Scholarlink

<p> Growing demands for high-precision machining, along with issues related to energy economy and structural stability, have contributed to considerable focus on optimizing machine tool design. The relationship of dynamic performance, static stiffness, and spatial motion is essential for achieving machining accuracy and operating dependability in high-precision gantry-type machine tools. Conventional methods for optimizing machine tools emphasize either static or dynamic characteristics, often overlooking combined spatial motion factors. This investigation presents a thorough optimization methodology for five-axis gantry-type machine tools to tackle these issues. An energy consumption model for the feed drive system is established by the analysis of kinematic and dynamic features. Finite element modelling (FEM) is utilized to assess structural deformation and dynamic stability at 27 spatial positions with 3 different planes (A, B, and C). Experimental static analysis under a 5000 N force in the X-axis load indicated spindle nose deformation of 67.26&#xa0;µm, bed deformation of 1.956&#xa0;µm, and table deformation of 7.53&#xa0;µm, along with a 1.56% disparity in X-axis stiffness between FEM and experimental data. Crucial structural characteristics affecting energy efficiency and accuracy are discovered and optimized via experimental validation. Modal analysis is performed to guarantee that natural frequencies are maintained outside resonance regions during operation. Ultimately, compensation values are calculated to rectify errors caused by deformation, hence enhancing accuracy and efficiency. The findings demonstrate a significant improvement in machining accuracy, accompanied by less distortion and improved energy efficiency across spatial arrangements. The outcomes illustrate possible applications in real-time error compensation, intelligent machine design, and Industry 4.0 frameworks by incorporating these insights into control systems, therefore advancing the aerospace, automotive, and precision manufacturing industries.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Probing energy-efficient design and spatial motion optimization of high-precision gantry machine tool

  • 期刊论文

  • 发表日期：

    2025年9月26日

查看全文
  • Aman Ullah,
  • Tzu-Chi Chan,
  • Arslan Munir,
  • Shinn-Liang Chang

摘要

Growing demands for high-precision machining, along with issues related to energy economy and structural stability, have contributed to considerable focus on optimizing machine tool design. The relationship of dynamic performance, static stiffness, and spatial motion is essential for achieving machining accuracy and operating dependability in high-precision gantry-type machine tools. Conventional methods for optimizing machine tools emphasize either static or dynamic characteristics, often overlooking combined spatial motion factors. This investigation presents a thorough optimization methodology for five-axis gantry-type machine tools to tackle these issues. An energy consumption model for the feed drive system is established by the analysis of kinematic and dynamic features. Finite element modelling (FEM) is utilized to assess structural deformation and dynamic stability at 27 spatial positions with 3 different planes (A, B, and C). Experimental static analysis under a 5000 N force in the X-axis load indicated spindle nose deformation of 67.26 µm, bed deformation of 1.956 µm, and table deformation of 7.53 µm, along with a 1.56% disparity in X-axis stiffness between FEM and experimental data. Crucial structural characteristics affecting energy efficiency and accuracy are discovered and optimized via experimental validation. Modal analysis is performed to guarantee that natural frequencies are maintained outside resonance regions during operation. Ultimately, compensation values are calculated to rectify errors caused by deformation, hence enhancing accuracy and efficiency. The findings demonstrate a significant improvement in machining accuracy, accompanied by less distortion and improved energy efficiency across spatial arrangements. The outcomes illustrate possible applications in real-time error compensation, intelligent machine design, and Industry 4.0 frameworks by incorporating these insights into control systems, therefore advancing the aerospace, automotive, and precision manufacturing industries.