Commenced in January 2007
Paper Count: 30855
Dynamic Clustering Estimation of Tool Flank Wear in Turning Process using SVD Models of the Emitted Sound Signals
Abstract:Monitoring the tool flank wear without affecting the throughput is considered as the prudent method in production technology. The examination has to be done without affecting the machining process. In this paper we proposed a novel work that is used to determine tool flank wear by observing the sound signals emitted during the turning process. The work-piece material we used here is steel and aluminum and the cutting insert was carbide material. Two different cutting speeds were used in this work. The feed rate and the cutting depth were constant whereas the flank wear was a variable. The emitted sound signal of a fresh tool (0 mm flank wear) a slightly worn tool (0.2 -0.25 mm flank wear) and a severely worn tool (0.4mm and above flank wear) during turning process were recorded separately using a high sensitive microphone. Analysis using Singular Value Decomposition was done on these sound signals to extract the feature sound components. Observation of the results showed that an increase in tool flank wear correlates with an increase in the values of SVD features produced out of the sound signals for both the materials. Hence it can be concluded that wear monitoring of tool flank during turning process using SVD features with the Fuzzy C means classification on the emitted sound signal is a potential and relatively simple method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080957Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1515
 E. Dimla, and S. Dimla , "Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods", International Journal of Machine Tools & Manufacture, vol. 40, pp. 1073- 1098, 2000.
 B. Sick, "On-line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research", Mechanical Systems and Signal Processing, vol. 16, no. 4, pp. 487- 546, 2002.
 J. Kopa, and S. Sali, "Tool wear monitoring during the turning process", Journal of Materials Processing Technology, vol. 113, Issues 1-3, pp. 312-316, 15 June 2001.
 A. G. Rehorn, J. Jiang, and P. E. Orban, "State-of the art methods and results in tool conditioning monitoring: a review", International Journal of Advanced Manufacturing Technology , vol. 26, pp. 693- 710, 2005.
 A.Samraj, S. Sayeed, L.C. Kiong., and N. E. Mastorokis, "Eliminating Forgers Based on Intra Trial Variability in Online Signature Verification Using Handglove and Photometric Signals", Journal of Information Security, vol. 1, pp. 23-28, 2010.
 N. S. Kamel, S. Sayeed and G.A. Ellis, "Glove-based approach to online signature verification", IEEE Transactions on Pattern Analysis and Machine Intelligence, USA, vol. 30, no. 6, pp. 1109-1113, 2008.
 S. Sayees, A. Samraj, R. Besar., and L. C. Kiong, "Forgery Detection in Dynamic Signature Verification by Entailing Principal Component Analysis". Discrete Dynamics in Nature and Society, vol. 2007 , pp. 1- 8, 2007.
 SVD and signal processing: Algorithms, Applications and Architectures,(1989) F. Deprettere, ed., North Holland Publishing Co.
 E.Biglieri, and K. Yao, "Some properties of singular value decomposition and their applications to digital signal processing", Signal Processing, vol. 18, Issue 3, pp. 277-289, November 1989,.
 J. Bezdek, "Pattern Recognition with Fuzzy Objective Functions", Plenum, New York, 1981.
 Mohanad A., Mohammad M., Abdullah R., "Optimizing of Fuzzy CMeans Clustering Algorithm Using GA", World Academy of Science, Engineering and Technology, Vol. 39, 2008.
 Bainian Li, Kongsheng Zhang, and Jian Xu, "Similarity measures and weighed fuzzy c-mean clustering algorithm", International Journal of Electrical and Computer Engineering, vol. 6, no. 1, pp. 1-4. 2010.