The idea, objectives and approach

• Project History

C t F• Current Focus– Addressing changing conditions– Interim results

• Next Steps– Short termShort term– Longer term project planning– Summary and discussion

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 7

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Previous Deliverables• Look at main cost drivers of scrap, unscheduled tool changes and

unscheduled maintenance

• Top Ten anomalies software installed at Global Engine andTop Ten anomalies software installed at Global Engine and evaluated

– Recognizing interesting events in process data– Generate Excel report including Paretos

» User interface design per Global Engine specifications

Lack of good data quality hurt effectiveness somewhat» User interface design per Global Engine specifications

• Matlab analysis module for maintenance event correlation installed at Global Engine and evaluated

– Automated drill-down tool for maintenance investigationg– C++ code auto-generated from MATLAB source no MATLAB license required

• Best practices for improving maintenance management and data qualityq y

– Analysis of PM scheduling and reporting data quality– Comparing maintenance practices to documented maintenance requirements

• Student internships at Global Engine for Summers ’06, ‘07 and ‘08

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 8

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Top 10 Pareto ChartO ti ith T F lt D ti

• Screen shot of viewing the top

Operations with Top Fault Durations

15000

20000

25000

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otal

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ec)

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viewing the top ten anomalies

– Faultiest Operations

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P180_

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P190_

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OperationOperations

– Fault Count– Fault Total Duration

OP18

OP19

OP18

OP25

OP30

OperationsOperations with Top Fault Count

• Excel output– Fast learning ramp-

up– Ease of drill-down 30

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– Ease of drill-down

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NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 9

_ _ _ _ _

Operations

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Previous Result: Strengthening Correlations with Normalized Overlay

A number of weaker Correlations can be grouped with overlays

After an overlay this operation demonstrates a gradual increaseAfter an overlay, this operation demonstrates a gradual increase in faults over the week leading up to the unscheduled Maintenance. This can be used as a basis for predicting and rescheduling downtime

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 10

rescheduling downtime.

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Outline• Introduction

– The idea, objectives and approach

• Project History

C t F• Current Focus– Addressing changing conditions– Interim results

• Next Steps– Short termShort term– Longer term project planning– Summary and discussion

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 11

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Current Focus• Failure Prediction

– Single fault type to maintenance

Leveraging improvements in data quality due to ongoing implementation of bestg yp

– All fault types to maintenance

Maintenance Under Low Production Conditions

implementation of best practice improvements

• Maintenance Under Low Production Conditions– Leveraging prediction information

• Reducing Redundant Maintenance– Best practices for matching unscheduled and scheduled

maintenance workmaintenance work– Leveraging knowledge of unscheduled maintenance events

in impacting scheduling maintenance

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 12

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Failure Prediction Through Event Data:Single Fault Type to MaintenanceSeveral Operations show strong correlations within the time scope examined, there are relationships that can be observed:

Leveraging improved maintenance data qualityContinuous Reject Faults maintenance data quality through implementation of best practices

Leveraging improved UM-ERC capabilities for

BOLTFEEDER NOT FEEDING BOLTS @ 18:21BOLTFEEDER NOT NOSE PIECE WONT

ERC capabilities for analyzing per specific fault type, and for more automated analysis

BOLTFEEDER NOT FEEDING BOLTS @23:48 NOT FEEDING BOLTS @

21:01NOT FEEDING BOLTS@ 0:49 (Next Day)

BOLT FEEDER NOT WORKING @ 6:05“ “ @ 15:39

NOSE PIECE WONT CLAMP TOGETHER @ 12:23“ “ @ 23:46- Feed & Torque Station

@ 23:46( y)

It is not clear that these correlations will hold in the long term. However, if a relationship is confirmed, maintenance practices can be updated to reflect this knowledge

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 13

practices can be updated to reflect this knowledge

The data is now of sufficient quality to where these relationships can be confirmed our next step

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Failure Prediction Through Event Data:All Fault Types to MaintenanceHigher correlations (once verified) means that we can use the prediction to improve impact of scheduled maintenance

160

180

200regression -7.545963e-001, mtype = AAA179608CPM type =grap all

Faults on System

BENEFITS• Reduces the probability of

unscheduled downtime

100

120

140

160 Incur cost due to lost units, slowing

production.

occurrences• Lower maintenance costs,

higher MTBF, lower MTTR, more predictable machine operation

40

60

80

100 predictable machine operation• Reduces the number of faults on

the system, increasing system performanceAlternative Schd.

Monthly Downtime

0 5 10 15 20 25 30 35 400

20

40

Unscheduled

• Allows maintenance scheduling to become adaptive

S

Monthly Downtime

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 14

Unscheduled Downtime

Schd. Monthly Downtime

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Maintenance Under Lower Production Conditions:“Low Load Maintenance”

• In today’s world factories are not operating at capacity– Focus is on reducing cost

• However maintenance practices are often designed for at-capacity operation

– Maintenance needs are often a function of production (e.g., part count) rather than timethan time

– Maintenance practices are not adjusted when operating below capacity– Money is being lost due to overly aggressive static maintenance practices

Solution: Implement part count or event based maintenance• Solution: Implement part-count or event-based maintenance– Leverage part tracking and maintenance prediction– Leverage fact that maintenance system supports part-count based

maintenance triggersmaintenance triggers

• Issues– Part tracking insufficient to support reliable count-based triggering

Maintenance prediction needs to be verified with new data

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 15

– Maintenance prediction needs to be verified with new data

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Maintenance Under Lower Production Conditions:“Low Load Maintenance”Example: Since the fault count on several operations track upward ahead of a maintenance operation as a result of part count, the prediction of the event can be used to trigger maintenance, resulting in longer times between maintenance in low load production conditionsp

High Fault Rate tracks High Fault Rate tracks System Load

BENEFITS• This alternative leverages existing information on the system

It d ’t i d t t th t t ki t• It doesn’t require an update to the part tracking system• The maintenance system can support event-based triggering

ISSUES• Need to verify causal relationships with newer higher quality data

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 16

y p g q y

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Reducing Redundant Maintenance (1)In a typical time-based maintenance system, a sudden failure can trigger an unscheduled maintenance event

Schd. PMSchd. PM Failure! Schd. PM

time

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 17

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Reducing Redundant Maintenance (2)If the unscheduled PM work is “matched” to the Scheduled PM, the PM schedule, if left unchanged is redundant and non-optimal

UnscheduledSchd. PMSchd. PM Schd. PM

Unscheduled PM

Redundant; mis-timed

time

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 18

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Reducing Redundant Maintenance (3)The maintenance scheduled should be “reset”, resulting in fewer maintenance events, lower downtime, and lower maintenance costs

UnscheduledSchd. PMSchd. PM Schd. PM

Unscheduled PM

KEYS TO MAKING THIS WORKKEYS TO MAKING THIS WORK• Linkage between unscheduled maintenance events and triggers in DataStream• Matching Unscheduled to Scheduled Maintenance work orders

best practices

time

Datastream Counter Reset

p

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 19

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Other Ongoing Efforts of Interest:Unified Data LayerEffort to integrate various data systems to increase the accuracy of regression analysis. An audit of all databases was performed to evaluate data quality and possibility of creating a unified data layer.

