Laser & Infrared, Volume. 54, Issue 5, 701(2024)

Depth prediction of K424 alloy etching based on machine learning

ZHANG Qing1,2,3, QIAO Hong-cao1,2、*, WANG Shun-shan1,2,3, and ZHAO Ji-bin1,2
Author Affiliations
  • 1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
  • 2Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    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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    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

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    Paper Information

    Category:

    Received: Aug. 7, 2023

    Accepted: May. 21, 2025

    Published Online: May. 21, 2025

    The Author Email: QIAO Hong-cao (hcqiao@sia.cn)

    DOI:10.3969/j.issn.1001-5078.2024.05.007

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