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http://dx.doi.org/10.14775/ksmpe.2022.21.03.077

A Study on the Wear Condition Diagnosis of Grinding Wheel in Micro Drill-bit Grinding System  

Kim, Min-Seop (Department of Mechanical Engineering(Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology)
Hur, Jang-Wook (Department of Mechanical Engineering(Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.21, no.3, 2022 , pp. 77-85 More about this Journal
Abstract
In this study, to diagnose the grinding state of a micro drill bit, a sensor attachment location was selected through random vibration analysis of the grinding unit of the micro drill-bit grinding system. In addition, the vibration data generated during the drill bit grinding were collected from the grinding unit for the grinding wheels under the steady and worn conditions, and data feature extraction and dimension reduction were performed. The wear of the micro-drill-bit grinding wheel was diagnosed by applying KNN, a machine-learning algorithm. The classification model showed excellent performance, with an accuracy of 99.2%. The precision, recall and f1-score were higher than 99% in both the steady and wear conditions.
Keywords
Micro Drillbit Grinding System; Machine Learning; Grinding Wheel; Wear Diagnostics;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Aung, S. S., Nagayama, I and Tamaki, S., "Dual-kNN for a Pattern Classification Approach", Journal of IEIE Transactions on Smart Processing & Computing, Vol. 6, No. 5, pp. 326-333, 2017.   DOI
2 Choi, H. G., "A Study on Micro Drill-Bit Measurement Using Images", Journal of the Korea Institute of Convergence Signal Processing, Vol. 16, No. 3, pp. 90-95, 2015.   DOI
3 Park, D. H., Choi, S. D. and Jo, Y. J., "Processing Technology for Regrinding the Micro Drillbit," Proceedings of the Korean Society of Precision Engineering Conference, pp. 873-874, 2013.
4 Roweis, S. T. and Saul, L. K., "Nonlinear Dimensionality Reduction by Locally Linear Embedding", Journal of Science, Vol. 290, No. 22, pp. 2323-2326, 2000.
5 Rameshkumar, K., Mouli, D. S. B. and Shivith, K., "Machine Learning Models for Predicting Grinding Wheel Conditions Using Acoustic Emission Features", SAE International Journal of Materials & Manufacturing, Vol. 14, No. 4, pp. 387-406, 2021.
6 Thomazella, R., Lopes, W., Aguiar, P., Alexandre, F., Fiocchi, A. and Bianchi, E., "Digital Signal Processing for Self-vibration Monitoring in Grinding: A New Approach based on the Time-frequency Analysis of Vibration Signals", Journal of Measurement, Vol. 145, pp. 71-83, 2019.   DOI
7 Choi, H. J., Jang, E. S., Kuk, Y. H. and Park, J. H., "Dynamic Analysis of the Index Robot used in the Drill bit Grinding Equipment", Proceedings of the KSMPE Conference, pp. 125-125, 2015.
8 Kim, S. I., Noh, Y. J., Kang, Y. J., Park, S. H. and Ahn, B. H., "Fault Classification Model Based on Time Domain Feature Extraction of Vibration Data", Journal of the Computational Structural Engineering Institute of Korea, Vol. 34, No. 1, pp. 25-33, 2021.   DOI
9 Jang, J. and Park, J. W., "Stabilization Determination Method of Laser Tracker Target Using Principal Component Analysis", Spring and Autumn Conference of the Korean Society of Mechanical Engineers, pp. 103-104, 2021.
10 Kim, J. S. and Youn, J. S. "Data Visualization using Linear and Non-linear Dimensionality Reduction Methods", Journal of the Korea Society of Computer and Information, Vol. 23, No. 12, pp. 21-26, 2018.   DOI
11 Lee, H. S., Kim, E. T. and Kim, D. Y., "Pattern Recognition System Combining KNN rules and New Feature Weighting algorithm", Journal of the Institute of Electronics Engineers of Korea - Computer and Information, Vol. 42, No. 4, pp. 43-50, 2005.
12 Song, W., Wang, L., Liu, P., "Improved T-SNE Based Maniford Dimensional Reduction for Remote Sensing Data Processing", Journal of Multimedia Tools and Applications, Vol. 78, pp. 4311-4326, 2019.   DOI
13 Kuk, Y. H and Choi, H. J., "Analysis of Fluid-Structure Interaction of Cleaning System of Micro Drill Bits", Journal of the Korean Society of Manufacturing Process Engineers, Vol. 15, No. 1, pp. 8-13, 2016.
14 Hassui, A, Diniz, A., Oliveira, J., Felipe, J., Gomes, J., "Experimental Evaluation on Grinding Wheel Wear through Vibration and Acoustic Emission", Journal of Wear, Vol. 217, No. 1, pp. 7-14, 1998.   DOI
15 Ahn, M. G., Kim, J. K. and Kwon, C. H., "Machine Learning Based Dimension Reduction Technique for Efficient Surveillance Reconnaissance of UAV", Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp. 748-749, 2019.