Tool wear prediction in machinning by using the adaptive neuro-fuzzy system

• Autorzy: Zuperl U. , Milfelner M. , Cus F.
• Konferencja: Third International Tribology Conference ITC 2004
• Języki publikacji: EN

Abstrakty

EN

The focus of this paper is to develop a reliable method to predict flank wear during end milling process. A neural-fuzzy scheme is applied to perform the prediction of flank wear from cutting force signals. In this contribution we also discussed the construction of a ANFIS system that seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the neural network. Machining experiments conducted using the proposed method indicate that using an appropriate force signals, the flank wear can be predicted within 4% of the actual wear for various end-milling conditions.

Słowa kluczowe

EN

estimation; flank wear; end milling; ANFIS

PL

wytrzymałość materiałów; oszacowanie; zużycie; frezowanie

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mechanics and Engineering
• Rocznik: 2004
• Tom: Vol. 9, no spec.
• Strony: 73--78
• Opis fizyczny: Bibliogr. 4 poz., rys., wykr.

Twórcy

autor

Zuperl U.

• Faculty of Mechanical Engineering, University of Maribor Smetanova 17, 2000 Maribor, SLOVENIA

autor

Milfelner M.

• Faculty of Mechanical Engineering, University of Maribor Smetanova 17, 2000 Maribor, SLOVENIA

autor

Cus F.

• Faculty of Mechanical Engineering, University of Maribor Smetanova 17, 2000 Maribor, SLOVENIA

Bibliografia

• [1] Chryssolouris G. and Domroese M. (1988): Sensor integration for tool wear estimating in machining. - Winter Annual Meeting of the ASME, vol.33, pp.l 15-123.
• [2] Cus F. and Balic J. (2000): Selection of cutting conditions and tool flow in flexible manufacturing system. -Int. J. for Manufacturing Science & Technology, vol.2, pp.101-106.
• [3] Emel E. (1992): Tool wear detection by neural network based acoustic emission sensing. - Control of Manufacturing Processes, ASME, pp.79-85.
• [4] Rahman M., Zhou Q. and Hong G.S. (1995): On-line cutting state recognition using a neural network. - Int. J. of Advanced Manufacturing Technology, No.2, pp.87-92.