ENBIS-8 in Athens

21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008

My abstracts


The following abstracts have been accepted for this event:

  • Complex PCA as an Exploratory Tool for 3-Dimensional Profiles

    Authors: Bianca M. Colosimo, Massimo Pacella
    Affiliation: Dipartimento di Meccanica, Politecnico di Milano, Milano, ITALY; Dipartimento di Ingegneria dell'Innovazione, Università del Salento, Lecce, ITALY
    Primary area of focus / application:
    Submitted at 14-May-2008 17:42 by Massimo Pacella
    Accepted (view paper)
    22-Sep-2008 14:40 Complex PCA as an Exploratory Tool for 3-Dimensional Profiles
    During the past few years, an increasing number of approaches and applications of profile monitoring have been proposed in the scientific literature. As a matter of fact, very often product and/or process quality is characterized by profiles or functional data. In this type of applications, the quality outcome (dependent variable) is actually a function of one or more spatial or temporal location variables (independent variables).
    Up to now, profile monitoring techniques have been constrained to situations in which the dependent variable is a scalar which can be modelled as a function of one or more independent variables via linear models or data-reduction approaches as PCA. As a matter of fact, PCA has been demonstrated to be an excellent exploratory method for interpreting and modelling profiles in a scalar field of data, obtained from manufacturing processes (Colosimo and Pacella 2007).
    However, when the quality of products is related to geometric tolerances, very often the profile cannot be simply modelled via a scalar variable, since the profile or curve lies in a 3-dimensional space. Examples range from the simplest requirement of axial straightness to very complex curves in free-form geometric tolerance.
    This paper explores problems arising when 3D curves has to be monitored over time proposing possible solutions. A real case dealing with the straightness of cylindrical components machined by turning is used as reference throughout the paper. In particular, an approach is discussed in which a generalization of PCA is used to model two-dimensional vector observations (i.e., directional data). The method is based on an appropriate use of complex, rather than real numbers in the analysis. The so-called “complex PCA” is routinely used in the case of horizontal velocity components in geophysical measurements (such an approach was firstly proposed by Hardy and Walton, 1978). Our aim is to explore the use of complex PCA also for modelling 3D profiles obtained from manufacturing processes.

    Colosimo B. M. and Pacella M., 2007. On the Use of Principal Component Analysis to Identify Systematic Patterns in Roundness Profiles. Quality and Reliability Engineering International, 23(6), 707-725.
    Hardy D. M. and Walton J. J., 1978. Principal Components of Vector Wind Measurements. Journal of Applied Meteorology, 17(8), 1153–1162.
  • Measurement System Analysis - a computer aided research

    Authors: Magdalena DIERING, MSc. Eng.; Edward PAJĄK, DSc PhD MSc. Eng., prof. PUT
    Affiliation: Poznan University of Technology, Division of Production Management
    Primary area of focus / application:
    Submitted at 14-May-2008 19:06 by Magdalena Diering
    Accepted (view paper)
    24-Sep-2008 09:20 Measurement System Analysis - a computer aided research
    This article is discussed about necessity for Measurement Systems Analysis (MSA) in production enterprises. Authors of this article are suggesting to make use of computer application program to MSA analysis. An example is presented.
    Manufacturing process engineers job involves continuously a necessity of making a decisions to keep the production process stable. To make correct decisions the process course must be regularly monitored for production quality and must be evaluated. Data of low quality can be the cause of making a wrong decisions about production process or products. To apply to MSA is as often as a requirement from demanding clients who consider that it is desirable that the given data must be a reliable source of information about the production process. In Polish enterprises MSA analyzing is still a rarity. Usually it is a result of low awareness of needs or even existing MSA methods or distrust to using them. Unconvinced of MSA benefits entrepreneurs may have an impression that conditions for carrying out a research, analysis and evaluation of measurement system, may interfere in a typical daily work of an organization. For a given research, one has to prepare parts, employees (participants of the experiment, a process engineer and a quality specialists), save up some place and, what is very time-consuming - carry out an analysis of a given data sheet and prepare MSA report. These things are connected with extra time and in the days of strong market competition, which every enterprise has to face – time means money. An aid for that issue may be, represented by the authors of the article a computer application program.
    The way to make a research of measurement system, its analysis and evaluation easier and faster that is more attractive for entrepreneurs is the purpose of this paper. The purpose of this paper is to describe one of MSA method for process engineers and quality managers as easy and useful instrument to estimate measurement system.
  • A Bayesian Approach to Model Shifts in Poisson Data

