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...

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Autores:
Correa Valencia, Maritza
Flores, Víctor
Quinonez, 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:10614/11616
Acceso en línea:
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
Palabra clave:
Center kernel alignment
Feature selection
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
Rights
openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
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oai_identifier_str oai:red.uao.edu.co:10614/11616
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
Center kernel alignment
Feature selection
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
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
Quinonez, Alma Yadira
dc.contributor.author.none.fl_str_mv Correa Valencia, Maritza
dc.contributor.author.spa.fl_str_mv Flores, Víctor
Quinonez, Alma Yadira
dc.subject.eng.fl_str_mv Center kernel alignment
Feature selection
Feature selection
Human motion
Kinematics
Motion capture data
Principal component analysis
Relevance
topic Center kernel alignment
Feature selection
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
dc.subject.lemb.eng.fl_str_mv Machining
Milling (metal-work)
Bayesian statistical decision theory
dc.subject.lemb.spa.fl_str_mv Mecanizado
Fresado (metalistería)
Teoría bayesiana de decisiones estadísticas
dc.subject.armarc.eng.fl_str_mv Manufacturing processes
High-speed machining
Micromachining
dc.subject.armarc.spa.fl_str_mv Mecanizado de alta velocidad
Procesos de manufactura
Corte de metales
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 2017-05-27
dc.date.accessioned.spa.fl_str_mv 2019-11-28T20:43:34Z
dc.date.available.spa.fl_str_mv 2019-11-28T20:43:34Z
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
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dc.identifier.citation.eng.fl_str_mv 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
dc.identifier.isbn.spa.fl_str_mv 978-3-319-59740-9 (en línea)
9783319597393 (impreso)
dc.identifier.issn.spa.fl_str_mv 1611-3349 (en línea)
0302-9743 (impresa)
dc.identifier.uri.spa.fl_str_mv 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
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-319-59740-9_23
identifier_str_mv 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
978-3-319-59740-9 (en línea)
9783319597393 (impreso)
1611-3349 (en línea)
0302-9743 (impresa)
url 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
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.haspart.eng.fl_str_mv Lecture Notes in Computer Science. 10338. Theoretical Computer Science and General Issues. 10338
dc.rights.spa.fl_str_mv Derechos Reservados - Universidad Autónoma de Occidente
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
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dc.rights.creativecommons.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
rights_invalid_str_mv Derechos Reservados - Universidad Autónoma de Occidente
https://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.spa.fl_str_mv application/pdf
dc.format.extent.spa.fl_str_mv Páginas 233-242
dc.coverage.spatial.spa.fl_str_mv Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí
dc.publisher.eng.fl_str_mv Springer, Cham
dc.source.spa.fl_str_mv instname:Universidad Autónoma de Occidente
reponame:Repositorio Institucional UAO
instname_str Universidad Autónoma de Occidente
institution Universidad Autónoma de Occidente
reponame_str Repositorio Institucional UAO
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dc.source.bibliographiccitation.spa.fl_str_mv 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)
