Diseño Generativo Realimentado (DGR) como soporte en los procesos de creación de productos

dc.contributor.advisorCordoba Nieto, Ernesto
dc.contributor.authorRestrepo Mendoza, Jhoan Sebastian
dc.contributor.researchgroupGrupo de Investigación: Grupo de trabajo en nuevas tecnologías de diseño y manufactura-automatización DIMA-UNspa
dc.date.accessioned2023-11-29T15:11:48Z
dc.date.available2023-11-29T15:11:48Z
dc.date.issued2023-11-28
dc.descriptionilustraciones, diagramas, fotografíasspa
dc.description.abstractEl presente trabajo de maestría se presenta como una metodología de diseño generativo realimentado para la creación de producto. Esta metodología se plantea considerando la integración de tecnología actual como lo es el diseño paramétrico en el software CAD y la propuesta de un ecosistema discreto de nube de puntos. Se usa conceptos de técnicas de optimización multiobjetivo para la exploración y explotación de un espacio, al generar un conjunto de soluciones que satisfacen los objetivos del nuevo producto. Se propone un desarrollo de modelamiento basado en estructuras de datos, específicamente grafos direccionales. Los grafos direccionales contienen en su nodos la información necesaria para operar sus entradas y generar valores de salida, que a su vez serán usados por otro nodo para operar y obtener otras salidas. Este proceso secuencial permite obtener el modelamiento de un componente, que sera tratado posteriormente en procesos de optimización multiobjetivo para obtener soluciones (nuevos productos) al clasificar los frentes de Pareto. La realimentación de esta propuesta se genera al desarrollar simulaciones de las etapas de manufactura, por tal motivo el ecosistema propuesto y la tecnología actual permite ingresar como objetivos estas etapas posteriores y reducir las iteraciones para obtener un producto funcional. Para la creación del ecosistema propuesto se implementa el uso de programación paralela en la obtención de soluciones que por métodos secuenciales no son viables. (Texto tomado de la fuente)spa
dc.description.abstractThe present master’s thesis is presented as a feedback-based generative design methodology for product creation. This methodology is proposed considering the integration of current technology such as parametric design in CAD software and the proposal of a discrete point cloud ecosystem. Concepts of multi-objective optimization techniques are used for the exploration and exploitation of a space, generating a set of solutions that satisfy the objectives of the new product. A modeling development based on data structures is proposed, specifically directed graphs. Directed graphs contain the necessary information in their nodes to operate their inputs and generate output values, which will in turn be used by another node to operate and obtain further outputs. This sequential process allows obtaining the modeling of a component, which will be subsequently subjected to multi-objective optimization processes to obtain solutions (new products) by classifying Pareto fronts. Feedback in this proposal is generated by simulating the manufacturing stages. For this reason, the proposed ecosystem and current technology allow incorporating these subsequent stages as objectives and reducing iterations to obtain a functional product. The creation of the proposed ecosystem involves the implementation of parallel programming to obtain solutions that are not viable through sequential methods.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagister en Automatización Industrialspa
dc.description.researchareaDiseño de productos y procesos industriales y preseriesspa
dc.format.extentxxiii, 206 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/85019
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrialspa
dc.relation.references[1] J. S. Restrepo Mendoza and E. Cordoba Nieto, “DISEÑO PARAMÉTRICO PARA CLASIFICACIÓN DE FAMILIAS DE PRODUCTOS EN MANUFACTURA DISCRETA EN EL LABFABEXUN,” in CONGRESO INTERNACIONAL DE INGENIERÍA MECÁNICA, MECATRÓNICA Y AUTOMATIZACIÓN - Memorias 2021, pp. 21–21, 2021.spa
dc.relation.referencesMartin Hankel, “RAMI4.0 – Reference Architecture Model Industry 4.0.,” 11 2016.spa
