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Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 30 , No. 1

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Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 30, No. 1, pp. 1-10
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
ISSN: 2508-5107 (Online)
Print publication date 15 Feb 2021
Received 23 Sep 2020 Revised 18 Nov 2020 Accepted 24 Nov 2020
DOI: https://doi.org/10.7735/ksmte.2021.30.1.1
이미지 피라미드와 중첩을 활용한 영상처리 기반의 인덱서블 엔드밀 공구마모영역 및 마모최대폭 측정기법
고형용^a ; 구정인^a^{, *}

A Study on the Measurement of Flank Wear Area and Its Maximal Length of Indexable End Mill based on Image Processing Techniques Using Image Pyramids and Edge Overlays
Hyoung Yong Ko^a ; Jeongin Koo^a^{, *}
aResearch Institute of Intelligent Manufacturing System, Korea Institute of Industrial Technology
Correspondence to : ^*Tel.: +82-41-5898-230 E-mail address: jikoo@kitech.re.kr (Jeongin Koo).



Funding Information ▼ Korea Institute of Industrial Technology KITECH EO 20 0040 Ministry of Trade, Industry and Energy 20004537

초록

Because the condition of machining tools has a significant influence on the machine downtime and the quality of machining products, maintaining it is the most important aspect in machining. This paper presents the detection process of the tool wear area and its maximal length based on image processing techniques. After collecting tool images from microscopes on the machine, we extracted region of interest (RoI) from them. Subsequently, we applied median filter, Sobel and Otsu method to RoI images to detect the flank wear area of them. To verify our proposed method, we compared it with the human measurement and image processing techniques proposed by prior literatures. Our experimental results were at least 6.7% more accurate because we used overlaid unworn tool images taken previously . With these techniques, we were able to detect tool wear area and its maximal length even for the worn tool wear area of curved surfaces.


Keywords: Toolwear monitoring, Image processing, Computer vision, Flank wear

Acknowledgments

This study has been conducted with the support of the Korea Institute of Industrial Technology as “Machinability Diagnosis and Control System based on Deep Learning for Self optimized Manufacturing System (KITECH EO 20 0040)” and has been supported by the Technology innovation Program (20004537, “Development of Manufacturing Big Data Collection and Analytics General Purpose Platform”) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).

References


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Hyoung Yong Ko

Research Engineer in the Department of Intelligent Manufacturing System, Korea Institute of Industrial Technology.His research interest is Smart Machining.

E-mail: kgu15460@kitech.re.kr

Jeongin Koo

Principal Research Engineer in the Department of Intelligent Manufacturing System, Korea Institute of Industrial Technology.His research interest is Operations Research, Management Science, and Smart Machining.

E-mail: E-mail: jikoo@kitech.re.kr