Scholarlink

<p>Wave edge end mills are widely used in rough machining for excellent chatter suppression and lower milling force. However, changes in their tooth profile geometric parameters affect milling force coefficients. Traditional methods require preparing tools of different sizes and conducting numerous experiments to obtain relevant data, leading to long development cycles and high costs. To solve this, this paper proposes a semi-analytical-based milling force modeling and tooth profile optimization method. It can predict the milling force of wave edge end mills with any tooth profile and optimize tooth profile parameters without repeated experiments. First, through geometric parametric modeling and milling force modeling of wave edge end mills, a mathematical description of tooth profile-mechanical response is constructed. Further, by combining finite element simulation, BP neural network and theoretical analysis, a discretized milling force coefficient identification model is established and integrated into the milling force solution process. Multiple milling experiments on X80 pipeline steel show good consistency between the model’s predictions and experimental measurements. Additionally, optimizing the wave edge tooth profile via genetic algorithm yields a scheme that reduces the average milling force by up to 10.30%, significantly outperforming ordinary end mills. This integrated modeling and optimization method greatly improves the design efficiency of wave edge end mills and provides an effective tool for the digital development of complex cutting tools.</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

Milling force modeling and tooth shape optimization of the wave-flute end mill based on the semi-analytical method

  • 期刊论文

  • 发表日期：

    2026年1月3日

查看全文
  • Xiaoquan Hao,
  • Feihong Yun,
  • Fei Guo,
  • Gang Wang,
  • Kefeng Jiao,
  • Haixia Gong,
  • Liquan Wang,
  • Peng Gao

摘要

Wave edge end mills are widely used in rough machining for excellent chatter suppression and lower milling force. However, changes in their tooth profile geometric parameters affect milling force coefficients. Traditional methods require preparing tools of different sizes and conducting numerous experiments to obtain relevant data, leading to long development cycles and high costs. To solve this, this paper proposes a semi-analytical-based milling force modeling and tooth profile optimization method. It can predict the milling force of wave edge end mills with any tooth profile and optimize tooth profile parameters without repeated experiments. First, through geometric parametric modeling and milling force modeling of wave edge end mills, a mathematical description of tooth profile-mechanical response is constructed. Further, by combining finite element simulation, BP neural network and theoretical analysis, a discretized milling force coefficient identification model is established and integrated into the milling force solution process. Multiple milling experiments on X80 pipeline steel show good consistency between the model’s predictions and experimental measurements. Additionally, optimizing the wave edge tooth profile via genetic algorithm yields a scheme that reduces the average milling force by up to 10.30%, significantly outperforming ordinary end mills. This integrated modeling and optimization method greatly improves the design efficiency of wave edge end mills and provides an effective tool for the digital development of complex cutting tools.