dc.relation.referencesTheOPCFoundation, “RAMI4.0 by Martin Hankel (Bosch-Rexroth) at OPC Day Europe 2016,” 2 2017.spa
dc.relation.referencesGonz´alez, “Measurement of Areas on a Sphere Using Fibonacci and Latitude-Longitude Lattices,” Mathematical Geosciences, vol. 42, pp. 49–64, 1 2010.spa
dc.relation.referencesDepartamento Nacional de Planeaci´on (DNP), Superintendencia de Industria y Comercio (SIC), Direcci´on Nacional de Derecho de Autor (DNDA), Instituto Colombiano Agropecuario (ICA), Organizaci´on Mundial de la Propiedad Intelectual (OMPI), and Misi´on permanente de Colombia ante las naciones Unidas, “Reporte sobre la informaci ´on en materia de Propiedad Intelectual en Colombia,” tech. rep., 2017.spa
dc.relation.referencesJ. Mountstephens and J. Teo, “Progress and challenges in generative product design: A review of systems,” 12 2020.spa
dc.relation.referencesZ. Jiang, H. Wen, F. Han, Y. Tang, and Y. Xiong, “Data-driven generative design for mass customization: A case study,” Advanced Engineering Informatics, vol. 54, 10 2022.spa
dc.relation.referencesP. Wolniak, B. Sauthoff, D. Kloock-Schreiber, and R. Lachmayer, “AUTOMATED PRODUCT FUNCTIONALITY and DESIGN OPTIMIZATION INSTANCING A PRODUCT-SERVICE SYSTEM,” in Proceedings of the Design Society: DESIGN Conference, vol. 1, pp. 1405–1414, Cambridge University Press, 2020.spa
dc.relation.referencesM. A. S. Al-Shamsi, “Review of Korean Imitation and Innovation in the Last 60 Years,” 3 2022.spa
dc.relation.referencesB. Bartikowski, F. Fastoso, and H. Gierl, “Luxury cars Made-in-China: Consequences for brand positioning,” Journal of Business Research, vol. 102, pp. 288–297, 9 2019spa
dc.relation.referencesK. D. Thoben, S. A. Wiesner, and T. Wuest, “Industrie 4.0 and smart manufacturing-a review of research issues and application examples,” 2017.spa
dc.relation.referencesD. G. Ullman, The Mechanical Design Process, vol. 1. 2010.spa
dc.relation.referencesA. M. Farid and N. P. Suh, Axiomatic Design in Large Systems. 2016.spa
dc.relation.referencesR. L. Norton, Dise˜no de m´aquinas. Un enfoque integrado. Pearson Educaci´on, cuarta edi ed., 2011.spa
dc.relation.referencesS. BuHamdan, A. Alwisy, and A. Bouferguene, “Generative systems in the architecture, engineering and construction industry: A systematic review and analysis,” International Journal of Architectural Computing, 2020.spa
dc.relation.referencesD. Nagaraj and D. Werth, “Towards a Generalized System for Generative Engineering,” in ACM International Conference Proceeding Series, Association for Computing Machinery, 1 2020.spa
dc.relation.referencesS. Fox, “A preliminary methodology for generative production systems,” Journal of Manufacturing Technology Management, vol. 22, no. 3, pp. 348–364, 2011.spa
dc.relation.referencesA. N. Pilagatti, G. Vecchi, E. Atzeni, L. Iuliano, and A. Salmi, “Generative Design and new designers’ role in the manufacturing industry,” in Procedia CIRP, vol. 112, pp. 364–369, Elsevier B.V., 2022.spa
dc.relation.referencesC. Hyunjin, “A Study on Application of Generative Design System in Manufacturing Process,” in IOP Conference Series: Materials Science and Engineering, vol. 727, Institute of Physics Publishing, 1 2020.spa
dc.relation.referencesJ.Wu, M. Li, Z. Chen, W. Chen, X.Wu, and Y. Xi, “Generative Design of the Roller Seat of the Wind Turbine Blade Turnover Machine Based on Cloud Computing,” ICMAE 2020 - 2020 11th International Conference on Mechanical and Aerospace Engineering, pp. 212–217, 2020.spa
dc.relation.referencesH. Li and R. Lachmayer, “Automated exploration of design solution space applying the generative design approach,” in Proceedings of the International Conference on Engineering Design, ICED, vol. 2019-August, pp. 1085–1094, Cambridge University Press, 2019.spa
dc.relation.referencesS. Khan and M. J. Awan, “A generative design technique for exploring shape variations,” Advanced Engineering Informatics, vol. 38, no. October, pp. 712–724, 2018.spa
dc.relation.referencesS. Khan, E. Gunpinar, and B. Sener, “GenYacht: An interactive generative design system for computer-aided yacht hull design,” Ocean Engineering, vol. 191, no. August, p. 106462, 2019.spa
