Prediction of roughness surface in textured by electrical erosion using bayesian networks
DOI:
https://doi.org/10.18046/syt.v11i27.1696Keywords:
A model for prediction of parameters that defined roughness surface [Ra] when this texture is produced by Electro Discharge Texturing [EDT] is presented. The non-linearity, instabilities and expensive experimentation in this process, are mainAbstract
A model for prediction of parameters that defined roughness surface [Ra] when this texture is produced by Electro Discharge Texturing [EDT] is presented. The non-linearity, instabilities and expensive experimentation in this process, are main causes for use predictive techniques by means of robust and reliable algorithms, for study factors that present characterization hard. Series of experiments were conducted to produce plane surface textures using a modified EDM machine ALIC-1. The data collected in experimental phase were used for trained Bayesian models with Naïve Bayes and Tree Augmented Naïve Bayes [TAN] classifiers. Results show acceptable behavior within the operating range, consistent with the physical phenomena governing EDT process. Find a surface roughness with particular specifications is demonstrated.
References
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Simão, J., Aspinwall, D.K., Wise, M.L.H., & El-Menshawy, F. (1996). Electrical discharge texturing of cold mill work rolls using different tool electrode materials. Iron and Steel Engineer, 73(3),42-47
Terpák, J., Dorak, L'. Revaj, J. (2010). Quality control of the electro-discharge texturing, Metabk, 49(1), 19-22
Chow, C.K. &Liu, C.N. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14(3), 462-467
Correa, M., Alique, J.R., & Bielza, C. (2008). Comparativa de modelos con aprendizaje supervisado: aplicación a un proceso industrial. En IV Simposio de control inteligente, 11-13 June, Santander, España, [CD]. Santander, España: Universidad de Cantabria
Correa, M., Bielza, C., &Pamies-Teixeira, J.J. (2009). Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Systems with Applications, 36, 7270-7279
Correa, M., Bielza, C., Ramirez, M.deJ., & Alique, J.R. (2008). A Bayesian network model for surface prediction in the machining process. International Journal of Systems Science, 39(12), 1181-1192
Correa, M., Ramirez, M.deJ., Alique, J.R., & Rodriguez, C.A. (2004). Factores que afectan el acabado superficial en los procesos de mecanizado: técnicas de análisis y modelos. En XXV Jornadas de automática, 8-10 Septiembre, Ciudad Real, España [en línea]. Recuperado de http://www.ceautomatica.es/old/actividades/jornadas/XXV/documentos/75-arlencicor.pdf
Díez, J. (2005, noviembre 10). Elvira [en línea]. Recuperado de www.ia.uned.es/~fjdiez/bayes/elvira/
Friedman, N., Geiger, D., & Goldszmit, M. (1997). Bayesian network classifiers. Machine Learning, 29, 131-161
Gao, Q., Zhang, Q., Su, S., & Zhang, J. (2008). Parameter
optimization model in electrical discharge machining process. Journal of Zhejiang University Science A. 9(1), 104-108
Ghoreishi, M. & Atkinson, J. (2002). A comparative experimental study of machining characteristics in vibratory, rotary and vibro-rotary electro-discharge machining. Journal of Materials Processing Technology, 120, 374-384
International Organization for Standardization [ISO]. (2013). ISO 4288:1996: Geometrical product specifications (GPS) -- Surface texture: Profile method -- Rules and procedures for the assessment of surface texture. Ginebra, Suiza: ISO
Ivancos, J., Luis, C.J., Ortiz, J.A., & González, H.A. (2005). Analysis of factors affecting the high speed side milling of hardened die steels. Journal of Materials Processing Technology, 162-163, 696-701
Langley, P., Iba, W., & Thompson, K. An analysis of Bayesian classifiers. En Proceedings of AAAI-92, (pp. 223-228). Palo Alto, CA: AAAI
Markopoulos, A., Vaxevanidis, N.M., Petropoulos, G., & Manolakos, D.E. (2006). Artificial neural networks modeling of surface finish in electro-discharge machining of tool steels, Proceedings of ESDA2006 8th Biennial ASME Conference on Engineering Systems Design and Analysis July 4-7, 2006, Torino, Italy. (V.4, pp.847-854). New York, NY: ASME. Disponible en http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1594550
Pamies-Teixeira, J.J. (2002). Fundamentos físicos do corte de metais. Lisboa, Portugal: Edinova
Pearl, J. (1988). Probabilistic reasoning in Intelligent Systems: networks of plausible inference, San Francisco, CA: Morgan Kaufmann
Pellicer, N., Ciurana, J., Ozel, T. (2009). Influence of process parameters and electrode geometry on feature micro-accuracy in electro discharge machining of tool steel. Materials and Manufacturing Processes, 24(12), 1282-1289
Pfestorf, M., Engel, U., & Geiger, M. (1998). 3D-Surface parameters and their application on deterministic textured metal sheets. International Journal of Machine Tools & Manufacture, 38(5-6), 607-614
Pradhan, M.K., Das, R., & Biswas, C.K. (2009). Comparisons of neural network models on surface roughness in electrical discharge machining. Journal of Engineering Manufacture, 223, 801-808 [Proceedings of the Institution of Mechanical Engineers, Part B:]
Simão, J., Aspinwall, D.K., Wise, M.L.H., & El-Menshawy, F. (1996). Electrical discharge texturing of cold mill work rolls using different tool electrode materials. Iron and Steel Engineer, 73(3),42-47
Terpák, J., Dorak, L'. Revaj, J. (2010). Quality control of the electro-discharge texturing, Metabk, 49(1), 19-22
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2013-12-28
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