BENEFITS

ActivPlant

Global Engin Databases BENEFITS• Allows for a more

comprehensive visualisation and

d t di f

META

DataStream

understanding of factory data.

• Facilitates correlation analysis across many

i blxxxxx

xxxxxx

x

A

DATataSt ea

MPTS

variables• Allows “islands of

automation” to be focused on the same

xxxxxx

xxxxx

xxxxxx

xxxxx

xxxx xxx xxxx

EngineerCorrelation across multiple variables

TA

LA

Scrap Data

MPTS factory objectivesISSUES* Poor data quality

prevents unification of

AYER

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 20

prevents unification of data.

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Other Ongoing Efforts of Interest:Improving Best Practices on Test Stands

Tear DownPass Engine Update the Book of Knowledge

1.) No Problems in the Engine (Engine Passed) (90%) of time2.) Minor Problems that can be fixed at cold stand (very rare) 3.) Major Problems, Engine needs to be Torn Down

) O ti 1 bl i t i th B k f k l d d

g

Test

a.) Option 1: problem exists in the Book of knowledge, and root cause can be identified. Result: Fix Operation Identified.b.) Option 2: problem's root cause cannot be identified. Engine is not immediately torn down due to lack of time and resourcesTest

Stand c.) Option 3: Engine is immediately torn down and operation may halt in order to determine root cause.

resources.

Operation One

Operation Two

Operation Three

Operation Four

KEYS TO IMPROVING BEST PRACTICES

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 21

• The main decision that needs to be made is whether a reject that has not been observed is worth examining the root causes of the problem

• The Largest Area of improvement is to determine a way for modeling the Likelihood of a reject seen occurring again

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Outline• Introduction

– The idea, objectives and approach

• Project History

C t F• Current Focus– Addressing changing conditions– Interim results

• Next Steps– Short termShort term– Longer term project planning– Summary and discussion

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 22

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Next Steps:Failure Prediction

• Project has shifted focus somewhat in light of the current economic conditions

– Focus on reducing cost NOW– Ideas for reducing cost in light of operation under capacity

• Data Quality best practices improvements are• Data Quality best practices improvements are starting to pay off

– We have evidence of correlations between faults and maintenance events in the latest datamaintenance events in the latest data

– We need to verify these correlations in new data coming in– Unfortunately older data (pre-best practices improvements) is

not that useful for verification– Unfortunately some of the newest data reflects operation much

under capacity» We have to decouple the capacity issue to verify correlation

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 23

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Next Steps:Low Production Load Maintenance• Correlation, if verified will allow for implementation of

event-based maintenance– Newer data (reflecting newer best practices) should be of sufficient

quality to (1) verify correlations, and (2) determine if maintenance is a function of part count as opposed to (or in addition to) time

• Implementation of improved part tracking and linking to p p p g gmaintenance for part-count based maintenance scheduling is a longer term issue

• Longer term all three types of maintenance scheduling• Longer term, all three types of maintenance scheduling should be in place, based on prediction information (or lack thereof)

Time-based maintenance– Time-based maintenance– Part-count based maintenance– Event-based maintenance– Combinations

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 24

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Next Steps:Reducing Redundant Maintenance

• Global Engine is aware of what needs to be done

• Fix is largely a combination of engineering and bestFix is largely a combination of engineering and best practices improvement at this point

– Linking of unscheduled corrective maintenance events into the preventative maintenance systempreventative maintenance system

– Aligning the work order descriptions of unscheduled maintenance operations with scheduled maintenance operations

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 25

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Next Steps:ROI Calculations• Impact of current efforts should be easy to

convert into ROI numbersE d d b f i t it ti X t f– E.g., reduced number of maintenances per unit time X cost of maintenance event in terms of man hours and consumables

– We hope to use ROI data to drive additional improvements in i t ti d b t tiintegration and best practices

» Improved part tracking / counting» Aligning of unscheduled and scheduled downtime work order

descriptionsdescriptions» Resetting capability for maintenance schedules

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 26

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Next Steps:Other Efforts

• Data consolidation / meta layer

• Improve best practices on test stands; decision process with rejects; analysis of continuous data

E l i ti MPTS d t i t d l f• Explore incorporating MPTS data into model for Unscheduled Downtime Prediction

C ti t i t i l t ti d fi t f• Continue to impact implementation and refinement of best practice improvements

– Data quality improvements to support prediction, consolidation and ROIData quality improvements to support prediction, consolidation and ROI goals

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 27

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Summary

• Implementation of best practices for improved data quality are starting to pay off

– We are seeing potential correlations between faults and maintenance– We are seeing potential correlations between faults and maintenance events

– Signal could be strong enough for prediction

• Current focus areas reflect economic environment

– Fault prediction maintenance schedulingau t p ed ct o a te a ce sc edu g– Low production load maintenance scheduling improvements– Reducing redundant maintenance

• Next steps focus on verifying analysis with new data and providing justification for best practice improvement investment through ROI analysis

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 28

improvement investment through ROI analysis

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Discussion

• Need to determine the type and level of longer term support for the project

– No Summer internship in 2009– No Summer internship in 2009– Will likely have close interaction with Global Engine via regular visits

• Need to determine if there is reusability– Elsewhere in Chrysler– Other ERC members

• If there is a desire to close the project 4 – 6• If there is a desire to close the project, 4 – 6 months additional to tidy up deliverables would be ideal

– There is still research to be done– Prioritization with other projects

• Questions?NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 29

Questions?

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Backup SlidesBackup Slides

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 30

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Examples of Data Quality Issues Encountered• System collects only a subset of the data generated from the PLCs.

There is often not enough information to identify strong correlations to support control.

• Historical Data is limited, making larger trends and behavior difficultHistorical Data is limited, making larger trends and behavior difficult to identify.

• Maintenance records have missing data.• Insufficient standardisation of data, manually entered data , y

unsuitable for computerised analysis.• Scrap code definition process results in unbounded growth of codes

nearly useless for correlation analysisAd-hoc scrap code creation

Scra

p s

Num

ber o

f SC

odes

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 31

Time

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Samples ofRecommendations for Improving Data Quality• Modular and Hierarchical “Reason Code” Scheme

– Incorporate an ID system for maintenance to help record keeping and reduce redundancy

– E.g., Maintenance and scrap databases

• System Wide Data Unification– Create a factory meta-data layer for access analysis and drill-downCreate a factory meta data layer for access, analysis and drill down– Create a unified labeling system so that a part can be tracked

through the system.

• Extended Access to Historical Data• Extended Access to Historical Data– Build in a mechanism for access to large vectors of archived data.