Benardos, P., Vosniakos, G.: Predicting surface roughness in machining: a review. Int. J. Mach. Tools Manuf. 43, 833–844 (2003)
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)
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)
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)
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)
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)
Friedman, N., Geiger, D., Goldszmit, M.: Bayesian network classifiers. Mach. Learn. 29, 131–161 (1997)
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)
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)
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
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)
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)
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)
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)
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)
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)
Stone, M.: Cross-validatory choice and assessment of statistical prediction. J. Roy. Stat. Soc. 36, 111–147 (1974)
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)
Zuperl, U., Cus, F.: Optimization of cutting conditions during cutting by using neural networks. Robot. Comput. Integr. Manuf. 19, 189–199 (2003)
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spelling Correa Valencia, Maritzavirtual::1377-1vflores@ucn.clmcorrea@uao.edu.coyadiraqui@uas.edu.mxFlores, Víctor3b45a8512db1519e39d991feb6db5397Quinonez, Alma Yadira9a04a8f20f90bbb31878910b9a031687Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-28T20:43:34Z2019-11-28T20:43:34Z2017-05-27Flores 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, Cham978-3-319-59740-9 (en línea)9783319597393 (impreso)1611-3349 (en línea)0302-9743 (impresa)http://hdl.handle.net/10614/11616https://link.springer.com/chapter/10.1007/978-3-319-59773-7_51https://link.springer.com/content/pdf/10.1007%2F978-3-319-59773-7.pdfhttps://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/pdfPáginas 233-242engSpringer, 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 233-242Lecture Notes in Computer Science. 10338. Theoretical Computer Science and General Issues. 10338Derechos 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_abf2instname:Universidad Autónoma de Occidentereponame:Repositorio Institucional UAOAhmad, 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_31Altintas, 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)Benardos, P., Vosniakos, G.: Predicting surface roughness in machining: a review. Int. J. Mach. Tools Manuf. 43, 833–844 (2003)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)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)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)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)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)Friedman, N., Geiger, D., Goldszmit, M.: Bayesian network classifiers. Mach. Learn. 29, 131–161 (1997)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)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)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/s00170-017-0128-1Lela, 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)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)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)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)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)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)Stone, M.: Cross-validatory choice and assessment of statistical prediction. J. Roy. Stat. Soc. 36, 111–147 (1974)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)Zuperl, U., Cus, F.: Optimization of cutting conditions during cutting by using neural networks. Robot. Comput. Integr. Manuf. 