Abstract
Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.
Similar content being viewed by others
References
Merchant ME (1998) Interpretative look on 20th century research on modeling of machining. Mach Sci Technol 2:157–163. doi:10.1080/10940349808945666
Finnie I (1956) Review of the metal-cutting analysis of the past hundred years. Mech Eng 78:715–721
Dixit PM, Dixit US (2008) Modeling of metal forming and machining processes: by finite element and soft computing methods. Springer, London
Deb S, Dixit US (2008) Intelligent machining: computational methods and optimization. In: Davim JP (ed) Machining: fundamentals and recent advances. Springer, London
Zarei O, Fesanghary M, Farshi B, Jalili Saffar R, Razfar MR (2009) Optimization of multi-pass face-milling via harmony search algorithm. J Mater Process Technol 209:2386–2392. doi:10.1016/j.jmatprotec.2008.05.029
Wang ZG, Rahman M, Wong YS, Sun J (2005) Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing. Int J Mach Tools Manuf 45:1726–1734. doi:10.1016/j.ijmachtools.2005.03.009
Zang JY, Liang SY, Yao J, Chen JM, Hang JL (2006) Evolutionary optimization of machining processes. J Intell Manuf 17:203–215. doi:10.1007/s10845-005-6637-z
Voß S (2001) Meta heuristics: the state of art. Lect Notes Comput Sci 2148:1–23. doi:10.1007/3-540-45612-0_1
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. doi:10.1016/S0019-9958(65)90241-X
Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 3:28–44
Goldberg GE (1989) Genetic algorithms in search optimization and machine learning. Addison Wesley, Reading, MA
Deb K (1995) Optimization for engineering design: algorithms and examples. Prentice-Hall, New York
Dorigo M (1996) The ant system: optimization by a colony of co-operating agents. IEEE Trans Syst Man Cybern Part B 26:1–13
Socha K, Dorigo M (2008) Ant colony optimization for continuous domain. Eur J Oper Res 185:1155–1173. doi:10.1016/j.ejor.2006.06.046
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN’95), Perth, Australia
Rangwala SS, Dornfeld DA (1989) Learning and optimization of machining operations using computing abilities of neural networks. IEEE Trans Syst Man Cybern 19:299–314
Azouzi R, Guillot M (1997) On-line prediction of surface finish and dimensional deviation in turning using neural network base sensor fusion. Int J Mach Tools Manuf 37:1201–1217. doi:10.1016/S0890-6955(97)00013-8
Chryssolouris G, Guillot M (1990) A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining. ASME J Eng Ind 112:112–131
Feng C-X, Wang X-F (2003) Surface roughness predictive modelling: neural networks versus regression. IIE Trans 35:11–27. doi:10.1080/07408170304433
Risbood KA, Dixit US, Sahasrabudhe AD (2003) Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132:203–214. doi:10.1016/S0924-0136(02)00920-2
Pal SK, Chakraborty D (2005) Surface roughness prediction in turning using artificial neural network. Neural Comput Appl 14:319–324. doi:10.1007/s00521-005-0468-x
Ozel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45:467–479. doi:10.1016/j.ijmachtools.2004.09.007
Kohli A, Dixit US (2005) A neural-network based methodology for the prediction of surface roughness in a turning process. Int J Adv Manuf Technol 25:118–129. doi:10.1007/s00170-003-1810-z
Ishbuchi H, Tanaka H (1991) Regression analysis with interval model by neural networks. Proc. IEEE Int Joint Conf. on Neural Networks Singapore, pp 1594−1599
