目的 在低碳节能的制造背景下,为同步兼顾激光淬火性能提升和激光抛光表面质量提升的特征。方法 提出了一种基于小龙虾优化算法（COA）的ZG45铸钢激光淬火-抛光复合工艺参数寻优方法。以表面粗糙度、平均淬透深度、峰谷差值和激光作用阶段能耗作为响应目标,使用拉丁超立方（LHS）设计试验,并采用贝叶斯优化（BO）的随机森林回归模型（RF）对试验数据进行拟合,进而构建针对4个响应目标的预测模型。通过COA多目标优化算法进行工艺参数寻优,得到相应的Parato解集,利用优劣解距离法（TOPSIS）结合多准则妥协解排序法(VIKOR)对Parato解集进行综合评分排序,得到最佳工艺参数组合,即激光功率为522 W,扫描速度为12 mm/s,搭接率为70%。结果 试验结果可知,模型预测值与试验真实值的误差不超过11.41%,激光淬火-抛光后表面粗糙度Ra由8.477 μm下降至2.585 μm,降幅为69.51%,表面显微硬度由230HV0.5升至515.8HV0.5,提升了2.24倍,表面体积磨损率由24.7×10-14 m3/(N·m)下降至8.7×10-14 m3/(N·m),降低64.78%;且最佳工艺参数与常规工艺参数对比时,激光作用阶段能耗由927 J降低至864 J,降幅为6.83%,表面粗糙度降低9.2%,淬透深度提升7.56%,峰谷差值降低29.89%。结论 研究成果可为面向高质量低能耗的ZG45铸钢激光淬火-抛光复合工艺参数优化提供有力的参考。
Abstract
The purpose is to simultaneously improve the surface quality of laser polishing and enhance the performance of laser quenching in the context of low-carbon and energy-saving manufacturing. This article proposes a parameter optimization method for the ZG45 cast steel laser quenching and polishing composite process based on the crayfish optimization algorithm (COA). Taking surface roughness, average hardening depth, peak valley difference, and energy consumption during the working process as response objectives, in order to better meet the requirements of machine learning algorithms for dataset uniformity, experiments are designed using Latin hypercube (LHS). Based on Bayesian optimization (BO), a random forest regression model (RF) is used to fit the experimental data, and a prediction model is constructed for the four response objectives. The COA multi-objective optimization algorithm is used to optimize process parameters, to take the minimum values for surface roughness, operating energy consumption, and peak valley difference, as well as the maximum value of the average hardening depth. Under this premise, the response objectives under different parameter combinations are obtained, and the corresponding Parato solution sets are obtained. The TOPSIS method combined with the VIKOR method is used to comprehensively score and rank the Parato solution sets, and the optimal process parameter combination is obtained. After the above evaluation system, the solutions with the highest comprehensive score, namely laser power of 522 W, scanning speed of 12 mm/s, and overlap rate of 70%, are selected for verification experiments in descending order of comprehensive score. The experimental results showed that the error between the predicted value of the model and the actual experimental value does not exceed 11.41%. Moreover, according to the process parameter combination with the highest comprehensive score, the surface roughness Ra after laser quenching polishing decreases from 8.477 μm to 2.585 μm, a decrease of 69.51%; and the surface micro hardness increases from 230HV0.5 to 515.8HV0.5, an increase of 2.24 times; the surface volume wear rate decreases from 24.7×10-14 m3/(N·m) to 8.7×10-14 m3/(N·m), a decrease of 64.78%. The energy consumption of laser is mainly related to laser power, scanning speed, and overlap rate, with laser power accounting for the highest proportion. Other parameters mainly affect the overall energy consumption by changing the laser working time. The conventional laser power is determined based on the carbon content of the material. When compared with conventional process parameters (i.e. laser power as a variable) while keeping all other parameters constant, the energy consumption of laser processing under this set of process parameters decreases from 927 J to 864 J, a decrease of 6.8%, the surface roughness is decreased by 9.2%, quenching depth indicated by 7.56%, and the peak valley difference is decreased by 29.89%. Based on the above, the following conclusion can be drawn: the application of COA optimization algorithm can provide reference for the optimization of laser quenching polishing process parameters for other cast steel materials, helping them determine the approximate range of process parameters. And the prediction model based on BO-RF fitting algorithm has good predictive performance for energy consumption in the machining process, high model stability, and can provide model reference for the calculation of carbon emissions in laser processing. The research results can provide strong reference for optimizing the laser quenching polishing composite process parameters of ZG45 cast steel for high-quality and low-energy consumption.
关键词
ZG45铸钢 /
激光复合工艺 /
COA多目标优化 /
拉丁超立方 /
激光能耗 /
拟合回归
Key words
ZG45 cast steel /
laser composite technology /
COA multi-objective optimization /
Latin hypercube /
laser energy consumption /
fitting regression
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基金
重庆市自然科学基金创新发展联合基金（CSTB2025NSCQ-LZX0133）; 湖北省科技重大专项资助（2023BCA006）; 重庆工商大学科学研究项目资助（1956057,2156015）
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