Open Access

Comparision of Dominant Features Identification for Tool Wear in Hard Turning of Inconel 718 by Using Vibration Analysis


[1] Ezugwu, E. O. “Key improvements in the machining of difficult to cut aerospace superalloys”, International Journal of Machine Tools and Manufacture 45 (12–13), pp. 1353 – 1367, 2005. DOI: 10.1016/j.ijmachtools.2005.02.00310.1016/j.ijmachtools.2005.02.003Open DOISearch in Google Scholar

[2] Olovsjo, S., Nyborg, L. “Influence of micro structure on wear behavior of uncoated WC tools in turning of Alloy718 and Waspalloy”, Wear 282–283, pp. 12 – 21, 2012. DOI: 10.1016/j.wear.2012.01.00410.1016/j.wear.2012.01.004Open DOISearch in Google Scholar

[3] Xiaozhi, C., Beizhi, L. “AE method for tool condition monitoring based on wavelet analysis”, International Journal of Advanced Manufacturing Technology 33 (9), pp. 968 – 976, 2007. DOI: 10.1007/s00170-006-0523-510.1007/s00170-006-0523-5Open DOISearch in Google Scholar

[4] Scheffer, C., Kratz, H., Heyns, S., Klocke, F. “Develpoment of tool wear monitoring system for hard turning”, International Journal of Machine Tools and Manufacture 43 (10), pp. 973 – 985, 2003. DOI: 10.1016/S0890-6955(03)00110-X10.1016/S0890-6955(03)00110-XSearch in Google Scholar

[5] Kurada, S., Bradley, C. “A review of machine vision sensors for tool condition monitoring”, Computers in Industry 34 (1), pp. 55 – 72, 1997. DOI: 10.1016/S0166-3615(96)00075-910.1016/S0166-3615(96)00075-9Open DOISearch in Google Scholar

[6] Kakade, S., Vijayaraghavan, L., Krishnamurthy, R. “Monitoring of tool status using intelligent acoustic emission sensing and decision based neural network”, IEEE, pp. 25 – 29, 1995. DOI: 10.1109/IACC.1995.46587310.1109/IACC.1995.465873Open DOISearch in Google Scholar

[7] Lalta, P., Rahul, K. “Study on Breaking Load of Single Lap Joint Using Hybrid Joining Techniques for Alloy Steel AISI 4140 and Mild Steel: Taguchi and Neural Network Approach”, Strojnícky časopis – Journal of Mechanical Engineering 68 (1), pp. 51 – 60, 2018. DOI: 10.2478/scjme-2018-000510.2478/scjme-2018-0005Open DOISearch in Google Scholar

[8] Milesich, T., Danko, J., Bucha, J. “Neural Networks - A Way to Increase the Fuel Efficiency of Vehicles”, Strojnícky časopis – Journal of Mechanical Engineering 68 (1), pp. 81 – 88, 2018. DOI: 10.2478/scjme-2018-000810.2478/scjme-2018-0008Search in Google Scholar

[9] Siddhpura, M., Siddhpura, A., Bhave, S. “Vibration as a parameter for monitoring the health of precision machine tools”, In: Conference, International conference on frontiers in design and manufacturing engineering, Coimbatore (India). Macmillan, India, 2008.Search in Google Scholar

[10] Dan, L., Mathew, J. “Tool wear and failure monitoring techniques for turning—a review”, International Journal of Machine Tools and Manufacture 30 (4), pp. 579 – 598, 1990. DOI: 10.1016/0890-6955(90)90009-810.1016/0890-6955(90)90009-8Search in Google Scholar

[11] Rao, S.B. “Tool wear monitoring through the dynamics of stable turning”, J Eng Ind 108 (3), pp. 183 – 190, 1986. DOI: 10.1115/1.318706210.1115/1.3187062Open DOISearch in Google Scholar

[12] Weller, E. J., Schrier, H. M., Weichbrodt, B. “What sound can be expected from worn tool?”, J Eng Ind 91(3), pp. 525 – 534, 1969. DOI: 10.1115/1.359162110.1115/1.3591621Open DOISearch in Google Scholar

[13] Del Taglia, A., Portunato, S., Toni, P. “An approach to online measurement of tool wear by spectrum analysis”, In: Proc 17th international MTDR conference 7, pp. 141 – 148, 1976. DOI: 10.1007/978-1-349-81484-8_1710.1007/978-1-349-81484-8_17Open DOISearch in Google Scholar

[14] Pandit, S.M., Kashou, S. “A data dependent system strategy of on-line tool wear sensing”, J Eng Ind 104 (3), pp. 217 – 223, 1982. DOI: 10.1115/1.318582210.1115/1.3185822Open DOISearch in Google Scholar

[15] Pandit, S. M., Kashou, S. “Variation in friction coefficient with tool wear”, Wear 84 (1), pp. 65 – 79, 1983. DOI: 10.1016/0043-1648(83)90119-910.1016/0043-1648(83)90119-9Open DOISearch in Google Scholar

[16] Jiang, C. Y., Zhang, Y. Z., Xu, H. J. “In-Process Monitoring of Tool Wear Stage by the Frequency Band - Energy Method”, CIRP Annals 36 (1), pp. 45 – 48, 1987. DOI: 10.1016/S0007-8506(07)62550-510.1016/S0007-8506(07)62550-5Open DOISearch in Google Scholar

[17] Teti, R., Jemielniak, K., O’Donnell, G., Dornfeld, D. “Advanced monitoring of machining operations”, CIRP Annals 59 (2), pp. 717 – 739, 2010. DOI: 10.1016/j.cirp.2010.05.01010.1016/j.cirp.2010.05.010Open DOISearch in Google Scholar

[18] Lu, M. – C., Kannatey – Asibu, E. Jr. “Analysis of Sound Signal Generation Due to Flank Wear in Turning”, J.. Manuf. Sci. Eng. 124 (4), pp. 799 – 808, 2002. DOI: 10.1115/1.151117710.1115/1.1511177Open DOISearch in Google Scholar

[19] Alonso, F. J., Salgado, D. R. “Analysis of the structure of vibration signals for tool wear detection”, Mechanical Systems and Signal Processing 22 (3), pp. 735 – 748, 2008. DOI: 10.1016/j.ymssp.2007.09.01210.1016/j.ymssp.2007.09.012Open DOISearch in Google Scholar

[20] Rajesh, V. G., Narayanan Namboothiri, V. N. “Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis”, Soft Compute 14, pp. 913 – 919, 2010. DOI: 10.1007/s00500-009-0466-510.1007/s00500-009-0466-5Open DOISearch in Google Scholar

[21] Ding, F., He, Z. “Cutting tool wear monitoring for reliability analysis using proportional hazards model”, International Journal of Advanced Manufacturing Technology 57 (5 – 8), pp. 565–574, 2011. DOI: 10.1007/s00170-011-3316-410.1007/s00170-011-3316-4Open DOISearch in Google Scholar

Publication timeframe:
2 times per year
Journal Subjects:
Engineering, Mechanical Engineering, Fundamentals of Mechanical Engineering, Mechanics