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

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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.

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Acknowledgements

This research has been supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51708346.

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Authors and Affiliations

  1. Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Liang Tian

  2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    Liang Tian & Yu Luo

  3. CRCC Bridge Engineering Bureau Group Co., Ltd, Tianjin, 300300, China

    Liang Tian

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Tian, L., Luo, Y. A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm. J Intell Manuf 31, 575–596 (2020). https://doi.org/10.1007/s10845-019-01469-w

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