| CNC machine tool has played an important role in the manufacturing industry. Improving the design and manufacturing level of CNC machine tool can promote the development of the manufacturing industry. C urrently Multidisciplinary Design Optimization(MDO) has been widely applied to various fields, such as aviation, aerospace, automotive, shipbuilding, construction, etc, and it has promoted the development of these areas. The application of the MDO in CNC machine tool design is relatively rare. Applying the MDO to CNC machine tool design can improve the level of China’s CNC machine tool.With CNC machine tool develops to be high speed, high precision and high relibility, such properties as being static, dynamic, quantitative and so on should be taken into consideration in design. Traditional experience-based design couldn’t meet the requirements of new design anymore. Through using multidisciplinary Design Optimization, one can get the optimal result by collaboratively optimizing different targets. In this paper, the author explores the problems that the optimization of key components and the lightweight design of machine tool, propose appropriate MDO methods and solve the problems by using those MDO methods. This paper is focused on the following aspects:(1) Reviewing and summarizing the background, definition and main content of the MDO theory, analyzing the domestic and international research status of MDO. Based on it, the research system of this paper is presented.(2) Having a research on the Multidisciplinary Design Optimization algorithms, summing up the advantages and disadvantages of each optimization algorithm, summarizing the domestic and international research status of MDO algorithms and pointing out the problems of MDO algorithms.(3) A new integrated multidisciplinary design optimization method is proposed by using a Latin hypercube sampling, a Kriging approximate model and a multi-objective genetic algorithm. Optimizing the column of a typical CNC machine tool with the proposed method. Design space and parametric model are built by choosing appropriate design variables and their value ranges. Samples in design space are generated by optimal Latin hypercube method and design variable contributions for design performance are discussed for aiding the designer’s judgments. The Kriging model is built by using polynomial approximation according to the response outputs of these samples. The optimal solution is identified by using a multi-objective genetic algorithm. Using this method, a satisfied solution is obtained.(4) For there are some deficiencies of Collaborative Optimization method, such as being sensitive to initial values, ineffective and so on, an improved measure is proposed. Improving the Collaborative Optimization and proving the improved algorithm effective by a classic example. By building the MDO model of a typical CNC machine tool spindle and solving the MDO problem with the improved algorithm, a satisfied solution is obtained. The improved CO is more effective and less sensitive for initial value than standard CO and it is not prone to such phenomenons as convergence, local convergence and so on.(5) A lightweight design method based on multi-objective particle swarm optimization algorithm and improved CO is proposed. Optimizing a typical CNC machine tool with the proposed lightweight design method. Analyzing the stiffness and modal of a typical CNC machine tool by parametric finite element analysis mothod. Verifing the correctness of the modal finite element analysis by modal experiment. Considerring various factors and establishing a reasonable equivalent model to ensure the credibility of the finite element analysis. Building the model of the MDO problem and solving this problem.The author optimizes the key components of the CNC machine tool and reduces the weight of the whole machine by using MDO method. This study provides new methods and technical support for MDO of CNC machine tool and provides a reference for further research. |
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