Journal Archive

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 27 , No. 5

[Paper List] [Go to Volume List]


[ Technical Papers ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 27, No. 5, pp. 472-477
Abbreviation: J. Korean Soc. Manuf. Technol. Eng.
ISSN: 2508-5107 (Online)
Print publication date 15 Oct 2018
Received 26 Jul 2018 Revised 28 Aug 2018 Accepted 30 Aug 2018
DOI: https://doi.org/10.7735/ksmte.2018.27.5.472
드릴링 추력을 이용한 드릴의 인프로세스 파손 검출
차예나^a ; 박동삼^b^{, *}


In-Process Detection of Drill Breakage Using Thrust Forces
Ye Na Cha^a ; Dong Sam Park^b^{, *}
aWonju Medical Instruments High School, 1756, Wonmun-ro, Munmak-eup, Wonju, Gangwon-do, 26370, Korea
bDept. of Mech. Eng., Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Korea
Correspondence to : ^*Tel.: +82-32-835-8418 Fax: +82-32-835-0793 E-mail address: dspark@inu.ac.kr (Dong Sam Park).



Funding Information ▼ Incheon National University

Abstract

To fully automate production processes, a monitoring system is required to detect abnormal phenomena that may occur during a cutting process, such as tool wear, chipping and fracture, excessive chatter, and built-up edge formation. In this study, a system is developed to detect drill breakage in real-time and in-process conditions during drilling. Through a number of preliminary experiments, it was confirmed that the thrust forces could be effectively used to detect drill breakage, and an algorithm for detecting breakage was thus established through signal processing of these thrust forces. The detection algorithm was implemented using LabVIEW on a PC. It is thus confirmed that drill breakages can be detected in-process and in real-time by applying the developed drill breakage detection system to actual NC drilling operations.


Keywords: Tool monitoring system, Drilling, Drill breakage, Thrust force, In-process

Acknowledgments

이 논문은인천대학교 2016년도자체연구비 지원에의하여 연구 되었음

References


1.	Park, D. S., 1992, A Study on the Chip-Breaking Frequency and Monitoring and Diagnosis of Cutting Process Using Neural Pattern Recognition Method, Doctoral Dissertation, Seoul National University, Republic of Korea.
2.	Cha, Y. N., 2017, A Study on the Detection of Drill Fracture Using Tool Dynamometer, Master Thesis, Incheon National University, Republic of Korea.
3.	Cai, G., Chen, X., Li, B., Chen, B., He, Z., 2012, Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information, Sensors 12 12964-12987.
4.	Chung, T. K., Yeh, P. C., Lee, H., Lin, C. M., Tseng, C. Y., Lo, W. T., Wang, C. M., Wang, W. C., Tu, C. J., Tasi, P. Y., Chang, J. W., 2016, An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/ Breakage-Condition Monitoring, Sensors 16 269-286.
5.	Bukkapatnam, Satish T. S., Kumara, Soundar R. T., Lakhtakia, A., 2000, Fractal Estimation of Flank Wear in Turning, ASME Journal of Dynamic Systems, Measurement and Control 122 89-94.
6.	Balsamo, V., Caggiano, A., Jemielniak, K., Kossakowska, J., Nejman, M., Teti, R., 2016, Multi Sensor Signal Processing for Catastrophic Tool Failure Detection in Turning, Procedia CIRP 41 939-944.
7.	Bombiński, S., Błażejak, K., Nejman, M., Jemielniak, K., 2016, Sensor Signal Segmentation for Tool Condition Monitoring, Procedia CIRP 46 155-160.
8.	Lauro, C. H., Brandão, L. C., Baldo, D., Reis, R. A., Davim, J. P., 2014, Monitoring and Processing Signal Applied in Machining Processes – A Review, Measurement 58 73-86.
9.	Nordmann GmbH & Co. KG, n.d. viewed 28 June 2018, <http://www.toolmonitoring.com>.