• “Deep” Analysis Of Key Operations– Select a single operation within the system and analyse its

behaviour over a long historical period to understand fault alarms and downtime behavior

– Use this process to better identify underlying data quality issues

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 32

Use this process to better identify underlying data quality issues

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ROI of Scaling Maintenance to Factory Load While there may be additional costs to implement a scaling system; the reduction of costs due to unnecessarily performed downtime indicate a high cost benefit ratio.

BENEFITSR d d t d t• Reduced costs due to un-necessary maintenances on the system.

INVESTMENTS• A system by which to

track parts.OR • Increased Production

time.• The ability to cope

variable production

--OR—• A mechanism for

scheduling downtimes w.r.t fault data. p

situations.

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 33

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Dataflow to Assist Analysis

MS Access (Manual)

ScrapGEMA

Int. Maint. HistoricalDatabase

ActivPlant PPS/PDCA Predict MS Access (Manual)

Machine

Operations with Top Fault Count

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Graphs

ActivPlant PPS/PDCA

Number

Unscheduled Downtime

ActivPlantJavaScript Excel

Faults

M i t

OP300_1OP110_2

OP60_3OP160_3OP280_3

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De A C P

Operations with Top Fault Count

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Graphs, Correlations

Number, duration of faults

DataStream MATLAB (C++)

JavaScriptAnalysis

Maint.CM, PM 0

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Maint, faults based on d ti

Graphs, Correlations

Excel (Manual)MPTS

Plan: Put machine faults together with machine rejects; incorporate information from Unschd. Tool Change and Scrap data if possible. (see

MachineReject

Unsch.Tool Change.

Excel (Manual)

duration

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 34

MPTS dotted line)Excel (Manual)

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UML DATA LAYER (CURRENT)

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering TA2-2, Slide 35

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NSF Engineering Research Center (ERC) forReconfigurable Manufacturing Systems (RMS)

C li W f Si l A l i f M it i

R h T

Cyclic Waveform Signal Analysis for Monitoring & Diagnosis of Powertrain Manufacturing Systems

Research Team:ERC/UM: Judy Jin, Kamran Paynabar, Yong Lei, Qiang Li, GM: John Agapiou (R&D);

Ed S ll & St N (GMPT Li i )Ed Sponseller & Steven Norman (GMPT- Livonia);Thomas Gustafson & Phillip Steinacker (GMPT-Pontiac)

Chrysler: James Wang, Eugene Kuo, Mark Skelly, John Gartner

March 16, 2009Presenter: Judy Jin

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 1

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Outline

• Project Overview & Process Background 

• Accomplishments & Tech Transfer Plan at GM• Accomplishments & Tech Transfer Plan at GM

• New investigations at ChryslerComparison of data collection between Chrysler and GM– Comparison of data collection between Chrysler and GM

– Analysis of available original waveform signals at Chrysler 

• New project proposed by GM• New project proposed by GM

• Milestone & Future Works

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 2

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Project OverviewProblem

Goal

Problem• Cyclic waveform signals are widely used for online monitoring of powertrain manufacturing process.• The trial and errors approach for features extraction & monitoring limits may not always be effective.

• To develop systematic waveform signal analysis methods to improve online monitoring systems for powertrain manufacturing processes.

Deliverables and benefitsD l i i l l f b d l f li f i i l tDevelop a generic signal analyzer for a broad class of cyclic waveform sensing signals to

• automatically set up monitoring limits to improve first time quality and reduce ramp up time for new production lines;

• effectively extract monitoring features from online sensing signals to reduce both false rejects and miss detection for reducing mfg cost;

Main tasksW k ith i t GM (&Ch l i thi t ) t ll t d ti /DOE d t f th

and miss detection for reducing mfg cost;• continuously learn and enhance diagnostic capability to quickly identify the root causes for

reducing defects & downtime.

• Work with engineers at GM (&Chrysler in this quarter) to collect production/DOE data from the selected process;

• Develop data analysis algorithms for data preprocessing/signal alignment/signal segmentation /online monitoring charts to characterize/monitor process operation states;

• Characterize process fault patterns to enhance diagnostic capability;

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 3

p p g p y;• Software development, plant testing, and validation.

Page 42: Quarterly Review 031609

Process Background

Valve Seat Pressing Machine Sciemetric

SystemProblem: A high false reject rate is a top concern.

Sensor data Goal: Improve production throughput by effectively using online sensor

i imonitoring systems.

Recorded manual inspection 28%

True Detection65%

2000

3000

4000

ad

Peak Force

GapUnrecorded 

inspection72%

Reduced to5% by ERC

0

1000

2000

Loa

Work

Force

Depth

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 4

-0.25 -0.2 -0.15 -0.1 -0.05 0Distance

p

Page 43: Quarterly Review 031609

Overview of Proposed New Monitoring Methods High dimensional Cyclic waveform

signal analyzerLow dimensional

physical interpreted features

High dimensional non-stationary sensing signals

Monitoringcontrol limits

SL

Root cause: misaligned signals.

SLfalse reject

3000

4000

5000

6000 Force • Alignment algorithms• Segmentation algorithms• Feature extraction algorithms• Online monitoring limits

-0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05-0.35-1000

0

1000

2000

LVDT

• Online monitoring limits• Fault classification

M lti l l t l i Sciemetric depthNew aligned depth by UMMultiscale wavelet analysis

yk1h

ykd,1

ykid ,

...0jh

l

ykjc ,10−

ykjd ,0

ykjc ,0

1lykc ,1 ...

ykic ,1−

ykic,

,ih

il

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 5

0jlkj ,0

Page 44: Quarterly Review 031609

Accomplishments & Tech Transfer Plan at GM

Major Accomplishments: ERC-team has proposed new algorithms for false reject reduction, the validations show:• UM algorithms can help reduce false rejects from the current 35% to 5%; • UM algorithms have the potential benefit to reduce miss detection of bad

products by setting more effective specification limits.

Current Tech Transfer Status:• GM is interested in implementing new algorithms and initiated efforts for the• GM is interested in implementing new algorithms and initiated efforts for the

ERC/UM team to work with the Sciemetric Company.• Technology transfer agreement was made between ERC, GM and Sciemetric

Company.p y• In this quarter, UM has provided the program codes to Sciemetric Company,

which will be implemented into GM monitoring system for further validation.

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 6

Page 45: Quarterly Review 031609

New investigations at gChrysler in this quarter

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 7

Page 46: Quarterly Review 031609

Comparison of Data Collection Schemes Between Chrysler and GMBetween Chrysler and GM

GMPT at Livonia Chrysler at Mac1

Accepted parts

Rejected parts

Accepted parts

Rejected parts

Original sensing signalsEvery 100 parts

All parts Last 25 parts Last 25 parts

Scimetric features data Not saved All parts All parts All partsScimetric features data Not saved All parts All parts All parts

Two analyses have been done based on available original waveform signals:y g g• For the rejected samples having regular signal profiles (8 samples), are they false

rejects?• For the rejected samples having irregular signal profiles (17samples), what are the

root causes?

