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|Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing
|Bock, 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)
|In 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.
|Machine learning; Feature selection; Numerical modelling; Heat transfer; Design of experiments; Explainable AI
|Year of Publication in PubData
|Medien- und Informationszentrum, Leuphana Universität Lüneburg
Information regarding first publication
|Title of the resource type
|Computer Methods in Applied Mechanics and Engineering
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