Depth prediction of K424 alloy etching based on machine learning
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 out-perform 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 oth-er 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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