Scholarlink

<p>A systematic study is conducted to address the issues of unclear grinding mechanism and insufficient surface quality prediction accuracy in the sand belt grinding process of DD6 single crystal high-temperature alloy. Firstly, based on the theory of elastic contact, a maximum undeformed cutting thickness model is established to systematically reveal the three-stage dynamic evolution characteristics and mechanism of abrasive belt grinding force. To overcome the weak generalization ability of traditional empirical models, a surface roughness prediction model with strong generalization ability is constructed using a genetic algorithm optimized BP neural network (GA-BP). Further combining three-dimensional single abrasive finite element simulation technology, quantitatively analyze the influence of grinding process parameters on the stress–strain field distribution. Through conducting experiments on robot sand belt grinding technology, the reliability of the finite element model was demonstrated. The grinding force exhibited typical fluctuation characteristics under the dynamic action of grinding and polishing pressure, with steady-state values ranging from 36 N ± 0.2 N. The developed GA-BP prediction model demonstrated excellent performance, with a maximum relative error of only 1.5703% and MAE and MSE as low as 0.53998 and 0.57313, respectively. Provide theoretical support and technical solutions for efficient and low damage precision machining of aircraft engine blades.</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

Study on the abrasive belt grinding removal mechanism and surface roughness prediction of DD6 single crystal superalloy

  • 期刊论文

  • 发表日期：

    2025年6月12日

查看全文
  • Pengfei Liu,
  • Pengyu Liu

摘要

A systematic study is conducted to address the issues of unclear grinding mechanism and insufficient surface quality prediction accuracy in the sand belt grinding process of DD6 single crystal high-temperature alloy. Firstly, based on the theory of elastic contact, a maximum undeformed cutting thickness model is established to systematically reveal the three-stage dynamic evolution characteristics and mechanism of abrasive belt grinding force. To overcome the weak generalization ability of traditional empirical models, a surface roughness prediction model with strong generalization ability is constructed using a genetic algorithm optimized BP neural network (GA-BP). Further combining three-dimensional single abrasive finite element simulation technology, quantitatively analyze the influence of grinding process parameters on the stress–strain field distribution. Through conducting experiments on robot sand belt grinding technology, the reliability of the finite element model was demonstrated. The grinding force exhibited typical fluctuation characteristics under the dynamic action of grinding and polishing pressure, with steady-state values ranging from 36 N ± 0.2 N. The developed GA-BP prediction model demonstrated excellent performance, with a maximum relative error of only 1.5703% and MAE and MSE as low as 0.53998 and 0.57313, respectively. Provide theoretical support and technical solutions for efficient and low damage precision machining of aircraft engine blades.