dc.relation.referencesE. Gunpinar and S. Gunpinar, “A shape sampling technique via particle tracing for CAD models,” Graphical Models, vol. 96, no. January, pp. 11–29, 2018spa
dc.relation.referencesS. Oh, Y. Jung, S. Kim, I. Lee, and N. Kang, “Deep generative design: Integration of topology optimization and generative models,” Journal of Mechanical Design, Transactions of the ASME, vol. 141, 11 2019spa
dc.relation.referencesP. Ghannad and Y. C. Lee, “Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (CoGAN),” Automation in Construction, vol. 139, 7 2022spa
dc.relation.referencesN. A. Kallioras and N. D. Lagaros, “DzAI: Deep learning based generative design,” Procedia Manufacturing, vol. 44, pp. 591–598, 2020spa
dc.relation.referencesN. A. Kallioras and N. D. Lagaros, “Mlgen: Generative design framework based on machine learning and topology optimization,” Applied Sciences (Switzerland), vol. 11, 12 2021spa
dc.relation.referencesJ. C. Garc´ıa Carrero, Planeaci´on de trayectorias en vuelo de un manipulador industrial para el Laboratorio F´abrica Experimental UN. PhD thesis, Universidad Nacional de Colombia, Bogot´a D.C, 2017spa
dc.relation.referencesC. Sarmiento Fautoque, Desarrollo Te´orico- Experimental en la Geometr´ıa de Maquinado M´ulti-ejes aplicando Ingenier´ıa Inversa Mixta. PhD thesis, Universidad Nacional de Colombia, Bogot´a D.C., 2014spa
dc.relation.referencesV. Granadeiro, L. Pina, J. P. Duarte, J. R. Correia, and V. M. Leal, “A general indirect representation for optimization of generative design systems by genetic algorithms: Application to a shape grammar-based design system,” Automation in Construction, vol. 35, pp. 374–382, 2013spa
dc.relation.referencesH. Li and R. Lachmayer, “Generative Design Approach for Modeling Creative Designs,” IOP Conference Series: Materials Science and Engineering, vol. 408, no. 1, 2018spa
dc.relation.referencesS. S. Pibal, K. Khoss, and I. Kovacic, “Framework of an algorithm-aided BIM approach for modular residential building information models,” International Journal of Architectural Computing, vol. 20, pp. 777–800, 12 2022spa
dc.relation.referencesM. Younus, C. Peiyong, L. Hu, and F. Yuqing, “MES development and significant applications in manufacturing -A review,” in ICETC 2010 - 2010 2nd International Conference on Education Technology and Computer, vol. 5, 2010spa
dc.relation.referencesT. A. Jauhar, M. Safdar, I. Kim, and S. Han, “Web-based Product Data Visualization and Feedback between PLM and MES,” in Proceedings - Web3D 2020: 25th ACM Conference on 3D Web Technology, Association for Computing Machinery, Inc, 11 2020spa
dc.relation.referencesG. Bruno, A. Faveto, and E. Traini, “An open source framework for the storage and reuse of industrial knowledge through the integration of plm and mes,” Management and Production Engineering Review, vol. 11, pp. 62–73, 6 2020spa
dc.relation.referencesE. Traini, G. Bruno, A. Awouda, P. Chiabert, and F. Lombardi, “Integration Between PLM and MES for One-of-a-Kind Production,” in IFIP Advances in Information and Communication Technology, vol. 565 IFIP, pp. 356–365, Springer, 2019spa
dc.relation.referencesM. I. Mahmoud, H. H. Ammar, M. M. Hamdy, and M. H. Eissa, “Production operation management using Manufacturing Execution Systems (MES),” in 2015 11th International Computer Engineering Conference: Today Information Society What’s Next?, ICENCO 2015, pp. 111–116, Institute of Electrical and Electronics Engineers Inc., 2 2016spa
dc.relation.referencesW. Qifeng and W. Zhangjian, “Web services-based system integration approach for manufacturing execution system,” in Proceedings - 2011 International Conference on Internet Computing and Information Services, ICICIS 2011, pp. 469–472, 2011spa
dc.relation.referencesS.-H. Jing, Q.-J. Meng, and W.-Q. Cao, “Cement Enterprise MES Key Technology Research and Application,” in 2007 International Conference on Machine Learning and Cybernetics, pp. 277–282, IEEE, 8 2007spa
dc.relation.referencesW. Qu, W. Cao, and Y. C. Su, “Design and implementation of smart manufacturing execution system in solar industry,” Journal of Ambient Intelligence and Humanized Computing, 2020spa
