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Volumn 78, Issue , 2014, Pages 16-27

Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process

Author keywords

Genetic programming; Stepwise regression; Support vector regression; Surface property; Surface roughness prediction; Turning

Indexed keywords

COMPLEX NETWORKS; FORECASTING; FUZZY LOGIC; FUZZY NEURAL NETWORKS; GENES; GENETIC ALGORITHMS; LEAST SQUARES APPROXIMATIONS; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; SOFT COMPUTING; SURFACE PROPERTIES; SURFACE ROUGHNESS; TURNING; UNCERTAINTY ANALYSIS;

EID: 84907225803     PISSN: 09659978     EISSN: 18735339     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2014.08.005     Document Type: Article
Times cited : (41)

References (73)
  • 1
    • 84862283893 scopus 로고    scopus 로고
    • Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel
    • U. Çaydaş, and S. Ekici Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel J Intell Manuf 23 2012 639 650
    • (2012) J Intell Manuf , vol.23 , pp. 639-650
    • Çaydaş, U.1    Ekici, S.2
  • 2
    • 74249115778 scopus 로고    scopus 로고
    • Application of soft computing techniques in machining performance prediction and optimization: A literature review
    • M. Chandrasekaran, M. Muralidhar, C.M. Krishna, and U. Dixit Application of soft computing techniques in machining performance prediction and optimization: a literature review Inter J Adv Manuf Technol 46 2010 445 464
    • (2010) Inter J Adv Manuf Technol , vol.46 , pp. 445-464
    • Chandrasekaran, M.1    Muralidhar, M.2    Krishna, C.M.3    Dixit, U.4
  • 3
    • 33750317589 scopus 로고    scopus 로고
    • Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ann
    • E. Bagci, and B. IşIk investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ann Inter J Adv Manuf Technol 31 2006 10 17
    • (2006) Inter J Adv Manuf Technol , vol.31 , pp. 10-17
    • Bagci, E.1    Işik, B.2
  • 4
    • 37049001016 scopus 로고    scopus 로고
    • Modeling of surface roughness in precision machining of metal matrix composites using ann
    • A.C. Basheer, U.A. Dabade, S.S. Joshi, V. Bhanuprasad, and V. Gadre Modeling of surface roughness in precision machining of metal matrix composites using ann J Mater Process Technol 197 2008 439 444
    • (2008) J Mater Process Technol , vol.197 , pp. 439-444
    • Basheer, A.C.1    Dabade, U.A.2    Joshi, S.S.3    Bhanuprasad, V.4    Gadre, V.5
  • 5
    • 0038298770 scopus 로고    scopus 로고
    • Predicting surface roughness in machining: A review
    • P. Benardos, and G.-C. Vosniakos Predicting surface roughness in machining: a review Int J Mach Tools Manuf 43 2003 833 844
    • (2003) Int J Mach Tools Manuf , vol.43 , pp. 833-844
    • Benardos, P.1    Vosniakos, G.-C.2
  • 6
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • C.J. Burges A tutorial on support vector machines for pattern recognition Data Min Knowl Disc 2 1998 121 167
    • (1998) Data Min Knowl Disc , vol.2 , pp. 121-167
    • Burges, C.J.1
  • 7
    • 42949106000 scopus 로고    scopus 로고
    • A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method
    • U. Çaydaş, and A. HasçalIk A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method J Mater Process Technol 202 2008 574 582
    • (2008) J Mater Process Technol , vol.202 , pp. 574-582
    • Çaydaş, U.1    Hasçalik, A.2
  • 8
    • 44749086674 scopus 로고    scopus 로고
    • Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ann models
