Prediction of the power supplied in friction-based joining process of metal-polymer hybrids through machine learning

Lambiase, F.; Grossi, V.; Scipioni, Silvia Ilaria; Paoletti, A.

doi:10.1016/j.jmapro.2021.06.001

Cited by 12 publications

(3 citation statements)

References 51 publications

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“…To this end, the higher percentage of experimental data, that is, 70% of data from Table 1, is chosen randomly for training sets, while the remaining 30% is used as testing sets. 6,18 The training set is utilized to determine weights and biases of ANN, SVM and GPR to establish regression models. The test data set helps to verify the efficiency of developed regression models.…”

Section: Modelling Using Machine Learning Modelsmentioning

confidence: 99%

“…1 Recently, a few researchers recently explored the feasibility of joining aluminium alloys (AA) and thermoplastic polymers using the FSW technique, and significant progress has been achieved. Lambiase et al 6 adopted a machine learning (ML) based artificial neural network (ANN) to friction power generated during friction-assisted joining of AA7075 and Polyamide66 (PA66). The studies found that the ANN tool is reliable for process design with a coefficient of determination ( R 2 ) value of 0.90.…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

Sandeep

Natarajan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Vehicle weight has been a critical concern in the aerospace and automobile industries for decades. Integrating dissimilar aluminium and polymer hybrid structures is beneficial for weight reduction without affecting structural performance. In the present work, aluminium alloy 7475 and polyphenylene sulfide (PPS) sheets were joined using the friction stir welding (FSW) technology in lap joint configuration. A series of FSW experiments have been performed by the design matrix developed using response surface methodology. Tensile lap shear strength (TLS) is calculated for each experimental run. In this study, an attempt has been made to assess the potential of machine learning algorithms to predict the TLS of the joint. It was found that the support vector machine (SVM) model with RBF kernel was the most effective for predicting the TLS. Furthermore, FSW process parameters are optimized by means of the desirability approach. The optimal set to attain maximum TLS is identified as the tilt angle of 2°, welding speed of 5.12 mm/min and tool rotational speed of 1185.92 r/min. Finally, a confirmation test was performed to validate the optimal set and the adequacy of the developed SVM model. From the confirmation test, the error percentage between experimental and prediction values is less than 5%. Metallographic analysis revealed that the joining mechanism is the macro and micromechanical interlocking assisted by chemical bonding.

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Section: Modelling Using Machine Learning Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

Sandeep

Natarajan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Machine learning (ML) has the potential to overcome these deficiencies. Recent studies show that machine learning can be used for the automation of different industries [14][15][16][17][18][19]. This paper will present a machine learning-based framework that can automatically identify the process and product fingerprints.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based framework for automatic identification of process and product fingerprints for smart manufacturing systems

Kundu

Luo

Qin

et al. 2022

Journal of Manufacturing Processes

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Impact of Pin Length on Joint Strength of Friction Lap Welded Aluminum Alloy 7475 and Polyphenylene Sulfide Thermoplastic Polymer Joints

Sandeep

Arivazhagan²

2022

J. of Materi Eng and Perform

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Prediction of the power supplied in friction-based joining process of metal-polymer hybrids through machine learning

Cited by 12 publications

References 51 publications

Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

Application of machine learning approaches to predict joint strength of friction stir welded aluminium alloy 7475 and PPS polymer hybrid joint

A machine learning-based framework for automatic identification of process and product fingerprints for smart manufacturing systems

Impact of Pin Length on Joint Strength of Friction Lap Welded Aluminum Alloy 7475 and Polyphenylene Sulfide Thermoplastic Polymer Joints

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