ENBIS-7 in Dortmund

24 – 26 September 2007

My abstracts


The following abstracts have been accepted for this event:

  • Efficient Design in Conjoint Analysis and Alike

    Authors: Rainer Schwabe
    Primary area of focus / application:
    Submitted at 7-Sep-2007 21:31 by
    Conjoint analysis is a popular tool in marketing research. Stated choice experiments are performed to evaluate the influence of various options on the comsumers' preferences. The quality of the outcome of such experiments heavily depends on its design, i.e. on which questions are asked. The present talk gives an overview of the results of a research project on "Efficient Design in Conjoint Analysis" carried out at the universities of Münster and Magdeburg (joint work with U. Graßhoff [Magdeburg], H. Großmann [London] and H. Holling [Münster]).
  • Calibration of instruments using LogVariance models

    Authors: Diego Zappa - Massimiliano Pesaturo
    Primary area of focus / application:
    Submitted at 7-Sep-2007 22:42 by
    Design of experiments are often programmed to estimate the mean response surface. Typically it is often assumed the homoschedasticity hypothesis over the experimental domain. The most recent literature has stressed the importance of evaluation also of the variance response surfaces both to assess the existence of heteroschedasticity and, in the latter case, for a matter of optimization (maximize /minimize the mean , minimizing the expected variance). In this context we exploit the so called Log Linear Variance models (also known as LogVariance models) to assess the calibration of a temperature sensors integrated inside MEMS Chip (Micro Electro Mechanical Systems) which is the core component of Lab-On-Chip (LOC) systems used in DNA clinical analysis. We will show the effectiveness of the procedure using data measured in a real experiment. In addition, because of the computational efforts and the needs of a sw tool easily sharable among researchers, it has been prepared an excel spreadsheet freely available from the authors.
  • Selecting explanatory variables with the modified version of Bayesian Information Criterion

    Authors: Malgorzata Bogdan (Purdue University, West Lafayette, IN, USA)
    Primary area of focus / application:
    Submitted at 8-Sep-2007 10:23 by
    Business or science data are often stored in large data bases. Looking for relationships between variables represented in such data bases is one of the most important aspects of data mining. In this talk we consider the problem of identifying factors related to a given continuous characteristic. The common approach to this problem relies on fitting the multiple regression model. The usual goal is to choose the simplest model which would include most of important factors related to the response variable. We will demonstrate that in the situation when the number of variables in the data base is much larger than the number of cases the standard model selection criteria like Akaike Information Criterion or Bayesian Information Criterion (BIC) have a tendency to include many spurious variables. This phenomenon is related to the well known problem of multiple testing. We will present the modified version of BIC which adjusts for this problem and its rank extension designed for the situation when the distribution of the response variable is strongly different from normal. We will illustrate the performance of our method by computer simulations and real data applications.
  • Design of Experiments for Mean and Variance

    Authors: Marta Emmett, Peter Goos, Eleanor Stillman
    Primary area of focus / application:
    Submitted at 8-Sep-2007 11:28 by
    The great majority of experimental designs are directed towards estimating the
    mean of a single response variable under homoscedasticity. However, in many
    practical applications the variance structure is not known and the variance, as
    well as the mean, needs to be estimated. Estimating the mean and variance
    simultaneously is particularly relevant in quality control experiments. The
    first person to bring attention to the importance of reducing variability in
    such experiments was Taguchi in the 1980s. Taguchi methods seek to design a
    product or a process whose performance meets a specified target on average and
    exhibits little variability. This variability may be a consequence of
    environmental factors, controllable and uncontrollable factors during the
    manufacturing process and component deterioration.

    More recently, Atkinson & Cook (1995) and Vining & Schaub (1996) developed
    optimal design theory for estimation of mean and variance functions
    simultaneously. Both papers assume that the variance function is estimated
    using the residuals of the regression function for the mean. However,
    researchers often prefer using sample variances for quantifying and modelling
    variation. This has the advantage that the responses of the variance function
    do not depend on the specification of the mean function. If sample variances
    are utilized, the optimal design approaches of Atkinson & Cook (1995) and
    Vining & Schaub (1996) are no longer ideal. Therefore, building on the work of
    Goos, Tack and Vandebroek (2001), we propose a new optimal design criterion for
    the simultaneous estimation of mean and variance functions, where it is assumed
    that sample variances are used for estimating the latter function.


    Atkinson, A.C. and Cook, R.D. (1995). D-optimum designs for heteroscedastic
    linear models. Journal of the American Statistical Association, 90, 204-212.

    Goos, Peter, Tack, L., Vandebroek, M. (2001). Optimal designs for variance
    function estimation in using sample variances. Journal of Statistical Planning
    and Inference. 92, 233-252.

    Vining, G.G. and Schaub, D. (1996). Experimental designs for estimating both
    mean and variance functions. Journal of Quality Technology. 28, 135-147.
  • Improvement of a manufacturing process by integrated physical and numerical experiments: a case-study in the textile industry

    Authors: Stefano Masala (1), Paola Pedone (2), Martina Sandigliano (1) and Daniele Romano (2)
    Primary area of focus / application:
    Submitted at 8-Sep-2007 11:49 by
    In hi-tech industry, like aerospace and microelectronics, the combined use of simulation and lab tests is a daily practice
    in the product development phase. It is easy to forecast that it will spread soon also in less knowledge-intensive sectors.
    However, although Design of Experiments and Computer Experiments provide sound methodologies for running experiments in
    physical and numerical settings respectively, the integration between the two kinds of investigation is still in its infancy.
    Yet in that case the sequential experimentation approach, introduced by George Box for physical experiments some fifty years
    ago, would have an even wider scope.

    The work describes the results of a research project which is currently taking place at Technova Srl, a medium size textile
    firm in Sardinia (Italy). The company produces flocked yarn, a component which, after weaving, becomes a fabric for a wide
    range of technical applications. Typical end products are coverings for seats and other components in car interiors. The
    yarn is formed by finely cut fibers (flock) applied to an adhesive coated carrier thread by the electrostatic force. The
    research focuses on the improvement of the manufacturing process. To this end, we exploit all kind of information sources
    available, from historical production data to physical experiments on pilot and production machines and experiments on
    different process simulators. We show that the results obtained by this approach are well beyond the initial expectations of
    the company in terms of enhanced product quality as well as process economy and flexibility.

    Keywords: DoE, Computer experiments, Sequential experimentation, Flocking process, Quality improvement.


    (1) Technova Srl, Olbia, martina.sandigliano@novafloor.it
    (2) University of Cagliari, Dept. of Mechanical Engineering, Cagliari, romano@dimeca.unica.it
  • An automotive experience in applying DoE to improve a process

    Authors: Laura Ilzarbe, M. Tanco, M. Jesús Alvarez, E. Viles
    Primary area of focus / application:
    Submitted at 8-Sep-2007 13:38 by
    Laser welding is becoming more widely used within the automotive industry because of its reputation for high quality and precision. However, achieving the best set of parameter settings for this process is no trivial task and the industry has encountered many problems in the implementation of laser welding. These problems lead to defects which can be very expensive, so the industry is keen to optimise the process to make it as cost effective as possible.

    In this paper we present the application of the design of experiments in a car manufacturing company to improve their technical knowledge of the laser welding process and the positive impact that this research already had on the number of defects observed.