    Authors: Panagiotis Tsiamyrtzis and Douglas M. Hawkins
    Affiliation: Dept. of Statistics, Athens University of Economics and Business
    Primary area of focus / application:
    Submitted at 15-May-2008 15:03 by Panagiotis Tsiamyrtzis
    24-Sep-2008 09:40 A Bayesian Approach to Model Shifts in Poisson Data
    We consider a process producing count data from a Poisson distribution. Our interest is in detecting on-line whether the Poisson parameter (mean and variance) shifts to either a higher value (causing worst process performance) or to smaller values (good scenario). The necessity for drawing inference sequentially as the observations become available leads us to adopt a Bayesian sequentially updated scheme of mixture of Gamma distributions. Issues regarding inference and prediction will be covered. The developed methodology is very appealing in cases like short runs and/or Phase I count data.

    Bayesian SPC by attributes, Change Point, Gamma mixture.
  • Estimated Process Capability Indices and Impact on the Proportion of Nonconforming Output

    Authors: Murat Caner Testik and Banu Yuksel Ozkaya
    Affiliation: Hacettepe University, Dept. of Industrial Engineering, Ankara TURKEY
    Primary area of focus / application:
    Submitted at 15-May-2008 16:31 by Murat Caner Testik
    22-Sep-2008 16:00 Estimated Process Capability Indices and Impact on the Proportion of Nonconforming Output
    Process capability analyses play a vital role in quality improvement programmes. Among the process capability analysis tools, commonly used are the process capability indices (PCIs). These are single number assessments of the relationship between process performance and specifications on a quality characteristic. Nevertheless, process parameters are often unknown in practice and need to be estimated for use in PCIs. Hence, estimated PCIs are subject to sampling error. In this study, two widely used process capability indices, namely Cp and Cpk, are considered when the observations are normally distributed but the process mean and/or variance are estimated. Marginal distributions for the proportion of nonconforming items are developed and computed for several sample sizes. Some recommendations are provided for practitioners.
  • Double Generalized Linear Model Estimates in the Case of Unreplicated Designs with Dispersion Effects

    Authors: Corinna Auer and Dr. Martina Erdbrügge
    Affiliation: Technical University of Dortmund, Germany
    Primary area of focus / application:
    Submitted at 15-May-2008 19:25 by Corinna Auer
    23-Sep-2008 11:15 Double Generalized Linear Model Estimates in the Case of Unreplicated Designs with Dispersion Effects
    In many technical or industrial applications it is necessary to not only optimize the mean but also the variance of a process. Using replicated designs it is possible to estimate functional dependencies between mean and variance as well as possible dispersion effects within the same model framework. However, in the situation of unreplicated designs usually constant variances or only a functional relationship between mean and variance is assumed. Here a method based on double generalized linear models (dGLMs) is proposed, which can also take possible dispersion effects into account. To fit a dGLM an extension of the likelihood as for example the extended quasi-likelihood (EQL) or the pseudo-likelihood (PL) is needed. The resulting estimates are examined concerning desirable statistical properties like efficiency and normality.
  • Control Charts for the Logarithmic Distribution

    Authors: Elisabeth Topalidou, Stelios Psarakis
    Affiliation: Dept. of Statistics, Athens University of Economics and Business
    Primary area of focus / application:
    Submitted at 15-May-2008 21:33 by Elisabeth Topalidou
    Accepted (view paper)
    23-Sep-2008 16:50 Control Charts for the Logarithmic Distribution
    Logarithmic distribution is one of the discrete distributions with various applications some of which are in ecology, engineering and water resources, pharmacology, biochemistry, molecular biology, genetics, environmental sciences, telecommunications and others. As a result of its variety of applications, it appears to be important that control charts to detect shifts in mean and variability should be constructed on the assumption that the distribution of the quality characteristic under study is the logarithmic distribution. Here we construct Shewhart-type and probability-type control charts for detecting parameter shifts for the logarithmic distribution. The performances of those two types of logarithmic control charts are compared. We also investigate the construction of CUSUM procedures for the logarithmic distribution. Finally the construction and the investigation of the performance of the proposed charts are illustrated using numerical examples.