In this article, an effective multiobjective optimization approach is exploited to search for the best milling parameters for CNC for complex 3d surfaces of SIMOLD 2083 alloy mold core. To improve the quality responses, the cutting factors are optimized by a combination of Taguchi method (TM), response surface method (RSM), and multiobjective water cycle algorithm (MWCA). Firstly, the design for initial series experiments of the cutting factors was generated via the TM. Thereafter, the regression models between the cutting factors and the surface roughness of the machined workpiece surface as well as milling time are formed via applying the RSM. Moreover, analysis of variance and sensitivity analysis are also executed to define the influences and crucial contributions of cutting parameters on the surface roughness and milling time. The results of analysis of variance showed that the factors which have main effects on surface roughness were spindle speed (42.42%), feed rate (29.40%), and cutting depth (6.59%), respectively. Meanwhile, the feed rate with the influence of 92.6% was the most significant factor in controlling the milling time. Ultimately, based on mathematical models, the MWCA is performed to define the optimal factors. The optimal results indicated that the optimized surface roughness was about 0.260 μm and the milling time was roughly 1012.767 (s). In addition, the errors between forecasted results and experimental verifications for the surface roughness and milling time are 2.04% and 5.39%, respectively. Therefore, the results of experimental verifications are suitable with the forecasted results from the proposed optimization method. These results depicted that the proposed integration approach can define effectively the optimal cutting factors for CNC milling and expand to apply for complex multiobjective optimization problems.

中文翻译：

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利用多目标水循环算法优化SIMOLD 2083合金型芯复杂3D曲面的CNC铣削参数

在本文中，利用一种有效的多目标优化方法来为SIMOLD 2083合金型芯的复杂3d表面搜索CNC的最佳铣削参数。为了改善质量响应，通过结合Taguchi方法（TM），响应面方法（RSM）和多目标水循环算法（MWCA）来优化切削因子。首先，通过TM生成了切削因子的初始系列实验的设计。此后，通过应用RSM形成切削因子与加工工件表面的表面粗糙度以及铣削时间之间的回归模型。此外，还执行方差分析和灵敏度分析，以定义切削参数对表面粗糙度和铣削时间的影响和关键作用。方差分析结果表明，影响表面粗糙度的主要因素分别是主轴转速（42.42％），进给速度（29.40％）和切削深度（6.59％）。同时，在92.6％的影响下进给速度是控制研磨时间的最重要因素。最终，基于数学模型，执行MWCA以定义最佳因子。最佳结果表明，优化后的表面粗糙度约为0.260 执行MWCA以定义最佳因子。最佳结果表明，优化后的表面粗糙度约为0.260 执行MWCA以定义最佳因子。最佳结果表明，优化后的表面粗糙度约为0.260 μ m和研磨时间为大约1012.767（小号）。此外，表面粗糙度和研磨时间的预测结果与实验验证之间的误差分别为2.04％和5.39％。因此，实验验证的结果与所提出的优化方法的预测结果相适应。这些结果表明，所提出的集成方法可以有效地定义用于CNC铣削的最佳切削因子，并可以扩展到适用于复杂的多目标优化问题。