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http://dx.doi.org/10.23087/jkicsp.2022.23.2.005

Machine Tool State Monitoring Using Hierarchical Convolution Neural Network  

Kyeong-Min Lee (Dept. of Computer Engineering, Silla University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.23, no.2, 2022 , pp. 84-90 More about this Journal
Abstract
Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.
Keywords
Machine Tool State Monitoring; Deep Learning; Hierarchical Convolution Neural Network; Acoustic Signal; Power Spectral Density;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 J. H. Kim, J. S. Yoon, and D. Y. Lee, "Estimation of Tangential Cutting Force using Spindle Load of CNC Machining Center," Journal of the Korean Society of Manufacturing Technology Engineers, vol. 28 no. 6, pp. 343-349, 2019.   DOI
2 B. H. Park, Y. J. Lee, and C. W. Lee, "Tool Condition Monitoring Using Deep Learning in Machining Process," Journal of the Korean Society for Precision Engineering, vol. 37, no. 6, pp. 415-420, 2020.   DOI
3 D. Y. Lee and S. J. Yun, "Development of Deburring Spindle up to 80,000 rpm for Precision Finishing," Korean Society for Precision Engineering Conference, pp. 441-442, 2017.
4 I. S. Yook, D. H. Lee, G. S. Han, W. K. Han, and J. H. Hwang, "Development of the Air Bearing Spindle for Precision Machining for Metal case of the Smart-phone," Korean Society for Precision Engineering Conference, pp. 280-281, 2016.
5 C. H. Park and T. G. Yoon, "Development of Drilling Spindle for Micro-hole Machining with Magnetic Bearings," Transactions of the Korean Society for Noise and Vibration Engineering, vol. 27, no. 4, pp. 510-517, 2017.   DOI
6 M. Saimurugan and K.I. Ramachandran, "Detection The Behavior of Smartphone Users using Time-division Feature Fusion Convolutional Neural Network," Journal of The Korean Institute of Information and Communication Engineering, vol. 24, no. 9, pp. 1224-1230, 2020.
7 S. J. Yoon, M. Y. Lee, J. H. Lee, S. H. Lee, and J. C. Na, "Fault Diagnosis Using Artificial Intelligence for the Spindle of Machine Tools," Transactions of the Korean Society of Mechanical Engineers, vol. 45, no. 5, pp. 401-408, 2021.   DOI
8 J. Shi, G. Si, S. Li, B. Oresanya, and Y. Zhang, "Feature extraction based on the fractional Fourier transform for vibration signals with application to measuring the load of a tumbling mill," Control Engineering Practice, vol. 84, pp. 238-246, 2019.   DOI
9 K. M. Lee, C. Vununu, K. S. Moon, S. H. Lee, and K. R. Kwon, "Automatic machine fault diagnosis system using discrete wavelet transform and machine learning," Journal of Korea Multimedia Society, vol. 20, no. 8, pp. 1299-1311, 2017.   DOI
10 M. Zhao, M. Kang, B. Tang, and M. Pecht, "Deep Residual Networks with Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes," IEEE Transactions on Industrial Electronics, vol. 65, issue 5, pp. 4290-4300, 2018.   DOI
11 K. M. Lee, "A Machine Fault Diagnosis System based on Convolution Neural Network using Acoustic Power Spectral Density," Ph. D. dissertation, Pukyong National University, Busan, Republic of Korea, 2019.
12 Y. J. An and J. Y. Kim, "A Study on Real-time Tool Breakage Monitoring on CNC Lathe Using Fusion Sensor," Tribology and Lubricants, vol. 28, no. 3, pp. 130~135, 2012.   DOI