Cutting temperature measurement in turning using fiber-optic multi-spectral radiation thermometry and its application in tool wear status recognition

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 数学 量子力学 统计 物理
作者
Jinghui Han,Zhiyong Liu,Kaiwei Cao,Long Xu,Tielin Shi,Guanglan Liao
出处
期刊:Measurement [Elsevier BV]
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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