The main objective of this research is to propose a multidisciplinary approach for the development and design of Computer Numerical Control (CNC) machine tools using numerical optimization methods combined Multi-Body Dynamic (MBD) analysis and to control design co-simulation. Metamodels based Sequential Approximate Optimization (SAO) for the co-simulation optimization problems are developed. The metamodels are constructed as approximate models for exact dynamic analysis responses by using simultaneous Kriging metamodeling method. SAO problems for single objective and multi-objective optimization designs are carried out based on the augmented Lagrange multiplier (ALM) method. An application of the proposed method on optimizing Proportional-Integral-Derivative (P-I-D) coefficients of PID controllers of a CNC machine tool model is performed to demonstrate the usefulness of integrating different research methods in numerical simulation. Therefore, this work overcomes a difficult task in tuning the PID controller which requires extensive experience and understandings of research and development (R&D) engineers. Moreover, the optimal PID controllers obtained by the multidisciplinary approach can help to increase the contouring accuracy of the CNC machine tools.

中文翻译：

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数控机床优化设计的多学科方法

本研究的主要目的是提出一种多学科方法，用于使用数值优化方法结合多体动力学 (MBD) 分析和控制设计协同仿真来开发和设计计算机数控 (CNC) 机床。开发了用于协同仿真优化问题的基于元模型的序列近似优化 (SAO)。通过使用同时克里金元建模方法，元模型被构建为精确动态分析响应的近似模型。单目标和多目标优化设计的 SAO 问题基于增强拉格朗日乘子 (ALM) 方法进行。将所提出的方法应用于优化数控机床模型 PID 控制器的比例积分微分 (PID) 系数，以证明在数值模拟中集成不同研究方法的有用性。因此，这项工作克服了调整 PID 控制器的艰巨任务，该任务需要研发 (R&D) 工程师的丰富经验和理解。此外，通过多学科方法获得的最佳PID控制器有助于提高数控机床的轮廓精度。D) 工程师。此外，通过多学科方法获得的最佳PID控制器有助于提高数控机床的轮廓精度。D) 工程师。此外，通过多学科方法获得的最佳PID控制器有助于提高数控机床的轮廓精度。