State-of-the-art in empirical modelling of rapid prototyping processes

Rapid Prototyping Journal

Volumn 20, Issue 2, 2014, Pages 164-178

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

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Artificial neural network; Empirical modelling; Fused deposition modeling; Rapid prototyping; Selective laser sintering; Stereolithograhpy

Indexed keywords

DESIGN/METHODOLOGY/APPROACH; EMPIRICAL MODELLING; FUSED DEPOSITION MODELING; FUSED DEPOSITION MODELLING; MATHEMATICAL EQUATIONS; PERFORMANCE CHARACTERISTICS; SELECTIVE LASER SINTERING; STEREOLITHOGRAHPY;

COMPRESSIVE STRENGTH; DEPOSITION; GENETIC PROGRAMMING; LASER HEATING; NEURAL NETWORKS; REGRESSION ANALYSIS;

RAPID PROTOTYPING;

EID: 84897878463     PISSN: 13552546     EISSN: None     Source Type: Journal    
DOI: 10.1108/RPJ-08-2012-0072     Document Type: Article

References (86)

1. Ahn, D., Kweon, J. H., Kwon, S., Song, J. and Lee, S. (2009), "Representation of surface roughness in fused deposition modeling", Journal of Materials Processing Technology, Vol. 209, pp. 5593-5600.
2. Aijun, L., Zhuohui, Z., Daoming, W. and Jinyong, Y. (2010), "Optimization method to fabrication orientation of parts in fused deposition modeling rapid prototyping", IEEE, January, pp. 416-419.
3. Ang, K. C., Leong, K. F., Chua, C. K. and Chandrasekaran, M. (2006), "Investigation of the mechanical properties and porosity relationships in fused deposition modellingfabricated porous structures", Rapid Prototyping Journal, Vol. 12, pp. 100-105.
4. Anitha, R., Arunachalam, S. and Radhakrishnan, P. (2001), "Critical parameters influencing the quality of prototypes in fused deposition modelling", Journal of Materials Processing Technology, Vol. 118, pp. 385-388.
5. Bacchewar, P., Singhal, S. and Pandey, P. (2007), "Statistical modelling and optimization of surface roughness in the selective laser sintering process", Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 221, p. 35.
6. Beal, V., Paggi, R., Salmoria, G. and Lago, A. (2009), "Statistical evaluation of laser energy density effect on mechanical properties of polyamide parts manufactured by selective laser sintering", Journal of Applied Polymer Science, Vol. 113, pp. 2910-2919.
7. Bernard, A. and Fischer, A. (2002), "New trends in rapid product development", CIRP Annals-Manufacturing Technology, Vol. 51, pp. 635-652.
8. Blum, A., Kalai, A. and Langford, J. (1999), "Beating the holdout: bounds for k-fold and progressive cross-validation", Proceedings of Twelfth Annual Conference on Computational Learning Theory, ACM, New York, NY, pp. 203-208.
9. Byun, H. S. and Lee, K. H. (2006), "Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making", Robotics & Computer-Integrated Manufacturing, Vol. 22, pp. 69-80.
10. Campanelli, S., Cardano, G., Giannoccaro, R., Ludovico, A. and Bohez, E. (2007), "Statistical analysis of the stereolithographic process to improve the accuracy", Computer-Aided Design, Vol. 39, pp. 80-86.
11. Casalino, G., De Filippis, L., Ludovico, A. and Tricarico, L. (2002), "An investigation of rapid prototyping of sand casting molds by selective laser sintering", Journal of Laser Applications, Vol. 14, p. 100.
12. Cervera, G. B. M. and Lombera, G. (1999), "Numerical prediction of temperature and density distributions in selective laser sintering processes", Rapid Prototyping Journal, Vol. 5, pp. 21-26.
13. Chang, D. Y and Huang, B. H. (2011), "Studies on profile error and extruding aperture for the RP parts using the fused deposition modeling process", The International Journal of Advanced Manufacturing Technology, Vol. 53, pp. 1027-1037.
