典型文献
Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning
文献摘要：
Tool failures in machining processes often cause severe damages of workpieces and lead to large quantities of loss,making tool condition monitoring an important,urgent issue.However,problems such as practicability still remain in actual machining.Here,a real-time tool condition monitoring method integrated in an in situ fiber optic temperature measuring apparatus is proposed.A thermal simulation is conducted to investigate how the fluctuating cutting heats affect the measuring temperatures,and an intermittent cutting experiment is carried out,verifying that the apparatus can capture the rapid but slight temperature undulations.Fourier transform is carried out.The spectrum features are then selected and input into the artificial neural network for classification,and a caution is given if the tool is worn.A learning rate adaption algorithm is introduced,greatly reducing the dependence on initial parameters,making training convenient and flexible.The accuracy stays 90％and higher in variable argument processes.Furthermore,an application program with a graphical user interface is constructed to present real-time results,confirming the practicality.
文献关键词：
中图分类号：
作者姓名：
Kaiwei CAO;Jinghui HAN;Long XU;Tielin SHI;Guanglan LIAO;Zhiyong LIU
作者机构：
State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China;Foxconn Industrial Internet Co.,Ltd.,Shenzhen 518109,China
文献出处：
引用格式：
[1]Kaiwei CAO;Jinghui HAN;Long XU;Tielin SHI;Guanglan LIAO;Zhiyong LIU-.Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning)[J].机械工程前沿,2022(01):82-96
A类：
undulations
B类：
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AB值：
0.665962
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