Classification-driven model selection approach of genetic programming in modelling of turning process

International Journal of Advanced Manufacturing Technology

Volumn 69, Issue 5-8, 2013, Pages 1137-1151

  Garg, A ^a   Rachmawati, L ^b   Tai, K ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b ROLLS ROYCE SINGAPORE PTE LTD   (Singapore)

Author keywords

Artificial neural network; Classification; Genetic programming; Model selection; Support vector machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; FORESTRY; FUZZY LOGIC; GENETIC ALGORITHMS; NEURAL NETWORKS; POPULATION STATISTICS; SUPPORT VECTOR MACHINES; SUPPORT VECTOR REGRESSION; TURNING; UNCERTAINTY ANALYSIS; WELL TESTING;

CLASSIFICATION AND REGRESSION TREE; CLASSIFICATION TECHNIQUE; EMPIRICAL MODELLING; MODEL SELECTION; PHYSICS-BASED MODELS; PROCESS COMPLEXITY; PROCESS PERFORMANCE; SUPPORT VECTOR REGRESSION (SVR);

GENETIC PROGRAMMING;

BURRING; CLASSIFICATION; FUZZY LOGIC; MACHINING; MATHEMATICAL MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS;

EID: 84887621431     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-013-5103-x     Document Type: Article

References (105)

1. Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inform Sci 210:81-88
2. Yildiz AR (2013) A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl Soft Comput 13(3):1561-1566
3. Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906-2912
4. Yildiz AR (2013) Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inform Sci 220:399-407
5. Yildiz AR (2013) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433-1439
6. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64(1-4):55-61
7. YIldIz AR (2009) A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing 25(2):261-270
8. YIldIz AR (2009) An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J Mat Processing Technol 209(6):2773-2780
9. Chandrasekaran M, Muralidhar M, Krishna CM, Dixit U (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46:445-464
10. Chu X, Tang C, Xue D, Su Y, Sun X (2012) Identification of machining features based on available resources of cutting tools. Int J Prod Res 50:4141-4157
11. Ic YT, Yurdakul M, Eraslan E (2012) Development of a component-based machining centre selection model using AHP. Int J Prod Res 50:6489-6498
12. Sun X, Chu X, Xue D, Su Y, Tang C (2012) An integrated setup/fixture planning approach for machining prismatic parts. Int J Prod Res 50:1009-1027
13. Garg A, Bhalerao Y, Tai K (in press) Review of empirical modeling techniques for modeling of turning process. Int J Model Ident Control
14. Rangwala SS, Dornfeld DA (1989) Learning and optimization of machining operations using computing abilities of neural networks. Systems, Man and Cybernetics, IEEE Transactions on 19:299-314
15. Azouzi R, Guillot M (1997) On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion. Int J Mach Tool Manuf 37:1201-1217
16. 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
17. Pal SK, Chakraborty D (2005) Surface roughness prediction in turning using artificial neural network. Neural Computing & Applications 14:319-324
18. Hadi Y, Ahmed SG (2006) Assessment of surface roughness model for turning process. Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing and Management 207:152-158
19. Yaldiz S, Unsacar F, Saglam H (2006) Comparison of experimental results obtained by designed dynamometer to fuzzy model for predicting cutting forces in turning. Mater Des 27:1139-1147
20. Kirby ED, Chen JC (2007) Development of a fuzzy-nets-based surface roughness prediction system in turning operations. Comput Ind Eng 53:30-42
21. Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tool Manuf 47:2140-2152
22. Davim JP, Gaitonde VN, Karnik SR (2008) Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models. J Mater Process Technol 205:16-23
23. Kaewkuekool S, Jirapattarasilp K, Pechkong K (2009). A study of influence factors affecting to surface roughness in stainless steel turning. Proceedings of International Conference on Computer Engineering and Technology (ICCET), pp 299-302
24. Nalbant M, Gokkaya H, Toktas I, Sur G (2009) The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks. Robotics and Computer-Integrated Manufacturing 25:211-223
25. Thakur DG, Ramamoorthy B, Vijayaraghavan L (2009) An experimental analysis of effective high speed turning of superalloy Inconel 718. J Mater Sci 44:3296-3304
26. Ozkan, I. A.; Saritas, I.; Yaldiz, S. (2010). A comparative study of ANN and FES for predicting of cutting forces and tool tip temperature in turning. Proceedings for 11th International Conference on Computer Systems and Technologies and workshop for Ph.D Students in Computing. pp. 177-185.
27. Rajasekaran T, Vinayagam BK, Palanikumar K, Prakash S (2010) Influence of machining parameters on surface roughness and material removal rate in machining carbon fiber reinforced polymer material. Frontiers in Automobile and, Mechanical Engineering (FAME), pp 75-80
28. Qian, Y.; Tian, J.; Liu, L.; Zhang, Y.; Chen, Y. (2010). A tool wear predictive model based on SVM. Control and Decision Conference (CCDC). pp. 1213-1217.
29. Aouici H, Yallese MA, Fnides B, Chaoui K, Mabrouki T (2011) Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness. J Mech Sci Technol 25:2843-2851
30. Chavoshi SZ (2011) Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite. Prod Eng 5:37-47
31. Lan TS (2011) Fuzzy parametric deduction for material removal rate optimization. J Math Stat 7:51-56
32. Rajasekaran T, Palanikumar K, Vinayagam BK (2011) Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Prod Eng 5:191-199
33. Zain AM, Haron H, Sharif S (2012) Integrated ANN-GA for estimating the minimum value for machining performance. Int J Prod Res 50:191-213
34. Judd, C. M.; Mcclelland, G. H.; Ryan, C. S. (2009). Data analysis: a model comparison approach. Routledge/Taylor & Francis, New York
35. Ezugwu EO, Arthur SJ, Hines EL (1995) Tool-wear prediction using artificial neural networks. J Mater Process Technol 49:255-264
36. 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
37. Tosun N, Özler 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