14. Chatterjee, A., Kumar, S., Saha, P., Mishra, P. and Choudhury, A. R. (2003), "An experimental design approach to selective laser sintering of low carbon steel", Journal of Materials Processing Technology, Vol. 136, pp. 151-157.
15. Chatterjee, S. and Hadi, A. S. (2006), Regression Analysis by Example, Wiley, New York, NY.
16. Chen, Z. H., Xu, B. G., Chi, Z. R. and Feng, D. G. (2012), "Mathematical formulation of knitted fabric spirality using genetic programming", Textile Research Journal, Vol. 82, pp. 667-676.
17. Cheng, R., Wu, X. and Zheng, J. (2010), "Improving dimensional accuracy of SLS processed part using Taguchi method", Proceedings of SPIE, Vol. 7997, 799715, 5 pp..
18. Choi, J. W., Quintana, R. and Wicker, R. B. (2011), "Fabrication and characterization of embedded horizontal micro-channels using line-scan stereolithography", Rapid Prototyping Journal, Vol. 17, pp. 351-361.
19. Chryssolouris, G., Lee, M. and Ramsey, A. (1996), "Confidence interval prediction for neural network models", IEEE Transactions on Neural Networks, Vol. 7, pp. 229-232.
20. Cong-Zhong, C, Jun-Fang, P., Yu-Feng, W, Xing-Jian, Z. and Ting-Ting, X. (2009), "Density prediction of selective laser sintering parts based on support vector regression", Acta Physica Sinica, Vol. 58, pp. S1-S19.
21. Daszykowski, M., Walczak, B. and Massart, D. (2002), "Representative subset selection", Analytica Chimica Acta, Vol. 468, pp. 91-103.
22. Dixit, P. M. and Dixit, U. S. (2008), Modeling of Metal Forming and Machining Processes: By Finite Element and Soft Computing Methods, Springer, Berlin.
23. Duan, B., Cheung, WL. and Wang, M. (2011), "Optimized fabrication of Ca-P/PHBV nanocomposite scaffolds via selective laser sintering for bone tissue engineering", Biofabrication, Vol. 6, pp. 4495-4505.
24. Equbal, A., Sood, A. K. and Mahapatra, S. (2011), "Prediction of dimensional accuracy in fused deposition modelling: a fuzzy logic approach", International Journal of Productivity and Quality Management, Vol. 7, pp. 22-43.
25. Garg, A. and Tai, K. (2011), "A hybrid genetic programming artificial neural network approach for modeling of vibratory finishing process", Proceedings of Computer Science and Information Technology (IPCSIT), Vol. 18, pp. 14-19.
26. Garg, A. and Tai, K. (2012a), "Review of genetic programming in modeling of machining processes", Proceedings of 2012 International Conference on Modelling, Identification and Control (ICMIC 2012) Wuhan, China, 24-26 June, IEEE, New York, NY, pp. 653-658.
27. Garg, A. and Tai, K. (2012b), "Comparison of regression analysis, artificial neural network and genetic programming in Handling the multicollinearity problem", Proceedings of 2012 International Conference on Modelling, Identification and Control (ICMIC 2012) Wuhan, China, 24-26 June, IEEE, New York, NY, pp. 353-358.
28. Garg, A., Tai, K., Lee, C. H. and Savalani, M. M. (2013), "A hybrid M5'-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process", Journal of Intelligent Manufacturing (in press).
29. Hardro, P., Wang, J. H. and Stucker, B. (1999), "Determining the parameter settings and capability of a rapid prototyping process", International Journal of Industrial Engineering, Vol. 6, pp. 203-213.
30. Hearst, M. A., Dumais, S., Osman, E., Platt, J. and Scholkopf, B. (1998), "Support vector machines", IEEE Intelligent Systems and their Applications, Vol. 13, pp. 18-28.