Sonar DK, Dixit US, Ojha DK (2006) The application of radial basis function neural network for predicting the surface roughness in a turning process. Int J Adv Manuf Technol 27:661–666. doi:10.1007/s00170-004-2258-5
Basak S, Dixit US, Davim JP (2007) Application of radial basis function neural networks in optimization of hard turning of AISI D2 cold-worked steel with a ceramic tool. Proc Inst Mech Eng B J Eng Manuf 221:987–998
Sarma DK, Dixit US (2007) A comparison of dry and air-cooled turning of grey cast iron with mixed oxide ceramic tool. J Mater Process Technol 190:160–172. doi:10.1016/j.jmatprotec.2007.02.049
Fang X, Jawahir IS (1994) Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters. Int J Prod Res 32:833–849. doi:10.1080/00207549408956974
Abburi NR, Dixit US (2006) A knowledge based system for the prediction of surface roughness in turning process. Robot Comput Integr Manuf 22:363–372. doi:10.1016/j.rcim.2005.08.002
Jiao Y, Pei ZS, Lei S, Lee ES, Fisher GR (2005) Fuzzy adaptive networks in machining process modeling dimensional error prediction for turning operations. Int J Prod Res 43:2931–2948. doi:10.1080/00207540500031964
Nandi AK, Pratihar DK (2004) An expert system based on FBFN using a GA to predict surface finish in ultra-precision turning. J Mater Process Technol 155–156:1150–1156. doi:10.1016/j.jmatprotec.2004.04.408
Ezugwu EO, Arthur SJ, Hines EL (1995) Tool-wear prediction using artificial neural networks. J Mater Process Technol 49:255–264. doi:10.1016/0924-0136(94)01351-Z
Dutta RK, Paul S, Chattopadhyay AB (2000) Applicability of the modified back-propagation algorithm in tool condition monitoring for faster convergence. J Mater Process Technol 98:299–309. doi:10.1016/S0924-0136(99)00295-2
Tosun N, Ozler L (2002) A study of tool life in hot machining using artificial neural networks and regression analysis method. J Mater Process Technol 124:99–104. doi:10.1016/S0924-0136(02)00086-9
Ojha DK, Dixit US (2005) An economic and reliable tool life estimation procedure for turning. Int J Adv Manuf Technol 26:726–732. doi:10.1007/s00170-003-2049-4
Quiza R, Figueira L, Davim JP (2008) Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel. Int J Adv Manuf Technol 37:641–648. doi:10.1007/s00170-007-0999-7
Natarajan U, Saravanan R, Periasamy VM (2006) Application of particle swarm optimization in artificial neural network for prediction of tool life. Int J Adv Manuf Technol 28:1084–1088. doi:10.1007/s00170-004-2460-5
Sick B (2002) On-line and indirect tool wear monitoring in turning with artificial neural net works: a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546. doi:10.1006/mssp.2001.1460
Das S, Roy R, Chattopadhyay AB (1996) Evaluation of wear of turning carbide inserts using neural networks. Int J Mach Tools Manuf 36:1639–1645. doi:10.1016/0890-6955(95)00089-5
Silva RG, Reuben RL, Baker KJ, Wilcox SJ (1998) Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors. Mech Syst Signal Process 12(2):319–332. doi:10.1006/mssp.1997.0123
Nadgir A, Ozel T (2000) Neural network modeling of flank wear for tool condition monitoring in orthogonal cutting of hardened steel. 4th Int. Conf on Eng. Design and Automation. Florida, USA, pp 1−6
Chungchoo C, Saini D (2002) On-line tool wear estimation in CNC turning operations using fuzzy neural network model. Int J Mach Tools Manuf 42:29–40. doi:10.1016/S0890-6955(01)00096-7
Khanchustambham RG, Zhang GM (1992) A neural network approach to on-line monitoring of a turning process. IEEE Trans 2:889–894
