Parameters optimization for laser shock peening without coating based on SSA-SVR and GA

IF 4.6 2区物理与天体物理 Q1 OPTICS

Optics and Laser Technology Pub Date : 2025-06-14 DOI:10.1016/j.optlastec.2025.113368

Yifeng Luo , Ke Li , Bowen Chen , Shuxun Cui , Jing Ni , Jindong Wang , Zhenbing Cai

{"title":"Parameters optimization for laser shock peening without coating based on SSA-SVR and GA","authors":"Yifeng Luo , Ke Li , Bowen Chen , Shuxun Cui , Jing Ni , Jindong Wang , Zhenbing Cai","doi":"10.1016/j.optlastec.2025.113368","DOIUrl":null,"url":null,"abstract":"<div><div>Laser shock peening without coating, as a typical surface strengthening technology, can effectively enhance the abrasion resistance of key components of aero-engine, and the reasonable selection of process parameters is the key to improving the abrasion resistance. In this paper, laser energy, spot diameter, overlapping rate, and impact number were used as test factors to construct a prediction model of wear rate using the machine learning method and response surface method (RSM), respectively, and process parameters were optimized with the objective of minimum wear rate. The results reveal that the response surface approach is challenging to depict the complicated, nonlinear connection between process factors and wear rate, and the prediction accuracy and objective optimization results are unsatisfactory. In contrast, the support vector regression (SVR) model optimized based on sparrow search algorithm (SSA) showed better results after being optimized by genetic algorithm (GA). The optimized wear rate is decreased by 20.11 % under the best process parameters and the wear resistance of the material is significantly improved. The proposed optimization method of laser shock peening without coating process parameters can significantly improve the resistance to fretting wear of the material.</div></div>","PeriodicalId":19511,"journal":{"name":"Optics and Laser Technology","volume":"191 ","pages":"Article 113368"},"PeriodicalIF":4.6000,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics and Laser Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030399225009594","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}

引用次数: 0

Abstract

Laser shock peening without coating, as a typical surface strengthening technology, can effectively enhance the abrasion resistance of key components of aero-engine, and the reasonable selection of process parameters is the key to improving the abrasion resistance. In this paper, laser energy, spot diameter, overlapping rate, and impact number were used as test factors to construct a prediction model of wear rate using the machine learning method and response surface method (RSM), respectively, and process parameters were optimized with the objective of minimum wear rate. The results reveal that the response surface approach is challenging to depict the complicated, nonlinear connection between process factors and wear rate, and the prediction accuracy and objective optimization results are unsatisfactory. In contrast, the support vector regression (SVR) model optimized based on sparrow search algorithm (SSA) showed better results after being optimized by genetic algorithm (GA). The optimized wear rate is decreased by 20.11 % under the best process parameters and the wear resistance of the material is significantly improved. The proposed optimization method of laser shock peening without coating process parameters can significantly improve the resistance to fretting wear of the material.

查看原文本刊更多论文

基于SSA-SVR和遗传算法的无涂层激光冲击强化参数优化

无涂层激光冲击强化作为一种典型的表面强化技术，可以有效提高航空发动机关键部件的耐磨性，而工艺参数的合理选择是提高耐磨性的关键。本文以激光能量、光斑直径、重叠率和冲击次数为测试因素，分别采用机器学习方法和响应面法（RSM）构建了磨损率预测模型，并以最小磨损率为目标对工艺参数进行了优化。结果表明，响应面法难以描述工艺因素与磨损率之间复杂的非线性关系，预测精度和客观优化结果不理想。相比之下，基于麻雀搜索算法（SSA）优化的支持向量回归（SVR）模型经过遗传算法（GA）优化后效果更好。在最佳工艺参数下，优化后的磨损率降低了20.11%，材料的耐磨性显著提高。提出的无涂层激光冲击强化工艺参数优化方法可显著提高材料的抗微动磨损性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Optics and Laser Technology 物理-光学

CiteScore

8.50

自引率

10.00%

发文量

1060

审稿时长

3.4 months

期刊介绍： Optics & Laser Technology aims to provide a vehicle for the publication of a broad range of high quality research and review papers in those fields of scientific and engineering research appertaining to the development and application of the technology of optics and lasers. Papers describing original work in these areas are submitted to rigorous refereeing prior to acceptance for publication. The scope of Optics & Laser Technology encompasses, but is not restricted to, the following areas: •development in all types of lasers •developments in optoelectronic devices and photonics •developments in new photonics and optical concepts •developments in conventional optics, optical instruments and components •techniques of optical metrology, including interferometry and optical fibre sensors •LIDAR and other non-contact optical measurement techniques, including optical methods in heat and fluid flow •applications of lasers to materials processing, optical NDT display (including holography) and optical communication •research and development in the field of laser safety including studies of hazards resulting from the applications of lasers (laser safety, hazards of laser fume) •developments in optical computing and optical information processing •developments in new optical materials •developments in new optical characterization methods and techniques •developments in quantum optics •developments in light assisted micro and nanofabrication methods and techniques •developments in nanophotonics and biophotonics •developments in imaging processing and systems