Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach

International Journal of Advanced Manufacturing Technology

Volumn 73, Issue 1-4, 2014, Pages 113-126

  Garg, A ^a   Tai, K ^a   Vijayaraghavan, V ^a   Singru, Pravin M ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

Author keywords

Burr height; Burr height prediction; Drilling; Over fitting

Indexed keywords

DRILLING; GENES; GENETIC PROGRAMMING; NEURAL NETWORKS; REGRESSION ANALYSIS; SURFACE ROUGHNESS;

BAYESIAN INFORMATION CRITERION; BURR HEIGHT; COMPUTATIONAL INTELLIGENCE METHODS; MULTI-GENE GENETIC PROGRAMMING; NON-LINEAR RELATIONSHIPS; OVERFITTING; PREDICTION OF PERFORMANCE; SUPPORT VECTOR REGRESSION (SVR);

GENETIC ALGORITHMS;

EID: 84903279556     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-014-5817-4     Document Type: Article

References (65)

1. Chandrasekaran M et al (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46(5):445-464
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 (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci: Int J 210:81-88
4. Yildiz AR (2013) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13: 2906-2912
5. Yildiz AR (2013) Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inf Sci: Int J 220: 399-407
6. Yildiz AR (2013) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433-1439
7. 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
8. Yildiz AR (2009) An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J Mater Process Technol 209(6):2773-2780
9. Garg A, Bhalerao Y, Tai K (2013) Review of empirical modelling techniques for modelling of turning process. Int J Model, Identif Control 20(2):121-129
10. Garg A, Rachmawati L, Tai K (2013) Classification-driven model selection approach of genetic programming in modelling of turning process. Int J Adv Manuf Technol 69(5-8):1137-1151
11. Garg A, Tai K, Savalani MM (2014) State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyp J 20(2): 164-178
12. Kumar S et al (2012) Drilling of glass fiber/vinyl ester composites with fillers. Mater Manuf Processes 27(3):314-319
13. Ozler L, Dogru N (2013) An experimental investigation of hole geometry in friction drilling. Mater Manuf Processes 28(4): 470-475
14. Rajmohan T, Palanikumar K (2011) Experimental investigation and analysis of thrust force in drilling hybrid metal matrix composites by coated carbide drills. Mater Manuf Processes 26(8):961-968
15. Gandomi AH et al (2013) Metaheuristic algorithms in modeling and optimization. In: Gandomi AH et al (eds) Metaheuristic applications in structures and infrastructures. Elsevier, Oxford, pp 1-24
16. Li X, Tso S (1999) Drill wear monitoring based on current signals. Wear 231(2):172-178 (Pubitemid 29466701)
17. Lee B, Liu H, Tarng Y (1998) Modeling and optimization of drilling process. J Mater Process Technol 74(1):149-157 (Pubitemid 128413302)
18. Chung B-M, Tomizuka M (2001) Fuzzy logic modeling and control for drilling of composite laminates. In Fuzzy systems. The 10th IEEE International Conference on 2001. IEEE
19. Abu-Mahfouz I (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43(7):707-720
20. Garg S, Pal SK, Chakraborty D (2007) Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear. Neural Comput Applic 16(4):407-417
21. Patra K, Pal SK, Bhattacharyya K (2007) Artificial neural network based prediction of drill flank wear from motor current signals. Appl Soft Comput 7(3):929-935 (Pubitemid 46551928)
22. Karnik S, Gaitonde V (2008) Development of artificial neural network models to study the effect of process parameters on burr size in drilling. Int J Adv Manuf Technol 39(5):439-453
23. Nandi AK, Paulo Davim J (2009) A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules. Mechatron 19(2):p. 218-232
24. Lin S, Ting C (1996) Drill wear monitoring using neural networks. Int J Mach Tools Manuf 36(4):465-475 (Pubitemid 126374953)
25. Sanjay C, NeemaM, Chin C (2005)Modeling of tool wear in drilling by statistical analysis and artificial neural network. J Mater Process Technol 170(3):p. 494-500 (Pubitemid 41814554)
26. Panda S et al (2006) Drill wear monitoring using back propagation neural network. J Mater Process Technol 172(2):283-290 (Pubitemid 43227339)
27. Tsao C, Hocheng H (2008) Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. J Mater Process Technol 203(1):p. 342-348
28. Sheng Y, Tomizuka M (2006) Intelligent modeling of thrust force in drilling process. J Dyn Syst, Meas Control 128(4):846-855 (Pubitemid 46034729)
29. Serapiao AB et al (2006) Classification of petroleum well drilling operations using support vector machine (SVM). In: Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on. IEEE
30. MA J-C, LIU T-S (2010) Application of support vector machine regression in the forecast of oil-gas drilling cost. J Xi'an Shiyou Univ (Nat Sci Ed) 3:p. 029