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 8

Page 47: Quarterly Review 031609

Analysis 1: Investigate whether Sciemetric rejected samples with 

regular signal profiles are false rejects or not (8samples).

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 9

Page 48: Quarterly Review 031609

Rejections Based on Old Sciemetric Depth

UL=0.5

(in)

Parts are rejected by the Sciemetric depth feature. (suspect of false reject)

LL=-0.5

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 1010

Page 49: Quarterly Review 031609

Signal Comparison between Rejected and Nonrejected Parts

Specification Limits

8000

10000 Specification Limits

Blue: 25 samples of accepted parts by Sciemetric

Red: 8 samples rejected by Sciemetric

6000

bs)

Red: 8 samples rejected by Sciemetric

(suspected false rejects)

4000

Forc

e (lb

Comment:

Misaligned signals may cause false rejects

0

2000g g y j

based on Sciemetric “depth” feature.

-35 -30 -25 -20 -15 -10 -5 0 5-2000

0

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 11

LVDT (in)

Page 50: Quarterly Review 031609

New Feature “Aligned Depth” after Signal Alignment

Algorithm of calculating aligned depth:Algorithm of calculating aligned depth:1. Use wavelet analysis (Harr transformation) to find contact point.

Contact point: The point where the press tool actually contacts the part leading to the change point of force signals.g g p g

2. Calculate new feature “aligned depth”. The difference between the contact point and the maximum LVDT value, which reflects the actual moving range.

---- Out-of-control---- In-control

3000

3500

4000

4500

3500

4000

4500

1500

2000

2500

1500

2000

2500

3000

Aligned by contact point

-500

0

500

1000

0 1 0 05 0 0 05 0 1 0 15 0 2-500

0

500

1000

Contact point

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Misaligned Signals-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 -0.1 -0.05 0 0.05 0.1 0.15 0.2

Aligned Depth

Page 51: Quarterly Review 031609

Signals After Alignment

8000

10000

Blue: 25 samples of accepted parts by

6000

(lbs)

Sciemetric

Red: 8 samples rejected by Sciemetric

(suspected false reject rate based on 254000

Forc

e (suspected false reject rate based on 25

samples=8/25=32%)

Comment:

0

2000 Comment:

Aligned depths are

within the limits.

-25 -20 -15 -10 -5 0 5 10-2000

LVDT (in)

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LVDT (in)

Page 52: Quarterly Review 031609

Monitoring Based on New Aligned Depth

UL=6.93

(in)

LL=5.93

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Page 53: Quarterly Review 031609

Request of New Data Collection for Further Validation at ChryslerFurther Validation at Chrysler

1. Record the original sensing signals for every 100 parts to verify

th “d th” f tthe new “depth” feature;

2. Record quality inspection data to verify rejections.

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Page 54: Quarterly Review 031609

Analysis 2:Cl if I l Si l P fil f R j d PClassify Irregular Signal Profiles of Rejected Parts

(17 samples)

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Page 55: Quarterly Review 031609

Irregular Shape Signals at Chrysler: Missing seats

8000

10000

10

-5

4000

6000

8000

e (lb

s)

-20

-15

-10

e (lb

s)(in

)

0

2000

4000

Forc

e

-30

-25

Forc

eLV

DT(

0 200 400 600 800 1000 1200 1400-2000

0

Time (ms)

0 500 1000 1500-35

Time (ms)

6000

8000

10000

2000

4000

Forc

e (lb

s)

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 17-35 -30 -25 -20 -15 -10 -5

-2000

0

LVDT (in)

Page 56: Quarterly Review 031609

100

120

28

-27.5Irregular Shape Signals at Chrysler: Interrupted operations

40

60

80

e (lb

s)-28.5

-28

DT

(in)

0

20

40

Forc

e

-29.5

-29LVD

1200 200 400 600 800 1000 1200

-40

-20

Time (ms)

0 200 400 600 800 1000 1200-30

Time (ms)

60

80

100

bs)

0

20

40

Forc

e (lb

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 18-30 -29.5 -29 -28.5 -28 -27.5

-40

-20

LVDT (in)

Page 57: Quarterly Review 031609

Mapping Table Developed based on GM Data___ Example of classified irregular profiles at GM

Fault type LVDT Force

N lHighly oscillatedLVDT

(spring problem)High LVDT

NormalHighly oscillated LVDT

Force (cable problem)

& LVDTOscillated LVDT(hi h i )

Normal shapeNo correct readingHighly oscillated

LVDT& LVDT

(spring problem)(high variance)

p

Missing Part NormalNo local forceHigh value of peak Normal

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 19

Page 58: Quarterly Review 031609

Improved Diagnostic Decision for Monitoring System

In‐comingSensor Signal Improvement by UM

Provide fault report &correction action Yes

No

• To Add a detection& classification module.

Detect & classify Irregular signal profiles?

Calculate monitoring features

• Developed better monitoring features (e.g. aligned depth)

Out of specification limits (SL)?

No

Confirm fault detection 

Yes

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Page 59: Quarterly Review 031609

New Project Proposed by GM__ Improve Leaking Test

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 21

Page 60: Quarterly Review 031609

New project at GM: Improve ATC Leaking Test

The Whole Leak Testing System

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 22

Page 61: Quarterly Review 031609

New Project Activities with GMSite visit:

Identify new projects:

•UM and GM teams visited Advanced Test Concepts (ATC) Inc. in Indianapolis on Feb 9&10;

Identify new projects:•Project 1: Signature analysis and self-learning of fault patterns for abnormal detection and root cause identification .

•Project 2: Verification of the adaptive test strategy to reduce the leak test cycle time and improve the production throughput.

•Project 3: Development of the standardized procedures for self-calibrating test•Project 3: Development of the standardized procedures for self-calibrating test tools at the tool setup stage for reducing the setting up time and engineers’ trial and error calibration efforts.

Next step•Work with the GM team to identify a plant, which can be used for the data collection and method/tool development in the next step.

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Page 62: Quarterly Review 031609

Milestones and Future Plans• Defined project scope and candidate manufacturing processes.

Last Yearp j p g p

• Collected production data at GMPT plants at Pontiac and Livonia.• Understand current practice and the GM need.• Conducted experimental tests and analyzed DOE data. • Developed signal alignment algorithms using Wavelets.

• Found the root cause of high false rejects of Sciemtetric system.• Developed new algorithms for reducing false rejects.• Prove the concept of profile feature extraction for gap detection.

June 2008

p g g g g

• Collect more data for further validation of new features and signal alignment algorithms.

• Integrating our proposed alignment algorithm with Scimetric software.Dec 2008

W k ith S i t i f ft i l t ti

• Propose an algorithm for determining specification limits.• Analyze irregular fault patterns to enhance diagnostic capability for

detecting and classifying sensor problems and missing part.