31. Hopkinson, N., Hague, R. and Dickens, P. (2006), Rapid Manufacturing, Wiley Online Library, Hoboken, NJ.
32. Jain, P. K., Pandey, P. M. and Rao, P. (2008), "Experimental investigations for improving part strength in selective laser sintering", Virtual and Physical Prototyping, Vol. 3, pp. 177-188.
33. Jayanthi, S., Keefe, M. and Gargiulo, E. P. (1994), Studies in Stereolithography: Influence of Process Parameters on Curl Distortion in Photopolymer Models, University of Texas, Austin, TX, pp. 250-258.
34. Judd, CM., Mcclelland, G. H. and Ryan, C. S. (2009), Data Analysis: A Model Comparison Approach, Routledge, London.
35. Kechagieas, J. (2007a), "An experimental investigation of the surface roughness of parts produced by LOM process", Rapid Prototyping Journal, Vol. 13, pp. 17-22.
36. Kechagias, J. (2007b), "Investigation of LOM process quality using design of experiments approach", Rapid Prototyping Journal, Vol. 13, pp. 316-323.
37. Klir, G. J. and Yuan, B. (1995), Fuzzy Sets and Fuzzy Logic, Prentice-Hall, Upper Saddle River, NJ.
38. Kroh, M., Bonten, C. and Eyerer, P. (2011), "Effects of process parameters on additive assisted laser sintering of polyetheretherketone", Proceedings of the Annual Technical Conference of the Society of Plastic Engineers (ANTEC) Boston, MA, 1-5 Mai, pp. 1806-1811.
39. Kruth, J. P. and Kumar, S. (2005), "Statistical analysis of experimental parameters in selective laser sintering", Advanced Engineering Materials, Vol. 7, pp. 750-755.
40. Kumar, G. P. and Regalla, S. P. (2012), "Optimization of support material and build time in fused deposition modeling (FDM) ", Applied Mechanics and Materials, Vol. 110, pp. 2245-2251.
41. Laeng, J., Khan, Z. A. and Khu, S. (2006), "Optimizing flexible behaviour of bow prototype using Taguchi approach", Journal of Applied Sciences, Vol. 6, pp. 622-630.
42. Lee, B., Abdullah, J. and Khan, Z. (2005), "Optimization of rapid prototyping parameters for production of flexible ABS object", Journal of Materials Processing Technology, Vol. 169, pp. 54-61.
43. Lee, S., Park, W., Cho, H., Zhang, W. and Leu, M. (2001), "A neural network approach to the modelling and analysis of stereolithography processes", Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 215, pp. 1719-1733.
44. Lee, S. H. (1998), "Optimal selection of process parameters and in-process measurement of cross-sectional shapes of products in stereolithography process", MS thesis, Korea Advanced Institute of Science and Technology, Daejeon.
45. Lee, S. J. and Siau, K. (2001), "A review of data mining techniques", Industrial Management & Data Systems, Vol. 101, pp. 41-46.
46. Liao, H. T. and Shie, J. R. (2007), "Optimization on selective laser sintering of metallic powder via design of experiments method", Rapid Prototyping Journal, Vol. 13, pp. 156-162.
47. Liu, W., Liu, Q., Ruan, F., Liang, Z. and Qiu, H. (2007), "Springback prediction for sheet metal forming based on GA-ANN technology", Journal of Materials Processing Technology, Vol. 187, pp. 227-231.
48. Li, C. L., Fu, G. Y. and Guo, K. B. (2011), "Study on forecast of forming temperature of abs resin during fused deposition manufacturing by fuzzy comprehensive evaluation", Key Engineering Materials, Vol. 464, pp. 264-267.
49. Li, X., Dong, J. and Zhang, Y. (2009), "Modeling and applying of RBF neural network based on fuzzy clustering and pseudo-inverse method", International Conference on Information Engineering and Computer Science, pp. 1-4.
