ENBIS-16 in Sheffield

11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016

My abstracts

 

The following abstracts have been accepted for this event:

  • Uncertainty Estimation of One-Step Ahead Railway Track Geometry Failure Predictions

    Authors: Bjarne Bergquist (Luleå University of Technology), Peter Söderholm (Swedish Transport Administration)
    Primary area of focus / application: Reliability
    Secondary area of focus / application: Metrology & measurement systems analysis
    Keywords: Railway, Remaining useful life (RUL), Uncertainty, Kalman filter, Time series, Prediction, Sweden
    Submitted at 31-May-2016 16:33 by Bjarne Bergquist
    Accepted (view paper)
    13-Sep-2016 15:40 Uncertainty Estimation of One-Step Ahead Railway Track Geometry Failure Predictions
    In this presentation, we exploit the prediction uncertainty of using recursively updated time series predictions of remaining useful life (RUL) for railway track. By RUL, we mean the time until dimensional properties of the track have degraded to a safety critical fault state, where actions such as speed restrictions and track alignment are required.

    We use Kalman filters for predicting the evolvement of track property data. The uncertainty of the predictions relates to: measurement uncertainty (instrument imprecisions including measurement location uncertainty); uncertainty of substructure properties at measurements (e.g. because of spring thaw and frost heave), and irregular sampling frequency. Repeated sampling consisting of returning measurement trains at the same track section in a short time interval support point estimate uncertainties and tests for violation of the Gaussian assumptions. We also discuss the sampling intervals in terms of the model’s prediction abilities in light of practical concerns such as the difficulties to perform measurements during different yearly seasons.
  • Accounting for Refractive Index Effects in Multilateration

    Authors: Alistair Forbes (National Physical Laboratory), Ben Hughes (National Physical Laboratory), Andrew Lewis (National Physical Laboratory)
    Primary area of focus / application:
    Keywords: Metrology, Multilateration, Refractive index, Uncertainty
    Submitted at 31-May-2016 18:13 by Alistair Forbes
    Accepted
    14-Sep-2016 09:20 Accounting for Refractive Index Effects in Multilateration
    Laser interferometry is used extensively in length metrology. The operational principle is based on a laser beam sent out from a measuring station and reflected back by a target to interfere with the outgoing beam. Changes in the length of the optical path can be estimated by counting the interferometric fringes. If three or more beams are fixed on a single reflecting target then the location of the target in three dimensions can be estimated, a process known as multilateration. The fringe counts measure the (change in the) optical path length and in order to convert to the (change in the) geometric path length it is necessary to know the average refractive index of the air along the path. The refractive index of air depends primarily on temperature, pressure and humidity, in order of relative importance, and the average refractive index is usually inferred from measurements of temperature and pressure at a small number of locations within the measuring volume. Most multilateration systems assume that the estimated geometric path length derived from the mean refractive index is a sample from a Gaussian distribution centred on the true geometric path and that the measurements of path lengths are statistically independent. However, the refractive index field is likely to be spatially correlated so that mean refractive indices along nearby paths are similar. This paper discusses ways of modelling the refractive index field, the correlating effect of refractive index on the estimates of path lengths and target locations and the evaluation of measurement uncertainty associated with multilateration systems.
  • Statistical Approach for Additive Manufacturing Process Characterisation

    Authors: Biagio Palumbo (Fraunhofer JL IDEAS-SQUARE, Department of Industrial Engineering, University of Naples Federico II), Francesco Del Re (Fraunhofer JL IDEAS-SQUARE, Department of Industrial Engineering, University of Naples Federico II), Massimo Martorelli (Fraunhofer JL IDEAS-CREAMI, Department of Industrial Engineering, University of Naples Federico II), Pasquale Corrado (MBDA ITALIA S.P.A), Giuseppe La Sala (MBDA ITALIA S.P.A)
    Primary area of focus / application: Design and analysis of experiments
    Secondary area of focus / application: Quality
    Keywords: Design of Experiment (DoE), Analysis Of Variance (ANOVA), Nested Effects Modeling (NEM), Additive Manufacturing (AM), Direct Metal Laser Sintering (DMLS)
    Submitted at 31-May-2016 18:56 by Biagio Palumbo
    Accepted
    13-Sep-2016 09:20 Statistical Approach for Additive Manufacturing Process Characterisation
    The Additive Manufacturing technology has been recognized as the next chapter in the industrial revolution. Exactly in the qualification phase of a new industrial process, when is inadequate the practitioner's experience, statistics play a strategic role to help management in decision-making by planning experimental activities, analyzing statistical results and catalyzing technological interpretations.
    The aim of this work is to estimate the effects of different laser exposure strategies on the final quality of produced parts, such as surface roughness, density and mechanical properties. Since the functional relationship between the large number of involved process parameters related to laser exposure settings, a Nested Effects Modeling (NEM) approach has been adopted.
    The proposed case study is a positive example of synergic collaboration and partnership between academic statisticians and industrial practitioners; it has been developed in MBDA industry, a world-class leader in missiles and missile systems.
  • Expert Knowledge Systems to Ensure Quality and Reliability in Direct Digital Manufacturing Environment