dc.relation.referencesY. Yue-Xina and R. Gong-Chang, “Design of Real Time Data Acquisition System Framework for Production Workshop Based on OPC Technology,” in MATEC Web of Conferences, vol. 128, EDP Sciences, 10 2017spa
dc.relation.referencesX. Zeng, “Design and implementation of production management system in aviation machining workshop based on MES,” in Proceedings - International Conference on Control Science and Electric Power Systems, CSEPS 2021, pp. 385–388, Institute of Electrical and Electronics Engineers Inc., 5 2021spa
dc.relation.referencesS. Mantravadi, C. Møller, C. LI, and R. Schnyder, “Design choices for next-generation IIoT-connected MES/MOM: An empirical study on smart factories,” Robotics and Computer-Integrated Manufacturing, vol. 73, 2 2022spa
dc.relation.referencesJ. Barata, P. R. da Cunha, A. S. Gonnagar, and M. Mendes, “Product traceability in ceramic industry 4.0: A design approach and cloud-based MES prototype,” in Lecture Notes in Information Systems and Organisation, vol. 26, pp. 187–204, Springer Heidelberg, 2018spa
dc.relation.referencesM. Ko, C. Lee, and Y. Cho, “Design and Implementation of Cloud-Based Collaborative Manufacturing Execution System in the Korean Fashion Industry,” Applied Sciences (Switzerland), vol. 12, 9 2022spa
dc.relation.referencesX. Han, M. Li, and X. Zhang, “Design and key technology of MES for spacecraft assembly,” in Proceedings - 2016 6th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2016, pp. 844–847, Institute of Electrical and Electronics Engineers Inc., 12 2016spa
dc.relation.referencesD. F. Tosse, S. Araujo, and E. C´ordoba, “Plataforma para integraci´on de m´aquinas en laboratorio f´abrica experimental con enfoque de Industria 4.0,” in Innovar para educar (Corporaci´on Cimted© 2020, ed.), vol. 1, pp. 125–146, Medell´ın, Antioquia – Colombia: Corporaci´on Centro Internacional de Marketing Territorial para la Educaci´on y el Desarrollo, primera ed., 2020spa
dc.relation.referencesR. Y. Zhong, G. Q. Huang, Q. Y. Dai, K. Zhou, T. Qu, and G. J. Hu, “RFID-enabled real-time manufacturing execution system for discrete manufacturing: Software design and implementation,” in 2011 International Conference on Networking, Sensing and Control, ICNSC 2011, pp. 311–316, 2011spa
dc.relation.referencesY. Wang, M. Wang, J. Wang, and Y. Zhou, “Design and implementation of device integration framework based on agent technology in MES,” in Procedia CIRP, vol. 83, pp. 485–489, Elsevier B.V., 2019spa
dc.relation.referencesG. D’Antonio, F. Segonds, J. S. Bedolla, P. Chiabert, and N. Anwer, “A proposal of manufacturing execution system integration in design for additive manufacturing,” in IFIP Advances in Information and Communication Technology, vol. 467, pp. 761–770, Springer New York LLC, 2016spa
dc.relation.referencesM. Naedele, H. M. Chen, R. Kazman, Y. Cai, L. Xiao, and C. V. Silva, “Manufacturing execution systems: A vision for managing software development,” Journal of Systems and Software, vol. 101, pp. 59–68, 3 2015spa
dc.relation.referencesT. Masood and R. H. Weston, “Modelling framework to support decision-making in manufacturing enterprises,” Advances in Decision Sciences, vol. 2013, 2013spa
dc.relation.referencesH. Habib, R. Menhas, and O. McDermott, “Managing Engineering Change within the Paradigm of Product Lifecycle Management,” Processes, vol. 10, 9 2022spa
dc.relation.referencesM. Hayat and H. Winkler, “Exploring the Basic Features and Challenges of Traditional Product Lifecycle Management Systems,” in IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2022-December, pp. 762–766, IEEE Computer Society, 2022spa
dc.relation.referencesS. R¨adler and E. Rigger, “A Survey on the Challenges Hindering the Application of Data Science, Digital Twins and Design Automation in Engineering Practice,” in Proceedings of the Design Society, vol. 2, pp. 1699–1708, Cambridge University Press, 5 2022spa
dc.relation.referencesS. Nzetchou, A. Durupt, S. Remy, and B. Eynard, “Semantic enrichment approach for low-level CAD models managed in PLM context: Literature review and research prospect,” Computers in Industry, vol. 135, 2 2022spa
dc.relation.referencesM. Lennartsson, S. Andr´e, and F. Elgh, “PLM support for design platforms in industrialized house-building,” Construction Innovation, 2 2021spa
