Mechanical Performance Forecast for BP Neural Network Materials Optimized by Genetic Algorithm
DOI:
https://doi.org/10.54097/e0hex025Keywords:
BP neural network, Genetic algorithm, Mechanical performance.Abstract
The mechanical properties of steel materials play a crucial role in their design, selection, and application. In order to better predict the mechanical properties through chemical composition and process parameters, this paper uses genetic algorithm to optimize the BP neural network to establish a mechanical property prediction model for steel materials. The model can predict three mechanical properties, including yield strength, tensile strength, and elongation, through chemical composition and process parameters. After optimization by genetic algorithm, the problems of insufficient convergence effect, random initialization of weights and thresholds in the BP neural network model are improved, and the prediction error is significantly reduced. The experimental results show that the GA-BP algorithm model has excellent performance in predicting the mechanical properties of steel materials.
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A. K. Tyagi, P.Chahal, “Artificial intelligence and machine learning algorithms. In Research Anthology on Machine Learning Techniques,” Methods, and Applications IGI Global, 2022, pp. 421-446.
Y. Juan, Y. Dai, and Y. Yang, “Accelerating materials discovery using machine learning [J]”. Journal of Materials Science & Technology, vol. 79, 2021, pp. 178-190.
Z. Xu, X. Huang, and L. Lin, “BP neural networks and random forest models to detect damage by Dendrolimus punctatus Walker[J] ”. Journal of forestry research, vol. 31, 2020, pp. 107-121.
W. Jin, Z. J. Li, and L. S. Wei, “The improvements of BP neural network learning algorithm[C]//WCC 2000-ICSP 2000”. 2000 5th international conference on signal processing proceedings. 16th world computer congress 2000. IEEE, vol. 3, 2000, pp.1647-1649.
I. Banerjee, Y. Ling, and M. C. Chen, “Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification[J]”. Artificial intelligence in medicine, vol. 97, 2019, pp. 79-88.
A. Yang, Y. Zhuansun, and C. Liu,”Design of intrusion detection system for internet of things based on improved BP neural network[J]”. Ieee Access, vol. 7, 2019, pp. 106043-106052.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors[J]”. nature, vol. 323, 1986, pp. 533-536.
S. L. yu, and J. Liu, “Convolutional recurrent neural networks for text classification[J]”. Journal of Database Management (JDM), vol. 32(4), 2021, pp. 65-82.
S. Wu, J. Yang, and G. Cao, “Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning[J]”. International Journal of Minerals, Metallurgy and Materials, vol. 28(8), 2021, pp. 1309-1320.
F. He, and L. Zhang, “Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules[J]”. The International Journal of Advanced Manufacturing Technology, vol. 95, 2018, pp. 4081-4089.
A. Lambora, K. Gupta, and K. Chopra, “Genetic algorithm-A literature review[C]//2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon)”. IEEE, 2019, pp. 380-384.
S. Ding, C. Su, J. Yu, “An optimizing BP neural network algorithm based on genetic algorithm[J]”. Artificial intelligence review, vol. 36, 2011, pp. 153-162.
H. Wang, Z. Zhang, and L. Liu, “Prediction and fitting of weld morphology of Al alloy-CFRP welding-rivet hybrid bonding joint based on GA-BP neural network[J]”. Journal of Manufacturing Processes, vol. 63, 2021, pp. 109-120.
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