Mechanical performance prediction for friction riveting joints of dissimilar materials via machine learning
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In: Procedia Manufacturing, Vol. 47, 05.2020, p. 615-622.
Research output: Journal contributions › Conference article in journal › Research › peer-review
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TY - JOUR
T1 - Mechanical performance prediction for friction riveting joints of dissimilar materials via machine learning
AU - Bock, Frederic Eberhard
AU - Blaga, Lucian Attila
AU - Klusemann, Benjamin
N1 - Conference code: 23
PY - 2020/5
Y1 - 2020/5
N2 - Solid-state joining techniques have become increasingly attractive for joining similar and dissimilar materials because it enables further optimization of lightweight components. In contrast to fusion-based joining processes, solid-state joining prevents the occurrence of typical defects such as pores or hot cracking. Machine learning algorithms are powerful tools to identify and quantify relationships between essential features along the process-property chain. In particular, different supervised machine learning algorithms can be used to perform regression analyses and establish correlations between process parameters as well as resulting properties. This can help to circumvent the demand for conducting a vast number of additional experiments to determine optimized process parameters for desired material properties. Additionally, this knowledge can be utilized to obtain a deeper understanding of the underlying mechanisms. In this study, a number of regression algorithms, such as support vector machines, decision trees, random forest and 2nd-order polynomial regression have been applied to correlate process parameters and materials properties for the solid-state joining process of force-controlled friction riveting. Experimental data generated via a central-composite Design of Experiments, serves as source of two separate data sets: one for training and one for testing the machine learning algorithms. The performances of the different algorithms are evaluated based on the determination coefficient R2 and the standard deviation of the predictions on the test data set. The trained algorithms with the best performance measures can be used as predictive models to forecast specific influences of process parameters on mechanical properties. Through the application of these models, optimized process parameters can be determined that lead to desired properties.
AB - Solid-state joining techniques have become increasingly attractive for joining similar and dissimilar materials because it enables further optimization of lightweight components. In contrast to fusion-based joining processes, solid-state joining prevents the occurrence of typical defects such as pores or hot cracking. Machine learning algorithms are powerful tools to identify and quantify relationships between essential features along the process-property chain. In particular, different supervised machine learning algorithms can be used to perform regression analyses and establish correlations between process parameters as well as resulting properties. This can help to circumvent the demand for conducting a vast number of additional experiments to determine optimized process parameters for desired material properties. Additionally, this knowledge can be utilized to obtain a deeper understanding of the underlying mechanisms. In this study, a number of regression algorithms, such as support vector machines, decision trees, random forest and 2nd-order polynomial regression have been applied to correlate process parameters and materials properties for the solid-state joining process of force-controlled friction riveting. Experimental data generated via a central-composite Design of Experiments, serves as source of two separate data sets: one for training and one for testing the machine learning algorithms. The performances of the different algorithms are evaluated based on the determination coefficient R2 and the standard deviation of the predictions on the test data set. The trained algorithms with the best performance measures can be used as predictive models to forecast specific influences of process parameters on mechanical properties. Through the application of these models, optimized process parameters can be determined that lead to desired properties.
KW - Engineering
KW - Decision trees
KW - Process parameters
KW - Random forests
KW - Solid state joining
KW - Support vector machines
KW - Ultimate tesnile force
UR - http://www.scopus.com/inward/record.url?scp=85085520581&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.04.189
DO - 10.1016/j.promfg.2020.04.189
M3 - Conference article in journal
AN - SCOPUS:85085520581
VL - 47
SP - 615
EP - 622
JO - Procedia Manufacturing
JF - Procedia Manufacturing
SN - 2351-9789
T2 - 23rd International Conference on Material Forming - 2020
Y2 - 4 May 2020 through 8 May 2020
ER -