Vibration based Assessment of Tool Wear in Hard Turning using Wavelet Packet Transform and Neural Networks

Document Type: Original Article

Authors

Department of Mechanical Engineering, University of Tabriz, Iran

Abstract

Demanding high dimensional accuracy of finished work pieces and reducing the scrap and production cost, call for devising reliable tool condition monitoring system in machining processes. In this paper, a tool wear monitoring system for tool state evaluation during hard turning of AISI D2 is proposed. The method is based on the use of wavelet packet transform for extracting features from vibration signals, followed by neural network for associating the root mean square values of extracted features with tool flank wear values of the cutting tool. From the result of performed experiments, coefficient of determination and root mean square error for the proposed tool wear monitoring system were found to be 99% and 0.0104 respectively. The experimental results show that wavelet packet transform of vibration signals obtained from the cutting tool has high accuracy in tool wear monitoring. Furthermore, the proposed neural network has the acceptable ability in generalizing the system characteristics by predicting values close to the actual measured ones even for the cutting conditions not encountered in the training stage.

Keywords

Main Subjects


[1]    Azizi, M. W., Belhadi, S., Athmane Yallese, M., Mabrouki, T., and Rigal, J. F., Surface Roughness and Cutting Forces Modeling for Optimization of Machining Condition in Finish Hard Turning of Aisi 52100 Steel, Journal of Mechanical Science and Technology, Vol. 26, No. 12, 2012, pp. 4105-4114.

[2]    Kong, D., Chen, Y., Li, N., and Tan, S., Tool Wear Monitoring Based on Kernel Principal Component Analysis and V-Support Vector Regression, The International Journal of Advanced Manufacturing Technology, Vol. 89, No. 1-4, 2016, pp. 175-190.

[3]    Dutta, S., Pal, S. K., and Sen, R., Progressive Tool Flank Wear Monitoring by Applying Discrete Wavelet Transform on Turned Surface Images, Measurement, Vol. 77, 2016, pp. 388-401.

[4]    Jianfeng, L., Yongqing, Z., Fangrong, C., Zhiren, T., and Yao, W., Wear and Breakage Monitoring of Cutting Tools by Optic Method (1st Part: Theory), Paper presented at the Proceedings of SPIE, 1996, pp. 481-489.

[5]    Jetley, S. K., A New Radiometric Method of Measuring Drill Wear, SME Manufacturing Engineering Transactions and 12 th NAMRC North American Manufacturing Research, 1984, pp. 255-259.

[6]    Sadílek, M., Kratochvíl, J., Petrů, J., Čep, R., Zlámal, T., and Stančeková, D., Cutting Tool Wear Monitoring with the Use of Impedance Layers, Vol. 21, No. 3, 2014, pp.639-644.

[7]    Su, J. C., Huang, C. K., and Tarng, Y. S., An Automated Flank Wear Measurement of Microdrills Using Machine Vision, Journal of Materials Processing Technology, Vol. 180, No. 1, 2006, pp. 328-335.

[8]    Ghosh, N, Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A. R., and Chattopadhyay, A. B., Estimation of Tool Wear During Cnc Milling Using Neural Network-Based Sensor Fusion, Mechanical Systems and Signal Processing, Vol. 21, No. 1, 2007, pp. 466-479.

[9]    Abu-Mahfouz, I., Drilling Wear Detection and Classification Using Vibration Signals and Artificial Neural Network, International Journal of Machine Tools and Manufacture, Vol. 43, No. 7, 2003, pp. 707-720.

[10] Bhaskaran, J., Murugan, M., Balashanmugam, N., and Chellamalai, M., Monitoring of Hard Turning Using Acoustic Emission Signal, Journal of Mechanical Science and Technology, Vol. 26, No. 2, 2012, pp. 609-615.

[11] D'Errico, G. E., An Adaptive System for Turning Process Control Based on Tool Temperature Feedback, Journal of Materials Processing Technology, Vol. 78, No. 1, 1998, pp. 43-47.

