Lacki, P.; Derlatka, A.; Więckowski, W.; Adamus, J. Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets. Materials2024, 17, 672.
Lacki, P.; Derlatka, A.; Więckowski, W.; Adamus, J. Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets. Materials 2024, 17, 672.
Lacki, P.; Derlatka, A.; Więckowski, W.; Adamus, J. Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets. Materials2024, 17, 672.
Lacki, P.; Derlatka, A.; Więckowski, W.; Adamus, J. Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets. Materials 2024, 17, 672.
Abstract
The article describes machine learning using artificial neural networks (ANN) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). A total of 608 experimental data were used to build the ANN. Two types of networks were built: the first one was used to classify good/bad connections with the MLP 7-19-2 topology, the second one was used to regress the tensile load-bearing capacity with the MLP 7-19-1 topology. Selected parameters of the FSW process were used as input data for ANN training: rotational speed, welding speed, joint and tool geometry. Based on a case study, the quality of the FSW joint was assessed in terms of microstructure and mechanical properties. The usefulness of both trained neural networks has been demonstrated. For the regression network, the quality of the validation set was approximately 93.6%. In the case of confusion matrix for the test set, errors never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. On their basis, predictive maps were developed and presented in the work allowing for the selection of optimal parameters of the FSW process for three types of joints.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
