Modeling of the Process Parameters using Soft Computing Techniques
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.
[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," Strojniški 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 Niš, 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 Niš, Serbia, 2009, (in serbian).
[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," Strojniški 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 Niš, 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 Niš, Serbia, 2009, (in serbian).
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:55524", author = "Miodrag T. Manić and Dejan I. Tanikić and Miloš S. Stojković and Dalibor M. ðenadić", title = "Modeling of the Process Parameters using Soft Computing Techniques", abstract = "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 = "fuzzy logic, manufacturing, neural networks", volume = "5", number = "11", pages = "2312-6", }