Lee BY, Tarang YS, Ma SC (1995) Modeling of the process damping force in chatter vibration. Int J Mach Tools Manuf 35:951–962. doi:10.1016/0890-6955(94)00046-M
Luong LHS, Spedding TA (1995) A neural network system for predicting machining behaviour. J Mater Process Technol 52:585–591. doi:10.1016/0924-0136(94)01626-C
Szecsi T (1999) Cutting force modeling using artificial neural networks. J Mater Process Technol 92:344–349. doi:10.1016/S0924-0136(99)00183-1
Lin JT, Bhattacharya D, Keeman V (2003) Multiple regression and neural networks analyses in composite machining. Compos Sci Technol 63:539–548. doi:10.1016/S0266-3538(02)00232-4
Ezugwu EO, Fadare DA, Bonney J, Da Silva RB, Sales WF (2005) Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int J Mach Tools Manuf 45:1375–1385. doi:10.1016/j.ijmachtools.2005.02.004
Hao W, Zhu X, Li X, Turagyenda G (2006) Prediction of cutting force for self-propelled cutting tool by artificial neural networks. J Mater Process Technol 180:23–29. doi:10.1016/j.jmatprotec.2006.04.123
Lin WS, Lee BY, Wu CL (2001) Modeling the surface roughness and cutting force for turning. J Mater Process Technol 108:286–293. doi:10.1016/S0924-0136(00)00835-9
Li X, Venuvinod PK, Chen MK (2000) Feed cutting force estimation from the current measurement with hybrid learning. Int J Adv Manuf Technol 16:859–862. doi:10.1007/s001700070002
Karpat Y, Ozel T (2005) Hard turning optimization using neural network modeling & swarm intelligence. Trans NAMRI/SME 33:179–186
Wang J (1993) A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information. Comput Ind Eng 25:389–392. doi:10.1016/0360-8352(93)90303-F
Lee YH, Yang BH, Moon KS (1999) An economic machining process model using fuzzy non-linear programming and neural network. Int J Prod Res 37:835–847. doi:10.1080/002075499191553
Hashmi K, El Baradie MA, Ryan M (1999) Fuzzy-logic based intelligent selection of machining parameters. J Mater Process Technol 94:94–111. doi:10.1016/S0924-0136(99)00086-2
Abburi NR, Dixit US (2007) Multi-objective optimization of multi-pass turning processes. Int J Adv Manuf Technol 32:902–910. doi:10.1007/s00170-006-0425-6
Kim SS, Kim IH, Mani V, Kim HJ (2008) Real-coded genetic algorithm for machining condition optimization. Int J Adv Manuf Technol 38:884–895. doi:10.1007/s00170-007-1144-3
Chen M-C, Tasi D-M (1996) A simulated annealing approach for optimization of multi-pass turning operation. Int J Prod Res 34:2803–2825. doi:10.1080/00207549608905060
Baykasoglu A, Dereli T (2002) Novel algorithm approach to generate the ‘number of passes’ and ‘ depth of cuts’ for the optimization routines of multi pass machining. Int J Prod Res 40:1549–1565. doi:10.1080/00207540210147043
Onwubolu GC, Kumalo T (2001) Optimization of multi pass turning operations with genetic algorithm. Int J Prod Res 39:3727–3745. doi:10.1080/00207540010005736
Chen MC, Chen KY (2003) Optimization of multi-pass turning operations with genetic algorithms: a note. Int J Prod Res 41:3385–3388. doi:10.1080/0020754031000118143
Wang X, Jawahir IS (2005) Optimization of multi-pass turning operations using genetic algorithms for the selection of cutting conditions and cutting tools with tool-wear effect. Int J Prod Res 43:3543–3559. doi:10.1080/13629390500124465
Srinivas J, Giri R, Yang SH (2009) Optimization of multi-pass turning using particle swarm intelligence. Int J Adv Manuf Technol 40:56–66. doi:10.1007/s00170-007-1320-5
