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

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...

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Bibliographic Details
Published in:Materials 2024-01, Vol.17 (3), p.672
Main Authors: Lacki, Piotr, Derlatka, Anna, Więckowski, Wojciech, Adamus, Janina
Format: Article
Language:English
Subjects:
Algorithms
Alloys
Aluminum
Aluminum alloys
Aluminum base alloys
Artificial intelligence
Artificial neural networks
Back propagation
Design of experiments
Friction stir welding
Lap joints
Machine learning
Mechanical properties
Metal sheets
Network topologies
Neural networks
Neurons
Optimization
Plates, Aluminum
Process parameters
Tensile strength
Tensile stress
Variance analysis
Welding
Welding parameters
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Summary: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.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma17030672