A hybrid \(\text{ M}5^\prime \) -genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process
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- 作者：A. Garg (1)
K. Tai (1)
C. H. Lee (1)
M. M. Savalani (2) - 关键词：$$\text{ M}5^\prime $$ ; Genetic programming ; Artificial neural network ; Trustworthiness ; Support vector regression ; Fused deposition modelling ; Rapid prototyping
- 刊名：Journal of Intelligent Manufacturing
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：25
- 期：6
- 页码：1349-1365
- 全文大小：1,573 KB
- 参考文献：1. Ahn, D., Kweon, J. H., Kwon, S., Song, J., & Lee, S. (2009). Representation of surface roughness in fused deposition modeling. / Journal of Materials Processing Technology, / 209, 5593鈥?600.
2. Aijun, L., Zhuohui, Z., Daoming, W., & Jinyong, Y. (2010). Optimization method to fabrication orientation of parts in fused deposition modeling rapid prototyping. IEEE / International Conference on Mechanic Automation and Control engineering (MACE) (pp. 416鈥?19).
3. Anitha, R., Arunachalam, S., & Radhakrishnan, P. (2001). Critical parameters influencing the quality of prototypes in fused deposition modelling. / Journal of Materials Processing Technology, / 118, 385鈥?88. CrossRef
4. Azadeh, A., Saberi, M., Anvari, M., & Mohamadi, M. (2011). An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units. / Journal of Intelligent Manufacturing, / 22, 229鈥?45. CrossRef
5. Basak, D., Pal, S., & Patranabis, D. C. (2007). Support vector regression. / Neural Information Processing-Letters and Reviews, / 11, 203鈥?24.
6. Baziar, M. H., Jafarian, Y., Shahnazari, H., Movhed, V., & Amin Tutunchian, M. (2011). Prediction of strain energy-based liquefaction resistance of sand-silt mixtures: An evolutionary approach. / Computers & Geosciences, / 37(11), 1883鈥?893.
7. Bernard, A., & Fischer, A. (2002). New trends in rapid product development. / CIRP Annals-Manufacturing Technology, / 51, 635鈥?52. CrossRef
8. Bhattacharya, B., & Solomatine, D. (2005). Neural networks and \(\text{ M}5^\prime \) model trees in modelling water level-discharge relationship. / Neurocomputing, / 63, 381鈥?96. CrossRef
9. Borges, C. E., Alonso, C. L., & Montana, J. L. (2010). Model selection in genetic programming. In / Genetic and evolutionary computation conference (GECCO) (pp. 985鈥?86). ACM.
10. Brezak, D., Majetic, D., Udiljak, T., & Kasac, J. (2012). Tool wear estimation using an analytic fuzzy classifier and support vector machines. / Journal of Intelligent Manufacturing, / 23, 797鈥?09. CrossRef
11. Buyukbingol, E., Sisman, A., Akyildiz, M., Alparslan, F. N., & Adejare, A. (2007). Adaptive neuro-fuzzy inference system (ANFIS): A new approach to predictive modeling in QSAR applications: A study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists. / Bioorganic & Medicinal Chemistry, / 15, 4265鈥?282. CrossRef
12. Byun, H. S., & Lee, K. H. (2006). Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making. / Robotics and Computer-Integrated Manufacturing, / 22, 69鈥?0.
13. Byvatov, E., & Schneider, G. (2003). Support vector machine applications in bioinformatics. / Applied Bioinformatics, / 2, 67鈥?7.
14. 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, / 39, 80鈥?6. CrossRef
15. Carrascal, A., & Alberdi, A. (2010). Evolutionary industrial physical model generation. / Hybrid Artificial Intelligence Systems, / 6076, 327鈥?34.
16. Casalino, G., De Filippis, L., Ludovico, A., & Tricarico, L. (2002). An investigation of rapid prototyping of sand casting molds by selective laser sintering. / Journal of Laser Applications, / 14, 100鈥?06. CrossRef
17. 脟aydas, U., & Ekici, S. (2012). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. / Journal of Intelligent Manufacturing, / 23, 639鈥?50. CrossRef
18. Chang, D. Y., & 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, / 53, 1027鈥?037. CrossRef
19. Chatterjee, S., & Hadi, A. S. (2006). / Regression analysis by example. New York: Wiley. CrossRef
20. Cheng, R., Wu, X., & Zheng, J. (2010). Improving dimensional accuracy of SLS processed part using Taguchi method. Fourth International seminar on Modern cutting and Measurement engineering. / Proceedings of the SPIE, / 7997, 799715鈥?99715-5. CrossRef
21. Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. / Journal of Intelligent and Fuzzy Systems, / 2, 267鈥?78.
