10.24423/EngTrans.674.2016
Drilling Projects by Tool Condition Monitoring System (TCMS)
References
Subramanian K., Cook N.H., Sensing of drill wear and prediction of drill life, ASME Journal of Engineering Industry, 99(2): 295–301, 1977, doi: 10.1115/1.3439211.
Ertunc H.M., Loparo K.A., A decision fusion algorithm for tool wear condition monitoring in drilling, International Journal of Machine Tools and Manufacture, 41(9): 1347–1362, 2001, doi: 10.1016/S0890-6955(00)00111-5.
Jantunen E., A summary of methods applied to tool condition monitoring in drilling, International Journal of Machine Tools and Manufacture, 42(9): 997–1010, 2002, doi: 10.1016/S0890-6955(02)00040-8.
Sanjay C., Neema M.L., Chin C.W., Modeling of tool wear in drilling by statistical analysis and artificial neural network, Journal of Materials Processing Technology, 170(3) :494–500, 2005, doi: 0.1016/j.jmatprotec.2005.04.072.
Liu H.S., Lee B.Y., Tarng Y.S., In-process prediction of corner wear in drilling operations, Journal of Materials Processing Technology, 101(1–3): 152–158, 2000, doi: 10.1016/S0924-0136(00)00434-9.
Abu-Mahfouz I., Drilling wear detection and classification using vibration signals and artificial neural network, International Journal of Machine Tools Manufacture, 43(7): 707–720, 2003, doi: 10.1016/S0890-6955(03)00023-3.
Kim H.Y., Ahn J.H., Kim S.H., Takata S., Real-time drill wear estimation based on spindle motor power, Journal of Materials Processing Technology, 124(3): 267–273, 2002, doi: .1016/S0924-0136(02)00111-5.
Salgado D.R., Cambero I., García-Sanz-Calcedo J. et al., A tool wear monitoring system for steel and aluminum alloys based on the same sensor, Materials Science Forum, Advances in Materials Processing Technologies, 797: 17–22, 2014, doi: 10.4028/www.scientific.net/MSF.797.17.
DOI: 10.24423/EngTrans.674.2016