38. Feng C-X, Wang X-F (2003) Surface roughness predictive modeling: neural networks versus regression. IIE Trans 35:11-27
39. Risbood K, Dixit U, Sahasrabudhe A (2003) Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process. J Mater Process Technol 132:203-214
40. Jiao Y, Lei S, Pei Z, Lee E (2004) Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations. Int J Mach Tool Manuf 44:1643-1651
41. 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
42. Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tool Manuf 45:467-479
43. Hao W, Zhu X, Li X, Turyagyenda G (2006) Prediction of cutting force for self-propelled rotary tool using artificial neural networks. J Mater Process Technol 180:23-29
44. Al-Ahmari A (2007) Predictive machinability models for a selected hard material in turning operations. J Mater Process Technol 190:305-311
45. Basak S, Dixit U, Davim J (2007) Application of radial basis function neural networks in optimization of hard turning of AISI D2 cold-worked tool steel with a ceramic tool. Proc Inst Mechanical Eng Part B: J Eng Manufact 221:987-998
46. Erzurumulu T, Oktem H (2007) Comparison of response surface model with neural network in determining the surface quality of moulded parts. Mater Des 28:459-465
47. Sarma D, Dixit U (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
48. Fang X, Jawahir I (1994) Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters. Int J Prod Res 32:833-849
49. Nandi A, Pratihar D (2004) An expert system based on FBFN using a GA to predict surface finish in ultra-precision turning. J Mater Process Technol 155:1150-1156
50. Abburi N, Dixit U (2006) A knowledge-based system for the prediction of surface roughness in turning process. Robotics and Computer-Integrated Manufacturing 22:363-372
51. Gajate A, Haber R, Deltoro R, Vega P, Bustillo A (2010) Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process. J Intell Manuf 23:869-882
52. Massol O, Li X, Gouriveau R, Zhou JH, Gan OP (2010). An exTS based neuro-fuzzy algorithm for prognostics and tool condition monitoring. International Conference on Control Automation Robotics and Vision, pp 1329-1334
53. Gill SS, Singh R, Singh J, Singh H (2012) Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 HSS turning tool for estimation of flank wear. Expert Syst Appl 39:4171-4180
54. Kothamasu R, Huang SH, Verduin WH (2005) Comparison of computational intelligence and statistical methods in condition monitoring for hard turning. Int J Prod Res 43:597-610
55. Özel T, Karpat Y, Figueira L, Davim JP (2007) Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. J Mater Process Technol 189:192-198
56. Mata F, Beamud E, Hanafi I, Khamlichi A, Jabbouri A, Bezzazi M (2010) Multiple regression prediction model for cutting forces in turning carbon-reinforced PEEK CF30. Adv Mater Sci Eng 2010:1-7
57. Prakash U, Yogavardhanaswamy GN, Ajit Prasad SL, Ravindra HV, Rajan TDP (2011) Tool wear prediction by regression analysis in turning A356 with 10% SiC. International Proceedings of Recent Advances in Intelligent Computational Systems (RAICS), pp 682-687
58. Zhang JY, Liang SY, Zhang G, Yen D (2006) Modeling of residual stress profile in finish hard turning. Mater Manuf Process 21:39-45
59. Gupta AK (2008) Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. Int J Prod Res 48:763-778
60. Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol 43:40-51
61. Çaydas U, Ekici S (2010) Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. J Intell Manuf 23:639-650
62. Chryssolouris G, Lee M, Ramsey A (1996) Confidence interval prediction for neural network models. IEEE Transactions on Neural Networks 7:229-232
63. Shao R, Martin E, Zhang J, Morris A (1997) Confidence bounds for neural network representations. Comput Chem Eng 21:S1173-S1178
64. Hinchliffe M, Hiden H, Mckay B, Willis M, Tham M, Barton G (1996). Modelling chemical process systems using a multi-gene genetic programming algorithm. Late breaking paper, GP'96, Stanford, USA, pp 28-31
65. Kovacic M, Brezocnik M (2003) Genetic programming approach for surface quality prediction. Tehnicki Vjesnik 10:19-24
66. Kovacic M, Balic J, Brezocnik M (2004) Evolutionary approach for cutting forces prediction in milling. J Mater Process Technol 155:1647-1652
67. Baziar MH, Jafarian Y, Shahnazari H, Movahed V, Amin Tutunchian M (2011) Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: an evolutionary approach. Comput Geosci 37:1883-1893
68. Garg A, Tai K (2011) A hybrid genetic programming - artificial neural network approach for modeling of vibratory finishing process. Int Proc Comput Sci Inform Technol (IPCSIT) 18:14-19
69. Garg A, Tai K (2012). Comparison of regression analysis, artificial neural network and genetic programming in Handling the multicollinearity problem. 2012 Proceedings of International Conference on Modelling, Identification and Control (ICMIC), Wuhan, China, IEEE, pp 353-358
70. Garg A, Tai K (2012) Review of genetic programming in modeling of machining processes. 2012 Proceedings of International Conference on Modelling, Identification and Control (ICMIC), Wuhan, China, IEEE, pp 653-658