19, 189–199 (2003)Center kernel alignmentFeature selectionFeature selectionHuman motionKinematicsMotion capture dataPrincipal component analysisRelevanceMachiningMilling (metal-work)Bayesian statistical decision theoryMecanizadoFresado (metalistería)Teoría bayesiana de decisiones estadísticasManufacturing processesHigh-speed machiningMicromachiningMecanizado de alta velocidadProcesos de manufacturaCorte de metalesPerformance of predicting surface quality model using softcomputing, a comparative study of resultsCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttps://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Publicationecc91e59-ce00-443d-bce6-ce85014e9629virtual::1377-1ecc91e59-ce00-443d-bce6-ce85014e9629virtual::1377-1https://scholar.google.com/citations?user=15MGkAQAAAAJ&hl=envirtual::1377-10000-0001-8464-2673virtual::1377-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001435997virtual::1377-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://red.uao.edu.co/bitstreams/fd92a88b-e250-469f-88fa-88b6b2b6cb17/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/ef237734-9698-44b4-bc70-f22a2ece5a70/download20b5ba22b1117f71589c7318baa2c560MD5310614/11616oai:red.uao.edu.co:10614/116162024-03-04 10:38:49.362https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidentemetadata.onlyhttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.coRUwgQVVUT1IgYXV0b3JpemEgYSBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgT2NjaWRlbnRlLCBkZSBmb3JtYSBpbmRlZmluaWRhLCBwYXJhIHF1ZSBlbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgbGEgTGV5IDQ0IGRlIDE5OTMsIGxhIERlY2lzacOzbiBhbmRpbmEgMzUxIGRlIDE5OTMsIGVsIERlY3JldG8gNDYwIGRlIDE5OTUgeSBkZW3DoXMgbGV5ZXMgeSBqdXJpc3BydWRlbmNpYSB2aWdlbnRlIGFsIHJlc3BlY3RvLCBoYWdhIHB1YmxpY2FjacOzbiBkZSBlc3RlIGNvbiBmaW5lcyBlZHVjYXRpdm9zLiBQQVJBR1JBRk86IEVzdGEgYXV0b3JpemFjacOzbiBhZGVtw6FzIGRlIHNlciB2w6FsaWRhIHBhcmEgbGFzIGZhY3VsdGFkZXMgeSBkZXJlY2hvcyBkZSB1c28gc29icmUgbGEgb2JyYSBlbiBmb3JtYXRvIG8gc29wb3J0ZSBtYXRlcmlhbCwgdGFtYmnDqW4gcGFyYSBmb3JtYXRvIGRpZ2l0YWwsIGVsZWN0csOzbmljbywgdmlydHVhbCwgcGFyYSB1c29zIGVuIHJlZCwgSW50ZXJuZXQsIGV4dHJhbmV0LCBpbnRyYW5ldCwgYmlibGlvdGVjYSBkaWdpdGFsIHkgZGVtw6FzIHBhcmEgY3VhbHF1aWVyIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2Nlci4gRUwgQVVUT1IsIGV4cHJlc2EgcXVlIGVsIGRvY3VtZW50byAodHJhYmFqbyBkZSBncmFkbywgcGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBvYmpldG8gZGUgbGEgcHJlc2VudGUgYXV0b3JpemFjacOzbiBlcyBvcmlnaW5hbCB5IGxhIGVsYWJvcsOzIHNpbiBxdWVicmFudGFyIG5pIHN1cGxhbnRhciBsb3MgZGVyZWNob3MgZGUgYXV0b3IgZGUgdGVyY2Vyb3MsIHkgZGUgdGFsIGZvcm1hLCBlbCBkb2N1bWVudG8gKHRyYWJham8gZGUgZ3JhZG8sIHBhc2FudMOtYSwgY2Fzb3MgbyB0ZXNpcykgZXMgZGUgc3UgZXhjbHVzaXZhIGF1dG9yw61hIHkgdGllbmUgbGEgdGl0dWxhcmlkYWQgc29icmUgw6lzdGUuIFBBUkFHUkFGTzogZW4gY2FzbyBkZSBwcmVzZW50YXJzZSBhbGd1bmEgcmVjbGFtYWNpw7NuIG8gYWNjacOzbiBwb3IgcGFydGUgZGUgdW4gdGVyY2VybywgcmVmZXJlbnRlIGEgbG9zIGRlcmVjaG9zIGRlIGF1dG9yIHNvYnJlIGVsIGRvY3VtZW50byAoVHJhYmFqbyBkZSBncmFkbywgUGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBlbiBjdWVzdGnDs24sIEVMIEFVVE9SLCBhc3VtaXLDoSBsYSByZXNwb25zYWJpbGlkYWQgdG90YWwsIHkgc2FsZHLDoSBlbiBkZWZlbnNhIGRlIGxvcyBkZXJlY2hvcyBhcXXDrSBhdXRvcml6YWRvczsgcGFyYSB0b2RvcyBsb3MgZWZlY3RvcywgbGEgVW5pdmVyc2lkYWQgIEF1dMOzbm9tYSBkZSBPY2NpZGVudGUgYWN0w7phIGNvbW8gdW4gdGVyY2VybyBkZSBidWVuYSBmZS4gVG9kYSBwZXJzb25hIHF1ZSBjb25zdWx0ZSB5YSBzZWEgZW4gbGEgYmlibGlvdGVjYSBvIGVuIG1lZGlvIGVsZWN0csOzbmljbyBwb2Ryw6EgY29waWFyIGFwYXJ0ZXMgZGVsIHRleHRvIGNpdGFuZG8gc2llbXByZSBsYSBmdWVudGUsIGVzIGRlY2lyIGVsIHTDrXR1bG8gZGVsIHRyYWJham8geSBlbCBhdXRvci4gRXN0YSBhdXRvcml6YWNpw7NuIG5vIGltcGxpY2EgcmVudW5jaWEgYSBsYSBmYWN1bHRhZCBxdWUgdGllbmUgRUwgQVVUT1IgZGUgcHVibGljYXIgdG90YWwgbyBwYXJjaWFsbWVudGUgbGEgb2JyYS4K

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