Multi-objective optimization of hybrid laser cleaning process parameters for carbon deposits based on bayesian-SVR and NSGA-II.

Yishun Su, Yong Hu, Qunli Zhang, Zhehe Yao, Zhijun Chen, Yanyi Yin, Zhenzhen Yang, Jianhua Yao

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Yishun SuCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Yong HuCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Qunli ZhangCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Zhehe YaoCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Zhijun ChenCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Yanyi YinCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Zhenzhen YangCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Jianhua YaoCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China. lam@zjut.edu.cn.

Funding

General Scientific Research Project of Zhejiang Provincial Department of Education Y202559285The Longyou County Science and Technology Program JHXM2023072The National Key Research and Development Program of China 2022YFB4601502The Natural Science Foundation of Zhejiang Province LQ24E050018The Zhejiang Provincial "Pioneer" Research and Development Program 2023C01064

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Abstract

Hybrid laser cleaning combines continuous-wave (CW) laser preheating with pulsed laser ablation to remove piston crown carbon deposits, but conventional tuning cannot simultaneously minimize surface roughness (Sa) and carbon residue rate (RC). Four machine learning models—Support Vector Regression (SVR), XGBoost, Random Forest (RF), and Backpropagation Neural Network (BPNN)—are compared. SVR achieves optimal Sa prediction (Test R2 = 0.9874, bootstrap R2 = 0.985–0.998). For RC, Bayesian optimization improves SVR from R2 = 0.5658 to R2 = 0.8965 (58.45% increase). Feature analysis reveals divergent sensitivities: Sa responds equally to all parameters, while RC is dominated by frequency and CW laser power. NSGA-II generates 66 Pareto-optimal solutions within narrow parameter windows, identifying three operational regimes: low-Sa (0.894μm, RC = 4.3%), balanced (0.958μm, RC = 3%), and low-RC (RC = 1.9%, Sa = 1.209μm). Comprehensive validation—bootstrap resampling, cross-validation, SHAP analysis, and sensitivity testing—confirms model reliability despite limited data. This framework enables intelligent laser cleaning optimization, reducing trial-and-error costs while providing insights into laser–material interactions.

Indexed as

Bayesian optimizationLaser cleaningMulti-objective optimizationNon-dominated sorting genetic algorithm II (NSGA-II)Support vector regression

Identifiers

PMID41792198

DOI10.1038/s41598-026-41748-0

PMCPMC12979649

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.