• Work with Sciemetric company for software implementation.• Work with Chrysler for algorithms validation and implementation• Identify the new projects for improving leaking test at GM

• Continue validation analysis at Chrysler.

Next Quarter

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 24

Continue validation analysis at Chrysler.• Work with GM for the new projects of improving leaking tests.Next year

Page 63: Quarterly Review 031609

Thank you!Thank you!

Q & AQ & A

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Page 64: Quarterly Review 031609

3. Analyzing Sciemetric feature data3. Analyzing Sciemetric feature data corresponding to accepted parts

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 26

Page 65: Quarterly Review 031609

00.20.4

th

Features box‐plot over timeDepth

1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88

-0.4-0.2

0

Dep

t

Date index

12000Work

1 2 3 4 6 8 9 10 11 12 1 16 1 18 19 29 30 36 3 38 39 40 43 44 4 46 4 49 1 2 3 6 8 9 63 64 6 66 6 0 1 2 8 9 80 81 84 8 86 8 88

400060008000

1000012000

1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88Date index

8000

9000

10000Peak

1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88

7000

Date index

3000

Force

1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88

1000

2000

D t i d

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Date indexSep 08 Oct 08 Nov 08 Dec 08

Date index

Page 66: Quarterly Review 031609

Depth box‐plot over time (good parts)

0.3

0.4

Sep 08

0.1

0.2Sep 08

h

-0.1

0

Dep

th

Oct 08 Nov 08 Dec 08

Dep

th

-0.3

-0.2• An obvious shift in depth from Oct 08 • Possible reason:

• Change in cylinder head raw part

1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88

-0.5

-0.4• Change in cylinder head raw part• Resetting the production process

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1 2 3 4 5 6 8 9 10 11 12 15 16 17 18 19 29 30 36 37 38 39 40 43 44 45 46 47 49 51 52 53 56 57 58 59 63 64 65 66 67 70 71 72 77 78 79 80 81 84 85 86 87 88Date indexDate index

Page 67: Quarterly Review 031609

Features Scatter Matrix (good parts)

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Page 68: Quarterly Review 031609

Features Scatter Matrix (Oct to Dec)

Low peak andLow peak and low depth

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Low Peak and high force

Page 69: Quarterly Review 031609

Projected features (Oct to Dec 08)

All data correspond to good parts

(based on Sciemetric report)

• Although all parts are good, they can beg p g yclustered in two classes by using features.

• To understand the reasons further study

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is needed.

Page 70: Quarterly Review 031609

8000

10000

6000

Pink: part # 2 which is close to upper limits

Blue: other accepted parts

bs)

4000

Forc

e(lb

0

2000

1600

1800

-7 -6 -5 -4 -3 -2 -1 0-2000

LVDT(in)1000

1200

1400

LVDT(in)

400

600

800

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-7 -6.8 -6.6 -6.4 -6.2 -6 -5.8 -5.6 -5.4 -5.2 -5-200

0

200

Page 71: Quarterly Review 031609

8000

6000

7000

8000

5000

6000

s)

Black: rejected parts close to the lower limit

Red: other rejected parts

3000

4000

Forc

e(lb

s

1000

2000

7 6 5 4 3 2 1 0-1000

0

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 33

-7 -6 -5 -4 -3 -2 -1 0LVDT(in)

Page 72: Quarterly Review 031609

10000

Aligned Signals

8000

6000

)

Blue: accepted parts

Black: rejected parts close to the lower limit

Red: other rejected parts4000

Forc

e (lb

s Red: other rejected parts

0

2000

1 0 1 2 3 4 5 6 7 8-2000

0

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 34

-1 0 1 2 3 4 5 6 7 8LVDT (in)

Page 73: Quarterly Review 031609

Detecting and Classifying Sensor Failures---- Proposed Mapping Algorithm by ERC/UM

Fault type LVDT ForceType I‐LVDT

(spring problem)Oscillated LVDT(high variance)

Normal(spring problem) (high variance)

Type II‐LVDTStep shape

(highly positive slope)Normal

Type III‐LVDTSpike shape

(highly positive & negative slope)

Normal

ith “ l f ”Type IV‐force

(cable problem)Normal

either “very low force” or over “peak force”

No correctType V‐force&LVDT

No correct reading/No slope

No correct reading/Oscillated force 

(high variance)

No correct 

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 35

Type VI‐missing Seat High LVDT reading reading/Oscillated force (high variance)

Page 74: Quarterly Review 031609

Signature OutputsQuick St bilit T tQuick Fill+Fill Stability Test

Master Part +Part +

EC

Master Part

Select limit to be used for leaking engines.

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 36

Page 75: Quarterly Review 031609

Identify Root Cause of High False Reject Rate6000

5000Blue: classified as a good part by Sciemetric

Red: false rejected as a bad part by Sciemetric

Force SL

3000

4000 Both signals seem to be good parts.

1000

2000 Root cause: misalignment on signals.

0

1000

-0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05-1000

Depth DepthSciemetric “depth” is based on LVDT absolute value, which is not atr e reflection of the act al mo ing range

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 37

p ptrue reflection of the actual moving range

Page 76: Quarterly Review 031609

NSF Engineering Research Center forReconfigurable Manufacturing SystemsReconfigurable Manufacturing Systems

Statistical Output Analysis of Steady-State p y ySimulation Models In SimuVeri Software

Sam Yang, Wencai Wang and Jack Hu (UM)Sam Yang, Wencai Wang and Jack Hu (UM) Susan Ostrowski and Annette Januszczak (Ford)

March 16, 2009

The University of Michigan, Ann Arbor

Page 77: Quarterly Review 031609

Outline

• Project Overviewj

• SimuVeri System Architecture

• Introduction to Statistical Output Analysis

• Application Case• Application Case

• What’s Next

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 2

Page 78: Quarterly Review 031609

Project Overview

• Model verification and experimenting

Background: The project arises from an earlier Ford-ERC project at CEP

• The manufacturing processes and models are very complex;Model verification and experimenting

are time consuming; • Models are not completely tested on

system level before experimenting;• Resulting in exaggerated throughput

and poor model applicability.Goals:

• To develop Computer Aided Testing (CAT) tools for automatic error checks;• To provide user friendly software• To provide user friendly software

platform supporting various validation, experimentation and optimization

techniques.

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 3

tec ques

Page 79: Quarterly Review 031609

Architecture of Application Software

Users select testing strategies

Engineers d l d l

Software executes tasks

& outputsdevelop model & outputs Results

WITNESS runs each scenario

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each scenario

Page 80: Quarterly Review 031609

Introduction of Statistical Output Analysis

• Random inputs/parameters in the simulation models result in random observation outputs (performanceresult in random observation outputs (performance measure estimates) with some distribution.