    Authors: Eva Scheideler (Hochschule Ostwestfalen-Lippe), Andrea Ahlemeyer (Ahlemeyer-Stubbe Data Mining and More)
    Primary area of focus / application: Design and analysis of experiments
    Keywords: Expert knowledge systems, Meta model, Direct digital manufacturing, Computer experiment
    Submitted at 31-May-2016 21:21 by Eva Scheideler
    Accepted (view paper)
    13-Sep-2016 10:10 Expert Knowledge Systems to Ensure Quality and Reliability in Direct Digital Manufacturing Environment
    Seamless process chains in the Direct Digital Manufacturing in the context of Industry 4.0 shorten the time between offering of individual designed products, production and the delivery of it. To reach that goal further automation is required. Therefore reliable automated and quick evaluation procedures are needed, which ensure the quality of the individual designed products in terms of product safety and product reliability.
    Until now human experts check based on their experience whether the consumer desired product parameters a likely to create a product that ensures the required safety level. If necessary the expert will change the product parameter after client consultation to meet the needed safety level. Depending on the specific application the review of the parameter is done so far with special often complex simulation tools. In most of the cases a human expert is need to run simulation tools.
    This talk aims to demonstrate, how a meta model generated on simulated data and adapted to the type of product can be used to ensure a reliable, automated and quick evaluation of the specific client preferred product parameters to guarantee the demanded characteristics of the final product with out consultation of human expert knowledge.
    A meta model that reach all this characteristics should only run on the client given parameters and information of the demanded characteristics or usages of the future product. In the further description these parameters are called input variables. Parameters based on expert knowledge should be covered by the meta model itself and not be influenced by clients requirements.
    In a first step a deterministic complex simulation model is created from the product. Design strategies for computer experiments are used to select data sets. Thereby the complexity and the nonlinearity between the input variables and the response has been taken into account. Deeper domain knowledge is also necessary to create these computer based experiments. The generated data are used to learn with Statistical prediction methods a valuable meta models which have a good forecast ability. The validation of the simplified prediction model is a crucial success factor.
    As proof of concept we choose a task from the field of construction. Our example is a vision panel in a facade or door. With sets of typical given parameters and sets of unusual but realistic once, we like to illustrate the power of the meta model. Our goal is it to see whether the safety requirements are based on the individual input variable and without expert interaction are confirmed by the meta model.
    Fast reliable prediction models derivative from complex simulation models are indispensable conditions for direct digital manufacturing. Using meta models in automation context is a foundation of manufacturing in future.
  • Design and Models for the Prediction of In-Flight Particle Properties in Thermal Spraying with Additive Day-Effects

    Authors: Sonja Kuhnt (Dortmund University of Applied Sciences and Arts)
    Primary area of focus / application: Design and analysis of experiments
    Keywords: Generalized linear models, Day effects, D-optimal design, Thermal spraying
    Submitted at 31-May-2016 22:48 by Sonja Kuhnt
    Accepted
    13-Sep-2016 09:20 Design and Models for the Prediction of In-Flight Particle Properties in Thermal Spraying with Additive Day-Effects
    Thermal spraying technology can be employed to apply a particle coating on a surface. Due to uncontrollable day-effects thermal spraying processes are often lacking in reproducibility. This fact, combined with a time-consuming and not immediately available analysis of the quality of the coating, leads us to measure and use in-flight properties of the particles. We derive separate generalized linear models suitable for describing multiple in-flight properties based on the assumption of a Gamma distribution. These models are needed for a later optimization procedure to search for settings of the process parameters which return values of the particles in-flight known to ensure a good quality of the final coating. We show how these models can be extended to include additional day-effects. The models are to be updated on a limited number of
    additional experiments on any day based on a suitable optimal experimental design. Our focus is on determinant type optimal design criteria maximizing the determinant of the Fisher information, which in case of generalized linear models may depend on the unknown parameter. As separate models are considered for the in-flight particles a design has to be determined which yields large determinants of all Fisher information matrices simultaneously. A classical D-optimal design for one component is not necessarily efficient for the other components. Therefore a new multi-objective optimality criterion is introduced which yields efficient designs for several models simultaneously. We demonstrate for the thermal spraying application that the constructed designs improve a reference design substantially.
  • Communication Challenges in Cross-Functional Research Teams: Misunderstanding and Talking at Cross Purposes

    Authors: Olivia Bluder (KAI GmbH)
    Primary area of focus / application: Other: Young Statisticians
    Secondary area of focus / application: Education & Thinking
    Keywords: Cross-functional team, Communication, Misunderstanding, Different technical disciplines, Interactive session
    Submitted at 31-May-2016 23:41 by Olivia Bluder
    Accepted
    13-Sep-2016 09:00 Communication Challenges in Cross-Functional Research Teams: Misunderstanding and Talking at Cross Purposes
    Often R&D challenges in industry can only be solved by the collaboration of a variety of experts from different fields. Consider for example that company X develops a new semiconductor device to switch on and off the light in the car. To simplify the procedure, let’s say that the necessary steps to achieve this goal are e.g. define the requirements, create a new design, select the materials and build the device. This procedure is not straight forward and often the initial design needs some iteration loops of re-designs until the device fulfills the requirements. This means that experts from electrical engineering, material science, chemistry, physics, simulation and of course statistics have to talk to each other to create a reliable product. Although all experts are technicians, the communication in such cross-functional teams can be challenging. Many disciplines use similar terms, they may have the same meaning on a high level, but for the individual disciplines they are associated with totally different methods.
    In this session examples on typical misunderstandings in cross-functional teams due to the usage of same terms with different meaning will be discussed. After this talk, strategies to avoid talking at cross purposes with team members from other disciplines will be developed in roleplays.