热加工工艺2024,Vol.53Issue(14) :27-32.DOI:10.14158/j.cnki.1001-3814.20212498

面向涂层裂纹的激光熔覆预测模型研究

Study on Prediction Model of Laser Cladding for Coating Crack

周浩南 孙文磊 王伟 张志虎
热加工工艺2024,Vol.53Issue(14) :27-32.DOI:10.14158/j.cnki.1001-3814.20212498

面向涂层裂纹的激光熔覆预测模型研究

Study on Prediction Model of Laser Cladding for Coating Crack

周浩南 1孙文磊 1王伟 1张志虎1
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作者信息

  • 1. 新疆大学 机械工程学院,新疆 乌鲁木齐 830047
  • 折叠

摘要

针对激光熔覆关键工艺参数与制备涂层之间因存在复杂非线性映射关系而产生的裂纹缺陷问题,通过将遗传算法与BP神经网络结合起来构建工艺参数与涂层裂纹密度之间的网络模型,利用MATLAB软件对建立的网络模型进行训练、拟合预测.结果表明,遗传算法对BP神经网络优化后的模型中预测误差最小为 3.06%,平均误差控制在11.57%以内,均方误差为 0.0008,模型预测精度高,性能稳定.验证了理论模型与实际试验相结合的可行性,减少了涂层裂纹工艺研究所需的大量重复性试验,为制备无裂纹镍基熔覆层具有重要意义.

Abstract

Aiming at the crack defects caused by the complex nonlinear mapping relationship between the key process parameters and the prepared coating in the process of laser cladding,the network model between the process parameters and the coating crack density was constructed by combining genetic algorithm and BP neural network,and the network model was trained,fitted and predicted by MATLAB software.The results show that the minimum prediction error of the BP neural network optimized model by genetic algorithm is 3.06%,the average error is controlled within 11.57%,and the mean square error is 0.0008.The prediction accuracy of the model is high and the performance is stable.It verifies the feasibility of combining the theoretical model with the actual test,and reduces a large number of repetitive tests required for the study of coating crack process.It is of great significance to prepare crack free nickel base cladding coating.

关键词

激光熔覆/裂纹密度/神经网络/遗传算法/模型预测

Key words

laser cladding/crack density/neural network/genetic algorithm/model prediction

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基金项目

新疆克拉玛依市科技重大专项项目(2018ZD002B)

自治区重点实验室开放基金项目(2020520002)

出版年

2024
热加工工艺
中国船舶重工集团公司热加工工艺研究所 中国造船工程学会船舶材料学术委员会

热加工工艺

CSTPCD北大核心
影响因子:0.55
ISSN:1001-3814
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