Dear Allstat,

Please find below details of the next event organised by the Business 
and Industrial Section (BIS) of the Royal Statistical Society. All are 
welcome, but please let RSS know by email so that we can arrange 
refreshments.



Kind regards,



David

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Screening Designs for Industrial Experiments



Wednesday, 4th June 2014, 16:00 – 18:15

Location: Royal Statistical Society, 12 Errol Street, London, EC1Y 8LX



"Design and Analysis of Screening Experiments when Factor Interactions 
are Present"

Angela Dean, The Ohio State University USA and University of 
Southampton UK.

 

It is increasingly common for screening experiments to be run during 
the development of high quality products and processes.  Screening 
involves sifting through a large number of potentially important 
factors to search, as economically and effectively as possible, for the 
few "active" factors. These are factors whose influence on the measured 
response is sufficiently large to be of value in improving the system. 
The active factors are followed up in later studies for building 
detailed models for prediction and optimization. In complex systems 
where there are several aspects which must function efficiently 
together, studies are needed to discover how factors interact. It is 
then vital that a screening strategy can identify active interactions 
as well as main effects.

 

This talk describes two types of screening design (supersaturated 
designs and two-stage group screening) illustrated by experiments run 
in the automotive engine and lubricant oil industries.  A variety of 
data analysis methods is investigated and recommendations on the choice 
of screening strategy are provided through simulation studies.

 

Joint work with Danel Draguljic, David Woods, Susan Lewis, Anna Vine



"Designed experiments for semi-parametric models and functional data 
with a case-study in Tribology"

Dave Woods, University of Southampton, UK

 

Experiments with functional data are becoming ubiquitous in science and 
engineering with the increasing use of online monitoring and 
measurement. Each run of the experiment results in the observation of 
data points that are realised from a smooth curve. Although large 
quantities of data may be collected from each run, it may still only be 
possible to perform small experiments with a limited number of runs. 

 

We describe statistical methodology for an example from Tribology, 
concerning the wear-testing of automotive lubricants. Here, we 
investigated how lubricant properties and process variables affected 
the shape of a functional response measuring wear. Novel techniques 
were developed for the initial design of a screening study where the 
levels of some of the factors could not be set directly. A two-stage 
semi-parametric modelling approach was applied, using a varying 
coefficient model and principal components. New methods for the design 
of follow-up experiments for such models were also developed and 
applied. In addition to the new methodology, we present conclusions 
from the case study about which factors had substantial effects, and 
how they influence the shape of the wear curves.

 

Joint work with Chris Marley and Sue Lewis (University of 
Southampton).



Open to both RSS and non-RSS members

 

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Tea and coffee will be served after the first talk

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