目的 为了解决球墨铸铁表面激光熔覆铁基合金过程中熔覆层塌陷、厚度不均等问题，确定旁轴送粉激光熔覆最优工艺参数组，并对参数寻优方法进行对比分析。方法 选取工艺参数(激光功率、扫描速度、送粉速度)为优化变量和熔覆层表面质量(表面粗糙度、硬度)为优化指标，通过设计L9(3⁴)正交试验进行极差分析，得到优化后的参数组合;通过神经网络预测模型结合NSGA-II多目标优化算法进行参数寻优。通过对比这两种优化方法对熔覆层表面质量的实际优化效果，确定最优工艺参数组。结果 3个工艺参数对综合质量的影响大小依次为激光功率>扫描速度>送粉速度，正交优化参数组合使得熔覆层表面粗糙度降低23.3%，硬度降低7.1%。而NSGA-II遗传算法优化参数组合可实现表面粗糙度降低40.5%，硬度提升6.6%。最优工艺参数组合为:激光功率4614 W，送粉速度2.6 r/min，扫描速度325.6 mm/min。结论 采用NSGA-II遗传算法能获得比正交试验更快更好的优化效果;通过合理选择工艺参数，能够解决熔覆层塌陷、厚度不均等问题，从而极大地改善熔覆层表面质量。

To solve the problems of cladding layer collapse and uneven thickness in the process of laser cladding Fe-based alloy on the surface of ductile cast iron, the optimal process parameter group of side-axis powder feeding laser cladding was determined, and the parameter optimization methods were compared and analyzed. Select the process parameters (laser power, scanning speed, powder feeding speed) as the optimization variables and the surface quality of the cladding layer (surface roughness, hardness) as the optimization indicators, and perform the range analysis by designing the L9(34) orthogonal test to get the optimized parameter combination; perform parameter optimization through the neural network prediction model combined with NSGA-II multi-objective optimization algorithm. By comparing the actual optimization effects of these two optimization methods on the surface quality of the cladding layer, the optimal process parameter group is determined. The influence of the three process parameters on the overall quality is in order:laser power, scanning speed, powder feeding speed, and the combination of orthogonal optimization parameters reduces the surface roughness of the cladding layer by 23.3% and the hardness by 7.1%; while the optimized parameter combination by the NSGA-II genetic algorithm can reduce the surface roughness by 40.5% and increase the hardness by 6.6%. The optimal combination of process parameters is:laser power 4614 W, powder feeding speed 2.6 r/min, scanning speed 325.6 mm/min. The use of NSGA-II genetic algorithm can obtain better optimization results than orthogonal experiments; through reasonable selection of process parameters, it can solve the problems of cladding layer collapse and thickness unevenness, thereby greatly improving the surface quality of the cladding layer.