Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach

International Journal of Advanced Manufacturing Technology

Volumn 73, Issue 1-4, 2014, Pages 375-388

  Garg, A ^a   Tai, K ^a   Savalani, M M ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Bead width prediction; Rapid prototyping modeling; Selective laser melting

Indexed keywords

3D PRINTERS; COMPRESSIVE STRENGTH; GENETIC ALGORITHMS; GENETIC PROGRAMMING; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; POWDER METALS; RAPID PROTOTYPING; SURFACE ROUGHNESS; TENSILE STRENGTH;

ARTIFICIAL NEURAL NETWORK MODELS; BEAD WIDTH; COMBINATION MECHANISMS; MULTI-GENE GENETIC PROGRAMMING; NON-LINEAR RELATIONSHIPS; OPTIMAL PARAMETER SETTINGS; RAPID PROTOTYPING MODEL; SELECTIVE LASER MELTING;

GENES;

ALGORITHMS; COMPRESSION STRENGTH; GENES; LASERS; MELTING; NEURAL NETWORKS; POWDER; PRINTERS; TENSILE STRENGTH;

EID: 84903308172     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-014-5820-9     Document Type: Article

References (49)

1. Krause FL, Chiesla M, Stiel C, Ulbrich A (1997) Enhanced rapid prototyping for faster product development processes. CIRP Annals-Manuf Technol 46(1):93-96
2. Wiedemann B, Jantzen HA (1999) Strategies and applications for rapid product and process development in Daimler-Benz AG. Comput Ind 39:11-25
3. Bernard A, Fischer A (2002) New trends in rapid product development. CIRPAnnals-Manuf Technol 51:635-652
4. Hopkinson N, Hague R, Dickens P (2006) Rapid manufacturing. Wiley, New York
5. Kruth JP, Froyen L, Van Vaerenbergh J, Mercelis P, Rombouts M, Lauwers B (2004) Selective laser melting of iron-based powder. J Mater Process Technol 149(1):616-622
6. Shiomi M, Osakada K, Nakamura K, Yamashita T, Abe F (2004) Residual stress within metallicmodelmade by selective laser melting process. CIRP Annals-Manuf Technol 53(1):195-198
7. Luo RC, Chang CL, Pan YL, Tzou JH (2005) Rapid tooling using laser powered direct metallic manufacturing process, Industrial Electronics Society. IECON 2005.31st Annual Conference of IEEE
8. Kumar S (2009) Manufacturing of WC-Co moulds using SLS machine. J Mater Process Technol 209:3840-3848
9. Shen Y, Mckown S, Tsopanos S, Sutcliffe CJ, Mines RAW, Cantwell WJ (2010) The mechanical properties of sandwich structures based on metal lattice architectures. J Sandw Struct Mater 12(2):159-180
10. Zhong W, Li F, Zhang Z, Song L, Li Z (2001) Short fiber reinforced composites for fused deposition modeling. Mater Sci Eng 301:125-130 (Pubitemid 32333825)
11. Masood SH, Song WQ (2004) Development of new metal/polymer materials for rapid tooling using fused deposition modelling. Mater Des 25:587-594 (Pubitemid 38763697)
12. Stampfl J, Liska R (2005) New materials for rapid prototyping applications. Macromol Chem Phys 206:1253-1256 (Pubitemid 40963802)
13. Badrossamay M, Childs THC (2007) Further studies in selective laser melting of stainless and tool steel powders. Int J Machine Tools and Manufacture 47(5):779-784 (Pubitemid 46162110)
14. Anitha R, Arunachalam S, Radhakrishnan P (2001) Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol 118:385-388 (Pubitemid 33148014)
15. Paul B, Voorakarnam V (2001) Effect of layer thickness and orientation angle on surface roughness in laminated object manufacturing. J Manuf Process 3:94-101
16. Pandey PM, Venkata Reddy N, Dhande SG (2003) Improvement of surface finish by staircase machining in fused deposition modeling. J Mater Process Technol 132:323-331 (Pubitemid 35433598)
17. Khan ZA, Lee BH, Abdullah J (2005) Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol 169(1):54-61 (Pubitemid 41406156)
18. Wang RJ, Wang L, Zhao L, Liu Z (2007) Influence of process parameters on part shrinkage in SLS. Int J Adv Manuf Technol 33: 498-504 (Pubitemid 46972370)
19. Ahn D, Kweon JH, Kwon S, Song J, Lee S (2009) Representation of surface roughness in fused deposition modeling. J Mater Proc Technol. pp. 5593-5600
20. Aijun L, Zhuohui Z, Daoming W, Jinyong Y (2010) Optimization method to fabrication orientation of parts in fused deposition modelling rapid prototyping. IEEE Int Conf Mech Autom Control eng. pp. 416-419
21. Byun HS, Lee KH (2006) Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making. Robot Comput-Integ Manuf 22:69-80 (Pubitemid 41497911)
