Selective laser melting (SLM) is one of the most common additive manufacturing (AM) technologies that shows great potential for intelligent and clean manufacturing. The process parameters involved in the SLM process have a significant impact on the part quality and energy consumption during the printing process. Since the relationships between the process parameters and various part quality characteristics cannot be expressed explicitly, it is impractical to decide the optimal process parameters intuitively. In this work, a hybrid multi-objective optimization approach by combining the ensemble of metamodels (EM) and non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to generate optimal process parameters to improve the energy consumption, the tensile strength, and the surface roughness of the as-built parts. First, the Taguchi experiment design is adopted and the corresponding SLM experiments are conducted to obtain the experimental results. Second, the correlations between the process parameters (i.e., laser power, layer thickness, scanning speed) and the three responses are fitted using the proposed EM. The comparative results show that the prediction ability of the EM outperforms the stand-alone metamodels. Then, the NSGA-II is used to search for multi-objective Pareto optimal solutions based on the constructed EM. Finally, the verification experiments were conducted to verify the optimal results obtained by the proposed multi-objective optimization approach. Results indicate that the optimal process parameters are effective and reliable. Besides, the main effects of process parameters on the responses are analyzed. Overall, the proposed hybrid multi-objective optimization method exhibits great ability to improve the effectiveness of SLM.

选择性激光熔化 (SLM) 是最常见的增材制造 (AM) 技术之一，在智能和清洁制造方面显示出巨大的潜力。SLM 工艺中涉及的工艺参数对打印过程中的零件质量和能耗有显着影响。由于工艺参数与各种零件质量特性之间的关系无法明确表达，因此直观地确定最佳工艺参数是不切实际的。在这项工作中，提出了一种通过结合元模型（EM）和非支配排序遗传算法-II（NSGA-II）的混合多目标优化方法来生成最佳工艺参数，以提高能耗、抗拉强度，以及完工零件的表面粗糙度。第一的，采用田口实验设计，进行相应的SLM实验，得到实验结果。其次，过程参数（即激光功率、层厚度、扫描速度）和三个响应之间的相关性使用建议的 EM 进行拟合。比较结果表明，EM 的预测能力优于独立元模型。然后，NSGA-II 用于基于构建的 EM 搜索多目标帕累托最优解。最后，通过验证实验来验证所提出的多目标优化方法获得的优化结果。结果表明，优化的工艺参数是有效且可靠的。此外，分析了工艺参数对响应的主要影响。全面的，