Please use this identifier to cite or link to this item: https://doi.org/10.48548/pubdata-131
Resource TypeJournal Article
TitleData-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing
DOI10.48548/pubdata-131
Handle20.500.14123/150
CreatorBock, Frederic E.  0000-0002-6541-2036 (Helmholtz-Zentrum Hereon  03qjp1d79)
Kallien, Zina  0009-0003-5133-0624 (Helmholtz-Zentrum Hereon  03qjp1d79)
Huber, Norbert  0000-0002-4252-9207 (Helmholtz-Zentrum Hereon  03qjp1d79)
Klusemann, Benjamin  0000-0002-8516-5087 (Institut für Produktionstechnik und -systeme (IPTS), Leuphana Universität Lüneburg  02w2y2t16)
AbstractIn the last decades, there has been an increase in the number of successful machine learning models that have served as a key to identifying and using linkages within the process-structure–property-performance chain for vastly different problems in the domains of materials mechanics. The consideration of physical laws in data-driven modelling has recently been shown to enable enhanced prediction performance and generalization while requiring less data than either physics-based or data-driven modelling approaches independently. In this contribution, we introduce a simulation-assisted machine learning framework applied to the solid-state layer deposition technique friction surfacing, suitable for solid-state additive manufacturing as well as repair or coating applications. The objective of the present study is to use machine learning algorithms to predict and analyse the influence of process parameters and environmental variables, i.e. substrate and backing material properties, on process behaviour and deposit geometry. The effects of maximum process temperatures supplied by a numerical heat transfer model on the predictions of the targets are given special attention. Numerous different machine learning algorithms are implemented, optimized and evaluated to take advantage of their varied capabilities and to choose the optimal one for each target and the provided data. Furthermore, the input feature dependence for each prediction target is evaluated using game-theory related Shapley Additive Explanation values. The experimental data set consists of two separate experimental design spaces, one for varying process parameters and the other for varying substrate and backing material properties, which allowed to keep the experimental effort to a minimum. The aim was to also represent the cross parameter space between the two independent spaces in the predictive model, which was accomplished and resulted in an approximately 44 % reduction in the number of experiments when compared to carrying out an experimental design that included both spaces.
LanguageEnglish
KeywordsMachine learning; Feature selection; Numerical modelling; Heat transfer; Design of experiments; Explainable AI
Year of Publication in PubData2024
Publishing TypeParallel publication
Publication versionPublished Version
Date issued2023-09-30
Creation ContextResearch
Published byMedien- und Informationszentrum, Leuphana Universität Lüneburg
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FieldValue
Resource typeJournal
Title of the resource typeComputer Methods in Applied Mechanics and Engineering
IdentifierDOI: 10.1016/j.cma.2023.116453
Publication year2024
Volume418
Number116453
Number typeArticle
PublisherElsevier
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