Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30982
Modeling of the Process Parameters using Soft Computing Techniques

Authors: Miodrag T. Manić, Dejan I. Tanikić, Miloš S. Stojković, Dalibor M. ðenadić


The design of technological procedures for manufacturing certain products demands the definition and optimization of technological process parameters. Their determination depends on the model of the process itself and its complexity. Certain processes do not have an adequate mathematical model, thus they are modeled using heuristic methods. First part of this paper presents a state of the art of using soft computing techniques in manufacturing processes from the perspective of applicability in modern CAx systems. Methods of artificial intelligence which can be used for this purpose are analyzed. The second part of this paper shows some of the developed models of certain processes, as well as their applicability in the actual calculation of parameters of some technological processes within the design system from the viewpoint of productivity.

Keywords: Neural Networks, Manufacturing, Fuzzy Logic

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1621


[1] I. Mukherjee, P. K. Ray, "A review of optimization techniques in metal cutting processes," Computers & Industrial Engineering, vol. 50, pp. 15-34, 2006.
[2] V. Venugopal, T. T. Narendran, "Neural network model for design retrieval in manufacturing systems," Computers in industry, vol. 20, pp. 11-23, 1992.
[3] S. V. Kamarthi, S. T. Kumara, F. T. S. Yu and I. Ham, "Neural networks and their applications in component design data retrieval," Journal of Intelligent Manufacturing, vol. 1, no. 2, pp. 125-140, 1990.
[4] T. W. Simpson, J. D. Peplinski, P. N. Koch and J. K. Allen, "Metamodels for computer-based engineering design: survey and recommendations," Engineering with Computers, vol. 17, pp. 129-150, 2001.
[5] W. L. Chan, M. W. Fu and J. Lu, "An integrated FEM and ANN methodology for metal-formed product design," Engineering Applications of Artificial Intelligence, vol. 21, no. 8, pp. 1170-1181, 2008.
[6] S. H. Yeo, M. W. Mak and S. A. P. Balon, "Analysis of decisionmaking methodologies for desirability score of conceptual design," Journal of Engineering Design, vol. 15, no. 2, pp. 195-208, 2004.
[7] J. H. Jahnke, "Cognitive support in software reengineering based on generic fuzzy reasoning nets," Fuzzy Sets and Systems, vol. 145, pp. 3- 27, 2004.
[8] S. T. Kumara, S. V. Kamarthy, "Function-to-structure transformation in conceptual design: An associative memory based paradigm," Journal of Intelligent Manufacturing, vol. 2, no. 5, pp. 281-292, 1991.
[9] K. Osakada, G. B. Yang, "Neural networks for process planning of cold forging," International Journal of Machine Tools and Manufacture, vol. 31, no. 4, pp. 577-587, 1991.
[10] J. L. Hwang, M. R. Henderson, "Applying the perceptron to threedimensional feature recognition," Journal of Design and Manufacturing, vol. 2, no. 4, pp. 187-198, 1992.
[11] L. Ding, J. Matthews, "A contemporary study into the application of neural network techniques employed to automate CAD/CAM integration for die manufacture," Computers & Industrial Engineering, vol. 57, no. 4, pp. 1457-1471, 2009.
[12] M. Santochi, G. Dini, "Use of neural networks in automated selection of technological parameters of cutting tools," Computer Integrated Manufacturing Systems, vol. 9, no. 3, pp. 137-148, 1996.
[13] M. G. Marchetta, R. Q. Forradellas, "An artificial intelligence planning approach to manufacturing feature recognition," Computer-Aided Design, vol. 42, no. 3, pp. 248-256, 2010.
[14] Y. P. S. Foo, Y. Takefuji, "Integer linear programming neural networks for job-shop scheduling," in Proc. 1988 Int. IEEE Conf. Neural Networks, vol. 2, 1988, pp.341-348