50. Mahesh, M., Fuh, J., Wong, Y. and Loh, H. (2005), "Benchmarking for decision making in rapid prototyping systems", CASE, IEEE, New York, NY, pp. 19-24.
51. Mansour, S. and Hague, R. (2003), "Impact of rapid manufacturing on design for manufacture for injection moulding", Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 217, p. 453.
52. Monzòn, M., Hernandez, P. M., Benitez, A. N., Marrero, M. D. and Fernandez, A. (2009), "Predictability of plastic parts behaviour made from rapid manufacturing", Tsinghua Science & Technology, Vol. 14, pp. 100-107.
53. Munguia, J., Ciurana, J. and Riba, C. (2009), "Neuralnetwork-based model for build-time estimation in selective laser sintering", Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 223, p. 995.
54. Nelson, J. C., Xue, S., Barlow, J. W., Beaman, J. J., Marcus, H. L. and Bourell, D. L. (1993), "Model of the selective laser sintering of bisphenol-A polycarbonate", Industrial & Engineering Chemistry Research, Vol. 32, pp. 2305-2317.
55. Pandey, P. M., Venkata Reddy, N. and Dhande, S. G. (2003), "Improvement of surface finish by staircase machining in fused deposition modeling", Journal of Materials Processing Technology, Vol. 132, pp. 323-331.
56. Pham, D. and Gault, R. (1998), "A comparison of rapid prototyping technologies", International Journal of Machine Tools & Manufacture, Vol. 38, pp. 1257-1287.
57. Poli, R., Langdon, W. B. and Mcphee, N. F. (2008), A Field Guide to Genetic Programming, Lulu Enterprises Uk Ltd, London.
58. Quintana, R., Choi, J. W., Puebla, K. and Wicker, R. (2010), "Effects of build orientation on tensile strength for stereolithography- manufactured ASTM D-638 type I specimens", The International Journal of Advanced Manufacturing Technology, Vol. 46, pp. 201-215.
59. Raghunath, N. and Pandey, P. M. (2007), "Improving accuracy through shrinkage modelling by using Taguchi method in selective laser sintering", International Journal of Machine Tools & Manufacture, Vol. 47, pp. 985-995.
60. Reddy, T., Kumar, Y. R. and Rao, C. (2006), Determination of Optimum Process Parameters using Taguchi's Approach to Improve the Quality of SLS Parts, ACTA Press, Anaheim, CA, pp. 228-233.
61. Reddy, B., Reddy, N. and Ghosh, A. (2007), "Fused deposition modelling using direct extrusion", Virtual and Physical Prototyping, Vol. 2, pp. 51-60.
62. Rong-ji, W., Xin-hua, L., Qing-ding, W. and Lingling, W. (2009), "Optimizing process parameters for selective laser sintering based on neural network and genetic algorithm", The International Journal of Advanced Manufacturing Technology, Vol. 42, pp. 1035-1042.
63. Savalani, M., Hao, L., Dickens, P., Zhang, Y., Tan, K. and Harris, R. (2012), "The effects and interactions of fabrication parameters on the properties of selective laser sintered hydroxyapatite polyamide composite biomaterials", Rapid Prototyping Journal, Vol. 18, pp. 16-27.
64. Searson, D. (2009), GPTIPS: Genetic Programming & Symbolic Regression for MATLAB.
65. Shao, R., Martin, E., Zhang, J. and Morris, A. (1997), "Confidence bounds for neural network representations", Computers & Chemical Engineering, Vol. 21, pp. 1173-1178.
66. Shen, X., Yao, J., Wang, Y. and Yang, J. (2004), "Density prediction of selective laser sintering parts based on artificial neural network", Advances in Neural Networks-ISNN, pp. 153-165.
67. Sood, A., Ohdar, R. and Mahapatra, S. (2009), "Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method", Materials & Design, Vol. 30, pp. 4243-4252.
