The prediction of tool life is of great significance to ensure the quality of parts and control the cost of machining. However, the tool wear process is complex and changeable, and it is difficult to accurately predict the residual life of the cutting tools affected by machining conditions. To solve the above problems, this paper presents a dynamic prediction method of tool life based on online learning. Using long-short term memory as base model and integrating the online learning module, the final model can automatically update the parameters during the machining process, and the accurate prediction of tool life under variable working conditions can be realized. The milling experiment was carried out, and the experimental results show that the dynamic prediction method of tool life can effectively improve the precision of tool life prediction.
基金资助:2015年国家重大专项(2015ZX04001002)。
作者简介: 王强:硕士研究生,主要研究方向为飞机复杂结构件智能数控加工技术。
引用本文:
王强,李迎光,郝小忠,刘长青,陈海吉. 基于在线学习的数控加工刀具寿命动态预测方法[J]. 航空制造技术, 2019, 62(7): 49-53.
WANG Qiang, LI Yingguang, HAO Xiaozhong, LIU Changqing, CHEN Haiji. Dynamic Prediction Method of Cutting Tool Life in NC Machining Based on Online Learning. Aeronautical Manufacturing Technology, 2019, 62(7): 49-53.