[12] Alonso, F. J., Salgado, D. R., Analysis of the Structure of Vibration Signals for Tool Wear Detection, Mechanical Systems and Signal Processing, Vol. 22, No. 3, 2008, pp. 735-748.

[13] Ebersbach, S., Peng, Z., Expert System Development for Vibration Analysis in Machine Condition Monitoring, Expert Systems with Applications, Vol. 34, No. 1, 2008, pp. 291-299.

[14] Chen, B., Chen, X., Li, B., He, Z., Cao, H., and Cai, G., Reliability Estimation for Cutting Tools Based on Logistic Regression Model Using Vibration Signals, Mechanical Systems and Signal Processing, Vol. 25, No. 7, 2011, pp. 2526-2537.

[15] Bhuiyan, M. S. H., Choudhury, I. A., and Dahari, M., Monitoring the Tool Wear, Surface Roughness and Chip Formation Occurrences Using Multiple Sensors in Turning, Journal of Manufacturing Systems, Vol. 33, No. 4, 2014, pp. 476-487.

[16] Tjepkema, D., Van Dijk, J., and Soemers, H. M. J. R., Sensor Fusion for Active Vibration Isolation in Precision Equipment, Journal of Sound and Vibration, Vol. 331, No. 4, 2012, pp. 735-749.

[17] Segreto, T., Simeone, A., and Teti, R., Multiple Sensor Monitoring in Nickel Alloy Turning for Tool Wear Assessment Via Sensor Fusion, Procedia CIRP, Vol. 12, 2013, pp. 85-90.

[18] Salgado, D. R., Cambero, I., Herrera Olivenza, J. M., García Sanz-Calcedo, J., Núñez López, P. J., and García Plaza, E., Tool Wear Estimation for Different Workpiece Materials Using the Same Monitoring System, Procedia Engineering, Vol. 63, 2013, pp. 608-615.

[19] Painuli, S., Elangovan, M., and Sugumaran, V., Tool Condition Monitoring Using K-Star Algorithm, Expert Systems with Applications, Vol. 41, No. 6, 2014, pp. 2638-2643.

[20] Aghdam, B. H., Vahdati, M., and Sadeghi, M. H., Vibration-Based Estimation of Tool Major Flank Wear in a Turning Process Using Arma Models, The International Journal of Advanced Manufacturing Technology, Vol. 76, No. 9-12, 2014, pp. 1631-1642.

[21] Wang, J., Xie, J., Zhao, R., Zhang, L., and Duan, L., Multisensory Fusion Based Virtual Tool Wear Sensing for Ubiquitous Manufacturing, Robotics and Computer-Integrated Manufacturing, Vol. 45, 2017, pp. 47-58.

[22] Silva, R. G., Reuben, R. L., Baker, K. J., and Wilcox, S. J., Tool Wear Monitoring of Turning Operations by Neural Network and Expert System Classification of a Feature Set Generated from Multiple Sensors, Mechanical Systems and Signal Processing, Vol. 12, No. 2, 1998, pp. 319-332.

[23] Siddhpura, A., Paurobally, R., A Review of Flank Wear Prediction Methods for Tool Condition Monitoring in a Turning Process, The International Journal of Advanced Manufacturing Technology, Vol. 65, No. 1-4, 2012, pp. 371-393.

[24] Liu, B., Ling, S. F., and Meng, Q., Machinery Diagnosis Based on Wavelet Packets, Journal of Vibration and Control, Vol. 3, No. 1, 1997, pp. 5-17.

[25] Wu, Y., Du, R., Feature Extraction and Assessment Using Wavelet Packets for Monitoring of Machining Processes, Mechanical Systems and Signal Processing, Vol. 10, No. 1, 1996, pp. 29-53.

[26] Xiaoli, L., Zhejun, Y., Tool Wear Monitoring with Wavelet Packet Transform—Fuzzy Clustering Method, Wear, Vol. 219, No. 2, 1998, pp. 145-154.