Vijayakumar K, Prabhaharan G, Asokan P, Saravanan R (2003) Optimization of multi-pass turning operations using ant colony system. Int J Mach Tools Manuf 43:1633–1639. doi:10.1016/S0890-6955(03)00081-6
Wang YC (2007) A note on ‘Optimization of multi-pass turning operations using ant colony system’. Int J Mach Tools Manuf 47:2057–2059. doi:10.1016/j.ijmachtools.2007.03.001
Ojha DK, Dixit US, Davim JP (2009) A soft computing based optimization of multi-pass turning processes. Int J Mater Prod Technol 35:145–166
Yeo SH (1995) A multi pass optimization strategy for CNC lathe operations. Int J Prod Econ 40:209–218. doi:10.1016/0925-5273(95)00052-1
Lo SP (2003) An adaptive-network based fuzzy inference system for prediction of work piece surface roughness in end milling. J Mater Process Technol 142:665–675. doi:10.1016/S0924-0136(03)00687-3
Ho WH, Tasi JT, Lin BT, Chou JH (2009) Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi- genetic learning algorithm. Expert Syst Appl 36:3216–3222. doi:10.1016/j.eswa.2008.01.051
Brezocnik M, Kovacic M, Ficko M (2004) Prediction of surface roughness with genetic programming. J Mater Process Technol 157–158:28–36. doi:10.1016/j.jmatprotec.2004.09.004
Koza JR (1992) Genetic programming. The MIT, Cambridge, MA
Reddy NSK, Rao PV (2005) Selection of optimum geometry and cutting conditions using surface roughness prediction model for end milling. Int J Adv Manuf Technol 26:1202–1210. doi:10.1007/s00170-004-2110-y
Oktem H, Erzurumlu T, Kutaran H (2005) Applications of response surface methodology in the optimization of cutting conditions for surface roughness. J Mater Process Technol 170:11–16. doi:10.1016/j.jmatprotec.2005.04.096
Reddy NSK, Rao PV (2006) Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms. Int J Adv Manuf Technol 28:463–473. doi:10.1007/s00170-004-2381-3
Prakasvudhisarn C, Kunnapapdeelert S, Yenradee P (2009) Optimal cutting condition determination for desired surface roughness in end milling. Int J Adv Manuf Technol 41:440–451. doi:10.1007/s00170-008-1491-8
Chen JC, Savage M (2001) A fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations. Int J Adv Manuf Technol 17:670–676. doi:10.1007/s001700170132
Iqbal A, He N, Li L, Dar NU (2007) A fuzzy expert system for optimizing parameters and predicting performance measures in hard-milling process. Expert Syst Appl 32:1020–1027. doi:10.1016/j.eswa.2006.02.003
Ghosh N, Ravi YB, Mukhopadyay S, Paul S, Mohanty AR, Chattopadyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21:466–479. doi:10.1016/j.ymssp.2005.10.010
Chen JC, Black JT (1997) A fuzzy-nets in process (FNIP) systems for tool-breakage monitoring in end-milling operations. Int J Mach Tools Manuf 37(6):783–800. doi:10.1016/S0890-6955(96)00023-5
Dutta RK, Paul S, Chattopadyay AB (2000) Fuzzy controlled back propagation neural network for tool condition monitoring in face milling. Int J Prod Res 38(13):2989–3010. doi:10.1080/00207540050117404
Susanto V, Chen JC (2003) Fuzzy logic based in-process tool wear monitoring system in face milling operations. Int J Adv Manuf Technol 3:186–192
Tansel IN, Bao WY, Reen NS, Kropas-Hughes CV (2005) Genetic tool monitor (GTM) for micro-end-milling operations. Int J Adv Manuf Technol 45:293–299
Dutta RK, Paul S, Chattopadyay AB (2006) The efficacy of back propagation neural network with delta bar delta learning in predicting the wear of carbide inserts in face milling. Int J Adv Manuf Technol 31:434–442. doi:10.1007/s00170-005-0230-7