• Questions about experimenting simulation models.Questions about experimenting simulation models.– How do we start the simulation (warm-up)?– How long should the model be run (how much simulated time

before stopping the run)?before stopping the run)?– How many samples of the performance measures should be

collected (how many replications)?H h ld th t t b l d?– How should the output be analyzed?

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Page 81: Quarterly Review 031609

Steady State Simulations

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Page 82: Quarterly Review 031609

Steady State Simulations

• The system has reached “steady state” where the performance is independent of the initial conditionsperformance is independent of the initial conditions.

• Examples– Production line simulations – the line starts where it left off at the

end of prior shifts.– Emergency rooms.Emergency rooms.– Airplane scheduling at airport.

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 7

Page 83: Quarterly Review 031609

Steady State and Running Time

• How to start the simulation?– Typically a warm-up period is used to minimize any impact of

initial conditions.– How long should the warm-up period be?How long should the warm up period be?– How long to run the simulation?

• Correlation Analysis

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 8

Page 84: Quarterly Review 031609

The Number of Replications

• Simulation models are used for experimentation.p– One simulation replication → a single sample (realization) of

each system performance measure.

n independent replications n independent samples from the– n independent replications → n independent samples from the same distribution.

• Confidence Interval Analysis

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 9

Page 85: Quarterly Review 031609

The Number of Replications

• Consider a single performance measure Let Xi be theConsider a single performance measure. Let Xi be the random variable that represents the value of the performance measure for the ith simulation replication.– xi = outcome/realization of Xi from the ith simulation replication.

• Since the Xi are independent and identically distributed d i bl th f b h t i drandom variables the performance can be characterized

using the “typical” confidence interval.

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 10

Page 86: Quarterly Review 031609

Analysis of Output (1)

• The approximate confidence interval%100*)1(• The approximate confidence interval %100*)1( α−

snszx *2/1 α−±

• Central limit Theorem: regardless of the distribution of h X ill b i t l di t ib t d)(Xeach Xi , will be approximately distributed as a

normal random variable when n ∞)(nX

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Page 87: Quarterly Review 031609

Analysis of Output (2)

• The approximate confidence interval%100*)1(• The approximate confidence interval %100*)1( α−

snstx n *2/1,1 α−−±

• Assumes the sample average is from a normal di t ib tidistribution.

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 12

Page 88: Quarterly Review 031609

The Number of Replications

• How to estimate number of independent replications required for a• How to estimate number of independent replications required for a desired precision.

• The half-width h of this confidence interval is

*2/1,1 nsth n α−−=

level). (precision desired afor 2

22

2/1,1 hhstn n α−−=⇒

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 13

Page 89: Quarterly Review 031609

The Number of Replications

• Substitute• Substitute

2/1,12/1 for tz n αα −−−

2

22

2/1 *hszn α−≈⇒

• Use this formula to approximate the number of replications needed to get a desired half-width (precision) for someneeded to get a desired half width (precision) for some performance measure.

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Page 90: Quarterly Review 031609

An Application Case

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 15

Page 91: Quarterly Review 031609

An Application Case

Time Observation

1.484055403 1.484055403

1.931330511 0.200569173

3.498247812 0.416304048

6.424550926 2.639221143

24.20430586 6.142266534

33.38210516 5.722407758

33.78572903 5.420538552

46.44300943 2.564226808

47 72927941 0 69581033447.72927941 0.695810334

60.97070454 12.17619063

80.76142078 30.96875537

AvgStddev

8.1870225.428285

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 16

Page 92: Quarterly Review 031609

An Application Case

2/1,12/1 for tz n αα −−−

2

22

2/1)A(*

hvgTISszn α−≈⇒

96.1025.01 =−z

4535.43.596.1 2

22 =≈n

Avg. 8.187022Stdev 5.428285

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 17

Page 93: Quarterly Review 031609

What’s NextLITERATURE REVIEW AND PROJECT DEFINITION

• Project needs and direction• Verification and validation approaches

WITNESS COMMUNICATION PROTOCOL• Witness file parser

• Witness file generator• Interfaces

VERIFICATION MODULE• Code the module

• Specific errors to be detected• Verification strategies

• Code the testing strategies

VALIDATION MODULE• Validation needs

• Validation strategies• Code strategies

OPTIMIZATION MODULE• Optimization needs

• Explore optimization methods• Code and test optimization

methods

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering # 18

Completed In Progress Future Work

Page 94: Quarterly Review 031609

Engineering Research Center for

Reconfigurable Manufacturing Systems

In-line Inspection of Engine Valve Seats

Dr Reuven Katz and Sankalp ArraboluDr. Reuven Katz and Sankalp Arrabolu

March,13th, 2009March,13 , 2009

The University of Michigan, College of Engineering

Page 95: Quarterly Review 031609

Seat angle

In-line inspection of engine valve seats

Gage Ф

Seat angle

Seat length

Valve guide

• Deck seat (±0 1o) throat anglesDeck, seat (±0.1 ), throat angles• Seat length• Seat roundness at gage• Seat runout at gage wrt valve guide

2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 96: Quarterly Review 031609

Project Goal

• In-line measurement of valve seat geometry (cycle time ~ 45 seconds)

• Rapid and accurate non-contact measurement

• Measurement of seat angles and seat length

• Preliminary repeatability test

• Evaluate in-line application feasibility

• Comment: All the study was done without having a defined specification of the problem

3NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 97: Quarterly Review 031609

Approach 1: Single Cross Section

4NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 98: Quarterly Review 031609

Approach 2: Least Squares Cone Fit w/ Five Cross Sections

Seat angleSeat angle

ERC Results CMM ResultsRun 1  Run 2 Run 3 Run1 Run2 Run3

60 segment angle 60.53 59.39 59.99

Seat Angle 45 18 45 18 44 98 45 15 45 34 45 18Seat Angle 45.18 45.18 44.98 45.15 45.34 45.1830 segment angle 30.28 30.33 30.27Seat Length 1.7137 1.6979 1.6991 1.729 1.772 1.738

5NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 99: Quarterly Review 031609

Valve seats R&R test results

Results of the repeatability testperformed for 50 measurements across 5 cross‐sections  

Seat Angle (degree)

Deck Angle (degree)

Throat Angle (degree)

Roundness (mm)

Seat Length (mm)

Gage Depth (mm)

Average 45.07468 60.40652 30.34172 0.02988506 1.747624 12.62412

Standard Deviation

0.017752769 0.043423985 0.017123847 0.002473562 0.012789657 0.002429748

6NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 100: Quarterly Review 031609

Accomplishments and Next Steps

Accomplishments:

• Angle measurement to within ± 1º degrees achieved using both happroaches

• Seat length measurement to within 0.2 mm

• Two axis demonstrator designed and built by ME450 studentsTwo axis demonstrator designed and built by ME450 students.

• Complete statistical analysis of cone-fits for improving accuracy.