[27] Mehrabi, M. G., Kannatey-Asibu Jr, E., Hidden Markov Model-Based Tool Wear Monitoring in Turning, Journal of Manufacturing Science and Engineering, Vol. 124, No. 3, 2002, pp. 651-658.

[28] Scheffer, C, Kratz, H., Heyns, P. S., and Klocke, F., Development of a Tool Wear-Monitoring System for Hard Turning, International Journal of Machine Tools and Manufacture, Vol. 43, No. 10, 2003, pp. 973-985.

[29] Velayudham, A, Krishnamurthy, R., and Soundarapandian, T., Acoustic Emission Based Drill Condition Monitoring During Drilling of Glass/Phenolic Polymeric Composite Using Wavelet Packet Transform, Materials Science and Engineering: A, Vol. 412, No. 1, 2005, pp. 141-145.

[30] Zhu, K., San Wong, Y., and Soon Hong, G., Wavelet Analysis of Sensor Signals for Tool Condition Monitoring: A Review and Some New Results, International Journal of Machine Tools and Manufacture, Vol. 49, No. 7, 2009, pp. 537-553.

[31] Chen, H., Huang, S., Li, D., and Fu, P., Turning Tool Wear Monitoring Based on Fuzzy Cluster Analysis, In Advances in Neural Network Research and Applications, Springer, Berlin, Germany, 2010, pp. 739-745.

[32] Lee, S., Tool Condition Monitoring System in Turning Operation Utilizing Wavelet Signal Processing and Multi-Learning Anns Algorithm Methodology, Int J Eng Res Innov, Vol. 2, No. 1, 2010, pp. 49-55.

[33] Mikołajczyk, T., Nowicki, K., Kłodowski, A., and Yu Pimenov, D., Neural Network Approach for Automatic Image Analysis of Cutting Edge Wear, Mechanical Systems and Signal Processing, Vol. 88, 2017, pp. 100-110.

[34] Teshima, T., Shibasaka, T., Takuma, M., Yamamoto, A., and Iwata, K., Estimation of Cutting Tool Life by Processing Tool Image Data with Neural Network, CIRP Annals-Manufacturing Technology, Vol. 42, No. 1, 1993, pp. 59-62.

[35] Kaya, B., Oysu, C., and Ertunc, H. M., Force-Torque Based on-Line Tool Wear Estimation System for Cnc Milling of Inconel 718 Using Neural Networks, Advances in Engineering Software, Vol. 42, No. 3, 2011, pp. 76-84.

[36] Yaqub, M. F., Gondal, I., and Kamruzzaman, J., Multi-Step Support Vector Regression and Optimally Parameterized Wavelet Packet Transform for Machine Residual Life Prediction, Journal of Vibration and Control, Vol. 19, No. 7, 2013, pp. 963-974.

[37] Karam, S., Teti, R., Wavelet Transform Feature Extraction for Chip Form Recognition During Carbon Steel Turning, Procedia CIRP, Vol. 12, 2013, pp. 97-102.

[38] Jemielniak, K., Kossakowska, J., Tool Wear Monitoring Based on Wavelet Transform of Raw Acoustic Emission Signal, Advances in Manufacturing Science and Technology, Vol. 34, No. 3, 2010, pp. 5-16.

[39] Fang, N., Srinivasa Pai, P., and Mosquea, S., Effect of Tool Edge Wear on the Cutting Forces and Vibrations in High-Speed Finish Machining of Inconel 718: An Experimental Study and Wavelet Transform Analysis, The International Journal of Advanced Manufacturing Technology,Vol. 52, No. 1, 2011, pp. 65-77.

[40] Davim, J. P., and Figueira, L., Comparative Evaluation of Conventional and Wiper Ceramic Tools on Cutting Forces, Surface Roughness, and Tool Wear in Hard Turning Aisi D2 Steel, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 221, No. 4, 2007, pp. 625-633.