22. Choi, J. W., Quintana, R., & Wicker, R. B. (2011). Fabrication and characterization of embedded horizontal micro-channels using line-scan stereolithography. / Rapid Prototyping Journal, / 17, 351鈥?61.
23. Dixit, P. M., & Dixit, U. S. (2008). / Modeling of metal forming and machining processes: By finite element and soft computing methods. Berlin: Springer.
24. Duan, B., Cheung, W. L., & Wang, M. (2011). Optimized fabrication of Ca-P/PHBV nanocomposite scaffolds via selective laser sintering for bone tissue engineering. / Biofabrication, / 3, 015001. CrossRef
25. Equbal, A., Sood, A. K., & Mahapatra, S. (2011). Prediction of dimensional accuracy in fused deposition modelling: A fuzzy logic approach. / International Journal of Productivity and Quality Management, / 7, 22鈥?3. CrossRef
26. Erginel, N. (2010). Modeling and analysis of packing properties through a fuzzy inference system. / Journal of Intelligent Manufacturing, / 21, 869鈥?74. CrossRef
27. Etemad-Shahidi, A., & Mahjoobi, J. (2009). Comparison between \(\text{ M}5^\prime \) 鈥?model tree and neural networks for prediction of significant wave height in Lake Superior. / Ocean Engineering, / 36, 1175鈥?181. CrossRef
28. Flores, J., & Graff, M. (2005). System identification using genetic programming and gene expression programming. / Computer and Information Sciences-ISCIS, / 3733(2005), 503鈥?11.
29. Gaitonde, V. N., & Karnik, S. R. (2012). Minimizing burr size in drilling using artificial neural network (ANN)-particle swarm optimization (PSO) approach. / Journal of Intelligent Manufacturing, / 23, 1783鈥?793. CrossRef
30. Garg, A., & Tai, K. (2011). A hybrid genetic programming-artificial neural network approach for modeling of vibratory finishing process. In / International Proceedings of Computer Science and Information Technology (IPCSIT) (vol. 18, pp. 14鈥?9).
31. Garg, A., & Tai, K. (2012a). Comparison of regression analysis, artificial neural network and genetic programming in handling the multicollinearity problem. In / Proceedings of 2012 international conference on modelling, identification & control (ICMIC 2012), Wuhan, China, 24鈥?6 June 2012 (pp. 353鈥?58), IEEE.
32. Garg, A., & Tai, K. (2012b). Review of genetic programming in modeling of machining processes. In / Proceedings of 2012 international conference on modelling, identification & control (ICMIC 2012), Wuhan, China, 24鈥?6 June 2012 (pp. 653鈥?58). IEEE.
33. Gologlu, C., & Arslan, Y. (2009). Zigzag machining surface roughness modelling using evolutionary approach. / Journal of Intelligent Manufacturing, / 20, 203鈥?10. CrossRef
34. Gunn, S. R. (1998). Support vector machines for classification and regression. / ISIS technical report, 14.
35. Gupta, A. K. (2008). Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression. / International Journal of Production Research, / 48, 763鈥?78. CrossRef
36. Hearst, M. A., Dumais, S., Osman, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. / IEEE Intelligent Systems and Their Applications, / 13, 18鈥?8. CrossRef
37. Hiden, H.G. (1998). Data-based modelling using genetic programming. PhD Thesis, Dept. Chemical and Process Engineering, University of Newcastle, UK.
38. Hinchliffe, M., Hiden, H., Mckay, B., Willis, M., Tham, M., & Barton, G. (1996). / Modelling chemical process systems using a multi-gene genetic programming algorithm (pp. 28鈥?1). Late breaking paper, GP鈥?6. Stanford.
39. Hopkinson, N., Hague, R., & Dickens, P. (2006). / Rapid manufacturing. New York: Wiley.
40. Iba, H., & Sasaki, T. (1996). Using genetic programming to predict financial data. In / IEEE Proceedings of congress on evolutionary computation (CEC) (vol. 1, pp. 244鈥?51).
41. Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. / IEEE Trans. Syst., Man, and Cyber, / 23, 665鈥?85.
