基于机器学习的K424合金刻蚀深度预测
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国家重点研发计划项目(No.2022YFB4601600)资助。


Depth prediction of K424 alloy etching based on machine learning
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    摘要:

    为探究水导激光加工过程中不同工艺参数对K424高温合金刻蚀深度的作用,对K424高温合金进行了包括激光功率、进给速度及加工次数在内的三个关键工艺参数的影响刻蚀实验,实验结果表明:较大的功率、较小的进给速度和多次加工会产生更深的刻蚀。此外采用XGBoost、RF、BPNN以及SVR四种模型建立了激光功率、进给速度和加工次数与加工深度之间的预测模型。在拟合效果上XGBoost与SVR模型表现优异,最大误差百分比均不到03;在预测结果方面显示,XGBoost最大误差百分比6698,优于另三种模型。最后得出XGBoost模型在拟合和预测K424高温合金加工深度方面有更好的性能。与传统的干式激光加工相比,水导激光加工技术减少了材料热损伤,提高了加工质量。该研究为水导激光加工K424高温合金提供了参考。

    Abstract:

    In order to research the influence of process parameters on the etching depth of K424 high temperature alloy during water jet guided laser(WJGL)processing,etching experiments on K424 high temperature alloy are carried out on the influence of three key process parameters including laser power,feed rate and number of times of processing.The experimental results show that higher power,smaller feed rate and multiple times of machining produce deeper etching.In addition,the prediction model between laser power,feed rate and number of times of machining and depth of machining is established by using four models,XGBoost,RF,BPNN and SVR.The XGBoost and SVR models outperform in terms of fitting effect,with the maximum percentage of error being less than 0.3%;in terms of prediction results,it shows that XGBoost has a maximum percentage of error percentage of 6.698%,which is better than the other three models.Finally,it is concluded that XGBoost model has better performance in fitting and predicting the depth of machining of K424 high temperature alloy.The water jet guided laser processing technique reduces material thermal damage and improves processing quality compared to conventional dry laser processing.This study provides a reference for water guided laser processing of K424 high temperature alloy.

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张青,乔红超,王顺山,赵吉宾.基于机器学习的K424合金刻蚀深度预测[J].激光与红外,2024,54(5):701~709
ZHANG Qing, QIAO Hong-cao, WANG Shun-shan, ZHAO Ji-bin. Depth prediction of K424 alloy etching based on machine learning[J]. LASER & INFRARED,2024,54(5):701~709

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  • 在线发布日期: 2024-05-14
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