Abstract
Solid state joining techniques have become increasingly attractive for joining similar or dissimilar materials which enable further optimization of light-weight 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; thus, to establish correlations between process parameters and 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 get a deeper understanding of the underlying mechanisms. In this study, a number of regression algorithms, such as support vector regression, random forest regression 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 experiment, served as data sets for training and testing of the machine learning algorithms. Performances of algorithms are evaluated based on the determination coefficient 𝑅𝑅2 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 that lead to desired properties can be determined.