42. Jeguirim, S. E. G., Dhouib, A. B., Sahnoun, M., Cheikhrouhou, M., Schacher, L., & Adolphe, D. (2011). The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics. / Journal of Intelligent Manufacturing, / 22, 873鈥?84. CrossRef
43. Katherasan, D., Elias, J. V., Sathiya, P., & Haq, A. N. (2012). Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. / Journal of Intelligent Manufacturing, / 23, 1鈥?0. CrossRef
44. Kecman, V. (2001). / Learning and soft computing: Support vector machines, neural networks, and fuzzy logic models. Cambridge, MA: MIT press.
45. Kennard, R. W., & Stone, L. A. (1969). Computer aided design of experiments. / Technometrics, / 11, 137鈥?48. CrossRef
46. Kotanchek, M., Smits, G., & Vladislaveva, 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, 201鈥?20.
47. Kovacic, M., Balic, J., & Brezocnik, M. (2004). Evolutionary approach for cutting forces prediction in milling. / Journal of Materials Processing Technology, / 155, 1647鈥?652. CrossRef
48. Kovacic, M., & Brezocnik, M. (2003). Genetic programming approach for surface quality prediction. / Tehnicki Vjesnik, / 10, 19鈥?4.
49. Koza, J. R. (1994). Genetic programming II: Automatic discovery of reusable programs.
50. Kroh, M., Bonten, C., & Eyerer, P. (2011). Effects of process parameters on additive assisted laser sintering of polyetheretherketone. In / Soceity of plastic engineers (SPE) annual technical conference (vol. 2, pp. 1806鈥?811).
51. Kruth, J. P., & Kumar, S. (2005). Statistical analysis of experimental parameters in selective laser sintering. / Advanced Engineering Materials, / 7, 750鈥?55. CrossRef
52. Kumagai, A., Liu, T. I., & Hozian, P. (2006). Control of shape memory alloy actuators with a neuro-fuzzy feedforward model element. / Journal of Intelligent Manufacturing, / 17, 45鈥?6. CrossRef
53. Kumar, G. P., & Regalla, S. P. (2012). Optimization of support material and build time in fused deposition modeling (FDM). / Applied Mechanics and Materials, / 110, 2245鈥?251.
54. Kuschu, I. (2002). Genetic programming and evolutionary generalization. / Evolutionary Computation, IEEE Transactions on, / 6, 431鈥?42.
55. Laeng, J., Khan, Z. A., & Khu, S. (2006). Optimizing flexible behaviour of bow prototype using Taguchi approach. / Journal of Applied Sciences, / 6, 622鈥?30. CrossRef
56. Lee, S., Park, W., Cho, H., Zhang, W., & 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, / 215, 1719鈥?733. CrossRef
57. Li, C. L., Fu, G. Y., & Guo, K. B. (2011). Study on forecast of forming temperature of ABS resign during fused deposition manufacturing by fuzzy comprehensive evaluation. / Key Engineering Materials, / 464, 264鈥?67. CrossRef
58. Li, T. S., Huang, C. L., & Wu, Z. Y. (2006). Data mining using genetic programming for construction of a semiconductor manufacturing yield rate prediction system. / Journal of Intelligent Manufacturing, / 17, 355鈥?61. CrossRef
59. Liu, W., Liu, Q., Ruan, F., Liang, Z., & Qiu, H. (2007). Springback prediction for sheet metal forming based on GA-ANN technology. / Journal of Materials Processing Technology, / 187, 227鈥?31. CrossRef
60. Mahesh, M., Fuh, J., Wong, Y., & Loh, H. (2005). Benchmarking for decision making in rapid prototyping systems. In / IEEE International Conference on Automation Science and, Engineering (pp. 19鈥?4).
61. Mansour, S., & 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, / 217, 453鈥?61. CrossRef
62. May, R., Maier, H. R., & Dandy, G. C. (2010). Data splitting for artificial neural networks using SOM-based stratified sampling. / Neural Networks, / 23, 283鈥?94. CrossRef
63. Monzon, M., Hernandez, P. M., Benitez, A. N., Marrero, M. D., & Fernandez, 脕. (2009). Predictability of plastic parts behaviour made from rapid manufacturing. / Tsinghua Science & Technology, / 14, 100鈥?07. CrossRef
64. Munguia, J., Ciurana, J., & Riba, C. (2009). Neural-network-based model for build-time estimation in selective laser sintering. / Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, / 223, 995鈥?002. CrossRef
65. Pandey, P. M., Venkata Reddy, N., & Dhande, S. G. (2003). Improvement of surface finish by staircase machining in fused deposition modeling. / Journal of materials processing technology, / 132, 323鈥?31. CrossRef
66. Park, T. C., Kim, U. S., Kim, L. H., Jo, B. W., & Yeo, Y. K. (2010). Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems. / Korean Journal of Chemical Engineering, / 27, 1063鈥?071. CrossRef
67. Pearson, R. K., & Pottmann, M. (2000). Gray-box identification of block-oriented nonlinear models. / Journal of Process Control, / 10, 301鈥?15. CrossRef
68. Pelckmans, K., Suykens, J. A. K., Vangestel, T., DE Brabanter, J., Lukas, L., Hamers, B., et al. (2002). / LS-SVMlab: A matlab/c toolbox for least squares support vector machines. Tutorial. Leuven: KULeuven-ESAT.
69. Pham, D., & Gault, R. (1998). A comparison of rapid prototyping technologies. / International Journal of Machine Tools and Manufacture, / 38, 1257鈥?287. CrossRef
70. Quinlan, J. R. (1992). Learning with continuous classes. In / Proceedings of the fifth Australian joint conference on artificial intelligence (pp. 343鈥?48). Singapore: World Scientific.
71. Quintana, R., Choi, J. W., Puebla, K., & 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, / 46, 201鈥?15. CrossRef
72. Reddy, T., Kumar, Y. R., & Rao, C. (2006). Determination of optimum process parameters using Taguchi鈥檚 approach to improve the quality of SLS parts. In / Proceedings of the 17th IASTED international conference on Modelling and simulation. ACTA Press (pp. 228鈥?33).
73. Rowland, J. (2003). Model selection methodology in supervised learning with evolutionary computation. / Biosystems, / 72, 187鈥?96. CrossRef
74. Salgado, D., & Alonso, F. (2007). An approach based on current and sound signals for in-process tool wear monitoring. / International Journal of Machine Tools and Manufacture, / 47, 2140鈥?152. CrossRef
75. Salgado, D., Alonso, F., Cambero, I., & Marcelo, A. (2009). In-process surface roughness prediction system using cutting vibrations in turning. / The International Journal of Advanced Manufacturing Technology, / 43, 40鈥?1. CrossRef
76. Saptoro, A., Tade, M. O., & Vuthaluru, H. (2012). A modified Kennard-stone algorithm for optimal division of data for developing artificial neural network models. / Chemical Product and Process Modeling, / 7, 13. CrossRef
77. Searson, D. P., Leahy, D. E., & Willis, M. J. (2010). GPTIPS: An open source genetic programming toolbox for multigene symbolic regression. In / International multiconference of engineers and computer scientists 2010 (vol. 1, pp. 77鈥?0).
78. Sharma, V. S., Sharma, S. K., & Sharma, A. K. (2008). Cutting tool wear estimation for turning. / Journal of Intelligent Manufacturing, / 19, 99鈥?08. CrossRef
79. Shen, X., Yao, J., Wang, Y., & Yang, J. (2004). Density prediction of selective laser sintering parts based on artificial neural network. In / Advances in neural networks-ISNN 2004 (vol. 3174/2004, pp. 153鈥?65).
80. Solomatine, D. P., & Siek, M. (2004). Flexible and optimal M5 model trees with applications to flow predictions. In Liong, Phoon, & Babovic (Eds.), / Proceedings of the sixth international conference on hydroinformatics. Singapore: World Scientific.
81. Solomatine, D. P., & Xue, Y. (2004). M5 model trees and neural networks: Application to flood forecasting in the upper reach of the Huai River in China. / Journal of Hydrologic Engineering, / 9, 491鈥?501.
82. Sood, A., Ohdar, R., & Mahapatra, S. (2010a). A hybrid ANN-BFOA approach for optimization of FDM process parameters. / Swarm, Evolutionary, and Memetic Computing, / 6466, 396鈥?03. CrossRef
83. Sood, A., Ohdar, R., & Mahapatra, S. (2010b). Parametric appraisal of fused deposition modelling process using the grey Taguchi method. / Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, / 224, 135鈥?45. CrossRef
84. Sood, A. K., Equbal, A., Toppo, V., Ohdar, R., & Mahapatra, S. (2011a). An investigation on sliding wear of FDM built parts. / CIRP Journal of Manufacturing Science and Technology, / 1, 48鈥?4.