71. Garg A, Tai K (2013) Comparison of statistical and machine learning methods in modelling of data with multicollinearity. Int J Model Ident Control 18(4):295-312
72. Garg A, Tai K, Lee CH, Savalani MM. A Hybrid M5'-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process, Journal of Intelligent Manufacturing (in press). doi: 10.1007/s10845-013-0734-1.
73. Garg A, Savalani MM, Tai K (in press) State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyping Journal
74. Garg A, Tai K (2013) Selection of a robust experimental design for the effective modeling of the nonlinear systems using genetic programming. Proceedings of 2013 IEEE Symposium on Computational Intelligence and Data mining (CIDM), Singapore, 2013, pp 293-298
75. Garg, A.; Sriram, S.; Tai K. (2013). Empirical analysis of model selection criteria for genetic programming in modeling of time series system. Proceedings of 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), Singapore, 2013, pp 84-88
76. Kotanchek M, Smits G, Vladislavleva E (2008) Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models. Genetic Programming Theory and Practice V, pp 201-220
77. Kushchu I (2002) Genetic programming and evolutionary generalization. IEEE Trans Evol Comput 6:431-442
78. Rowland J (2003) Model selection methodology in supervised learning with evolutionary computation. Biosystems 72:187-196
79. Borges E, Alonso CL, Montana JL (2010) Model selection in genetic programming. Proceedings of 12th annual conference on Genetic and Evolutionary Computation ACM, pp 985-986
80. Neseli S, Yaldiz S, Turkes E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement: J Int Measure Confederation 44:580-587
81. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11:137-148
82. May R, Maier HR, Dandy GC (2010) Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Networks 23:283-294
83. Saptoro A, Tade MO, Vuthaluru H (2012) A modified Kennard-Stone algorithm for optimal division of data for developing artificial neural network models. Chem Prod Process Model 7:13
84. Koza JR (1996) On the programming of computers by means of natural selection. MIT Press, Cambridge
85. Chen ZH, Xu BG, Chi ZR (2012) Mathematical formulation of knitted fabric spirality using genetic programming. Text Res J 82:667-676
86. Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Int MultiConfer Eng Comput Sci 2010 1:77-80
87. Hiden HG (1998) Data-based modelling using genetic programming. PhD thesis, Department of Chemical and Process Engineering, University of Newcastle, UK
88. Hearst MA, Dumais S, Osman E, Platt J, Scholkopf B (1998) Support vector machines. Intelligent Systems and their Applications, IEEE 13:18-28
89. Byvatov E, Schneider G (2003) Support vector machine applications in bioinformatics. Appl Bioinforma 2:67-77
90. Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logicmodels. MIT Press, Cambridge
91. Vapnik V (1995) The nature of statistical learning. Springer, New York
92. Pelckmans K, Suykens JAK, Vangestel T, De Brabanter D, Lukas J, Hamers B et al (2002) LS-SVMlab: a matlab/c toolbox for least squares support vector machines. Tutorial. KULeuven-ESAT, Leuven
93. Patterson DW (1998) Artificial neural networks: theory and applications. Prentice-Hall, Englewood Cliffs
94. Bustillo A et al (2011) Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. Int J Adv Manuf Technol 57(5-8):521-532
95. Farahnakian M et al (2011) The selection of milling parameters by the PSO-based neural network modeling method. Int J Adv Manuf Technol 57(1-4):49-60
96. 2 laser cutting experiment. Int J Adv Manuf Technol 59(9-12):1103-1111
97. Zhang Y, Yang J, Jiang H (2012) Machine tool thermal error modeling and prediction by grey neural network. Int J Adv Manuf Technol 59(9-12):1065-1072
98. Zinati RF, Razfar MR (2012) Constrained optimum surface roughness prediction in turning of X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm. Int J Adv Manuf Technol 58(1-4):93-107
99. Gaitonde VN, Karnik SR, Figueira L, Davim JP (2011) Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling. Int J Adv Manuf Technol 52:101-114
100. Niaki STA, Akbari-Nasaji S (2011) A hybrid method of artificial neural networks and simulated annealing in monitoring auto-correlated multi-attribute processes. Int J Adv Manuf Technol 56:777-788
101. Pontes FJ, Ferreira JR, Silva MB, Paiva AP, Balestrassi PP (2010) Artificial neural networks for machining processes surface roughness modeling. Int J Adv Manuf Technol 49:879-902
102. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks 17:113-126
103. Sall J, Ng K, Hecht M, Tilley D, Potter R, Corcoran B (1999). SAS Institute, Cary
104. YIldIz AR (2013) Comparison of evolutionary-based optimization algorithms for structural design optimization. Eng Appl Artif Intel 26(1):327-333
105. YIldIz AR (2012) A new hybrid particle swarm optimization approach for structural design optimization in automotive industry. Proc Inst Mechanical Eng, Part D: J Automobile Eng 226(10):1340-1351