    • J.P. Davim, V. Gaitonde, and S. Karnik investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ann models J Mater Process Technol 205 2008 16 23
    • (2008) J Mater Process Technol , vol.205 , pp. 16-23
    • Davim, J.P.1    Gaitonde, V.2    Karnik, S.3
  • 9
    • 84883578713 scopus 로고    scopus 로고
    • Review of empirical modeling techniques for modeling of turning process
    • A. Garg, Y. Bhalerao, and K. Tai Review of empirical modeling techniques for modeling of turning process Int J Model Ident Control 20 2 2013 121 129
    • (2013) Int J Model Ident Control , vol.20 , Issue.2 , pp. 121-129
    • Garg, A.1    Bhalerao, Y.2    Tai, K.3
  • 10
    • 20444464409 scopus 로고    scopus 로고
    • Modelling the correlation between cutting and process parameters in high-speed machining of inconel 718 alloy using an artificial neural network
    • E. Ezugwu, D. Fadare, J. Bonney, R. Da Silva, and W. Sales 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 2005 1375 1385
    • (2005) Int J Mach Tools Manuf , vol.45 , pp. 1375-1385
    • Ezugwu, E.1    Fadare, D.2    Bonney, J.3    Da Silva, R.4    Sales, W.5
  • 11
    • 84860495884 scopus 로고    scopus 로고
    • An integrated setup/fixture planning approach for machining prismatic parts
    • X. Sun, X. Chu, D. Xue, Y. Su, and C. Tang An integrated setup/fixture planning approach for machining prismatic parts Int J Prod Res 50 2012 1009 1027
    • (2012) Int J Prod Res , vol.50 , pp. 1009-1027
    • Sun, X.1    Chu, X.2    Xue, D.3    Su, Y.4    Tang, C.5
  • 12
    • 1842484481 scopus 로고    scopus 로고
    • Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel
    • I. Korkut, M. Kasap, I. Ciftci, and U. Seker Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel Mater Des 25 2004 303 305
    • (2004) Mater des , vol.25 , pp. 303-305
    • Korkut, I.1    Kasap, M.2    Ciftci, I.3    Seker, U.4
  • 13
    • 34047149432 scopus 로고    scopus 로고
    • A multivariate hybrid approach applied to AISI 52100 hardened steel turning optimization
    • A.P. Paiva, J.R. Ferreira, and P.P. Balestrassi A multivariate hybrid approach applied to AISI 52100 hardened steel turning optimization J Mater Process Technol 189 2007 26 35
    • (2007) J Mater Process Technol , vol.189 , pp. 26-35
    • Paiva, A.P.1    Ferreira, J.R.2    Balestrassi, P.P.3
  • 14
    • 46249124721 scopus 로고    scopus 로고
    • Estimation of cutting forces and surface roughness for hard turning using neural networks
    • V.S. Sharma, S. Dhiman, R. Sehgal, and S. Sharma Estimation of cutting forces and surface roughness for hard turning using neural networks J Intell Manuf 19 2008 473 483
    • (2008) J Intell Manuf , vol.19 , pp. 473-483
    • Sharma, V.S.1    Dhiman, S.2    Sehgal, R.3    Sharma, S.4
  • 15
    • 79953705955 scopus 로고    scopus 로고
    • Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool
    • T. Rajasekaran, K. Palanikumar, and B. Vinayagam Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool Prod Eng Res Devel 5 2011 191 199
    • (2011) Prod Eng Res Devel , vol.5 , pp. 191-199
    • Rajasekaran, T.1    Palanikumar, K.2    Vinayagam, B.3
  • 17
    • 85009526594 scopus 로고    scopus 로고
    • A study of influence factors affecting to surface roughness in stainless steel turning. Computer engineering and technology, ICCET'09
    • Kaewkuekool S, Jirapattarasilp K, Pechkong K. A study of influence factors affecting to surface roughness in stainless steel turning. computer engineering and technology, ICCET'09. In: International Conference On, 2009. IEEE, vol. 2, 2009, p. 99-302.
