Abstract:Selective laser melting (SLM) is one of the most widely used technologies for additive manufacturing of metallic materials, and its forming accuracy still cannot meet the requirements of practical applications due to the complex thermo-physical processes in the forming process. Therefore, in order to improve the dimensional accuracy of SLM formed parts, this study proposes an integrated Response Surface Methodology (RSM) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) approach to optimize the dimensional accuracy of SLM formed GH3265 superalloy. Firstly, a model of SLM process parameters with dimensional errors in X, Y and Z directions was developed using response surface methodology, and then the model was optimized by NSGA-II for multi-objective optimization. The results show that: the model constructed by the response surface method has high prediction accuracy, and the correlation coefficients R2 are 0.9456, 0.9842, and 0.9704 in order; The optimization algorithm is able to obtain the optimal interval of the processing parameters at 1500 iterations: the laser power is 250.8-310W, the scanning speed is 1028-1400mm/s, the hatching space is 0.071-0.084 mm; The experimental validation results show the high reliability of the integrated method with ARE of 5.95%, 4.92%, and 3.97% for dimensional errors in X, Y, and Z directions, in that order.