%0 conference lecture
%@ 
%A Klusemann, B., Bock, F., Suhuddin, U., Blaga, L.

%D 2020
%J Materials Science and Engineering Congress - MSE 2020

 

%T Prediction of mechanical performances in Solid-State Joining Processes via Machine Learning
%U 
 
%X Presented will be results from the application of various machine-learning-models to correlate required process parameters with desired joint properties for two solid-state joining techniques: Refill Friction Stir Spot Welding and Friction Riveting. The employed machine-learning-models for regression and classification tasks include decision trees, random forests and support vector machines as well as convolutional neural networks. Training and testing data was generated through Central-Composite and Box-Behnken Designs of Experiments. The models were trained and tested based on different performance measures in order to evaluate the suitability of the different approaches for the current processes. The results illustrate the predicting capabilities with respect to certain process parameters on mechanical properties and performances, which enable inverse identification of optimized process parameters to manufacture desired mechanical properties.