刀具磨损
高温计
温度测量
机械加工
机械工程
材料科学
校准
自编码
计算机科学
工程类
人工神经网络
人工智能
统计
物理
数学
量子力学
作者
Jinghui Han,Zhiyong Liu,Kaiwei Cao,Long Xu,Tielin Shi,Guanglan Liao
出处
期刊:Measurement
[Elsevier BV]
日期:2022-05-28
卷期号:198: 111413-111413
被引量:17
标识
DOI:10.1016/j.measurement.2022.111413
摘要
The cutting temperature is essential for phenomena understanding and quality improvement in metal cutting while its in-situ online measurement is still a challenge. This paper presents a near-infrared fiber-optic multi-spectral method for in-situ online cutting temperature measurement. Using thermal radiation spectrum for temperature measurement, the method optimizes the lower limit of temperature measurement to 150 °C while improving accuracy. The calibration shows that in the range of above 250 °C, the average relative error of temperature measurement is stable below 0.5%. The titanium alloy cutting experiments are carried out. In-situ online measurement of tool temperatures in dry/wet cuttings are realized using the self-developed system. The influence of cutting parameters on cutting temperature is studied, and the real-time response of the temperature measurement system to the cutting state is verified. As for industrial application, the capability of the system in heavy-duty turning is proved by railway wheelsets turning experiments. Tool wear experiments are conducted, and a positive correlation between the cutting temperature and tool wear is revealed. Tool wear status recognition is realized based on cutting temperature by sparse autoencoder and k-means clustering, and a recognition accuracy of 97.3% is achieved. These results indicate promising prospects in cutting mechanism research, machining status monitoring and industrial applications, empowering the advancement of intelligent manufacturing and industry 4.0.
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