Development of FSW Process Parameters for Lap Joints Made of Thin 7075 Aluminum Alloy Sheets

Abstract

The article describes machine learning using artificial neural networks (ANNs) to develop the parameters of the friction stir welding (FSW) process for three types of aluminum joints (EN AW 7075). The ANNs were built using a total of 608 experimental data. Two types of networks were built. The first one was used to classify good/bad joints with MLP 7-19-2 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 2 neurons), and the second one was used to regress the tensile load-bearing capacity with MLP 7-19-1 topology (one input layer with 7 neurons, one hidden layer with 19 neurons, and one output layer with 1 neuron). FSW parameters, such as rotational speed, welding speed, and joint and tool geometry, were used as input data for ANN training. The quality of the FSW joint was assessed in terms of microstructure and mechanical properties based on a case study. The usefulness of both trained neural networks has been demonstrated. The quality of the validation set for the regression network was approximately 93.6%, while the errors for the confusion matrix of the test set never exceeded 6%. Only 184 epochs were needed to train the regression network. The quality of the validation set was approximately 87.1%. 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.

Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

“…This research advocates for the thorough integration of ML into the FSW process, transforming it from a mere material joining technique to a sophisticated procedure that greatly benefits from the analysis of real-time data and applied artificial intelligence. Furthermore, Lacki et al [13] utilized ANNs to formulate process parameters for aluminum joint FSW. Their findings highlight the effectiveness of ANN in FSW, establishing computational artificial intelligence as a crucial element for improving not only the process's efficiency but its consistency as well.…”

Influence of Process Parameters on the Mechanical Properties and Corrosion Resistance of Dissimilar Friction Stir Welded Joints of AA2024-O and AA6061-O Aluminum Alloys

“…These joints are called dissimilar welded joints. High-quality dissimilar joints are a true challenge to conventional welding processes [19][20][21][22][23][24]. The friction stir welding process is a successful alternative in these cases [21,25,26].…”

New Approach for Automated Explanation of Material Phenomena Using Artificial Neural Networks

“…ANNs were also predicted tool wear in aluminum matrix composites (AMC) by incorporating parameters as vibration acceleration, cutting forces, and varying cutting speeds, thus contributing to improved machining processes and tool life optimization [10]. 2 For friction stir welding (FSW) of aluminum joints, ANNs classified joint quality and regressed tensile load-bearing capacity, optimizing welding parameters with high validation accuracy [11]. ANNs were able to predict the yield and ultimate tensile strength of metallic alloys, including aluminum, achieving greater than 95% confidence by leveraging chemical composition, tempers, and hardness data [12].…”