[15] J. C. Vidal, M. Mucientes, A. Bugarín and M. Lama, "Machine scheduling in custom furniture industry through neuro-evolutionary hybridization," Applied Soft Computing, vol. 11, no. 2, pp. 1600-1613, 2011.
[16] T. Karim, B. Reda and H. Georges, "Multi-objective supervisory flow control based on fuzzy interval arithmetic: Application for scheduling of manufacturing systems," Modelling Practice and Theory, vol. 19, no. 5, pp. 1371-1383, 2011.
[17] Y.-R. Shiue, R.-S. Guh, "Study of SOM-based intelligent multicontroller for real-time scheduling," Applied Soft Computing, to be published.
[18] J. M. Cadenas, M. C. Garrido and E. Mu├▒oz, "Facing dynamic optimization using a cooperative metaheuristic configured via fuzzy logic and SVMs," Applied Soft Computing, to be published.
[19] W.-C. Chen, G.-L. Fu, P.-H. Tai and W.-J. Deng, "Process parameter optimization for MIMO plastic injection molding via soft computing," Expert Systems with Applications, vol. 36, no. 2, pp. 1114-1122, 2009.
[20] N. Thitipong, N. V. Afzulpurkar, "Optimization of tile manufacturing process using particle swarm optimization," Swarm and Evolutionary Computation, vol. 1, no. 2, pp. 97-109, 2011.
[21] G. K. M. Rao, G. Rangajanardhaa, D. H. Rao, M. S. Rao, "Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm," Journal of Materials Processing Technology, vol. 209, no. 3, pp. 1512-1520, 2009.
[22] H. C. W. Lau, E. N. M. Cheng, C. K. M. Lee and G. T. S. Ho, "A fuzzy logic approach to forecast energy consumption change in a manufacturing system," Expert Systems with Applications, vol. 34, no. 3, pp. 1813-1824, 2008.
[23] M. Salehi, A. Bahreininejad and I. Nakhai, "On-line analysis of out-ofcontrol signals in multivariate manufacturing processes using a hybrid learning-based model," Neurocomputing, vol. 74, no. 12-13, pp. 2083- 2095, 2011.
[24] S. S. Rangwala and D. A. Dornfeld, "Learning and optimization of machining operations using computing abilities of neural networks," IEEE Transactions on System, Man, and Cybernetics, vol. 19, no. 2, pp. 299-314, 1989.
[25] Y. S. Tarng, T. C. Wang, W. N. Chen and B. Y. Lee, "The use of neural networks in predicting turning forces," Journal of Materials Processing Technology, vol. 47, pp. 273-289, 1995.
[26] J. Yu, L. Xi and X. Zhou, "Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble," Engineering Applications of Artificial Intelligence, vol. 22, no. 1, pp. 141-152, 2009.
[27] M. T. Hayajneh, A. M. Hassan and A. T. Mayyas, "Artificial neural network modeling of the drilling process of self-lubricated aluminum/alumina/graphite hybrid composites synthesized by powder metallurgy technique," Journal of Alloys and Compounds, vol. 478, no. 1-2, pp. 559-565, 2009.
[28] I. Korkut, A. Ac─▒r and M. Boy, "Application of regression and artificial neural network analysis in modelling of tool-chip interface temperature in machining," Expert Systems with Applications, vol. 38, no. 9, pp. 11651-11656, 2011.
[29] D. Tanikić, M. Manić, G. Devedžić, Z. Stević, "Modelling Metal Cutting Parameters Using Intelligent Techniques," Strojni┼íki vestnik - Journal of Mechanical Engineering, vol. 56, no. 1, pp. 52-62, 2010.
[30] D. Tanikić, M. Manić, G. Devedžić, Ž. ─åojba┼íić, "Modelling of the Temperature in the Chip-Forming Zone Using Artificial Intelligence Techniques," Neural Network World, vol. 20, no. 2, pp. 171-187, 2010.
[31] D. Tanikić, "Modeling of the correlations among metal cutting process parameters using the adaptive neuro-fuzzy systems," Phd thesis, Mechanical Engineering Faculty of the University of Ni┼í, 2009, (in serbian).
[32] D. Lazarević, "Modeling correlation between the parameters of the plasma cutting and analysis of heat balance using the method of artificial intelligence," PhD thesis, Mechanical Engineering Faculty of the University of Ni┼í, Serbia, 2009, (in serbian).