Ching-kao C, Lu HS (2007) The optimal cutting-parameter selection of heavy cutting process in side milling for SUS304 stainless steel. Int J Adv Manuf Technol 34:440–447. doi:10.1007/s00170-006-0630-3
Tandon V, El-Mounayari H (2001) A novel artificial neural networks force model for end milling. Int J Adv Manuf Technol 18:693–700. doi:10.1007/s001700170011
Zuperl U, Cus F (2004) Tool cutting force modeling in ball end milling using multilevel perceptron. J Mater Process Technol 153(154):268–275. doi:10.1016/j.jmatprotec.2004.04.309
Radhakrishnan T, Nandan U (2005) Milling force prediction using regression and neural networks. J Intell Manuf 16:93–102. doi:10.1007/s10845-005-4826-4
Briceno JF, El-Mounayri H, Mukhopadhyay S (2002) Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process. Int J Mach Tools Manuf 42:663–674. doi:10.1016/S0890-6955(02)00008-1
Zuperl U, Cus F, Mursec B, Ploi T (2006) A generalized neural network model of ball end milling force system. Int J Mach Tools Manuf 175:98–108
Aykut S, Golcu M, Semiz S, Ergur HS (2007) Modelling of cutting forces as function of cutting parameters for face milling of satellite 6 using artificial neural network. J Mater Process Technol 190:199–203. doi:10.1016/j.jmatprotec.2007.02.045
Tandon V, El-Mounayri H, Kishawy H (2002) NC end milling optimization using evolutionary computation. J Mach Tools Manuf 42:595–605. doi:10.1016/S0890-6955(01)00151-1
Shunmugam MS, Bhaskara Reddy SV, Narendran TT (2000) Selection of optimal conditions in multi-pass face-milling using a genetic algorithm. Int J Mach Tools Manuf 40:401–414. doi:10.1016/S0890-6955(99)00063-2
Cus F, Milfelner M, Balic J (2006) An intelligent system for monitoring and optimization of ball-end milling process. J Mater Process Technol 175:90–97. doi:10.1016/j.jmatprotec.2005.04.041
Sreeram S, Senthilkumar A, Rahman M, Zaman MT (2006) Optimization of cutting parameters in micro end milling operations under dry cutting conditions using genetic algorithms. Int J Adv Manuf Technol 30:1030–1039. doi:10.1007/s00170-005-0148-0
Wang ZG, Wong YS, Rahman M (2004) Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. Int J Adv Manuf Technol 24:727–732. doi:10.1007/s00170-003-1789-5
Suh SH, Shin YS (1996) Neural network modeling for tool path planning of rough cut in complex pocket milling. J Manuf Syst 15:295–304. doi:10.1016/0278-6125(96)84192-6
Ali YM, Zhang LC (1999) Surface roughness prediction of ground components using a fuzzy logic approach. J Mater Process Technol 89–90:561–568. doi:10.1016/S0924-0136(99)00022-9
Nandhi AK, Pratihar DK (2004) Design of a genetic-fuzzy system to predict surface finish and power requirement in grinding. Fuzzy Sets Syst 148:487–504. doi:10.1016/j.fss.2003.10.001
Kim GH (2002) Evaluation of pre-estimation model to the in process surface roughness for grinding operations. Int J Korean Soc Precis Eng 3:24–30
Samhouri MS, Surgenor BW (2005) Surface roughness in grinding: On-line prediction with adaptive neuro-fuzzy inference system. Trans NAMRI/SME 33:57–64
Wang LX, Mandel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Trans Neural Netw 3:807–814. doi:10.1109/72.159070
Nandi AK, Banerjee MK (2005) FBF-NN-based modeling of cylindrical plunge grinding process using a GA. J Mater Process Technol 162–163:655–664. doi:10.1016/j.jmatprotec.2005.02.080
Deivanathan R, Vijayaraghavan L, Krishnamurthy R (1999) In-process monitoring of grinding burn in cylindrical grinding of steel. J Mater Process Technol 91:37–42. doi:10.1016/S0924-0136(98)00408-7
