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

Author

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  • Liang Tian

    (Shijiazhuang Tiedao University
    Shanghai Jiaotong University
    CRCC Bridge Engineering Bureau Group Co., Ltd)

  • Yu Luo

    (Shanghai Jiaotong University)

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.

Suggested Citation

  • Liang Tian & Yu Luo, 2020. "A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 575-596, March.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01469-w
    DOI: 10.1007/s10845-019-01469-w
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    References listed on IDEAS

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    1. Kuanfang He & Xuejun Li, 2016. "A quantitative estimation technique for welding quality using local mean decomposition and support vector machine," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 525-533, June.
    2. Hamed Pashazadeh & Yousof Gheisari & Mohsen Hamedi, 2016. "Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 549-559, June.
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    Cited by:

    1. Dayuan Wu & Ping Yan & You Guo & Han Zhou & Jian Chen, 2022. "A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2321-2339, December.
    2. Roham Sadeghi Tabar & Kristina Wärmefjord & Rikard Söderberg & Lars Lindkvist, 2021. "Critical joint identification for efficient sequencing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 769-780, March.
    3. Mengrui Zhu & Yun Yang & Xiaobing Feng & Zhengchun Du & Jianguo Yang, 2023. "Robust modeling method for thermal error of CNC machine tools based on random forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2013-2026, April.
    4. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.

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