CNC machine tool is a typical complex product related with multidisciplinary fields, complex structure and high performance requirements. It is difficult to identify the overall optimal solution of the machine tool structure for their multiple objectives. A new integrated multidisciplinary design optimization method is then proposed by using a Latin hypercube sampling, a Kriging approximate model and a multi-objective genetic algorithm. 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 multidisciplinary design model is established based on three optimization objectives, i.e. setting mass, optimum deformation and first-order natural frequency, and two constraints, i.e. second-order natural frequency and third-order natural frequency. The optimal solution is identified by using a multi-objective genetic algorithm. The proposed method is applied in a multidisciplinary optimization case study for a typical CNC machine tool. In the optimal solution, the mass decrease 3.35% and the first order natural frequency increases 4.34% contrast to the original solution.
CNC machine tool plays an important role in different industries, which is key equipment for manufacture of mechanical products. The machine tool is going to be high speed, high precision and lightweight. Its structure design is a typical complex multidisciplinary optimization problem, which must consider the static and dynamic stiffness, mass and etc. The traditional subjective decision-making methods or single-objective optimization methods are difficult to deal with the issue for multi-objective coupling. Multidisciplinary design optimization (MDO) method can take full advantages of the synergistic effects among multiple disciplines and obtain the optimal solution by collaborating optimization for multiple objectives. Currently, MDO method has been applied in a lot of industry fields of aviation, aerospace, shipbuilding, automotive and etc.