31. Cevik A (2007) Genetic programming based formulation of rotation capacity of wide flange beams. J Constr Steel Res 63(7):884-893 (Pubitemid 46365935)
32. Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a new strategy to nonlinear system modeling. Inf Sci 181(23):5227-5239
33. Gandomi AH et al (2010) Genetic programming and orthogonal least squares: a hybrid approach to modeling the compressive strength of CFRP-confined concrete cylinders. J Mech Mater Struct 5(5):735-753
34. Garg A, Garg A, Tai K (2014) A multi-gene genetic programming model for estimating stress-dependent soil water retention curves. Comput Geosci 18:45-56
35. Garg A et al (2014) Estimation of factor of safety of rooted slope using an evolutionary approach. Ecol Eng 64:314-324
36. Garg A et al (2014) An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes. Eng Appl Artif Intell. doi:10.1016/j.engappai.2013. 12.011
37. Garg A, SriramS, Tai K (2013) Empirical analysis ofmodel selection criteria for genetic programming in modeling of time series system. In Computational Intelligence for Financial Engineering & Economics (CIFEr), 2013 I.E. Conference on. IEEE
38. Garg A, Tai K (2013) Selection of a robust experimental design for the effective modeling of nonlinear systems using genetic programming. In Computational Intelligence and Data Mining (CIDM), 2013 I.E. Symposium on. IEEE
39. Garg A et al (2013) A hybrid\ text {M} 5 \ prime-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process. J Intell Manuf 1-17, (in press: doi: 10.1007/s10845-013-0734-1
40. Garg A et al (2014) Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Syst Appl 41(4):1389-1399
41. Gandomi AH, Alavi AH (2012) A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems. Neural Comput Appl 21(1):171-187
42. Vijayaraghavan Vet al (2013) Estimation of mechanical properties of nanomaterials using artificial intelligence methods.Appl Phys A 1-9, (in press: doi: 10.1007/s00339-013-8192-3
43. Vijayaraghavan V et al (2014) Measurement of properties of graphene sheets subjected to drilling operation using computer simulation. Measurement 50:50-62
44. Kushchu I (2002) Genetic programming and evolutionary generalization. Evol Comput, IEEE Trans 6(5):431-442 (Pubitemid 35327562)
45. Rowland JJ (2003) Model selection methodology in supervised learning with evolutionary computation. Biosystems 72(1):187-196 (Pubitemid 37464798)
46. Borges CE, Alonso CL, Montaña JL (2010) Model selection in genetic programming. ACM
47. Chan KYet al (2011) Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming. Appl Soft Comput 11(2):1648-1656
48. Koza JR (1996) On the programming of computers by means of natural selection. Vol. 1. MIT press
49. GargA, Tai K(2012) Reviewof genetic programming inmodeling of machining processes. In Modelling, Identification & Control (ICMIC), 2012 Proceedings of International Conference on IEEE
50. Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox for multigene symbolic regression. Proc Int Multi 1:pp.77-80
51. Hinchliffe M et al (1996) Modelling chemical process systems using a multi-gene genetic programming algorithm. Late breaking paper, GP'96. Stanford, USA
52. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461-464
53. Hearst MA et al (1998) Support vector machines. Intel Syst Their Appl, IEEE 13(4):18-28
54. Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press
55. Byvatov E, Schneider G (2003) Support vector machine applications in bioinformatics. Appl Bioinforma 2(2):67
56. Gupta AK (2010) Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. Int J Prod Res 418(3):763-778
57. Vapnik VN (1998) Statistical learning theory
58. Pelckmans K et al (2002) LS-SVMlab: a matlab/c toolbox for least squares support vector machines. Tutorial. KULeuven-ESAT. Leuven, Belgium
59. Asadi P et al (2012) Predicting the grain size and hardness of AZ91/SiC nanocomposite by artificial neural networks. Int J Adv Manuf Technol 63(9-12):1095-1107
60. Di Angelo L, Di Stefano P (2011) A neural network-based build time estimator for layer manufactured objects. Int J AdvanManuf Technol 57(1-4):215-224
61. FarahnakianMet al (2011) The selection ofmilling parameters by the PSO-based neural network modeling method. Int J Adv Manuf Technol 57(1-4):49-60
62. Fathy A, Megahed AA (2012) Prediction of abrasive wear rate of in situ Cu-Al 2O 3 nanocomposite using artificial neural networks. Int J Adv Manuf Technol 62(9-12):953-963
63. Gaitonde VN et al (2011) Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling. Int J Adv Manuf Technol 52(1-4):101-114
64. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113-126 (Pubitemid 38019003)
65. Gandomi AH, Roke DA (2013) Intelligent formulation of structural engineering systems. In Seventh M.I.T. Conference on Computational Fluid and Solid Mechanics-Focus: Multiphysics \& Multiscale, Cambridge, MA 02142, USA