Stiffness design and multi-objective optimization of machine tool structure based on biological inspiration

Abstract

This paper proposes a novel method to design the internal stiffeners layout of the supporting parts of a machine tool. This method skillfully adopts the natural growth law of leaf veins. Firstly, the similarity between the growth law of leaf veins and the layout of stiffeners in the supporting parts is dealt with in detail. The mechanical properties of different growth types of leaf veins are compared and analyzed by the finite element simulation. In addition, the optimality of the adaptive growth law of leaf veins is also analyzed. Then, a parameter optimization method of stiffeners layout based on a variable cross-section structure is proposed. Distinct from the traditional design of stiffeners layout, the method proposed in this paper not only adopts the idea of a variable cross-section structure, but also simulates the adaptive growth law of leaf veins through a parametric method. In the process of establishing a meta model, three modeling methods are compared, and the excellent performance of the genetic algorithm and BP neural network (GABPNN) method in meta model accuracy is determined. And a marine predator algorithm is implemented to optimize the meta model. Finally, the method proposed in this paper is applied to the design of the column part of a machine tool, and the effectiveness of the proposed method is verified by simulation and experiments, which provides a good idea for the design of stiffeners layout of the supporting parts of a machine tool.