基于深度学习算法的合金零件表面切削加工参数优化
Optimization of Surface Cutting Parameters of Alloy Parts Based on Deep Learning Algorithm
张开金1
作者信息
- 1. 甘肃省理工中等专业学校,武威 733000
- 折叠
摘要
为提高合金零件表面切削加工效率和生产能力,提高加工时的材料去除率,引入深度学习算法,开展合金零件表面切削加工参数优化研究.通过构建深度学习模型,实现对切削加工参数的智能预测与优化模型建立.模型构建过程中有效处理材料去除率与表面粗糙度等约束函数,并合理设置优化条件,以确保优化结果的实用性和准确性,最终通过深度学习算法求解得到合金零件表面切削加工的最优参数组合.对比实验结果表明,该方法显著提高了材料去除率,同时验证了深度学习在切削加工参数优化中的有效性.
Abstract
In order to improve the surface cutting efficiency and production capacity of alloy parts and promote the material removal rate during processing,the deep learning algorithm is introduced to carry out the optimization research of the surface cutting machining parameters of alloy parts.By constructing the deep learning model,the intelligent prediction and optimization model establishment of the cutting processing parameters are realized.During the model construction process,the constraint functions such as the material removal rate and the surface roughness are effectively treated,and the optimization conditions are reasonably set up to ensure the practicability and accuracy of the optimization results.Finally,the optimal parameter combination of the alloy parts is solved by deep learning algorithm.The comparative experimental results show that the proposed method significantly improves the material removal rate,and also verifies the effectiveness of deep learning in optimizing cutting parameters.
关键词
深度学习算法/表面切削加工/合金零件Key words
deep learning algorithm/surface cutting processing/alloy parts引用本文复制引用
出版年
2024
NETL
NSTL
万方数据