A novel method based on Bayesian regularized artificial neural networks for measurement uncertainty evaluation

Moschos Papananias, Simon Fletcher, Andrew Longstaff, Azibananye Mengot

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Coordinate measuring machines (CMMs) are complex measuring systems that are widely used in manufacturing industry for form, size, position, and orientation assessment. In essence, these systems collect a set of individual data points that in practice is often a relatively small sample of an object. Their software then processes these points in order to produce a geometric result or to establish a local coordinate system from datum features. The subject of CMM evaluation is a broad and multifaceted one. This paper is concerned with the uncertainty in the coordinates of each point within the measuring volume of the CMM. Therefore, a novel method for measurement uncertainty evaluation using limited-size data sets is conceived and developed. The proposed method is based on a Bayesian regularized artificial neural network (BRANN) model consisting of three inputs and one output. The inputs are: The nominal coordinates; the ambient temperature; and the temperature of the workpiece. The output is the measured (actual) coordinates. An algorithm is developed and implemented before training the BRANN in order to map each nominal coordinate associated with the other inputs to the target coordinate. For validation the model is trained using a relatively small sample size of ten data sets to predict the variability of a larger sample size of ninety data sets. The calculated uncertainty is improved by more than 80% using the predicted variability compared to the uncertainty from the limited sample data set.

    Original languageEnglish
    Title of host publicationProceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2016
    EditorsP. Bointon, R. K. Leach, N. Southon
    PublisherEuspen
    Pages97-98
    Number of pages2
    ISBN (Electronic)9780956679086
    Publication statusPublished - 30 May 2016
    Event16th International Conference of the European Society for Precision Engineering and Nanotechnology - Nottingham, United Kingdom
    Duration: 30 May 20163 Jun 2016

    Conference

    Conference16th International Conference of the European Society for Precision Engineering and Nanotechnology
    Abbreviated titleEUSPEN 2016
    Country/TerritoryUnited Kingdom
    CityNottingham
    Period30/05/163/06/16

    Keywords

    • Bayesian regularized artificial neural network (BRANN)
    • coordinate measuring machine (CMM)
    • uncertainty of measurement
    • UKRI
    • EPSRC
    • EP/I033424/1

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