Adventures in industry. Sue Lewis Southampton Statistical Sciences Research Institute University of Southampton. sml @ maths.soton.ac.uk. Outline. Experiments on many factors - with Jaguar Cars - using two-stage group screening - to find the important factors
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- with Jaguar Cars
- using two-stage group screening
- to find the important factors
- where values of factors cannot be set
- with Hosiden Besson, Sauer Danfoss, Goodrich
control x noise interactions
Also main effects and control x control interactions
For conventional factorial designs
large number of factors large number of runs
- identify the important factors
- to estimate both main effects and interactions
Disadvantage: could miss factors that interact with noise
Stage 1: perform an experiment on the grouped factors
to decide which groups are important
- estimate main effects and/or interactions
Stage 2: dismantle those groups found to be important and experiment on their individual factors
- estimate both main effects and interactions
- and their levels
Local brainstorming – but experts often at different sites
- via a dynamic questionnaire
- with free form comments
- explores the resources needed for various strategies and factor groupings
- estimates the risk of missing important factors through simulation of experiments
Assess possible grouping strategies
- resource required
- risk of missing an important factor
Individual factors are classified as
Very likely to be active
Less likely to be active
Not worth including
eg 0.7 and 0.2
Control – very likely Noise
Plug type* Temperature
Plug gap* Injector tip leakage
Air fuel ratio
Control – less likely
Spark during crank
Spark time during run-up
Higher idle speed
* hard-to-change: grouped together
Group 1: Plug type* & Plug gap*
Group 2: Air to fuel ratio & Injection timing
Group 3: Spark time during crank & During run-up
Group 4: Higher idle speed & Idle flare
Group 5: Injector tip leakage
Group 6: Temperature
Half-replicate (I=123456) in 4 sessions of 8 runs
Included large interactions
(Afr & Injection timing) x Temperature
(Higher idle speed & Idle flare) x Injector tip leakage
- both grouped control x noise interactions
6 factors to investigate at the Second Stage Experiment
- for the individual factors
- could have been smaller
Preliminary findings include
Experiments on assembled products
Aim: mean sound output
close to target
with reduced variation
Aim: reduce mean leakage and variation in leakage
- under varying pressure and speed
Obtain parts with required factor values by
- making special components
- measuring large samples and using components with required factor values
For our examples: too slow and costly
In our examples: cannot reuse components
Use a specially developed search algorithm with
- a low order polynomial to describe the response
- a design chosen for accurate estimation of the coefficients of the model (D-optimality)
Software (DEAP) has been developed that
- assists with product and component definition
- provides access to the design algorithm
The most important factors for improving the product performance were:
For the sounder : the pip height and skill of operator
For the pump: positioning of the cover and the alignment of gears
- large numbers of factors
- assembled products
- freely available
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