dc.relation.referencesV. Kopei, O. Onysko, C. Barz, P. Daˇsi´c, and V. Panchuk, “Designing a Multi-Agent PLM System for Threaded Connections Using the Principle of Isomorphism of Regularities of Complex Systems,” Machines, vol. 11, 2 2023spa
dc.relation.referencesY. Liao, F. Deschamps, E. d. F. R. Loures, and L. F. P. Ramos, “Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal,” International Journal of Production Research, vol. 55, no. 12, pp. 3609–3629, 2017spa
dc.relation.referencesJ. W. Veile, D. Kiel, J. M. M¨uller, and K. I. Voigt, “Lessons learned from Industry 4.0 implementation in the German manufacturing industry,” Journal of Manufacturing Technology Management, 2019spa
dc.relation.references“Status Report Reference Architecture Model Industrie 4.0 (RAMI4.0),” tech. rep., 2015spa
dc.relation.referencesA. F. Cifuentes G´omez, Implementaci´on de sistemas de gesti´on de informaci´on del ciclo de vida de producto basado en el desarrollo de un molde de inyecci´on. PhD thesis, Universidad Nacional de Colombia, Bogot´a, Colombia, 2021spa
dc.relation.referencesP. Andr´es and C. Parra, “Modelo e-Manufacturing bajo la arquitectura Cloud Manufacturing para el Laboratorio F´abrica Experimental UN,” tech. rep., 2015spa
dc.relation.referencesM. K. Mohanty, P. Gahan, and S. Choudhury, “Why most of the supplier development programs fail in discrete manufacturing – findings from selected Indian discrete manufacturing industries,” 2014spa
dc.relation.referencesT. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K. H. Johansson, “A survey of distributed optimization,” Annual Reviews in Control, vol. 47, pp. 278–305, 2019spa
dc.relation.referencesA. K. Sethi and S. P. Sethi, “Flexibility in manufacturing: A survey,” International Journal of Flexible Manufacturing Systems, vol. 2, no. 4, pp. 289–328, 1990.spa
dc.relation.referencesS. K. Saren and V. Tiberiu, “Review of Flexible Manufacturing System Based on Modeling and Simulation,” ANNALS OF THE ORADEA UNIVERSITY. Fascicle of Management and Technological Engineering., vol. Volume XXV, no. 1, 2016.spa
dc.relation.referencesG. Kim, Y. Kwon, E. S. Suh, and J. Ahn, “Analysis of Architectural Complexity for Product Family and Platform,” Journal of Mechanical Design, Transactions of the ASME, vol. 138, no. 7, pp. 1–11, 2016spa
dc.relation.referencesO. Asikoglu and T. W. Simpson, “A new method for evaluating design dependencies in product architectures,” 12th AIAA Aviation Technology, Integration and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, no. September, pp. 1–11, 2012spa
dc.relation.referencesM. Lafou, L. Mathieu, S. Pois, and M. Alochet, “Manufacturing System Flexibility: Product Flexibility Assessment,” Procedia CIRP, vol. 41, pp. 99–104, 2016.spa
dc.relation.referencesF. M. Kasie, G. Bright, and A. Walker, “Decision support systems in manufacturing: a survey and future trends,” Journal of Modelling in Management, vol. 12, no. 3, pp. 432– 454, 2017.spa
dc.relation.referencesJ. Igba, K. Alemzadeh, P. M. Gibbons, and K. Henningsen, “A framework for optimising product performance through feedback and reuse of in-service experience,” Robotics and Computer-Integrated Manufacturing, vol. 36, pp. 2–12, 2015spa
dc.relation.referencesM. von Stietencron, K. A. Hribernik, C. C. Røstad, B. Henriksen, and K. D. Thoben, “Applying closed-loop product lifecycle management to enable fact based design of boats,” in IFIP Advances in Information and Communication Technology, vol. 517, pp. 522–531, Springer New York LLC, 2017spa
dc.relation.referencesC. a. Coello Coello and G. B. Lamont, Applications Of Multi-Objective Evolutionary Algorithms. 2004spa
dc.relation.referencesH. Ishibuchi, N. Tsukamoto, and Y. Nojima, “Evolutionary many-objective optimization: A short review,” in 2008 IEEE Congress on Evolutionary Computation, CEC 2008, 2008.spa
dc.relation.referencesG. Chiandussi, M. Codegone, S. Ferrero, and F. E. Varesio, “Comparison of multiobjective optimization methodologies for engineering applications,” Computers and Mathematics with Applications, vol. 63, no. 5, pp. 912–942, 2012.spa
