Volume 1, Issue 2, June 2013, Page: 59-63
Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor
Soheil Mohtaram, Mechanical engineering, Islamic azad university science & Research branch, Yazd, Iran
Mohammad Amin Nikbakht, Mechatronic engineering, Islamic azad university of khomeinishahr, Isfahan, Iran
Received: May 23, 2013;       Published: Jun. 30, 2013
DOI: 10.11648/j.ijmea.20130102.15      View  2875      Downloads  139
Abstract
The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear predictor. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. A neural network is used in tool condition monitoring system (TCM) as a decision making system to discriminate different malfunction states from measured signal. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modeling. By developed tool condition monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit.
Keywords
Machining Process, Simulation, Wear Estimation, ANFIS
To cite this article
Soheil Mohtaram, Mohammad Amin Nikbakht, Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor, International Journal of Mechanical Engineering and Applications. Vol. 1, No. 2, 2013, pp. 59-63. doi: 10.11648/j.ijmea.20130102.15
Reference
[1]
MULC, T., UDILJAK, T., CUS, F., MILFELNER, M. (2004) Monitoring cutting- tool wear using signals from the control system, Journal of Mechanical Engineering, Vol.50, No.12, pp 568-579
[2]
KUO, R.J. (2003) Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network, Engineering Applications of Artificial Intelligence, Vol.3, pp 49-261
[3]
ACHICHE, S., BALAZINSKI, M., BARON, L., JEMIELNIAK, K. (2008) Tool wear monitoring using genetically-generated fuzzy knowledge bases, Engineering Applications of Artificial Intelligence, Vol.15, pp 303-314
[4]
KOPAC, J. (2002) Cutting forces and their influence on the economics of machining, Journal of Mechanical Engineering, Vol.48, No 3, pp 72-79.
[5]
IQBAL, A., HE, N., DAR, N.U., LI, L. (2009) Comparison of fuzzy expert system based strategies of offline and online estimation of flank wear in hard milling process, Expert Systems with Applications, Vol.33, pp 61-66
[6]
CHIEN, W.T., TSAI, C.S. (2005) The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel, Journal of Materials Processing Technology, Vol.140, pp 340-345
[7]
A.Chiba, T.Fukao, O.Ichikawa, M. Oshima, M. Takemoto & D.G.Dorrell, (2005), Magnetic bearing and bearing less drives Dreier, M. E., McKeown,W. L. and Scott, H. W. (1996) A fuzzy logic controller to drill small holes.In Chen, C. H. (ed.), Fuzzy Logic and Neural Network Handbook. New York: McGraw-Hill, pp. 22.1–22.8.
[8]
Aspin wall DK, Dewesa RC, Ng EG, Sage C, Soo SL (2007) the influence of cutter orientation and work piece angle on mach inability when high-speed milling Inconel 718 under finishing conditions. Int J Mach Tools Manuf 47:1839–1846.
[9]
Rech J, Kermouche G, Carcia-Rosales C, Khellouki A, Garcia-NavasV (2008) Characterization and modeling of the residual stresses induced by belt finishing on a AISI52100 hardened steel. J Mater Process Techno doi: 10.1016/j.jmatprotec.2007.12.133
[10]
El Mansori M, Sura E, Ghidossi P, Deblaise S, Dal Negro T, Khanfir H (2007) Toward physical description of form and finish performance in dry belt finishing process By a tribo-energetic approach. J Mater Process Technol 182:498–511.
[11]
Axinte DA, Kritmanorot M, Gindy NNZ (2005) Investigations on belt polishing of Heat-resistant titanium alloys. J Mater Process Technol 166:398–404.
Browse journals by subject