A Novel Data-Driven Lightweight Optimization Method Based on Meta-Structure of CNC Machine Tool

Shaoqing Wu, Congbo Li, Huajun Cao, Xinyu Li, Huishi Liu

Abstract: Computer numerical control (CNC) machine tools consume a large amount of raw materials in its manufacturing process. With the emphasis on the economy of machine tools, reducing the material consumption becomes significant. The lightweight optimization is regarded an effective way for material saving, since it can reduce the cost while improving machining performance. However, existing lightweight studies of machine tool mainly depend on experience, which cannot ensure the accuracy and efficiency, and the design scheme is hard for manufacturing. On this basis, a novel data-driven lightweight optimization method with meta-structure integrating the topological and size optimization is proposed in this article. Meta-structures are modeled to form the topological configuration of machine tool for avoiding time-consuming design analysis. The surrogate models with a novel adaptive sequential sampling method are built to fit accurate relationships between structure and performances. A multi-objective size optimization frame is developed for further reducing the mass, deformation and enhancing natural frequency of machine tool. The gear honing machine tool is taken as the case study, where results indicate that the lightweight optimization can reduce mass by 13.72% under the premise of structure safety. Note to Practitioners—This article provides one method for the lightweight optimization of CNC machine tools at the design stage. The traditional methods only focus on the experience computation of the machine tool design and ignore the data modeling, leading to a limitation of the lightweight potential of machine tools. This article suggests a data-driven lightweight optimization method of CNC machine tools, which integrates the topological optimization and size optimization to enhance the lightweight results. Based on the meta-structure and surrogate assisted methods, the multi-objective lightweight optimization model can be easier obtained. A case study on the gear honing machine is conducted where simulation results indicate that this method can significantly reduce the mass while ensuring the structure safety. It may assist decision makers in providing optimal design schemes of machine tools under the need of material saving and structure optimization.

External IDs:dblp:journals/tase/WuLCLL25