Abstract
The complexity and difficulties in modelling the most of nowadays real world problems increase as the computational capacity does, specially in those processes where relatively new technology arises. One of such processes is the steel sheet incremental cold shaping. The steel sheet incremental cold shaping process is a new technique for shaping metal sheets when a reduced amount of pieces per lots should be manufactured. As it is a relatively new technique, there is a lack of knowledge in defining the operating conditions, so in order to fit them, before manufacturing a lot a trial and error stage is carried out. A decision support system to reduce the cost of processing and to assist in defining the operating conditions should be studied. This study focus on the analysis and design of the decision support system, and as it is going to be shown, the most suitable features have been found using a wrapper feature selection method, in which genetic algorithms support vector machines and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn.
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References
Altun, A.A., Allahverdi, N.: Neural Network Based Recognition by Using Genetic Algorithm for Feature Selection of Enhanced Fingerprints. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 467–476. Springer, Heidelberg (2007)
Bustillo, A., Sedano, J., Curiel, L., Villar, J.R., Corchado, E.: A Soft Computing System for Modelling the Manufacture of Steel Components. Advances in Soft Computing 57(3), 601–609 (2008)
Casillas, J., Cordón, O., del Jesus, M.J., Herrera, F.: Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process. Information Sciences 136(1-4), 135–157 (2001)
de la Cal, E., Fernández, E.M., Quiroga, R., Villar, J., Sedano, J.: Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds.) HAIS 2010. LNCS, vol. 6077, pp. 143–150. Springer, Heidelberg (2010)
Fung, G.M., Mangasarian, O.L.: A Feature Selection Newton Method for Support Vector Machine Classification. Computational Optimization and Applications 28(2), 185–202 (2004)
Huanga, C.-L., Wang, C.-J.: A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with Applications 31(2), 231–240 (2006)
Mackay, D.J.C.: Bayesian interpolation. Neural Computation 4(3), 415–447 (1992)
The MathWorks, MATLAB - The Language Of Technical Computing (2011), http://www.mathworks.com/products/matlab/
Mohanty, I., Datta, S., Bhattacharjeeb, D.: Composition-Processing-Property Correlation of Cold-Rolled IF Steel Sheets Using Neural Network. Materials and Manufacturing Processes 24(1), 100–105 (2009)
Redondo, R., Santos, P., Bustillo, A., Sedano, J., Villar, J.R., Correa, M., Alique, J.R., Corchado, E.: A Soft Computing System to Perform Face milling Operations. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 1282–1291. Springer, Heidelberg (2009)
Sedano, J., Corchado, E., Curiel, L., Villar, J., Bravo, P.: The Application of a Two-Step AI Model to an Automated Pneumatic Drilling Process. Int. J. of Comp. Mat. 86(10-11), 1769–1777 (2008)
Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A Soft Computing Based Method for Detecting Lifetime Building Thermal Insulation Failures. Int. Comp. Aided Eng. 17(2), 103–115 (2009)
Sedano, J., Villar, J.R., Curiel, L., de la Cal, E., Corchado, E.: Improving Energy Efficiency in Buildings Using Machine Intelligence. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 773–782. Springer, Heidelberg (2009)
Villar, J.R., Suárez, M.R., Sedano, J., Mateos, F.: Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 565–572. Springer, Heidelberg (2009)
Villar, J.R., Berzosa, A., de la Cal, E., Sedano, J., Garcıa-Tamargo, M.: Multi-objective Simulated Annealing in Genetic Algorithm and Programming learning with low quality data. In publication for Neural Computing (2011)
Villar, J.R., Sedano, J., Corchado, E., Puigpinós, L.: Soft Computing decision support for a steel sheet incremental cold shaping process. Accepted for the Proc. of the 12th Int. Conf. on Intel. Data Eng. and A. L, IDEAL 2011 (2011)
Wong, M.L.D., Nandi, A.K.: Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Proc. 84(2), 351–365 (2004)
Yang, J., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13(2), 44–49 (1998)
Zhang, P., Verma, B., Kumar, K.: Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pat. Recog. Letters 28(7), 909–919 (2005)
Zhao, J., Cao, H.Q., Ma, L.X., Wang, F.Q., Li, S.B.: Study on intelligent control technology for the deep drawing of an axi-symmetric shell part. J. of Materials Processing Tech. 151(1-3), 98–104 (2005)
Zhao, J., Wang, F.: Parameter identification by neural network for intelligent deep drawing of axisymmetric workpieces. J. of Materials Processing Tech. 166(3), 387–391 (2005)
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Puigpinós, L., Villar, J.R., Sedano, J., Corchado, E., de Ciurana, J. (2011). Steel Sheet Incremental Cold Shaping Improvements Using Hybridized Genetic Algorithms with Support Vector Machines and Neural Networks. In: Pelta, D.A., Krasnogor, N., Dumitrescu, D., Chira, C., Lung, R. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2011). Studies in Computational Intelligence, vol 387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24094-2_22
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DOI: https://doi.org/10.1007/978-3-642-24094-2_22
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