ArticleScientific reports2025
Research on multi-objective optimization design for high-precision turning-milling machine tool bed based on taguchi method -FEA.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Bionic optimization design of bed for high-precision turn-milling compound machine tool based on the honeycomb structure.Scientific reports · 2026Article
- Lightweight Design Method for Micromanufacturing Systems Based on Multi-Objective Optimization.Micromachines · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
As a high-precision machining equipment, the turning-milling machine tool requires its bed structure the primary load-bearing component to exhibit excellent dynamic and static characteristics that significantly influence machining accuracy and efficiency. To ensure machining precision, this paper proposes a multi-objective collaborative optimization design methodology integrating Finite Element Analysis(FEA) and Taguchi Method. This approach employs FEA technology to obtain the static and dynamic characteristics of the machine tool bed(MTB) structure, followed by multi-objective collaborative optimization using Taguchi Method to achieve comprehensive performance enhancement. Compared with the traditional research methods for optimizing machine tool beds, the FEA technology can handle more complex geometric shapes and boundary conditions, improve the accuracy and reliability of data, and enhance the optimization efficiency through the Taguchi method, achieving multi-objective joint optimization. Comparative analysis demonstrates that the optimized structure achieves 5.14% reduction in maximum deformation, 1.75% decrease in mass, and 1.04% improvement in fourth-order natural frequency. The results validate the effectiveness of this design methodology in achieving efficient MTB optimization for turning-milling machine tools, simultaneously enhancing dynamic-static performance while realizing lightweight design. This research provides valuable references for advancing precision improvement and green manufacturing research in turning-milling machine tools.
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