dc.relation.referencesG. Ortega, E. Filatovas, E. M. Garz´on, and L. G. Casado, “Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU,” Journal of Global Optimization, vol. 69, pp. 607–627, 11 2017spa
dc.relation.referencesL. B. De Oliveira, C. G. Marcelino, A. Milanes, P. E. Almeida, and L. M. Carvalho, “A successful parallel implementation of NSGA-II on GPU for the energy dispatch problem on hydroelectric power plants,” in 2016 IEEE Congress on Evolutionary Computation, CEC 2016, pp. 4305–4312, Institute of Electrical and Electronics Engineers Inc., 11 2016.spa
dc.relation.referencesK. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.spa
dc.relation.referencesE. Zitzler, M. Laumanns, and L. Thiele, “SPEA2: Improving the Strength Pareto Evolutionary Algorithm,” Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100, 2001spa
dc.relation.referencesP. K. Tripathi, S. Bandyopadhyay, and S. K. Pal, “Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients,” Information Sciences, vol. 177, pp. 5033–5049, 11 2007.spa
dc.relation.referencesX. Li, “A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization,” Genetic and Evolutionary Computation — GECCO 2003, vol. 2723, pp. 37–48, 6 2003spa
dc.relation.referencesR. Sedgewick and K. Wayne, “Algorithms,” tech. repspa
dc.relation.referencesJ. J. Craig, P. Prentice, and P. P. Hall, “Introduction to Robotics Mechanics and Control Third Edition,” tech. rep., 2005.spa
dc.relation.referencesI. Viana, J.-J. Orteu, N. Cornille, and F. Bugarin, “Inspection of aeronautical mechanical parts with a pan-tilt-zoom camera: an approach guided by the computer-aided design model,” Journal of Electronic Imaging, vol. 24, no. 6, p. 061118, 2015spa
dc.relation.referencesH. Huang, J. Liu, S. Liu, T. Wu, and P. Jin, “A method for classifying tube structures based on shape descriptors and a random forest classifier,” Measurement: Journal of the International Measurement Confederation, vol. 158, p. 107705, 2020spa
dc.relation.referencesF. Hui, P. Payeur, and A. M. Cretu, “Visual tracking of deformation and classification of non-rigid objects with robot hand probing,” Robotics, vol. 6, no. 1, 2017.spa
dc.relation.referencesJ. K. Oh, S. Lee, and C. H. Lee, “Stereo vision based automation for a bin-picking solution,” International Journal of Control, Automation and Systems, vol. 10, no. 2, pp. 362–373, 2012spa
dc.relation.referencesE. Gunpinar and S. Khan, A multi-criteria based selection method using non-dominated sorting for genetic algorithm based design. No. 65, Springer US, 2019spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.subject.ddc670 - Manufacturaspa
dc.subject.ddc620 - Ingeniería y operaciones afinesspa
dc.subject.ddc680 - Manufactura para usos específicosspa
dc.subject.ddc600 - Tecnología (Ciencias aplicadas)spa
dc.subject.lembDiseño industrialspa
dc.subject.lembDesign, industrialeng
dc.subject.lembAutomatizaciónspa
dc.subject.lembAutomationeng
dc.subject.lembProductos nuevosspa
dc.subject.lembNew productseng
dc.subject.lembDiseños de productosspa
dc.subject.lembDesarrollo de nuevos productosspa
dc.subject.lembNew product developmenteng
dc.subject.proposalDiseño generativo realimentadospa
dc.subject.proposalDiseño generativospa
dc.subject.proposalOptimización multiobjetivospa
dc.subject.proposalDiseño paramétricospa
dc.subject.proposalGrafos direccionalesspa
dc.subject.proposalFrentes de Paretospa
dc.subject.proposalProgramación paralelaspa
dc.subject.proposalFeedback-based generative designeng
dc.subject.proposalFeedback generative designeng
dc.subject.proposalGenerative designeng
dc.subject.proposalMulti-objective optimizationeng
dc.subject.proposalParametric designeng
dc.subject.proposalDirected graphseng
dc.subject.proposalPareto frontseng
dc.subject.proposalparallel programmingeng
dc.titleDiseño Generativo Realimentado (DGR) como soporte en los procesos de creación de productosspa
dc.title.translatedFeedback Generative Design (FGD) as Support in Product Creation Processeseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
1033777324.2023.pdf
Tamaño:
28.21 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ingeniería - Ingeniería Industrial

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción: