ENBIS-7 in Dortmund

24 – 26 September 2007

My abstracts


The following abstracts have been accepted for this event:

  • Investigating the Impact on Product Quality of Raw Material Variability for a Chemical Process: A DoE Approach

    Authors: Ewan Polwart (Fujifilm Imaging Colorants Ltd)
    Primary area of focus / application:
    Submitted at 5-Sep-2007 07:43 by
    Determining the impact on product quality of batch-to-batch variation in raw
    materials is important in specification setting, establishing critical
    parameters and for process understanding for chemical and biochemical
    processes. Where historical data exists on the raw material variability it
    is possible to consider this to look at the impact on product quality.
    Where changes in the process and / or product grade occur data-mining may
    prove infeasible and experimental design may be a more suitable alternative.

    This paper will present one possible strategy for carrying out such an
    experimental design that exploits the inherent correlation within the
    characteristics of the raw material to give a usefully small number of
    experiments. Principle component analysis (PCA) was applied to the
    historical chemical analysis data for the raw material. D-optimal
    experimental design was applied to the principle component scores to select
    batches for inclusion in the DoE.
  • Why DoE is not widely used among engineers in Europe?

    Authors: Martín Tanco; Elisabeth Viles; María Jesus Alvarez; Laura Ilzarbe
    Primary area of focus / application:
    Submitted at 5-Sep-2007 08:59 by
    Engineers perform experiments and analyse data as an integral part of their job. Whether or not engineers have learned statistics, they will do statistics. However, we still have a wide gap between theoretical development of Design of Experiments (DoE) and its effective application in industries. Despite efforts by specialists in quality and statistics, DoE has yet to be applied as widely as it could and should be.

    A vast bibliographic study was carried out for detecting the barriers for why DoE is not widely used among engineers in Europe. The barriers detected were firstly grouped and reduced into sixteen groups. Afterwards, a brief survey was carried out to obtain first-hand information about the importance of each barrier. Four different initiatives were carried out in April 2007 for obtaining response from ENBIS members, which allow us not only to access academician but also practitioners interested in DoE. It was mainly an online survey, which is still available on the web at the following direction: http://examinador.tecnun.es/mtanco/encuesta.asp.

    We introduce in the following work a deep statistical analysis of the mentioned survey. The most important intended goal of our research is to rank and group the barriers in order to suggest some ideas or solutions to allow DoE become closer to industries. We believe our conclusions will help to identify pitfalls and generate a realm of discussion of the situation in Europe.
  • A Corrected Likelihood-Based Confidence Area for Weibull Distribution Parameters and Large-Scale Life Time Data

    Authors: Haselgruber, Nikolaus
    Primary area of focus / application:
    Submitted at 7-Sep-2007 06:48 by
    The Weibull distribution is, in particular for technical applications, a common life time model and data often will be observed in large-scale experiments. Several methods are available to estimate the distribution parameters and confidence areas usually are computed applying the large sample theory for maximum likelihood estimators. Large-scale life time experiments are expensive, consequently samples tend to be small and of short duration which causes right-censored data. The large sample theory looses its applicability.

    This presentation suggests a correction of the likelihood-based confidence area which significantly increases its accuracy for small and moderately censored samples.
  • The use of intelligent Experimental Designs for Optimal Automotive Engine Calibration Online at Engine Test Bench.

    Authors: Thierry Dalon (Siemens VDO Automotive AG, Regensburg, Germany)
    Primary area of focus / application:
    Submitted at 7-Sep-2007 07:31 by
    Control-unit calibration for modern internal combustion engines is currently facing a conflict caused by the additional effort needed to calibrate increasingly complex engine data with a growing number of parameters, together with extremely ambitious objectives regarding the period of time and the resources needed for calibration, performance, consumption, and comfort expected by the customer and emissions levels which are more and more stringent.

    To reduce costs we look for reducing testing time at test bench and hence use minimal number of measurements. That leads to Optimal Experimental Design approaches. Designing experiments often leads to trade-offs between local and global search: local criteria encompass achieving best calibration i.e. the optimization of a target (for example performance) under many constraints (emissions, consumption), whereas global criteria tend to explore the whole domain or improve model quality.

    We present here the context and methods investigated at Siemens VDO Automotive for optimal engine calibration online at the test bench.
    The approach will be illustrated on a practical industrial engine calibration example.

    Keywords: Automotive Engine Calibration, Design of Experiments, Online Optimization, Model-based/ surrogate optimization

    Specifics: It is a presentation related to Dr. Karsten Roepke expertise field.
  • Efficient experimental designs in the presence of more than one hard-to-change variable

    Authors: Heidi Arnouts, Peter Goos (University of Antwerp, Antwerp, Belgium)
    Primary area of focus / application:
    Submitted at 7-Sep-2007 08:04 by Heidi Arnouts
    In ''real-life'' experiments, especially in an industrial environment, experimental
    factors are often not independently reset for each run. This is often due to time
    and/or cost restrictions in the production process. A lot of research has been done
    for the situation in which there is only one hard-to-change variable in the experi-
    ment, the so called ''split-plot'' experimental design. In industrial settings however
    there are often more factors that are ''hard-to-change'' and therefore it is also inter-
    esting to search for optimal designs that involve several hard-to-change variables.
    Some published research deals with this topic but under the restriction that all the
    hard-to-change variables are reset at the same time which reduces this problem to
    a split-plot experiment. In our research, we relax this constraint and look for D-
    optimal designs allowing the various hard-to-change variables to be reset at different
    points in time.
  • Designs for first-order interactions in choice experiments with binary attributes

    Authors: Heiko Grossmann, Rainer Schwabe, Steven G. Gilmour
    Primary area of focus / application:
    Submitted at 7-Sep-2007 11:17 by
    Choice experiments aim at understanding how preferences for goods or services are influenced by the features of competing options and applications in marketing, health economics and other fields abound. In recent years, the efficient design of choice experiments has attracted considerable interest. Typically, these designs have been derived within the framework of the multinomial logit (MNL) model. When it is assumed that the choice probabilities within each choice set are equal, the design problem for the MNL model is equivalent to the corresponding problem for an approximating linear model. By using the correspondence between the design problems, in this talk for choice experiments involving pairs of options described by a common set of two-level factors new exact designs are derived which allow the efficient estimation of main effects and first-order interactions. These designs compare favorably with available alternatives in the literature in that for high efficiencies they usually require the same or a considerably smaller number of choice sets. Similarly, for the same number of choice sets they possess the same or a higher efficiency.