A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm

Liang Tian () and Yu Luo
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Liang Tian: Shijiazhuang Tiedao University
Yu Luo: Shanghai Jiaotong University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 3, No 4, 575-596

Abstract: Abstract The inherent deformation method has a significant advantage in evaluating the total welding deformations for large and complex welded structures. The prerequisite for applying this approach is that the inherent deformations of corresponding weld joints should be known beforehand. In this study, an intelligent model based on support vector machine (SVM) and genetic algorithm (GA) was established to predict the inherent deformations of a fillet-welded joint. The training samples were obtained from numerical experiments conducted by the thermal–elastic–plastic finite element analysis. In the developed SVM model, the welding speed, current, voltage and plate thickness were considered as input parameters, and the longitudinal and transverse inherent deformations were corresponding outputs. The correlation coefficients and percentage errors for all the samples were calculated to evaluate the prediction performance of the SVM model. The research results demonstrate that the SVM model optimized by GA can be used to assess the longitudinal and transverse inherent deformations for the T-joint fillet weld with acceptable accuracy.

Keywords: T-joint fillet welding; Inherent deformations; Numerical simulation; Support vector machine; Genetic algorithm; Finite element method (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-019-01469-w

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