Real-Time Cutting Tool State Recognition Based on Cutting Force Signals, Geometric Information and Process Information

HUA Jiaqi; LI Yingguang; LIU Changqing

doi:10.16080/j.issn1671-833x.2018.11.048

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Real-Time Cutting Tool State Recognition Based on Cutting Force Signals, Geometric Information and Process Information

更新时间：2025-10-30

- Real-Time Cutting Tool State Recognition Based on Cutting Force Signals, Geometric Information and Process Information
- Aeronautical Manufacturing Technology Vol. 61, Issue 11, Pages: 48-54(2018)
- 作者机构：
  
  南京航空航天大学机电学院,南京,210016
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671-833x.2018.11.048
  CLC：
- Published：2018
- 稿件说明：
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华家玘，李迎光，刘长青. 基于切削力信号-几何信息-工艺信息的铣削加工刀具状态实时辨识*[J]. 航空制造技术, 2018, 61(11): 48-54.

HUA Jiaqi, LI Yingguang, LIU Changqing. Real-Time Cutting Tool State Recognition Based on Cutting Force Signals, Geometric Information and Process Information. Aeronautical Manufacturing Technology, 2018, 61(11): 48-54.
华家玘，李迎光，刘长青. 基于切削力信号-几何信息-工艺信息的铣削加工刀具状态实时辨识*[J]. 航空制造技术, 2018, 61(11): 48-54. DOI： 10.16080/j.issn1671-833x.2018.11.048.

HUA Jiaqi, LI Yingguang, LIU Changqing. Real-Time Cutting Tool State Recognition Based on Cutting Force Signals, Geometric Information and Process Information. Aeronautical Manufacturing Technology, 2018, 61(11): 48-54. DOI： 10.16080/j.issn1671-833x.2018.11.048.

摘要

刀具状态的实时辨识对提高数控加工效率和质量、降低加工成本具有重要作用。对于单件小批量生产模式下的复杂零件，加工过程中的几何形状和切削参数不断变化，为刀具状态的准确辨识带来很大挑战。针对以上问题，提出了一种基于切削力信号- 几何信息- 工艺信息的铣削加工刀具状态辨识方法。采集加工过程中不同刀具状态的切削力信号，并对其做时域和时频分析，提取切削力信号的特征量，与加工工艺信息和零件几何信息相关联，建立输入向量，构建基于BP 神经网络的刀具状态辨识模型并训练，在实际加工中通过神经网络模型实现刀具状态的实时辨识。经过试验验证，该方法可以基本满足铣削加工刀具状态的实时辨识。

Abstract

Real-time recognition of cutting tool states plays an important role to improve the efficiency and quality and reduce the cost of CNC machining. For complex parts with single-piece or small batch production

the geometry and cutting parameters in the machining process are constantly changing which makes a great challenge for accurate recognition of cutting tool state. To address the above problems

this paper presents a cutting tool state recognition approach based on cutting force signals

geometric information and process information. The cutting force signals of different tool states are collected and analyzed. The time analysis and time-frequency analysis methods are used to extract the features of cutting force signals. Associated with the machining process information and the geometric information of the parts

the input vector is established. Cutting tool state recognition model based on neural network is established and trained. The real-time recognition of cutting tool state is realized with the neural network model during the actual machining process. Experiments show that the approach can solve the real-time recognition of cutting tool states.

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