Performance of predicting surface quality model using softcomputing, a comparative study of results

This paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data obtained from two machine-tools with different characteristics, but using the s...

Full description

Autores:
Correa Valencia, Maritza
Flores, Víctor
Quiñónez, Alma Yadira
Tipo de recurso:
Part of book
Fecha de publicación:
2017
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/11191
Acceso en línea:
http://hdl.handle.net/10614/11191
https://doi.org/10.1007/978-3-319-59740-9_23
Palabra clave:
Mecanizado
Machining
Mecanizado de alta velocidad
High-speed machining
High-speed milling
Softcomputing
Bayesian networks
Predictive models
Rights
openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
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oai_identifier_str oai:red.uao.edu.co:10614/11191
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv Performance of predicting surface quality model using softcomputing, a comparative study of results
title Performance of predicting surface quality model using softcomputing, a comparative study of results
spellingShingle Performance of predicting surface quality model using softcomputing, a comparative study of results
Mecanizado
Machining
Mecanizado de alta velocidad
High-speed machining
High-speed milling
Softcomputing
Bayesian networks
Predictive models
title_short Performance of predicting surface quality model using softcomputing, a comparative study of results
title_full Performance of predicting surface quality model using softcomputing, a comparative study of results
title_fullStr Performance of predicting surface quality model using softcomputing, a comparative study of results
title_full_unstemmed Performance of predicting surface quality model using softcomputing, a comparative study of results
title_sort Performance of predicting surface quality model using softcomputing, a comparative study of results
dc.creator.fl_str_mv Correa Valencia, Maritza
Flores, Víctor
Quiñónez, Alma Yadira
dc.contributor.author.none.fl_str_mv Correa Valencia, Maritza
Flores, Víctor
Quiñónez, Alma Yadira
dc.subject.lemb.spa.fl_str_mv Mecanizado
topic Mecanizado
Machining
Mecanizado de alta velocidad
High-speed machining
High-speed milling
Softcomputing
Bayesian networks
Predictive models
dc.subject.lemb.eng.fl_str_mv Machining
dc.subject.armarc.spa.fl_str_mv Mecanizado de alta velocidad
dc.subject.proposal.eng.fl_str_mv High-speed machining
High-speed milling
Softcomputing
Bayesian networks
Predictive models
description This paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data obtained from two machine-tools with different characteristics, but using the same experimental model. In both cases, work pieces (probes) of the same material were machined (steel and aluminum probes) with cutting parameters and characteristics proper of production processes in industries such as aeronautics and automotive. The main objective of this study was to compare surface quality prediction models created in two machining centers to establish differences in outcomes and the possible causes of these differences. In addition, this paper deals with the validation of each model concerning surface quality obtained, along with comparing the quality of the models with other predictive surface quality models based on similar techniques
publishDate 2017
dc.date.issued.spa.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2019-10-10T13:07:49Z
dc.date.available.none.fl_str_mv 2019-10-10T13:07:49Z
dc.type.spa.fl_str_mv Capítulo de libro
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dc.type.content.eng.fl_str_mv Text
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dc.identifier.isbn.spa.fl_str_mv 9783319597393 (impreso)
978-3-319-59740-9 (en línea)
dc.identifier.issn.spa.fl_str_mv 1611-3349 ( en línea)
0302-9743 (impresa)
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10614/11191
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-319-59740-9_23
identifier_str_mv 9783319597393 (impreso)
978-3-319-59740-9 (en línea)
1611-3349 ( en línea)
0302-9743 (impresa)
url http://hdl.handle.net/10614/11191
https://doi.org/10.1007/978-3-319-59740-9_23
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.eng.fl_str_mv 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
dc.relation.citationedition.spa.fl_str_mv Primera edición
dc.relation.citationendpage.none.fl_str_mv 242
dc.relation.citationstartpage.none.fl_str_mv 233
dc.relation.cites.spa.fl_str_mv 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
dc.relation.ispartofbook.eng.fl_str_mv Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science
dc.relation.references.none.fl_str_mv 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
10. Friedman, N., Geiger, D., Goldszmit, M.: Bayesian network classifiers. Mach. Learn. 29, 131–161 (1997) CrossRefzbMATHGoogle 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
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rights_invalid_str_mv Derechos Reservados - Universidad Autónoma de Occidente
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spelling Correa Valencia, Maritzavirtual::1375-1Flores, Víctor3b45a8512db1519e39d991feb6db5397Quiñónez, Alma Yadira9a04a8f20f90bbb31878910b9a031687Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-10-10T13:07:49Z2019-10-10T13:07:49Z20179783319597393 (impreso)978-3-319-59740-9 (en línea)1611-3349 ( en línea)0302-9743 (impresa)http://hdl.handle.net/10614/11191https://doi.org/10.1007/978-3-319-59740-9_23This paper describes a comparative study of performance of two models predicting surface quality in high-speed milling (HSM) processes using two different machining centers. The models were created with experimental data obtained from two machine-tools with different characteristics, but using the same experimental model. In both cases, work pieces (probes) of the same material were machined (steel and aluminum probes) with cutting parameters and characteristics proper of production processes in industries such as aeronautics and automotive. The main objective of this study was to compare surface quality prediction models created in two machining centers to establish differences in outcomes and the possible causes of these differences. In addition, this paper deals with the validation of each model concerning surface quality obtained, along with comparing the quality of the models with other predictive surface quality models based on similar techniquesapplication/pdf10 páginasengSpringer, ChamNatural 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 233-242Primera edición242233Flores, 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_23Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science1. 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 Scholar2. Altintas, Y., Weck, M.: Chatter stability of metal cutting and grinding. CIRP Ann. Manuf. Technol. 53, 40–51 (2004) Google Scholar3. 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 Scholar5. 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 Scholar6. 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 Scholar7. 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 Scholar8. 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 Scholar9. 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 Scholar10. Friedman, N., Geiger, D., Goldszmit, M.: Bayesian network classifiers. Mach. Learn. 29, 131–161 (1997) CrossRefzbMATHGoogle Scholar11. 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 Scholar12. 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 Scholar13. 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/s00170-017-0128-114. 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 Scholar15. 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 Scholar16. 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 Scholar17. 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 Scholar18. 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 Scholar19. 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 Scholar20. Stone, M.: Cross-validatory choice and assessment of statistical prediction. J. Roy. Stat. Soc. 36, 111–147 (1974) MathSciNetzbMATHGoogle Scholar21. 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 Scholar22. Zuperl, U., Cus, F.: Optimization of cutting conditions during cutting by using neural networks. Robot. Comput. Integr. Manuf. 19, 189–199 (2003) CrossRefzbMATHGoogle ScholarDerechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Performance of predicting surface quality model using softcomputing, a comparative study of resultsCapítulo de librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85MecanizadoMachiningMecanizado de alta velocidadHigh-speed machiningHigh-speed millingSoftcomputingBayesian networksPredictive modelsPublicationecc91e59-ce00-443d-bce6-ce85014e9629virtual::1375-1ecc91e59-ce00-443d-bce6-ce85014e9629virtual::1375-1https://scholar.google.com/citations?user=15MGkAQAAAAJ&hl=envirtual::1375-10000-0001-8464-2673virtual::1375-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001435997virtual::1375-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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