Application of BP Neural Networks on the Thickness Prediction of Sherardizing Coating

被引：0
|
作者：
J. B. Long
X. B. Li
Y. C. Zhong
D. Peng
机构：
[1] Xiangtan University,School of Materials Science and Engineering
[2] Key Laboratory of Materials Design and Preparation Technology of Hunan Province,undefined
来源：
Transactions of the Indian Institute of Metals | 2019年 / 72卷
关键词：
Sherardizing; Fe–Zn coating; BP neural network; Prediction;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Sherardizing is a surface protection method for obtaining Fe–Zn coating on the steel surface by thermal diffusion. The thickness of coating is critical for its performance. Therefore, it is necessary to propose a model to predict coating thickness based on different process parameters. The model for the relationship between sherardizing process parameters and the total thickness and each layer phase thickness of the coating is built using a three-layer backpropagation (BP) artificial neural network based on Levenberg–Marquardt algorithm. The results show that neural network model has a good predictive effect on the total thickness and each layer phase thickness of Fe–Zn coating. The prediction error of the model on the thickness of the coating is within 4.71%, and the coefficient of determination is 0.99974. It presents a new method for prediction of the thickness of sherardizing coating.
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页码：2443 / 2448
页数：5
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