    • (2009) International Conference On, 2009. IEEE , vol.2 , pp. 99-302
    • Kaewkuekool, S.1    Jirapattarasilp, K.2    Pechkong, K.3
  • 19
    • 29744469780 scopus 로고    scopus 로고
    • Modeling of residual stress profile in finish hard turning
    • J.Y. Zhang, S.Y. Liang, G. Zhang, and D. Yen Modeling of residual stress profile in finish hard turning Mater Manuf Processes 21 2006 39 45
    • (2006) Mater Manuf Processes , vol.21 , pp. 39-45
    • Zhang, J.Y.1    Liang, S.Y.2    Zhang, G.3    Yen, D.4
  • 20
    • 0033887646 scopus 로고    scopus 로고
    • Applicability of the modified back-propagation algorithm in tool condition monitoring for faster convergence
    • R. Dutta, S. Paul, and A. Chattopadhyay Applicability of the modified back-propagation algorithm in tool condition monitoring for faster convergence J Mater Process Technol 98 2000 299 309
    • (2000) J Mater Process Technol , vol.98 , pp. 299-309
    • Dutta, R.1    Paul, S.2    Chattopadhyay, A.3
  • 21
    • 0037233403 scopus 로고    scopus 로고
    • Surface roughness predictive modeling: Neural networks versus regression
    • C.-X. Feng, and X.-F. Wang Surface roughness predictive modeling: neural networks versus regression IIE Trans 35 2003 11 27
    • (2003) IIE Trans , vol.35 , pp. 11-27
    • Feng, C.-X.1    Wang, X.-F.2
  • 22
    • 0037427589 scopus 로고    scopus 로고
    • Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process
    • K. Risbood, U. Dixit, and A. Sahasrabudhe Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process J Mater Process Technol 132 2003 203 214
    • (2003) J Mater Process Technol , vol.132 , pp. 203-214
    • Risbood, K.1    Dixit, U.2    Sahasrabudhe, A.3
  • 23
    • 1642335673 scopus 로고    scopus 로고
    • A study of tool life in hot machining using artificial neural networks and regression analysis method
    • N. Tosun, and L. Özler A study of tool life in hot machining using artificial neural networks and regression analysis method J Mater Process Technol 124 2002 99 104
    • (2002) J Mater Process Technol , vol.124 , pp. 99-104
    • Tosun, N.1    Özler, L.2
  • 24
    • 0029252416 scopus 로고
    • Tool-wear prediction using artificial neural networks
    • E. Ezugwu, S. Arthur, and E. Hines Tool-wear prediction using artificial neural networks J Mater Process Technol 49 1995 255 264
    • (1995) J Mater Process Technol , vol.49 , pp. 255-264
    • Ezugwu, E.1    Arthur, S.2    Hines, E.3
  • 25
    • 33846844788 scopus 로고    scopus 로고
    • Tool wear predictive model based on least squares support vector machines
    • D. Shi, and N.N. Gindy Tool wear predictive model based on least squares support vector machines Mech Syst Signal Process 21 2007 1799 1814
    • (2007) Mech Syst Signal Process , vol.21 , pp. 1799-1814
    • Shi, D.1    Gindy, N.N.2
  • 27
    • 0028407979 scopus 로고
    • Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters
    • X. Fang, and I. Jawahir Predicting total machining performance in finish turning using integrated fuzzy-set models of the machinability parameters Inter J Prod Res 32 1994 833 849
    • (1994) Inter J Prod Res , vol.32 , pp. 833-849
    • Fang, X.1    Jawahir, I.2
  • 28
    • 84862296569 scopus 로고    scopus 로고
    • Tool wear monitoring using neuro-fuzzy techniques: A comparative study in a turning process
    • A. Gajate, R. Haber, R. Del Toro, P. Vega, and A. Bustillo Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process J Intell Manuf 23 2012 869 882
    • (2012) J Intell Manuf , vol.23 , pp. 869-882
    • Gajate, A.1    Haber, R.2    Del Toro, R.3    Vega, P.4    Bustillo, A.5
  • 29
    • 82255175702 scopus 로고    scopus 로고
    • Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 Hss turning tool for estimation of flank wear
    • S.S. Gill, R. Singh, J. Singh, and H. Singh Adaptive neuro-fuzzy inference system modeling of cryogenically treated AISI M2 Hss turning tool for estimation of flank wear Expert Syst Appl 39 2012 4171 4180
    • (2012) Expert Syst Appl , vol.39 , pp. 4171-4180
    • Gill, S.S.1    Singh, R.2    Singh, J.3    Singh, H.4
  • 30
    • 10044288212 scopus 로고    scopus 로고
    • An expert system based on FBFN using a ga to predict surface finish in ultra-precision turning
    • A. Nandi, and D. Pratihar An expert system based on FBFN using a ga to predict surface finish in ultra-precision turning J Mater Process Technol 155 2004 1150 1156
    • (2004) J Mater Process Technol , vol.155 , pp. 1150-1156
    • Nandi, A.1    Pratihar, D.2
  • 31
    • 83655190741 scopus 로고    scopus 로고
    • Fuzzy parametric deduction for material removal rate optimization
    • T.-S. Lan Fuzzy parametric deduction for material removal rate optimization J Math Stat 7 2011 51 56
    • (2011) J Math Stat , vol.7 , pp. 51-56
    • Lan, T.-S.1
  • 32
    • 67349237442 scopus 로고    scopus 로고
    • An experimental analysis of effective high speed turning of superalloy inconel 718
    • D. Thakur, B. Ramamoorthy, and L. Vijayaraghavan An experimental analysis of effective high speed turning of superalloy inconel 718 J Mater Sci 44 2009 3296 3304
    • (2009) J Mater Sci , vol.44 , pp. 3296-3304
    • Thakur, D.1    Ramamoorthy, B.2    Vijayaraghavan, L.3
  • 33
    • 17844396496 scopus 로고    scopus 로고
    • Genetic equation for the cutting force in ball-end milling
    • M. Milfelner, J. Kopac, F. Cus, and U. Zuperl Genetic equation for the cutting force in ball-end milling J Mater Process Technol 164 2005 1554 1560
    • (2005) J Mater Process Technol , vol.164 , pp. 1554-1560
    • Milfelner, M.1    Kopac, J.2    Cus, F.3    Zuperl, U.4
  • 34
    • 16444384668 scopus 로고    scopus 로고
    • Generation of a model for cutting forces using artificial intelligence
    • M. Milfelner, U. Zuperl, and F. Cus Generation of a model for cutting forces using artificial intelligence Stroj Vestn 51 2005 41 54
    • (2005) Stroj Vestn , vol.51 , pp. 41-54
    • Milfelner, M.1    Zuperl, U.2    Cus, F.3
  • 35
    • 79956104093 scopus 로고    scopus 로고
    • An integrated evolutionary approach for modelling and optimization of wire electrical discharge machining
    • D. Kondayya, and A.G. Krishna An integrated evolutionary approach for modelling and optimization of wire electrical discharge machining Proc Inst Mech Eng, Part B: J Eng Manuf 225 2011 549 567
    • (2011) Proc Inst Mech Eng, Part B: J Eng Manuf , vol.225 , pp. 549-567
    • Kondayya, D.1    Krishna, A.G.2
  • 36
    • 10044242354 scopus 로고    scopus 로고
    • Evolutionary approach for cutting forces prediction in milling
    • M. Kovacic, J. Balic, and M. Brezocnik Evolutionary approach for cutting forces prediction in milling J Mater Process Technol 155 2004 1647 1652
    • (2004) J Mater Process Technol , vol.155 , pp. 1647-1652
    • Kovacic, M.1    Balic, J.2    Brezocnik, M.3
  • 37
    • 62549130699 scopus 로고    scopus 로고
    • Zigzag machining surface roughness modelling using evolutionary approach
    • C. Gölo Lu, and Y. Arslan Zigzag machining surface roughness modelling using evolutionary approach J Intell Manuf 20 2009 203 210
    • (2009) J Intell Manuf , vol.20 , pp. 203-210
    • Gölo Lu, C.1    Arslan, Y.2
  • 38
    • 79961031997 scopus 로고    scopus 로고
    • Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCS using genetic expression programming
    • M. Kök, E. Kanca, and Ö. Eyercio Lu Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCS using genetic expression programming Inter J Adv Manuf Technol 55 9-12 2011 1 14
    • (2011) Inter J Adv Manuf Technol , vol.55 , Issue.912 , pp. 1-14
