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A DPSO-BP NN modeling for predicting mechanical property: a case of 6181H18 aluminum alloy

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Abstract

Aluminum alloy is widely used in daily life due to their good properties. In order to get the change rule of the mechanical properties of 6181H18 aluminum alloy, a detecting particle swarm optimization (DPSO) algorithm was adopted to update weights and thresholds of back propagation neural network (BP NN) in an innovative way. In this way, a DPSO-BP NN prediction model was established to improve the prediction accuracy and was applied to predict the peak stresses of 6181H18 aluminum alloy. The results show that the predicted values obtained based on BP NN and DPSO-BP NN are both very close to the experimental ones and they can reflect the variation law of the stresses of 6181H18 aluminum alloy. It is confirmed that the DPSO-BP NN has a higher prediction accuracy by the mean relative error, standard residual, R-squared and root mean square error (RMSE). The established DPSO-BP NN prediction model owns better prediction capability compared with the traditional BP NN model. The results of this study can provide a scientific basis for the improvement of mechanical properties of alloy materials, and offer a technical reference for technical workers in related fields.

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Acknowledgements

This work is sponsored by the Program of Foundation of Science and Technology Commission of Shanghai Municipality (22dz1206005, 22dz1204202), National Natural Science Foundation of China (12172228, 11572187), Natural Science Foundation of Shanghai (22ZR1444400), Shanghai Professional Technical Service Platform for Intelligent Operation and Maintenance of Renewable Energy (22DZ2291800) and Science and Technology Foundation of Shanghai Dong Hai Wind Power Co., Ltd.

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Jianping Zhang & Chenghua Hu

  2. Intelligent Medical Information and Smart Healthcare Research Center, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai, 200093, China

    Jianping Zhang

  3. Department of Pathology, Seventh People’s Hospital of Shanghai University of TCM, Shanghai, 200137, China

    Hongzhu Yan

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  1. Jianping Zhang
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  2. Chenghua Hu
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JZ (Corresponding Author) (First Author): conceptualization, methodology, supervision, project administration, funding acquisition, resources. CH methodology, data curation, writing-original draft and editing. HY validation, data curation, supervision.

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Correspondence to Jianping Zhang.

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Zhang, J., Hu, C. & Yan, H. A DPSO-BP NN modeling for predicting mechanical property: a case of 6181H18 aluminum alloy. Appl. Phys. A 130, 228 (2024). https://doi.org/10.1007/s00339-024-07356-3

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