摘要
刀具磨损及寿命预测一直是刀具管理研究的重要领域,但由于加工工艺、使用强度及环境因素的极度未确知性,很难取得对刀具寿命的准确判断,因而增加了机床或工件损坏的风险,所以需要构建模型实现对磨损程度的有效预测。本文首先通过一定条件下的刀具磨损实验,测量出刀具在相同工作时间内的磨损值,然后采用新信息优先的新陈代谢EGM(1,1)模型对特定条件下的数据序列进行拟合与预测,并与传统EGM(1,1)进行比较。结论发现新陈代谢EGM(1,1)模型具有更高的预测精度、更小的拟合误差,对提高刀具磨损及寿命预测的准确性具有重要方法意义。
It has always been an important field for tool wear and life prediction in tool management research.But due to the extreme uncertainty of processing technology,strength of use and environmental factors,it is difficult to obtain an accurate judgment of tool life,thus resulting in risk of machine tool or workpiece damage.In order to realize effective prediction of wear degree,the tool wear values were measured by tool wear experiments in certain conditions.Then,metabolism EGM(1,1)model was employed to perform fitting and prediction of data sequences.And by comparison with traditional EGM(1,1),it is found that the metabolism EGM(1,1)model has higher prediction accuracy,and as an effective life prediction method,it presents important significance to improve the accuracy of tool wear.
作者
李光宇
李守军
Li Guang-yu;Li Shou-jun(School of Mechanical&Electrical Engineering,Suqian College,Suqian 223800,China)
出处
《内燃机与配件》
2022年第22期108-110,共3页
Internal Combustion Engine & Parts
基金
宿迁市科技计划项目(Z2019106)
江苏高校品牌专业建设工程资助项目(PPZY2015C252)。