A Predictive Model for Weld Properties in AA-7075-FSW: A Heterogeneous AMIS-Ensemble Machine Learning Approach

Matitopanum, Surasak; Luesak, Peerawat; Chiaranai, Somphop; Pitakaso, Rapeepan; Srichok, Thanatkij; Sirirak, Worapot; Jirasirilerd, Ganokgarn

doi:10.1016/j.iswa.2023.200259

Cited by 4 publications

(4 citation statements)

References 30 publications

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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%

“…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. Study [8] discusses the development of an integrated prediction model for ultimate tensile strength (UTS), maximum hardness (MH), and heat input (HI) in friction stir welding of AA-7075. The aim of the work was to improve FSW welding procedures by incorporating four control parameters: tilt angle, rotation speed, welding speed, and shoulder diameter.…”

Section: Parameter Optimization Using a Machine Learning Approachmentioning

confidence: 99%

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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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Section: Parameter Optimization Using a Machine Learning Approachmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…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%

“…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. Study [8] discusses the development of an integrated prediction model for ultimate tensile strength (UTS), maximum hardness (MH), and heat input (HI) in friction stir welding of AA-7075. The aim of the work was to improve FSW welding procedures by incorporating four control parameters: tilt angle, rotation speed, welding speed, and shoulder diameter.…”

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.

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Machine learning metamodels for thermo-mechanical analysis of friction stir welding

Burande,

Kalita,

Gupta

et al. 2024

Int J Interact Des Manuf

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A Predictive Model for Weld Properties in AA-7075-FSW: A Heterogeneous AMIS-Ensemble Machine Learning Approach

Cited by 4 publications

References 30 publications

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

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

Machine learning metamodels for thermo-mechanical analysis of friction stir welding

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