22. Chang DY, Huang BH (2011) Studies on profile error and extruding aperture for the RP parts using the fused deposition modelling process. Int J Adv Manuf Technol 53:1027-1037
23. Li CL, Fu GY, Guo KB (2011) Study on forecast of forming temperature of ABS resign during fused deposition manufacturing by fuzzy comprehensive evaluation. Key Eng Mater 464:264-267
24. Sood A, Ohdar R, Mahapatra S, Sood A, Ohdar R, Mahapatra S (2010a) A hybrid ANN-BFOA approach for optimization of FDM process parameters. Swarm Evol Memet Comput 6466:396-403
25. SoodA,Ohdar R, Mahapatra SS (2010) Parametric appraisal of fused deposition modelling process using the grey Taguchi method. Proceedings of the Institution of Mechanical Engineers. J Eng Manuf 224:135-145
26. Sood AK, Equbal A, Toppo V, Ohdar R, Mahapatra SS (2011a) An investigation on sliding wear of FDM built parts. CIRP J Manuf Sci Technol 1:48-54
27. Hinchliffe M, Hiden H, Mckay B, Willis M, Tham M, Barton G (1996a) Modelling chemical process systems using a multi-gene genetic programming algorithm. 28-31
28. Gandomi AH, Alavi AH, Arjmandi P, Aghaeifar A, Seyednoor M (2010) Genetic programming and orthogonal least squares: a hybrid approach to modeling the compressive strength of cfrp-confined concrete cylinders. J Mech Mater Struct 5:735-753
29. Gandomi AH, Alavi AH (2011) Multi-stage genetic programming: a New strategy to nonlinear system modeling. Inf Sci 181:5227-5239
30. Chan KY, Kwong CK, Dillon TS, Tsim YC (2011) Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming. Appl Soft Comput 11:1648-1656
31. Gonçalves I, Silva S, Melo J, Carreiras JB (2012) Random sampling technique for overfitting control in genetic programming. In: Moraglio A, Silva S, Krawiec K, Machado P, Cotta C (eds) Genetic Programming. Springer, Berlin Heidelberg
32. 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 Series on Computational Intelligence and Data Mining (CIDM), Singapore, pp. 293-298
33. Garg A, Bhalerao Y, Tai K (2013) Review of Empirical modeling techniques for modeling of turning process. Int J Model Identif Control 20(2):121-129
34. 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 69:1137-1151
35. Garg A, Tai K, Savalani MM (2013) State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyp J 20(2): 164-178
36. Garg A, SriramS, Tai K (2013) Empirical analysis ofmodel 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, pp. 84-88
37. Garg A, Tai K, Lee CH, SavalaniMM(2013) A HybridM5-Genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDMprocess, J IntellManuf (in press, doi: 10.1007/s10845-013-0734-1)
38. Garg A, Garg A, Tai K (2014) A multi-gene genetic programming model for estimating stress-dependent soil water retention curves. Comput Geosci 18(1):45-56
39. Vijayaraghavan Vet al (2013) Estimation of mechanical properties of nanomaterials using artificial intelligence methods. Appl Phys A. p. 1-9. doi:10.1007/s00339-013-8192-3
40. Garg A, Vijayaraghavan V, Mahapatra SS, Tai K, Wong CH (2013) Performance evaluation ofmicrobial fuel cell by artificial intelligence methods. Expert Syst Applns 41(4):1389-1399
41. GargA, Tai K(2012) Reviewof genetic programming inmodeling of machining processes, in Proceedings of 2012 International Conference on Modelling, Identification and Control (ICMIC2012), Wuhan, China, IEEE. pp. 653-658
42. Searson DP, Leahy DE, Willis MJ (2010) GPTIPS: an open source genetic programming toolbox formultigene symbolic regression. Int Multiconference Eng Comput Sci 1:77-80
43. 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
44. 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
45. Yang CB, Deng CS, Chiang HL (2012) Combining the Taguchi method with artificial neural network to construct a prediction model of a CO 2 laser cutting experiment. Int J AdvManuf Technol 59(9-12):1103-1111
46. 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
47. 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
48. Zhao D, Zhu Q, Li N, Li S (2014) Synchronized control with neuroagents for leader-follower based multiple robotic manipulators. Neurocomputing 124(26):149-161
49. Dongya Zhao, Quanmin Zhu, Shaoyuan Li (2013) RBF neural networks based robot non-smooth adaptive control, Chinese Control Conference (CCC), 32nd Chinese, 583-587