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1Book
مصطلحات موضوعية: Mecanizado, Machining, Mecanizado de alta velocidad, High-speed machining, High-speed milling, Softcomputing, Bayesian networks, Predictive models
Time: Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí
وصف الملف: application/pdf; 10 páginas
Relation: Natural and Artificial Computation for Biomedicine and Neuroscience : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part I. Páginas 233-242; Primera edición; 242; 233; Flores, V., Correa, M., Quiñonez, Y. (2017). Desempeño del modelo de predicción de la calidad de la superficie usando Softcomputing, un estudio comparativo de resultados. En: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_23; Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science; 1. Ahmad, N., Janahiraman, T.V.: Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, K.-A. (eds.) Proceedings of ELM-2014 Volume 2. PALO, vol. 4, pp. 321–329. Springer, Cham (2015). doi:10.1007/978-3-319-14066-7_31Google Scholar; 2. Altintas, Y., Weck, M.: Chatter stability of metal cutting and grinding. CIRP Ann. Manuf. Technol. 53, 40–51 (2004) Google Scholar; 3. Badu, S., Vinayagam, B.: Surface roughness prediction model using adaptive particle swarm optimization (APSO) algorithm. Intell. Fuzzy Syst. 28, 345–360 (2015); 4. Benardos, P., Vosniakos, G.: Predicting surface roughness in machining: a review. Int. J. Mach. Tools Manuf. 43, 833–844 (2003) CrossRefGoogle Scholar; 5. Correa, M., Bielza, C., Ramírez, M., Alique, J.R.: A Bayesian network model for surface roughness prediction in the machining process. Int. J. Syst. Sci. 39, 1181–1192 (2008) CrossRefzbMATHGoogle Scholar; 6. Correa, M., Bielza, C., Pamies-Teixeira, P.: Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Syst. Appl. 36(3), 7270–7279 (2009) CrossRefGoogle Scholar; 7. D‘Mello, G., Pai, S.: Prediction of surface roughness in high speed machining: a comparison. Proc. Int. J. Res. Eng. Technol. 1, 519–525 (2014) Google Scholar; 8. Ezugwua, E., Faderea, D., Onney, J., Bonney, J., Silva, R., Sales, W.: Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using artificial neural network. Int. J. Mach. Tools Manuf. 45, 1375–1385 (2005) CrossRefGoogle Scholar; 9. Flores, V., Correa, M., Alique, J.R.: Modelo Pre-Proceso de predicción de la Calidad Superficial en Fresado a Alta Velocidad basado en Soft Computing. Revista Iberoamericana de Automática e Informática Industrial RIAI 8(1), 38–43 (2011) CrossRefGoogle Scholar; 11. Hao, W., Zhu, X., Li, X.: Prediction of cutting force for self-propelled rotary tool using artificial neural network. J. Mater. Process. Technol. 180, 23–29 (2006) CrossRefGoogle Scholar; 12. Izamshah, R., Yuhazri, M., Hadzley, M., Amran, M.: Effects of end mill helix angle on accuracy for machining thin-rib aerospace component. Appl. Mech. Mater. 315, 773–777 (2013) CrossRefGoogle Scholar; 13. Jiang, B., He, T., Gu, Y., et al.: Method for recognizing wave dynamics damage in high-speed milling cutter. Int. J. Adv. Manuf. Technol. (2017). doi:10.1007/s00170-017-0128-1; 14. Lela, B., Bajie, D., Jozié, S.: Regression analysis, support vector machines, and Bayesian neural network approaches to modelling surface roughness in face milling. Adv. Manuf. Technol. 42, 1082–1089 (2009) CrossRefGoogle Scholar; 15. MacQueen, J.: Some methods for classification analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (2003) Google Scholar; 16. Shang, S., Li, J.: Tool wear and cutting forces variation in high-speed end-milling Ti-6Al-4V alloy. Int. J. Adv. Manuf. Technol. 46, 69–78 (2010) CrossRefGoogle Scholar; 17. Ozel, T., Esteves, A., Davim, J.: Neural network process modelling for turning of steel parts using conventional and wiper inserts. Int. J. Mater. Prod. Technol. 35, 246–258 (2009) CrossRefGoogle Scholar; 18. Ramírez-Cadena, M., Correa, M., Rodríguez-González, C., Alique, J.R.: Surface roughness modeling based on surface roughness feature concept for high speed machining. Am. Soc. Mech. Eng. Manuf. Eng. Div. 16(1), 811–815 (2005) Google Scholar; 19. Soleimanimehr, H., Nategh, M., Amini, S.: Modelling of surface roughness in vibration cutting by artificial neural network. Proc. World Acad. Sci. Eng. Technol. 40, 386–390 (2009) Google Scholar; 20. Stone, M.: Cross-validatory choice and assessment of statistical prediction. J. Roy. Stat. Soc. 36, 111–147 (1974) MathSciNetzbMATHGoogle Scholar; 21. Zhou, L., Cheng, K.: Dynamic cutting process modelling and its impact on the generation of surface topography and texture in nano/micro cutting. In: Proceedings of IMechE-2009, vol. 233, pp. 247–266 (2009) Google Scholar; 22. Zuperl, U., Cus, F.: Optimization of cutting conditions during cutting by using neural networks. Robot. Comput. Integr. Manuf. 19, 189–199 (2003) CrossRefzbMATHGoogle Scholar; 1611-3349 ( en línea); 0302-9743 (impresa); http://hdl.handle.net/10614/11191; https://doi.org/10.1007/978-3-319-59740-9_23
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2Book
المصدر: instname:Universidad Autónoma de Occidente ; reponame:Repositorio Institucional UAO ; Ahmad, N., Janahiraman, T.V.: Modelling and prediction of surface roughness and power consumption using parallel extreme learning machine based particle swarm optimization. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, K.-A. (eds.) Proceedings of ELM-2014 Volume 2. PALO, vol. 4, pp. 321–329. Springer, Cham (2015). doi:10.1007/978-3-319-14066-7_31 ; Altintas, Y., Weck, M.: Chatter stability of metal cutting and grinding. CIRP Ann. Manuf. Technol. 53, 40–51 (2004) ; Badu, S., Vinayagam, B.: Surface roughness prediction model using adaptive particle swarm optimization (APSO) algorithm. Intell. Fuzzy Syst. 28, 345–360 (2015) ; ....
مصطلحات موضوعية: Center kernel alignment, Feature selection, Human motion, Kinematics, Motion capture data, Principal component analysis, Relevance, Machining, Milling (metal-work), Bayesian statistical decision theory, Mecanizado, Fresado (metalistería), Teoría bayesiana de decisiones estadísticas, Manufacturing processes, High-speed machining, Micromachining, Mecanizado de alta velocidad, Procesos de manufactura, Corte de metales
Time: Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí
وصف الملف: application/pdf; Páginas 233-242
Relation: Natural and Artificial Computation for Biomedicine and Neuroscience : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part I. Páginas 233-242; Lecture Notes in Computer Science. 10338. Theoretical Computer Science and General Issues. 10338; Flores V., Correa M., Quiñonez Y. (2017) Performance of Predicting Surface Quality Model Using Softcomputing, a Comparative Study of Results. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science, vol 10337. Springer, Cham; 1611-3349 (en línea); 0302-9743 (impresa); http://hdl.handle.net/10614/11616; https://link.springer.com/chapter/10.1007/978-3-319-59773-7_51; https://link.springer.com/content/pdf/10.1007%2F978-3-319-59773-7.pdf; https://doi.org/10.1007/978-3-319-59740-9_23