Wang Z, Willet P, Deaguiar PR, Webster J (2001) Neural network detection of grinding burn from acoustic emission. Int J Mach Tools Manuf 41:283–309. doi:10.1016/S0890-6955(00)00057-2
Ali YM, Zhang LC (2004) A fuzzy model for predicting burns in surface grinding of steel. J Mater Process Technol 44:563–571
Liu Q, Chen X, Gindy N (2005) Fuzzy pattern recognition of AE signals for grinding burn. Int J Mach Tools Manuf 45:811–818. doi:10.1016/j.ijmachtools.2004.11.002
Lezanski P (2001) An intelligent system for grinding wheel condition monitoring. J Mater Process Technol 109:258–263. doi:10.1016/S0924-0136(00)00808-6
Fuh KH, Wang SB (1997) Force modeling and forecasting in creep feed grinding using improved BP neural network. Int J Mach Tools Manuf 37:1167–1178. doi:10.1016/S0890-6955(96)00012-0
Kawak JS, Ha MK (2004) Neural network approach for diagnosis of grinding operation by acoustic emission and power signals. J Mater Process Technol 147:65–71. doi:10.1016/j.jmatprotec.2003.11.016
Liao TW, Chen LJ (1994) A neural network approach for grinding processes: modelling and optimization. Int J Mach Tools Manuf 34:919–937. doi:10.1016/0890-6955(94)90105-8
Govindasamy JJ, McLoone SF, Irwin GW, French JJ, Doyle RP (2005) Neural modeling, control and optimization of an industrial grinding process. Control Eng Pract 13:1243–1258. doi:10.1016/j.conengprac.2004.11.006
Brinksmeier E, Tonshoff HK, Czenkusch C, Heinzel C (1998) Modelling and optimization of grinding processes. J Intell Manuf 9:303–314. doi:10.1023/A:1008908724050
Lee CW, Shin YC (2000) Evolutionary modeling and optimization of grinding process. Int J Prod Res 38(12):2787–2813. doi:10.1080/002075400411484
Saravanan R, Sachithanandam M (2001) Genetic algorithm (GA) for multivariable surface grinding process optimisation using a multi-objective function model. Int J Adv Manuf Technol 17:330–338. doi:10.1007/s001700170167
Gopal AV, Rao PV (2003) Selection of optimum conditions for maximum material removal rate with surface finish and damage as constraints in SiC grinding. Int J Mach Tools Manuf 43:1327–1336. doi:10.1016/S0890-6955(03)00165-2
Baskar N, Saravanan R, Asokan P, Prabhaharan G (2004) ant colony algorithm approach for multi-objective optimization of surface grinding operations. Int J Adv Manuf Technol 23:311–317. doi:10.1007/s00170-002-1533-6
Mathews PG, Shunmugam MS (1999) Neural-network approach for predicting hole quality in reaming. Int J Mach Tools Manuf 39:723–730. doi:10.1016/S0890-6955(98)00061-3
Taso CC, Hochang H (2008) Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. J Mater Process Technol 203:342–348. doi:10.1016/j.jmatprotec.2006.04.126
Nandi AK, Davim JP (2009) A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules. Mechatronics 19:218–232. doi:10.1016/j.mechatronics.2008.08.004
Biglari FR, Fang XD (1995) Real-time fuzzy logic control for maximizing the tool life of small-diameter drills. Fuzzy Sets Syst 72:91–101. doi:10.1016/0165-0114(94)00261-5
Lin SC, Ting CJ (1999) Drill wear monitoring using neural networks. Int J Adv Manuf Technol 36:465–475
Liu HS, Lee BY, Tarang YS (2000) In-process prediction of corner wear in drilling operations. Int J Adv Manuf Technol 101:152–158
Abu-Mahfouz I (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43:707–720. doi:10.1016/S0890-6955(03)00023-3
