Web-based knowledge elicitation and application to planned experiments for product development Sue...
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Web-based knowledge elicitation and application to planned experiments for
product development
Sue Lewis
University of Southampton, UK
David Dupplaw, David Brunson,
Anna-Jane Vine, Colin Please, Angela Dean, Andy Keane, Marcus Tindall
EPSRC, Jaguar Cars, Hosiden Besson, Goodrich Engine Control Systems
Outline
1. Background- conventional versus web-based approach
2. Design of experiments- screening many factors
3. Methodology
4. Software implementation
5. Case study- Jaguar Cars- optimisation of cold start engine
performance
Knowledge elicitation
• List of factors to be varied/ held constant
• Available knowledge on how factors affect performance
Two-stage group screening
• aims to find factors whose effects are sufficiently large to produce a substantive improvement in performance
• each factor is investigated at two levels
- “high” and “low”
• factors are of two kinds: control (design) or noise
• factors divided into groups
• factors within each group are varied together
- new grouped factor
Two-stage group screening
Stage 1 experiment– on small number of grouped factors– decide which groups are important
Stage 2 experiment– on individual factors from important
groups– estimate main effects and interactions
Strategies: at stage 1– factor main effects only
• classical group screening– both main effects and interactions
• interaction group screening
Criteria for choosing a grouping and strategy
1. Total number of runs (Stage 1 + Stage 2)– total no. S of main effects and interactions
that have to be examined by experiment
Want E(S) as small as possibleAlso P(S>target) as small as possible
2. Risk of failing to detect important main effects and interactions– as small as possible
• tension between these aims• software to guide our choice
Software implementation • central server - Linux and Windows XP• all interactions through a web browser
- Internet Explorer, Netscape, Opera• different levels of user
- administrators- users- guests
• uses open source software• requires installation of
Apache web server, MySQL, PHP tools, grouping and simulation code
Methodology
Setup (administrator)Eg define performance measure and initial factors Acquisition of information (users and guests)Eg elicit factor importance, experts’ confidence, new
factors
Summary of importance (admin. and users)Eg guide factor groupings
Choice of groupings and strategy (admin. and users)Eg consider probability distribution of total size of
experiment
Simulation (admin. and users)Eg examine how often important effects may be missed
Comparison of 4 strategies under the criterion minimize P(S>target)
Simulation software then run to calculate the proportions of times important effects
are missed
Case Study
12 control or design factors– AFR– spark time– calibration– engine off timing– idle speed– plug type– injection timing– spark advance– transient fuel with
calibration– plug gap– variable valve timing– injector spray angle and
direction
2 noise factors- injector tip variation- humidity/temperature
Performance measure: given no. of engine cycles with a low spark resistance
Planning the experiment: size of S
Grouping
0.010.1557.25102.3322,2,2,12,3
P(S>120)P(S>110)Var(S)E(S)
2 indiv noise
7 indiv des
5 v. likely ind design
Probability of 5 very likely design main effects being active = 1.0
Distribution of S
Risk of exceeding a target
Planning the experiment: risks
2 – 15 52 – 56 design x noise interactions
3 – 24 2 – 18 design x design interactions
00 design main effects
separategrouped
Noise factors
Use simulation to calculate percentages of active effects missed
Conclude: keep noise factors separate –not grouped together
Choosing a strategy: risks
2 – 15 71 – 73 design x noise interactions
3 – 24 69 – 70 design x design interactions
00 – 1individual design main effects
IGSCGS
Strategy
Percentages of active effects missed
CGS misses more active individual effects than IGS
Conclusions
• Group screening methodology defined• Software implementation validated in
industry• Software at beta testing stage
- enquiries and feedback welcome