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基于GA-BP与NSGA-Ⅱ算法的42CrMo表面激光熔覆参数多目标优化

Multi-objective optimization of laser cladding parameters for 42CrMo surface based on GA-BP and NSGA-II algorithms

  • 摘要: 为解决42CrMo轴承表面常有的凹坑、微小裂纹等缺陷问题,获得主轴承高强度钢匹配的修复工艺参数,设计了三因素五水平L25(53)单层单道激光熔覆正交试验,研究Ni60A粉末在42CrMo上熔覆的最优工艺参数组合,通过反向传播神经网络(back propagation neural network,BP神经网络)建立以激光功率、送粉速度、扫描速度3个参数为优化变量,构建以稀释率、热影响区深度、显微硬度3个熔覆层性能为优化目标的非线性数学模型,并通过遗传算法对模型进行优化,基于第二代非支配遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)进行多目标寻优,从帕雷托(Pareto)解中确定最优工艺参数组合.结果表明,在激光功率1 050 W、送粉速度0.65 r/min、扫描速度8.8 mm/s工况下,熔覆层稀释率为20.00%,热影响区深度为0.650 2 mm,显微硬度为603.6 HV.在最优工艺参数组合下稀释率降低了44.38%,热影响区深度降低了6.15%,显微硬度提高了6.42%,有效提高了熔覆层质量,综合性能达到最佳.

     

    Abstract: In order to solve the defects such as pits and microcracks on the surface of 42CrMo bearings, the repair process parameters matched by the high-strength steel of the main bearing were obtained. A three-factor five-level L25(53) single-layer and single-channel laser cladding orthogonal test was designed to study the optimal combination of process parameters for Ni60A powder cladding on 42CrMo. By using the back propagation (BP) neural network, a model was established with the laser power, powder feeding speed, and scanning speed as the optimization variables. A nonlinear mathematical model with the dilution rate, depth of heat affected zone, and microhardness as the optimization objectives was established, and the model was optimized by a genetic algorithm. Based on the non-dominated sorting genetic algorithm-Ⅱ (NSGA-II), the multi-objective optimization was carried out, and the optimal combination of process parameters was determined from the Pareto solution. The results show that under the conditions of laser power of 1 050 W, powder feeding speed of 0.65 r/min, and scanning speed of 8.8 mm/s, the dilution rate of the cladding layer is 20.00%; the depth of the heat affected zone is 0.650 2 mm, and the microhardness is 603.6 HV. Under the optimal combination of process parameters, the dilution rate decreases by 44.38%; the depth of the heat affected zone reduces by 6.15%, and the microhardness increases by 6.42%. This effectively improves the quality of the cladding layer and achieves the best overall performance.

     

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