    • Kök, M.1    Kanca, E.2    Eyercio Lu Ö.3
  • 39
    • 80052923685 scopus 로고    scopus 로고
    • Multi-stage genetic programming: A new strategy to nonlinear system modeling
    • A.H. Gandomi, and A.H. Alavi Multi-stage genetic programming: a new strategy to nonlinear system modeling Inf Sci 181 2011 5227 5239
    • (2011) Inf Sci , vol.181 , pp. 5227-5239
    • Gandomi, A.H.1    Alavi, A.H.2
  • 40
    • 77956952491 scopus 로고    scopus 로고
    • Genetic programming and orthogonal least squares: A hybrid approach to modeling the compressive strength of CFRP-confined concrete cylinders
    • A.H. Gandomi, A.H. Alavi, P. Arjmandi, A. Aghaeifar, and M. Seyednoor Genetic programming and orthogonal least squares: a hybrid approach to modeling the compressive strength of CFRP-confined concrete cylinders J Mech Mater Struct 5 2010 735 753
    • (2010) J Mech Mater Struct , vol.5 , pp. 735-753
    • Gandomi, A.H.1    Alavi, A.H.2    Arjmandi, P.3    Aghaeifar, A.4    Seyednoor, M.5
  • 41
    • 0013485092 scopus 로고    scopus 로고
    • PhD Thesis, UK: Dept. Chemical and Process Engineering, University of Newcastle
    • Hiden HG. Data-based modelling using genetic programming. PhD Thesis, UK: Dept. Chemical and Process Engineering, University of Newcastle; 1998.
    • (1998) Data-based Modelling Using Genetic Programming
    • Hiden, H.G.1
  • 45
    • 38949166031 scopus 로고    scopus 로고
    • Co-evolution of non-linear PLS model components
    • D. Searson, M. Willis, and G. Montague Co-evolution of non-linear PLS model components J Chemom 21 2007 592 603
    • (2007) J Chemom , vol.21 , pp. 592-603
    • Searson, D.1    Willis, M.2    Montague, G.3
  • 46
    • 79955683058 scopus 로고    scopus 로고
    • A robust data mining approach for formulation of geotechnical engineering systems
    • A.H. Alavi, and A.H. Gandomi A robust data mining approach for formulation of geotechnical engineering systems Eng Comput, Emerald 28 3 2011 242 274
    • (2011) Eng Comput, Emerald , vol.28 , Issue.3 , pp. 242-274
    • Alavi, A.H.1    Gandomi, A.H.2
  • 47
    • 84856002704 scopus 로고    scopus 로고
    • A new multi-gene genetic programming approach to nonlinear system modeling. Part II: Geotechnical and earthquake engineering problems
    • A.H. Gandomi, and A.H. Alavi A new multi-gene genetic programming approach to nonlinear system modeling. Part II: geotechnical and earthquake engineering problems Neural Comput Appl 21 1 2012 189 201
    • (2012) Neural Comput Appl , vol.21 , Issue.1 , pp. 189-201
    • Gandomi, A.H.1    Alavi, A.H.2
  • 48
    • 84892886876 scopus 로고    scopus 로고
    • Hybridizing genetic programming with orthogonal least squares for modeling of soil liquefaction
    • A.H. Gandomi, and A.H. Alavi Hybridizing genetic programming with orthogonal least squares for modeling of soil liquefaction Inter J Earthquake Eng Hazard Mitigation, Praise Worthy Prize 1 1 2013 1 8
    • (2013) Inter J Earthquake Eng Hazard Mitigation, Praise Worthy Prize , vol.1 , Issue.1 , pp. 1-8
    • Gandomi, A.H.1    Alavi, A.H.2
  • 49
    • 84896402736 scopus 로고    scopus 로고
    • A multi-gene genetic programming model for estimating stress-dependent soil water retention curves
    • A. Garg, A. Garg, and K. Tai A multi-gene genetic programming model for estimating stress-dependent soil water retention curves Comput Geosci 2014 1 12
    • (2014) Comput Geosci , pp. 1-12
    • Garg, A.1    Garg, A.2    Tai, K.3
  • 50
    • 84896393469 scopus 로고    scopus 로고
    • An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes
    • A. Garg An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes Eng Appl Artif Intell 2014
    • (2014) Eng Appl Artif Intell
    • Garg, A.1
  • 51
    • 85135237068 scopus 로고    scopus 로고
    • Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach
    • V. Vijayaraghavan Predicting the mechanical characteristics of hydrogen functionalized graphene sheets using artificial neural network approach J Nanostruct Chem 3 1 2013 83
    • (2013) J Nanostruct Chem , vol.3 , Issue.1 , pp. 83
    • Vijayaraghavan, V.1
  • 52
    • 84906315197 scopus 로고    scopus 로고
    • Estimation of mechanical properties of nanomaterials using artificial intelligence methods
    • V. Vijayaraghavan Estimation of mechanical properties of nanomaterials using artificial intelligence methods Appl Phys A 2013 1 9
    • (2013) Appl Phys A , pp. 1-9
    • Vijayaraghavan, V.1
  • 53
    • 84892927535 scopus 로고    scopus 로고
    • Measurement of properties of graphene sheets subjected to drilling operation using computer simulation
    • V. Vijayaraghavan Measurement of properties of graphene sheets subjected to drilling operation using computer simulation Measurement 2014
    • (2014) Measurement
    • Vijayaraghavan, V.1
  • 54
    • 84896983883 scopus 로고    scopus 로고
    • An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material
    • A. Garg An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material Simul Model Pract Theory 44 2014 1 13
    • (2014) Simul Model Pract Theory , vol.44 , pp. 1-13
    • Garg, A.1
  • 55
    • 84888298203 scopus 로고    scopus 로고
    • Performance evaluation of microbial fuel cell by artificial intelligence methods
    • A. Garg Performance evaluation of microbial fuel cell by artificial intelligence methods Expert Syst Appl 41 4 2014 1389 1399
    • (2014) Expert Syst Appl , vol.41 , Issue.4 , pp. 1389-1399
    • Garg, A.1
  • 56
    • 84897878463 scopus 로고    scopus 로고
    • State-of-the-art in empirical modelling of rapid prototyping processes
    • A. Garg, K. Tai, and M. Savalani State-of-the-art in empirical modelling of rapid prototyping processes Rapid Prototyping J 20 2 2014 164 178
    • (2014) Rapid Prototyping J , vol.20 , Issue.2 , pp. 164-178
    • Garg, A.1    Tai, K.2    Savalani, M.3
  • 57
    • 84892838008 scopus 로고    scopus 로고
    • Estimation of factor of safety of rooted slope using an evolutionary approach
    • A. Garg Estimation of factor of safety of rooted slope using an evolutionary approach Ecol Eng 64 2014 314 324
    • (2014) Ecol Eng , vol.64 , pp. 314-324
    • Garg, A.1
  • 58
    • 84901986976 scopus 로고    scopus 로고
    • A computational intelligence-based genetic programming approach for the simulation of soil water retention curves
    • A. Garg A computational intelligence-based genetic programming approach for the simulation of soil water retention curves Transp Porous Media 2014 1 17
    • (2014) Transp Porous Media , pp. 1-17
    • Garg, A.1
  • 59
    • 81455135143 scopus 로고    scopus 로고
    • Modeling and optimization of hard turning of X38crmov5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness
    • H. Aouici, M.A. Yallese, B. Fnides, K. Chaoui, and T. Mabrouki 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 2011 2843 2851
    • (2011) J Mech Sci Technol , vol.25 , pp. 2843-2851
    • Aouici, H.1    Yallese, M.A.2    Fnides, B.3    Chaoui, K.4    Mabrouki, T.5
  • 60
    • 84894887900 scopus 로고
    • Computer aided design of experiments
    • R.W. Kennard, and L.A. Stone Computer aided design of experiments Technometrics 11 1969 137 148
    • (1969) Technometrics , vol.11 , pp. 137-148
    • Kennard, R.W.1    Stone, L.A.2
  • 61
    • 74149090502 scopus 로고    scopus 로고
    • Data splitting for artificial neural networks using som-based stratified sampling
    • R. May, H.R. Maier, and G.C. Dandy Data splitting for artificial neural networks using som-based stratified sampling Neural Networks 23 2010 283 294
