机电工程技术2024，Vol.53Issue(2)：29-34,123,7.DOI:10.3969/j.issn.1009-9492.2024.02.006
基于机器学习的刀具磨损状态智能预测方法研究
Research on Intelligent Prediction Method for Tool Wear Status Based on Machine Learning
摘要
Abstract
The intelligent prediction of tool wear state is successfully realized by taking the tool as the research carrier and applying advanced technologies such as artificial intelligence and intelligent optimization.The research focuses on the establishment of an effective tool wear state prediction method,which comprehensively analyzes key information such as tool wear mechanism,form and dullness standard.At the same time,a self-mining tool wear state monitoring platform is constructed to collect and process relevant data.In the data processing process,wavelet filtering and EMD-Shannon energy entropy are used for feature screening,and a feature space dataset is constructed to provide a solid data foundation for the subsequent construction of the prediction model.The support vector machine classification algorithm and intelligent optimization algorithm are combined to construct an intelligent prediction framework for tool wear state.This framework not only improves the prediction accuracy,but also provides a powerful tool for maintenance personnel,which facilitates better tool wear state prediction and maintenance work.In order to enhance the practical application value,the obtained results are integrated into the MATLAB GUI-based tool wear state intelligent monitoring prototype system,and the prediction results are presented in a graphical interface,so that the user can intuitively understand and master the tool wear state.The results show that the method has high accuracy,and the recognition accuracy of tool wear state can reach 84%,which provides reliable technical support for related fields.关键词
刀具磨损/智能监测系统/特征选择/智能优化算法/支持向量机Key words
tool wear/intelligent monitoring system/feature selection/intelligent optimization algorithm/support vector machine分类
机械制造引用本文复制引用
梁璐娜,魏建安,袁雅阁,吴国阳,徐军..基于机器学习的刀具磨损状态智能预测方法研究[J].机电工程技术,2024,53(2):29-34,123,7.基金项目
贵州省科技支撑计划(黔科合支撑[2023]一般433) （黔科合支撑[2023]一般433）
贵州大学自然科学专项(特岗)科研基金项目(贵大特岗合字(2022)40号) （特岗）
