表面技术2025，Vol.54Issue(5)：217-232,275,17.DOI:10.16490/j.cnki.issn.1001-3660.2025.05.017
基于OOA-RF的H13钢激光表面硬化工艺参数优化
Optimization of Laser Hardening Process Parameters for Surface of H13 Steel Based on OOA-RF
摘要
Abstract
To enhance the hardening effect of laser action on the surface of H13 steel,this study proposed a method for predicting the process parameters of laser surface hardening based on the Osprey Optimization Algorithm(OOA)optimized Random Forest(RF)algorithm.Firstly,a finite element model was established to simulate the temperature field changes on the surface of the workpiece during laser scanning.Experiments were then conducted under the same process parameters,and the maximum quench depth was measured to validate the effectiveness of the model.The results showed a relative error of 11.0%for the maximum quench depth,indicating that the established finite element model accurately reflected the laser surface hardening process and provided support for selection of process parameter ranges in subsequent studies.The finite element model was utilized to determine the process parameter ranges for laser power,scanning speed,and overlap rate.Subsequently,a three-factor,five-level central composite test(CCD)was conducted within these ranges to derive hardened layer parameters through the finite element model.Then,a response surface methodology(RSM)surface hardening layer prediction model,an RF surface hardening layer prediction model,and a surface hardening layer prediction model based on OOA-RF were constructed separately,and the prediction accuracy of the three models was analyzed and compared.The OOA-RF model was found to have a higher goodness of fit(R2)for the response target compared with the RSM and RF models,indicating its better applicability.Additionally,the mean absolute percentage error values of the OOA-RF model were consistently lower than those of the RSM and RF models,further highlighting its higher fitting accuracy for the response target.By using the multi-objective genetic algorithm(NSGA-Ⅱ)to optimize the established OOA-RF model for process parameters,constraints needed to be applied to both the range of process parameters and the response objectives to achieve a good quenching effect.After optimization,the Pareto front solution set was obtained.To select the best solution from the solution set,the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS)and the Entropy Weight Method(EWM)were combined to re-rank the optimization solution set and obtain the best combination of process parameters.In the verification test conducted at the optimal process parameters with a power of 517 W,a scanning speed of 5 mm/s,and an overlap rate of 48%,a cross section perpendicular to the scanning path was cut.After polishing and etching the cross section,the average hardened depth was observed to be 723.3 μm,with a relative error of 11.15%compared with the predicted value.Additionally,a peak-to-valley difference of 58.75 μm was achieved with a relative error of 3.77%from the predicted value,and hardened surfaces were relatively flat,without visible depressions.The hardness before laser surface hardening was(165.2±9.2)HV0.5.After surface hardening,the hardness increased to(381.4±86.2)HV0.5,indicating an average hardness enhancement of 1.3 times.The elemental content of hardened and non-hardened areas was similar,and the main reason for hardening was the martensitic transformation of the hardened layer.This method demonstrates potential for optimizing laser surface hardening process parameters for alloys.关键词
激光表面硬化/随机森林算法/有限元模型/平均淬透深度/中心复合试验/多目标遗传算法Key words
laser surface hardening/random forest algorithm/finite element model/average hardening depth/central composite test/multi-objective genetic algorithm分类
信息技术与安全科学引用本文复制引用
梁强,徐彬源,徐永航,杜彦斌,李永亮..基于OOA-RF的H13钢激光表面硬化工艺参数优化[J].表面技术,2025,54(5):217-232,275,17.基金项目
重庆市自然科学基金面上项目(cstc2020jcyj-msxmX0276) （cstc2020jcyj-msxmX0276）
重庆市高校创新研究群体资助项目(CXQT21024) General Project of Chongqing Natural Science Foundation(cstc2020jcyj-msxmX0276) （CXQT21024）
Innovative Research Group of Universities in Chongqing Municipality(CXQT21024) （CXQT21024）
