Hybrid modeling of mechanical properties and hardness of aluminum alloy 5083 and C100 Copper with various machine learning algorithms in friction stir welding

Ye, Xubo; Su, Zhanguo; Dahari, Mahidzal; Su, Yiping; Alsulami, Samirah; Aldhabani, Musaad S.; Abed, Azher M.; Ali, H. Elhosiny; Bouzgarrou, Souhail

doi:10.1016/j.istruc.2023.06.094

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…The employment of diverse regression models and cross-validation techniques by Anandan and Manikandan [7] has introduced a structured approach to forecasting FSW results. Moreover, this capacity for prediction is expanded through hybrid modeling employing various ML algorithms, as investigated by Ye et al [8]. This research offers insights into the mechanical properties and behaviors of materials undergoing FSW, demonstrating the extensive applicability of ML beyond standard process parameters and into the sphere of advanced materials science.…”

Section: Introductionmentioning

confidence: 94%

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

Myśliwiec,

Kubit,

Szawara

2024

Materials

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This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints’ maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency.

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Section: Introductionmentioning

confidence: 94%

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

Myśliwiec,

Kubit,

Szawara

2024

Materials

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“…Artificial intelligence (AI) offers possibilities for predicting and optimizing FSW process parameters. Particularly, machine learning techniques can be employed to develop predictive models [7][8][9][10][11]. These models can analyze historical data related to FSW, considering factors, such as tool speed and force and material properties, to predict optimal process parameters for the desired outcomes, like weld strength, quality, and defect minimization.…”

Section: Parameter Optimization Using a Machine Learning Approachmentioning

confidence: 99%

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

Lacki,

Derlatka,

Więckowski

et al. 2024

Materials

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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.

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“…Artificial Intelligence (AI) offers possibilities for predicting and optimizing FSW process parameters. Particularly machine learning techniques, can be employed to develop predictive models [7][8][9][10][11]. These models can analyze historical data related to FSW, considering factors such as tool speed, force and material properties, to predict optimal process parameters for the desired outcomes like weld strength, quality, and defect minimization.…”

Section: Parameters Optimization Using a Machine Learning Approachmentioning

confidence: 99%

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

Lacki,

Derlatka,

Więckowski

et al. 2024

Preprint

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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.

show abstract

Hybrid modeling of mechanical properties and hardness of aluminum alloy 5083 and C100 Copper with various machine learning algorithms in friction stir welding

Cited by 9 publications

References 21 publications

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

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

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

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

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