Sanjay C, Neema ML, Chin CW (2005) Modeling of tool wear in drilling by statistical analysis and neural network. J Mater Process Technol 170:494–500. doi:10.1016/j.jmatprotec.2005.04.072
Panda SS, Singh AK, Chakraborty PSK (2006) Drill wear monitoring using back propagation neural network. J Mater Process Technol 172:283–290. doi:10.1016/j.jmatprotec.2005.10.021
Patra K, Pal SK, Bhattacharyya K (2007) Artificial neural network based on prediction of drill flank wear motor current signals. Appl Soft Comput 7:927–935. doi:10.1016/j.asoc.2006.06.001
Garg S, Pal SK, Chakraborty D (2007) Evaluation of the performance of back propagation and radial basis function neural networks in predicting the drill flank wear. Neural Comput Appl 16:407–417. doi:10.1007/s00521-006-0065-7
Choi YJ, Park MS, Chu CN (2008) Prediction of drill failure using features extraction in time and frequency domains of feed motor current. Int J Mach Tools Manuf 48:29–39. doi:10.1016/j.ijmachtools.2007.08.009
Khajavi AN, Komanduri R (1993) On multisensor approach in drill wear monitoring. Ann CIRP 42:71–74. doi:10.1016/S0007-8506(07)62394-4
Liu TI, Chen WY (1998) Intelligent detection of drill wear. Mech Syst Signal Process 12:863–873. doi:10.1006/mssp.1998.0165
Li X, Tso SK (1999) Drill wear monitoring based on current signals. Wear 231:172–178. doi:10.1016/S0043-1648(99)00130-1
Stone R, Krishnamurthy K (1996) A neural network thrust force controller to minimize delaminating during drilling of graphite–epoxy laminates. Int J Mach Tools Manuf 36:985–1003. doi:10.1016/0890-6955(96)00013-2
Chung BM, Tomizuka M (2001) Fuzzy logic modeling and control for drilling of composite laminates. 10th IEEE International Conference on Fuzzy Systems, Melbourne, pp 509−512
Karri V (1999) RBF neural network for thrust and torque predictions in drilling operations. 3rd International conference on computational intelligence and multimedia applications (ICCIMA’99), New Delhi, pp 55–59
Sheng Y, Tomizuka M (2006) Intelligent modeling of thrust force in drilling process. J Dyn Syst Meas Control 128(4):846–856. doi:10.1115/1.2361322
Lee BY, Liu HS, Tarang YS (1998) Modeling and optimization of drilling process. J Mater Process Technol 74:149–157. doi:10.1016/S0924-0136(97)00263-X
Hashmi K, Graham ID, Mills B (2000) Fuzzy logic based data selection for the drilling process. J Mater Process Technol 108:55–61. doi:10.1016/S0924-0136(00)00597-5
Ghaiebi H, Solimanpur M (2007) An ant algorithm for optimization of hole-making operations. Comput Ind Eng 52:308–319. doi:10.1016/j.cie.2007.01.001
Zhu GY, Zhang WB (2007) Drilling path optimization by particle swarm optimization algorithm with global convergence characteristics. Int J Prod Res 46:2299–2311. doi:10.1080/00207540601042480
Chryssolouris G, Lee M, Ramsey A (1996) Confidence interval prediction for neural network models. IEEE Trans Neural Netw 7:229–232. doi:10.1109/72.478409
Shao R, Martin RB, Zhang J, Morris AJ (1997) Confidence bounds for neural network representations. Comput Chem Eng 21:S1173–S1178
Geerdes WM, Alvardo MAT (2008) An application of physics-based and artificial neural network-based hybrid temperature prediction scheme in a hot strip mill. J Manuf Sci Eng 130:014501 (5 pages)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chandrasekaran, M., Muralidhar, M., Krishna, C.M. et al. Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46, 445–464 (2010). https://doi.org/10.1007/s00170-009-2104-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-009-2104-x