Artificial neural network to predict the weld status in laser welding of copper to aluminum

Mathivanan, Karthik; Plapper, Peter

doi:10.1016/j.procir.2021.10.009

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…The average percentage error between actual and predicted data was 1.22 and 1.86 for overlap factor and tensile strength, respectively. The shear force of dissimilar lap-welded joints made of copper and aluminum was predicted using the ANN technique [ 44 ]. A prediction accuracy of 91% was found between the predicted and experimental data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

Moustafa

Elsheikh

2023

Polymers

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This study investigates the application of a coupled multi-layer perceptrons (MLP) model with Archimedes optimizer (AO) to predict characteristics of dissimilar lap joints made of polymethyl methacrylate (PMMA) and polycarbonate (PC). The joints were welded using the laser transmission welding (LTW) technique equipped with a beam wobbling feature. The inputs of the models were laser power, welding speed, pulse frequency, wobble frequency, and wobble width; whereas, the outputs were seam width and shear strength of the joint. The Archimedes optimizer was employed to obtain the optimal internal parameters of the multi-layer perceptrons. In addition to the Archimedes optimizer, the conventional gradient descent technique, as well as the particle swarm optimizer (PSO), was employed as internal optimizers of the multi-layer perceptrons model. The prediction accuracy of the three models was compared using different error measures. The AO-MLP outperformed the other two models. The computed root mean square errors of the MLP, PSO-MLP, and AO-MLP models are (39.798, 19.909, and 2.283) and (0.153, 0.084, and 0.0321) for shear strength and seam width, respectively.

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Section: Introductionmentioning

confidence: 99%

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

Moustafa

Elsheikh

2023

Polymers

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“…The plates exhibited significant deformation during laser welding, and the change in thickness was large, showing a wavy surface. The method predicting the joint strength was proposed [30]. However, laser welding has the problem of unavoidable changes in the mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

Impact Joining of Pure Copper C1100 and Aluminum Alloy A6061-T6 Plates at Edges

Yamashita

Iwatsuka

Taguchi

et al. 2022

Metals

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Joining of pure copper C1100 and aluminum alloy A6061-T6 plates of 5 mm thickness was investigated. The method was developed by one of the authors, in which the newly created surfaces of a pair of plates obtained by high-speed shear were immediately in contact with a sliding motion with a small overlap length. The total processing time was just about a few milliseconds. To create the new surface, high-speed shaving was also tested. The joining was not possible for the full thickness of the plates. A sharp notch was observed at the joint boundary due to a large shear droop in the copper. Shaving decreased the shear droop, and the joint length through the plate thickness became longer. The joining performance was evaluated by a uniaxial tensile test. The joint efficiency reached 100% using the specimen cut out from the really joined boundary. The affected zone of joining was confirmed by the hardness distribution near the boundary. It was about 30% of the thickness of the plate, which was much smaller than that in welding by heat, and no softened zone was found in both materials.

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A performance comparison of deep learning and shallow machine learning in acoustic emission monitoring of aluminium alloy pulsed laser welding

Zeng,

Wu,

Luo

et al. 2024

Soft Comput

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Artificial neural network to predict the weld status in laser welding of copper to aluminum

Cited by 7 publications

References 16 publications

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

Impact Joining of Pure Copper C1100 and Aluminum Alloy A6061-T6 Plates at Edges

A performance comparison of deep learning and shallow machine learning in acoustic emission monitoring of aluminium alloy pulsed laser welding

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