Browse > Article
http://dx.doi.org/10.15207/JKCS.2019.10.11.015

A Study on the Prediction of CNC Tool Wear Using Machine Learning Technique  

Lee, Kangbae (MIS, Donga University)
Park, Sungho (MIS, Donga University)
Sung, Sangha (MIS, Donga University)
Park, Domyoung (Taekwang Co., Ltd.)
Publication Information
Journal of the Korea Convergence Society / v.10, no.11, 2019 , pp. 15-21 More about this Journal
Abstract
The fourth industrial revolution is noted. It is a smarter factory. At present, research on CNC (Computerized Numeric Controller) is actively underway in the manufacturing field. Domestic CNC equipment, acoustic sensors, vibration sensors, etc. This study can improve efficiency through CNC. Collect various data such as X-axis, Y-axis, Z-axis force, moving speed. Data exploration of the characteristics of the collected data. You can use your data as Random Forest (RF), Extreme Gradient Boost (XGB), and Support Vector Machine (SVM). The result of this study is CNC equipment.
Keywords
Random Forest; XGBoost; CNC; SVM; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 J. H. Ryu. (2019). Monitoring of Milling Processes through Measurement of Power Consumption. Master dissertation, Seoul University, Seoul.
2 J. J. Kim. (2019). A Detection and Stabilization Method for CNC Tool Vibration using Acoustic Sensor. Journal of Korea Institute of Information, Electronics, and Communication Technology, 12(2), 120-146. DOI : 10.17661/jkiiect.2019.12.2.120   DOI
3 Y C Liu, X F Hu & S. X. Sun. (2019, July). Remaining Useful Life Prediction of Cutting Tools Based on Support Vector Regression. IOP Conference Series: Materials Science and Engineering (pp. 1-8). China : IOP Publishing.
4 J. H. Oh & J. S. Kim. (2017. June). Prediction of Housing Price using Machine Learning: Focusing on MARS, Korea Housing Association 2017 Spring Conference (pp. 153-17)1, Korea : the Korean Association for Housing Policy Studies
5 A. Kumar et al. (2019). An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools, Computers & Industrial Engineering, 128, 1008-1014. DOI : 10.1016/j.cie.2018.05.017   DOI
6 S. T. Jung. (2018). Prediction and Experiments of Cutting Forces in Down Milling of Hardened Mold Steel, Journal of the Korean Society of Manufacturing Technology Engineers, 27(4), 346-350. DOI : 10.7735/ksmte.2018.27.4.346   DOI
7 D. Wu et al. (2017). A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests, Journal of Manufacturing Science and Engineering, 139(7), 1-19. DOI : 10.1115/1.4036350
8 J. Wang & R. Zhao. (2017). Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks, Sensors 2017, 17(2), 2-18 DOI : 10.3390/s17020273   DOI
9 AMIT JAIN & BHUPESH KUMARLAD. (2016). Data Driven Models for Prognostics of High Speed Milling Cutters, International Journal of Performability Engineering, 12(1), 3-12 DOI : 10.23940/ijpe.16.1.p3.mag
10 S. H. Kang. (2016). Multivariate Monitoring of the Metal Frame Process in Mobile Device Manufacturing, Journal of the Korean Institute of Industrial Engineers, 42(6), 1-9 DOI : 10.7232/JKIIE.2016.42.6.395   DOI
11 T. Benkedjouh (2015). Health assessment and life prediction of cutting tools based on support vector regression, Journal of Intelligent Manufacturing 2015, 26(2), 213-223 DOI : 10.1007/s10845-013-0774-6   DOI
12 A. Gouarir & G. Martinez-Arellano. (2018. June). In-process Tool Wear Prediction System Based on Machine, 8th CIRP Conference on High Performance Cutting, (pp. 501-504), Hungary : Procedia CIRP DOI : 10.1016/j.procir.2018.08.253   DOI
13 H. J. Kim. (2019). Machine Learning-based Quality Control and Error Correction Using Homogeneous Temporal Data Collected by IoT Sensors, Journal of the Korea Convergence Society, 10(4), 17-23 DOI : 10.15207/JKCS.2019.10.4.017   DOI
14 S. W. Bae. (2018). Estimation of the Apartment Housing Price Using the Machine Learning Methods: The Case of Gangnam-gu, Journal of the Korea Real Estate Analysts Association, 24(1), 69-85 DOI : 10.19172/KREAA.24.1.5   DOI
15 H. J. KIM. (2019). Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments, International Journal of Geo-Information, 7(5), 1-16 DOI : 10.3390/ijgi7050168