• Design repeatability set-up and testing

Next Steps:

• Evaluate the implementation feasibility i e increase measurement• Evaluate the implementation feasibility i.e. increase measurement speed, optimize data collecting path

• Test serial robot and a evaluate the use of a PKM

7NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 101: Quarterly Review 031609

Operational Data of the Current Measuring System

• Optimet Conoprobe Laser Scan Frequency : 3000 KHz

• The Motion Stage Forward Motion Maximum Speed Possible : 5000 mm/min

• Current System (presently not optimized for time)y (p y p )Forward Speed used : 100mm/minBackward Speed used : 1000 mm/minStop time between scans : 1 secp

8NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 102: Quarterly Review 031609

Methods for Time Optimization Methods for Time Optimization

with above Specificationswith above Specifications

9NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 103: Quarterly Review 031609

Current Method Time Taken = 193 sec Time Taken= 176 sec

Time Taken = 88sec

Page 104: Quarterly Review 031609

Challenges

To find the minimum time : we need to increase the d f thspeed of the scan.

Increasing speed reduces the number of data points capturedcaptured.

The Speed Vs Data Capture is an OPEN ISSUE.

Need to decide which parameter to compromise on forNeed to decide which parameter to compromise on, for the best time optimized performance.

11NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 105: Quarterly Review 031609

Engineering Research Center for

Reconfigurable Manufacturing Systems

Parallel Kinematic Mechanism (PKM) for P i L ti f O ti l SPrecise Location of Optical Sensors

Dr. Hagay BambergerDr. Reuven Katz

Date: 3/16/2009

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 106: Quarterly Review 031609

The Goal & The MethodThe Goal & The Method

Develop & build a PKM demonstrator, which will precisely locateoptical sensors like camera or laseroptical sensors, like camera or laser

Use PKM for Valve Seat measurements as well as for SmallBores inspection projectsp p j

The suggested PKM possesses 4 degrees of freedom that arerequired for locating precisely optical sensors, within a desiredworkspace

The advantages of a PKM:A• Accuracy

• High rigidity• Large payload capacity• Fast dynamic response

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

• Fast dynamic response

Page 107: Quarterly Review 031609

Description of the Suggested PKMDescription of the Suggested PKM

The mechanism consists of:M i l tf bl f 4 d f f d• Moving platform capable of 4 degrees of freedom:

two translations and two rotations• 4 linear motors on the base

Sight PipeMotion stageLaser

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 108: Quarterly Review 031609

Project LayoutProject Layout

Analysis & Synthesis:Ki ti D i W k Si l iti• Kinematics, Dynamics, Workspace, Singularities,

Structure, Joints …Detailed mechanical designDetailed control designBuilding and calibratingTests:Tests:• Accuracy, Repeatability, …

Required resources:Required resources:• CAD designer• Control expert

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

• Budget of ~$30K for hardware

Page 109: Quarterly Review 031609

Estimated Timetable for PKM projectEstimated Timetable for PKM project

Starting: 4/1/2009

Mechanical design review: 5/2009

Control design review: 7/2009

Component purchasing &Component purchasing &manufacturing: 9/2009

Working prototype: 11/2009Working prototype: 11/2009

Tests: 12/2009

NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 110: Quarterly Review 031609

Engineering Research Center for

Reconfigurable Manufacturing Systems

Internal Thread MeasurementInternal Thread Measurement

Dr. Reuven Katz, Dr. Hongwei Zhang and Dr. En Hongg g g

Mar. 16, 2009

The University of Michigan, College of Engineering

Page 111: Quarterly Review 031609

Project Overview

Goals:Goals:• Develop methodologies for the inspection of geometrical features of

internal threads in machined automotive parts.

• The two methods to be presented enable in-process internal thread q alit erification sing optical sensors

Deliverables and benefits

quality verification using optical sensors.

• The approaches allow to extract thread pitch, major and minor diameter, flank angle and even the starting point of the thread with respect to a reference location on the perimeterreference location on the perimeter.

Main tasks• Laser scan measurement using Optimet sensorLaser scan measurement using Optimet sensor

• Optical inspection using a CCD camera with sightpipe

• R&R test to be done partially

2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 112: Quarterly Review 031609

Methodology using a laser sensor and set-up

Principle

Rotary

Internal Thread

Principle

Z axisProbe

Z

Rotary Motion Mirror

OpticalSensor

X

Y Motorized

periscopeMotion Stages

X axis

Y axis

Rotary Stage

Motion StagesRotary Motion

Periscope

Sensor

Method:

Measuring internal threads using a Laser Range Finder (Optimet Sensor) Sensor

Stepper Motor

45˚ Mirror inside

integrated with a motorized periscope designed at ERC.

3NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 113: Quarterly Review 031609

Measurements results of M12X1.25 internal thread

Minor Diameter --D1 Pitch Diameter --D2 Major Diameter --DMin Max Min Max Tol Min Max

STANDARD: ANSI/ASME B1.13M-1983 (R1995)M12 X 1.25 Unit: mm

Measured Parameters

Min. Max. Min. Max. Tol. Min. Max.10.647 10.912 11.188 11.368 0.18 12 12.360

0˚ 180˚ 90˚ 270˚ AveragePitch (mm) 1.248 1.251 1.252 1.246 1.249

Height (mm) 0.639 0.696 0.539 0.615 0.622Major Dia. (mm) 12.014 12.127 12.071Minor Dia (mm) 10 667 10 871 10 769 Δ Pitch

Major D

ia.M

inor Dia.

Minor Dia. (mm) 10.667 10.871 10.769

Conclusion 1:

Δ

Compared to the standard data, all the parameters we get are

within acceptable limits for the designated thread type. axial cross section

4NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 114: Quarterly Review 031609

Measurements results of starting location of a thread

Method:

• Measuring the axial distance between the edge of the threadedMeasuring the axial distance between the edge of the threaded

bore and the center point of the bottom of the first thread tooth.

• Four measurements were taken at four different angles. The area

Area ofThread Started

90˚ Δ=2.288

of thread started is determined by comparing these four values.

• Theoretically, the largest and smallest must be neighbors and the

t ti i t f th th d i l t d b t th Thread Started

0˚ Δ=2.531

III

IVIII

180˚ Δ=1.942

starting point of the thread is located between them.

Conclusion 2:

radial cross section

270˚ Δ=1.384

* The starting point of the thread is located in quadrant IV.

* The helix is clockwise.

5NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 115: Quarterly Review 031609

360 degree view from

Methodology and setup based on a CCD and a Sight-pipe

360 degree view fromthe sight pipe Selected annular zone Reconstruct final

image

Stitching Line strip Define the annular Zone of each Frame

Extract and unwrap the annular Zone of each

Frame

Sight Pipe

Lens & Illumination

CCD

360-degree-view Line Scan Flow

Motion stages

Tilt Stages

LEDIllumination Internal Thread Measurement System

Conical Lens

Optical principle of the sight-PipeThe integrated sensor

6NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

The integrated sensor

Page 116: Quarterly Review 031609

Measurements results

1. A smoothing filter followed by Prewitt filter is used to bring out edges.

2. LabVIEW Shape detection is used to extract average angle of threads.

3. A series of line profiles are generated perpendicular to the thread lines and peaks are detected.

7NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 117: Quarterly Review 031609

Discussion on Method 2 (3D Digitalization)

8NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 118: Quarterly Review 031609

Challenges on 3D Digitalization

Phase shifting could be achieved by using Light Modulation Technique. With digital light source we can fulfill the phase shift.

