基于在线学习的数控加工刀具寿命动态预测方法

Dynamic Prediction Method of Cutting Tool Life in NC Machining Based on Online Learning

预测刀具寿命对保证零件质量和控制加工成本意义重大,但刀具磨损过程复杂多变,刀具剩余寿命受工况影响难以准确预测。针对以上问题,提出了一种基于在线学习的刀具寿命动态预测方法,以长短时记忆网络为基础模型,融合在线学习模块,使得模型能够在加工过程中自动更新参数,实现变工况下刀具寿命的精确预测。进行了铣削加工试验,结果表明,刀具寿命动态预测方法可以有效提升刀具寿命预测精度。

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.