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Bending deformation prediction in a welded square thin-walled aluminum alloy tube structure using an artificial neural network

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Abstract

In this study, an effective artificial neural network (ANN) model was established to predict the bending deformation in a welded square thin-walled aluminum alloy tube structure. The input variables of the ANN model include four controllable positional parameters of welding, whereas the bending deformations in the X-axis and Y-axis directions are considered the target outputs. To estimate the bending distortion rapidly and accurately, a supervised multilayer feed-forward backpropagation (BP) neural network was proposed. A total of 270 finite element (FE) numerical simulations were performed to generate a database for training the designed ANN model. Moreover, a series of experiments were conducted to confirm the accuracy of the FE models. The predicted values from the designed BP neural network and the FE simulation results were compared to evaluate the performance of the proposed ANN model. The comparative study demonstrated that the proposed BP neural network model can accurately predict welding-induced bending deformations within an available range of welding position parameters.

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Funding

This work was supported by a 2020 Yeungnam University Research Grant.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Graduate School of Yeungnam University, Gyeongsan, Gyeongbuk, 38541, South Korea

    Chunbiao Wu

  2. School of Mechanical Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China

    Chao Wang

  3. School of Mechanical Engineering, Yeungnam University, Gyeongsan, Gyeongbuk, 38541, South Korea

    Jae-Woong Kim

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  1. Chunbiao Wu
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  2. Chao Wang
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Correspondence to Jae-Woong Kim.

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Wu, C., Wang, C. & Kim, W. Bending deformation prediction in a welded square thin-walled aluminum alloy tube structure using an artificial neural network. Int J Adv Manuf Technol 117, 2791–2805 (2021). https://doi.org/10.1007/s00170-021-07884-9

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  • DOI: https://doi.org/10.1007/s00170-021-07884-9

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