How to find the Sensitivity factor by changing the cylinder diameter as the reference plane moves.

The angle of the illumination and optical axis is either very small or immeasurable which could cause K unsolvable ( we may start with flat surface calibration. )

The misalignment of the sight pipe system and surface quality variationmay lead to errors in phase shifting measurement.

Next step: Prove if it is doable or not.

9NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 119: Quarterly Review 031609

The EndThe End

10NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 120: Quarterly Review 031609

Discussion on Method 2 (3D Digitalization)

11NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 121: Quarterly Review 031609

Challenges on 3D Digitalization

Phase shifting could be achieved by using Light Modulation Technique. With digital light source we can fulfill the phase shift.

How to find the Sensitivity factor by changing the cylinder diameter as the reference plane moves.

The angle of the illumination and optical axis is either very small or immeasurable which could cause K unsolvable ( we may start with flat surface calibration. )

The misalignment of the sight pipe system and surface quality variationmay lead to errors in phase shifting measurement.

Next step: Prove if it is doable or not.

12NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 122: Quarterly Review 031609

Engineering Research Center for

Reconfigurable Manufacturing Systems

Outlier Teeth Detection in Sprocketsp

Dr Reuven KatzDr. Reuven Katz Saikrishnan Ramachandran

March 13 2009March 13, 2009

The University of Michigan, College of Engineering

Page 123: Quarterly Review 031609

Goals

The Objective

To decide if there is an agreement to initiate a projectTo decide if there is an agreement to initiate a project

Project Goals

• Detect the presence of misaligned teeth i.e. outliers in sprocketsDetect the presence of misaligned teeth i.e. outliers in sprockets

• Measure in line the location and the extent of deformation for each outlier relative to its neighbors while the sprocket rotates

2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 124: Quarterly Review 031609

Experimental setup

Optimet laser sensor

Sprocket

Aerotech rotaryAerotech rotary stage

3NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 125: Quarterly Review 031609

Suggested Method

• Measure the distance to tip of the teeth of a rotating sprocketMeasure the distance to tip of the teeth of a rotating sprocketusing a non-contact single-point laser range finder

• Each tooth is clearly observed as a bar (several points)

• Outliers are observed as peaks

4NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 126: Quarterly Review 031609

Evaluation of the method

Apriori

Rotating speed (sprocket) w = 300 rpm f = 5HzRotating speed (sprocket) w = 300 rpm f = 5Hz

Tooth width (sprocket) L = 5 mm

Diameter (sprocket) D = 175 mm

Data collection frequency (Optimet) fop = 3000 Hz

Number of points collected/revolution N = 3000/5 = 600

Number of points per tooth/revolution n = NL/ (πD) 3000 /(175 ) 5 45= 3000 /(175π) = ~5.45

= 5-6 points

Conclusion: Possible to be tested !!!

5NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Conclusion: Possible to be tested !!!

Page 127: Quarterly Review 031609

Stationary Experiment

Measurement of the profile of two sets of teeth for a stationary sprocket and linearly moving Optimet sensor: one containing all normal teeth (blue) and the second containing outliers (red)

Observation : 3 outliers detected whose deformations are ~0 35 mm 1mm and 0 21mm respectively

6NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

~0.35 mm, 1mm and -0.21mm respectively

Page 128: Quarterly Review 031609

Evaluation of the method360 d t fil f th ti f th k t t l d360 degree rotary scan profile of the tip of the sprocket at a slow speed gives the outlier tooth location (w.r.t. starting point) and the deformation

Outliers are observed at teeth 5, 6 and 7 (w.r.t. start tooth) and the measured displacements are 0 21mm; 0 98mm and 0 35mm respectivelymeasured displacements are -0.21mm; 0.98mm and 0.35mm respectively

7NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 129: Quarterly Review 031609

Evaluation of the method

• To check the performance of the method, the number of collected points per tooth is down sampled from thousands to 5

• The outlier displacement values are calculated in each of the two cases

• Results are tabulated below

Displacement Thousands of points

5 random points

Outlier 1 - 0.2088 mm - 0.2107 mm

Outlier 2 0.9899 mm 0.9902 mm

Outlier 3 0 3585 mm 0 3622 mmOutlier 3 0.3585 mm 0.3622 mm

Note: negative deformation signifies inward deformation

8NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

g g

Page 130: Quarterly Review 031609

Conclusion

1. The proposed method is an accurate way for the detection of outliers in sprocketsof outliers in sprockets

2. It is also accurate in estimating the deformation/displacement of outliers

3. Can be applied in an industrial application with a rotating sprocket

9NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 131: Quarterly Review 031609

Engineering Research Center for

Reconfigurable Manufacturing Systems

Valve Seat Gap Inspection

March13th, 2009

The University of Michigan, College of Engineering

Page 132: Quarterly Review 031609

Project Goal

Goals:• To develop a methodology to measure the small gap between valve

seat and cylinder head

I li t t h i th t bl hi h d t ti

y

Expected Deliverables:• In-line measurement technique that can enable high speed automatic

inspection

Work done:

• Proof on concept using a laser probe with a motorized periscope

2NSF Engineering Research Center for Reconfigurable Manufacturing SystemsUniversity of Michigan College of Engineering

Page 133: Quarterly Review 031609

Infrared Valve Seat GappDetection System

Dan SimonQuality Network

Planned MaintenancePlanned [email protected]

Page 134: Quarterly Review 031609

Infrared Study of poorly “seated” exhaust Valve Seats

Seat “gaps” set @ .001, .003, g p @ , ,006 & .010 inches in L-6 aluminum head.

L-6 head warmed up w/ quartz lamp

Dan Simon (313) 324-5353 [ [email protected] ]

Page 135: Quarterly Review 031609

Exhaust Valve Seat w/ .010in gap @ 113F

48.0°C48

47

46.0°C

Page 136: Quarterly Review 031609

L6 Engine Head w/ .006 in gap in exhaust insert seat @ 118F Head Temperature

48.0°C48

47

46.0°C

Page 137: Quarterly Review 031609

Exhaust Valve seat w/ .003 in. gap @ 120F

49.9°C

49

48

47

46.0°C

Page 138: Quarterly Review 031609

Exhaust Valve Seat w/ .001 in. gap @ 114 F

47.0°C4747

46

45.0°C45

Page 139: Quarterly Review 031609

Exhaust Valve Seat w/ .001 in Gap @ 114 F(X 2 magnified)

47.0°C4747

46

45.0°C45