    • (2010) Neural Networks , vol.23 , pp. 283-294
    • May, R.1    Maier, H.R.2    Dandy, G.C.3
  • 62
    • 84887622153 scopus 로고    scopus 로고
    • A modified Kennard-Stone algorithm for optimal division of data for developing artificial neural network models
    • A. Saptoro, M.O. Tadé, and H. Vuthaluru A modified Kennard-Stone algorithm for optimal division of data for developing artificial neural network models Chem Prod Process Model 7 2012 1934 2659
    • (2012) Chem Prod Process Model , vol.7 , pp. 1934-2659
    • Saptoro, A.1    Tadé, M.O.2    Vuthaluru, H.3
  • 64
    • 84891492327 scopus 로고    scopus 로고
    • A hybrid genetic programming-artificial neural network approach for modeling of vibratory finishing process
    • A. Garg, and K. Tai A hybrid genetic programming-artificial neural network approach for modeling of vibratory finishing process Inter Proc Comput Sci Inform Technol 18 2011 14 19
    • (2011) Inter Proc Comput Sci Inform Technol , vol.18 , pp. 14-19
    • Garg, A.1    Tai, K.2
  • 65
    • 0345040882 scopus 로고    scopus 로고
    • Support vector machine applications in bioinformatics
    • E. Byvatov, and G. Schneider Support vector machine applications in bioinformatics Appl Bioinform 2 2003 67 77
    • (2003) Appl Bioinform , vol.2 , pp. 67-77
    • Byvatov, E.1    Schneider, G.2
  • 67
    • 74549139703 scopus 로고    scopus 로고
    • Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression
    • A.K. Gupta Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression Int J Prod Res 48 2010 763 778
    • (2010) Int J Prod Res , vol.48 , pp. 763-778
    • Gupta, A.K.1
  • 69
    • 34548361987 scopus 로고    scopus 로고
    • An approach based on current and sound signals for in-process tool wear monitoring
    • D.R. Salgado, and F.J. Alonso An approach based on current and sound signals for in-process tool wear monitoring Int J Mach Tools Manuf 47 2007 2140 2152
    • (2007) Int J Mach Tools Manuf , vol.47 , pp. 2140-2152
    • Salgado, D.R.1    Alonso, F.J.2
  • 70
    • 67650000504 scopus 로고    scopus 로고
    • In-process surface roughness prediction system using cutting vibrations in turning
    • D.R. Salgado, F.J. Alonso, I. Cambero, and A. Marcelo In-process surface roughness prediction system using cutting vibrations in turning Int J Adv Manuf Technol 43 2009 40 51
    • (2009) Int J Adv Manuf Technol , vol.43 , pp. 40-51
    • Salgado, D.R.1    Alonso, F.J.2    Cambero, I.3    Marcelo, A.4
  • 71
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of svm parameters and noise estimation for svm regression
    • V. Cherkassky, and Y. Ma Practical selection of svm parameters and noise estimation for svm regression Neural Networks 17 2004 113 126
    • (2004) Neural Networks , vol.17 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 72
    • 79953662840 scopus 로고    scopus 로고
    • A hybrid computational approach to derive new ground-motion prediction equations
    • A.H. Gandomi, A.H. Alavi, M. Mousavi, and S.M. Tabatabaei A hybrid computational approach to derive new ground-motion prediction equations Eng Appl Artif Intell 24 4 2011 717 732
    • (2011) Eng Appl Artif Intell , vol.24 , Issue.4 , pp. 717-732
    • Gandomi, A.H.1    Alavi, A.H.2    Mousavi, M.3    Tabatabaei, S.M.4
  • 73
    • 84888201161 scopus 로고    scopus 로고
    • An evolutionary approach for modeling of shear strength of RC deep beams
    • Springer [in press]. doi:10.1617/s11527-013-0039-z
    • Gandomi AH, Yun GJ, Alavi AH. An evolutionary approach for modeling of shear strength of RC deep beams. Materials and structures, Springer; 2013. [in press]. doi:10.1617/s11527-013-0039-z.
    • (2013) Materials and Structures
    • Gandomi, A.H.1    Yun, G.J.2    Alavi, A.H.3


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