Ultrasonic spot welding of aluminum-copper dissimilar metals: A study on joint strength by experimentation and machine learning techniques

Satpathy, Mantra Prasad; Mishra, Sourav; Sahoo, Susanta Kumar

doi:10.1016/j.jmapro.2018.04.020

Cited by 60 publications

(17 citation statements)

References 26 publications

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“…were used extensively in several welding problems to predict weld geometries, cooling rate, stresses, UTS etc. 2,3,24 Lostado et al 25 combined ANN, genetic algorithm (GA), regression trees and FEM to study the influence of input parameters on the weld geometries, thermal cycle and distortion during GMAW for weld optimization. Escribano et al 26 combined finite element analysis and data mining techniques to develop models to predict the optimized force, pressure etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Das

Pratihar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the present study, machine learning algorithms have been used to predict residual stress during electron beam welding of stainless steel using the information of input process parameters and natural frequency of vibrations. Accelerating voltage, beam current and welding speed have been considered as input process parameters. Both residual stress and natural frequencies of vibration of the weld obtained using each set of the input parameters are measured experimentally. A number of machine learning algorithms, namely M5 algorithm-based Model Trees Regression, Random forest, Support Vector Regression, Reduced Error Pruning Tree, Multi-layer perceptron, Instance-based k-nearest neighbor algorithm, and Locally weighted learning have been used for the said purpose. Support vector regression and Locally weighted learning are found to perform consistently good and bad, respectively. The predicted welding residual stresses have been validated experimentally through X-ray diffraction (XRD) and good agreements are obtained. In addition, statistical tests are conducted, and the estimated reliability values of the employed models are analyzed through Monte-Carlo simulations.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Das

Pratihar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…[6][7][8][9]. UMW is particularly useful in joining multiple layers of thin, conductive and dissimilar metals [10,11]. A notable application of UMW is battery-tab joining process in manufacturing of lithium-ion battery packs for electrical vehicles.…”

Section: Introductionmentioning

confidence: 99%

Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning

Nazir

Shao

2021

Journal of Manufacturing Processes

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“…Up to now, many researchers pay much attention to welding methods, optimizing process parameters and etc to improve the performance of Al/Cu joining component, such as ultrasonic spot welding [8][9][10], explosive welding [11][12], friction stir welding [13][14][15][16] and laser welding [17][18][19][20][21], that have been applied in various industrial production. But the formation of the representative brittle phase of (intermetallic compounds) IMC is inevitable, which causes a seriously degradation of weld properties and large melt point difference [22].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation of 2A16 Aluminum and Pure Copper Magnetic Assisted Dissimilar Laser Wire Feed Welding Brazing

Wang¹,

Jiang²,

Xiong³

et al. 2021

Preprint

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Magnetic field assisted welding could suppress defects and improve weld bead properties obviously. In this paper, a full factorial experiment with coaxial magnetic field assisted welding was conducted to study the mechanism of the magnetic field on the dissimilar laser wire feed welding brazing profile of aluminum and copper. Furthermore, a three dimensional numerical simulation model was developed to reveal the influence of the magnetic field on the weld bead. It was found that with the magnetic flux density increase from 10 mT to 50 mT, the properties of the weld bead were improved significantly, and the wetting angle decreased from 53°to 26°, the main fluid flow direction of the weld bead changed in to horizontal direction. Meanwhile, EDS and XRD results showed that the main intermetallic compounds (IMC) of Al2Cu and CuZn composition changed to Al4.2Cu3.2Zn0.7 in welding beads. Computed and measured distortions illustrated good agreement in the fusion zone.

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Ultrasonic spot welding of aluminum-copper dissimilar metals: A study on joint strength by experimentation and machine learning techniques

Cited by 60 publications

References 26 publications

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning

Experimental and Numerical Investigation of 2A16 Aluminum and Pure Copper Magnetic Assisted Dissimilar Laser Wire Feed Welding Brazing

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