Abstract
This paper presents a monitoring method for on-line detection and indication of the occurrence of a cutting tool failure during high-speed face milling. The method consists of processing of the vibration signal using a reconfigurable infinite impulse response (IIR) bandpass digital filter and statistical techniques. The healthy tool threshold and the filter passband are adjusted and configured based on the cutting parameters that were set up during the machining process. For this process, sets of filter coefficients are pre-calculated for a number of defined insert passing frequencies ranges. The method is verified on-line during machining tests that are carried out at different tool failure levels and using various cutting parameters. In all experimental tests, the method allows the tool condition to be detected and indicated correctly. The proposed method is therefore shown to be simple, fast, computationally efficient, and reliable for the detection and indication of the presence of several types of tool failures for various cutting parameters, and the use of this method does not require any modification of the machine tool structure.
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Sevilla-Camacho PY, Herrera-Ruiz G, Robles-Ocampo JB, Jáuregui-Correa JC (2011) Tool breakage detection in CNC high-speed milling based in feed-motor current signals. Int J Adv Manuf Technol 53:1141–1148. doi:10.1007/s00170-010-2907-9
Rivero A, Lopez de Lacalle LN, Penalva ML (2008) Tool wear detection in dry high-speed milling based upon the analysis of machine internal signals. Mechatronics 18:627–633. doi:10.1016/j.mechatronics.2008.06.008
Zhang S, Li JF, Liu FS, Jiang F (2008) Tool wear in high-speed milling of Ti-6Al-4V alloy. Key Eng Mater 375–376:435–439. doi:10.4028/www.scientific.net/KEM.375-376.435
Zhang S, Li JF, Sun J, Jiang F (2010) Tool wear and cutting forces variation in high-speed end-milling Ti-6 Al-4V alloy. Int J Adv Manuf Technol 46:69–78. doi:10.1007/s00170-009-2077-9
Haber RE, Jiménez JE, Peres CR, Alique JR (2004) An investigation of tool-wear monitoring in a high-speed machining process. Sensors Actuators 116:539–545. doi:10.1016/j.sna.2004.05.017
Zeng H, Thoe TB, Li X, Zhou J (2006) Multi-modal sensing for machine health monitoring in high speed machining. 4th IEEE Int Conf on Ind Informatics 1217–1222. doi: 10.1109/INDIN.2006.275812
Dimla DE Sr (2002) The correlation of vibration signal features to cutting tool wear in a metal turning operation. Int J Adv Manuf Technol 19:705–713. doi:10.1007/s001700200080
Orhan S, Er OA, Camuscu N, Aslan E (2007) Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness. NDT&E Int 40:121–126. doi:10.1016/j.ndteint.2006.09.006
Zhang JZ, Chen JC (2008) Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. Int J Adv Manuf Technol 39:118–128. doi:10.1007/s00170-007-1186-6
Sevilla PY, Jauregui JC, Herrera G, Robles JB (2013) Efficient method for detecting tool failures in high-speed machining process. Proc Inst Mech Eng B J Eng Manuf 227:473–482. doi:10.1177/0954405412473906
Siddiqui RA, Amer W, Ahsan Q, Grosvenor RI, Prickett PW (2007) Multi-band infinite impulse response filtering using microcontrollers for e-Monitoring applications. Microprocess Microsyst 31:370–380. doi:10.1016/j.micpro.2007.02.007
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Sevilla-Camacho, P.Y., Robles-Ocampo, J.B., Muñiz-Soria, J. et al. Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering. Int J Adv Manuf Technol 81, 1187–1194 (2015). https://doi.org/10.1007/s00170-015-7302-0
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DOI: https://doi.org/10.1007/s00170-015-7302-0