85. Sood, A. K., Ohdar, R., & Mahapatra, S. (2009). Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. / Materials & Design, / 30, 4243鈥?252. CrossRef
86. Sood, A. K., Ohdar, R. K., & Mahapatra, S. S. (2011b). Experimental investigation and empirical modelling of FDM process for compressive strength improvement. / Journal of Advanced Research, / 3, 81鈥?0. CrossRef
87. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of system and its applications to modelling and control. / IEEE Trans. Syst., Man, and Cyber, / 15, 116鈥?32. CrossRef
88. Thrimurthuli, K., Pandey, P. M., & Venkata Reddy, N. (2004). Optimum part deposition orientation in fused deposition modeling. / International Journal of Machine Tools and Manufacture, / 44, 585鈥?94. CrossRef
89. Upcraft, S., & Fletcher, R. (2003). The rapid prototyping technologies. / Assembly Automation, / 23, 318鈥?30. CrossRef
90. Vapnik, V. (1995). / The nature of statistical learning. New York: Springer.
91. Vosniakos, G., Maroulis, T., & Pantelis, D. (2007). A method for optimizing process parameters in layer-based rapid prototyping. / Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, / 221, 1329鈥?340.
92. Wang, G., Wang, Y., Zhao, J., & Chen, G. (2012). Process optimization of the serial-parallel hybrid polishing machine tool based on artificial neural network and genetic algorithm. / Journal of Intelligent Manufacturing, / 23, 365鈥?74.
93. Wang, R. J., Li, J., Wang, F., & Li, X. (2009). ANN model for the prediction of density in selective laser sintering. / International Journal of Manufacturing Research, / 4, 362鈥?73. CrossRef
94. Wang, W., Cheah, C., Fuh, J., & Lu, L. (1996). Influence of process parameters on stereolithography part shrinkage. / Materials & Design, / 17, 205鈥?13. CrossRef
95. Wang, Y., & Witten, I. H. (1996). / Induction of model trees for predicting continuous classes (Working paper 96/23). Hamilton, New Zealand: University of Waikato, Department of Computer Science
96. Wiedemann, B., & Jantzen, H. A. (1999). Strategies and applications for rapid product and process development in Daimler-Benz AG. / Computers in Industry, / 39, 11鈥?5. CrossRef
97. Willis, M., Hiden, H., Hinchliffe, M., Mckay, B., & Barton, G. W. (1997). Systems modelling using genetic programming. / Computers & Chemical Engineering, / 21, S1161鈥揝1166. CrossRef
98. Witten, I. H., & Frank, E. (2005). / Data mining: Practical machine learning tools and techniques (2nd ed.). Burlington, MA: Morgan Kaufmann.
99. Yan, X., & Gu, P. (1996). A review of rapid prototyping technologies and systems. / Computer-Aided Design, / 28, 307鈥?18. CrossRef
100. Zadeh, L. A. (1994). Fuzzy logic, neural networks, and soft computing. / Communications of the ACM, / 37, 77鈥?4. CrossRef
101. Zhang, Y., & Bhattacharyya, S. (2004). Genetic programming in classifying large-scale data: An ensemble method. / Information Sciences, / 163, 85鈥?01. CrossRef - 作者单位：A. Garg (1)
K. Tai (1)
C. H. Lee (1)
M. M. Savalani (2)
1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong - ISSN：1572-8145
文摘
Recent years have seen various rapid prototyping (RP) processes such as fused deposition modelling (FDM) and three-dimensional printing being used for fabricating prototypes, leading to shorter product development times and less human intervention. The literature reveals that the properties of RP built parts such as surface roughness, strength, dimensional accuracy, build cost, etc are related to and can be improved by the appropriate settings of the input process parameters. Researchers have formulated physics-based models and applied empirical modelling techniques such as regression analysis and artificial neural network for the modelling of RP processes. Physics-based models require in-depth understanding of the processes which is a formidable task due to their complexity. The issue of improving trustworthiness of the prediction ability of empirical models on test (unseen) samples is paid little attention. In the present work, a hybrid M5 \(^{\prime }\) -genetic programming (M5 \(^{\prime }\) -GP) approach is proposed for empirical modelling of the FDM process with an attempt to resolve this issue of ensuring trustworthiness. This methodology is based on the error compensation achieved using a GP model in parallel with a M5 \(^{\prime }\) model. The performance of the proposed hybrid model is compared to those of support vector regression (SVR) and adaptive neuro fuzzy inference system (ANFIS) model and it is found that the M5 \(^{\prime }\) -GP model has the goodness of fit better than those of the SVR and ANFIS models.