68. Sood, A., Ohdar, R. and Mahapatra, S. S. (2010a), "A hybrid ANN-BFOA approach for optimization of FDM process parameters", Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science, Springer, Berlin, pp. 396-403.
69. Sood, A., Ohdar, R. and Mahapatra, S. (2010b), "Parametric appraisal of fused deposition modelling process using the grey Taguchi method", Proceddings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 224, pp. 135-145.
70. Sood, A., Equbal, A., Toppo, V., Ohdar, R. and Mahapatra, S. S. (2011a), "An investigation on sliding wear of FDM built parts", CIRP Journal of Manufacturing Science and Technology, Vol. 5 No. 1, pp. 48-54.
71. Sood, A., Ohdar, R. and Mahapatra, S. S. (2011b), "Experimental investigation and empirical modelling of FDM process for compressive strength improvement", Journal of Advanced Research, Vol. 3, pp. 81-90.
72. Thrimurthulu, K., Pandey, P. M. and Venkata Reddy, N. (2004), "Optimum part deposition orientation in fused deposition modeling", International Journal of Machine Tools & Manufacture, Vol. 44, pp. 585-594.
73. Upcraft, S. and Fletcher, R. (2003), "The rapid prototyping technologies", Assembly Automation, Vol. 23, pp. 318-330.
74. Vosniakos, G., Maroulis, T. and Pantelis, D. (2007), "A method for optimizing process parameters in layerbased rapid prototyping", Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 221, pp. 1329-1340.
75. Wang, W., Cheah, C., Fuh, J. and Lu, L. (1996), "Influence of process parameters on stereolithography part shrinkage", Materials & Design, Vol. 17, pp. 205-213.
76. Weissman, E. and Hsu, M. (1991), "A finite element model of multi-layered laser sintered parts", Proceedings of the Solid Freeform Fabrication Symposium, Austin, TX, USA, pp. 86-94.
77. Wiedemann, B. and Jantzen, H. A. (1999), "Strategies and applications for rapid product and process development in Daimler-Benz AG", Computers in Industry, Vol. 39, pp. 11-25.
78. Williams, R., Komaragiri, S., Melton, V. and Bishu, R. (1996), "Investigation of the effect of various build methods on the performance of rapid prototyping (stereolithography) ", Journal of Materials Processing Technology, Vol. 61, pp. 173-178.
79. Xu, G., Zhang, X., Zhu, J., Zhao, W. and Lu, B. (2006), "Optimizing build parameters for improving dimension accuracy of high-resolution stereolithography system", Zhongguo Jixie Gongcheng (China Mechanical Engineering), Vol. 17, pp. 559-562.
80. Yan, X. and Gu, P. (1996), "A review of rapid prototyping technologies and systems", Computer-Aided Design, Vol. 28, pp. 307-318.
81. Yingwei, L., Sundararajan, N. and Saratchandran, P. (1998), "Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm", IEEE Transactions on Neural Networks, Vol. 9, pp. 308-318.
82. Zadeh, L. A. (1994), "Fuzzy logic, neural networks, and soft computing", Communications of the ACM, Vol. 37, pp. 77-84.
83. Zhang, H., Xu, J. and Wang, G. (2003), "Optimization of plasma deposition manufacturing parameters using a hybrid ANN-GAs method", IEEE 30th International Conference on Plasma Science, p. 447.
84. Hiden, H. G. (1998), "Data-based modelling using genetic programming", PhD Thesis, Dept. Chemical and Process Engineering, University of Newcastle, Newcastle upon Tyne.
85. Hinchliffe, M., Hiden, H., Mckay, B., Willis, M., Tham, M. and Barton, G. (1996), "Modelling chemical process systems using a multi-gene genetic programming algorithm", in Koza, J. R. (Ed.), Later Breaking Papers at the Gentic Programming 1996 Conference, Standford Bookstore, Standford University, CA, pp. 28-31.
86. Koza, J. R. (1996), On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA.