Drilling projects by tool condition monitoring system (TCMS)

• Autorzy: Garc´Ia-Sanz-Calcedo J. , Salgado D. R. , Gonzalez A. G.
• Języki publikacji: EN

Abstrakty

EN

In this paper, an online tool condition monitoring system (TCMS) for drilling is presented. The method is based on monitoring the spindle and feed motor currents. Root mean square (RMS) values of the spindle and feed motor currents, drill diameter, spindle speed and feed rate are the inputs to the network, and drill wear is the output. Drilling experiments were carried out over a wide range of cutting conditions to map the relationship between the input information and a tool wear. The performance and the architecture of the neural network have been validated with experiments, and a good agreement in an estimation of the tool condition was found. The results show that this method can be effectively used to verify and determine the tool wear.

Słowa kluczowe

EN

mechanical project; monitoring; drilling project; tool condition monitoring system; TCMS; root mean square

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Engineering Transactions
• Rocznik: 2016
• Tom: Vol. 64, iss. 4
• Strony: 555--561
• Opis fizyczny: Bibliogr. 8 poz., rys., tab., wykr.

Twórcy

autor

Garc´Ia-Sanz-Calcedo J.

• University of Extremadura Graphical Expression Department Avda. Elvas s/n (06070) Badajoz, Spain

autor

Salgado D. R.

• University of Extremadura Mechanical, Energetic and Materials Engineering Department Avda. Elvas s/n (06070) Badajoz, Spain

autor

Gonzalez A. G.

• University of Extremadura Mechanical, Energetic and Materials Engineering Department Avda. Elvas s/n (06070) Badajoz, Spain

Bibliografia

• 1. 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.
• 2. 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.
• 3. 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.
• 4. 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: 10.1016/j.jmatprotec.2005.04.072.
• 5. 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.
• 6. 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.
• 7. 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: 10.1016/S0924-0136(02)00111-5.
• 8. Salgado D.R., Cambero I., Garc´ıa-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.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).