Reminder of this Wednesday's 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
>---
>
>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
>
>
>
>Please register via email to [log in to unmask] so we can plan
>catering.
>
>
>
>Tea and coffee will be served after the first talk
>
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