Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence
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ORIGINAL ARTICLE
Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence Aoping Qin 1 & Liang Guo 1 & Zhichao You 1 & Hongli Gao 1
&
Xiangdong Wu 1 & Shoubing Xiang 1
Received: 6 April 2020 / Accepted: 17 August 2020 / Published online: 22 September 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Tool wear is an important factor affecting the quality of finished products, productivity, and the normal operation of machine tools, so tool condition monitoring (TCM) has become a research hotpot in the field of intelligent manufacturing. Compared with traditional monitoring methods, vision-based tool condition monitoring methods are more accurate and intuitive. However, the existing visual monitoring method requires manual adjustment of the tool position, and the degree of automation needs to be improved. Therefore, this paper proposes automatic face milling cutter condition monitoring method based on dynamic image sequence. We first acquire the dynamic image sequence of face milling cutter with the spindle rotating, then forward the dynamic image sequence to the image processing module to extract target area. And the images after image processing are propagated to the image selection module to obtain the image to be measured. Finally, forward the selected image to wear value measurement module to obtain the wear value. The presented automatic face milling cutter condition monitoring method is verified on a fiveaxis milling center. Compared with the direct measurement results of industrial digital microscope, the measurement error of the proposed method is within 4%, which is a reliable and effective online monitoring method for milling cutter wear. Keywords Automatic monitoring . Dynamic image sequence . Machine vision . Shape feature
1 Introduction Tool condition is of great significance for machining. According to statistics, machine tool downtime caused by tool wear accounts for 20% of the total downtime [1]. Therefore, it is very necessary to monitor the tool wear in the process of metal cutting, which can greatly improve finished product quality and productivity. Studies have shown that an accurate and reliable tool condition monitoring system can reduce machine tool downtime by 75% and increase production efficiency by 10–50% [2]. Therefore, the research on the tool condition monitoring system is of great significance for industrial production. With the development of advanced sensing technology and computing techniques, the condition monitoring data of mechanical components is available now, which raises the research attention on data-driven prognostic methods of machinery [3]. * Hongli Gao [email protected] 1
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
According to the relationship between different condition monitoring data and tool condition, the tool condition monitoring methods are mainly divided into direct method [4] and indirect method [5]. A comprehensive review on using direct an
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