Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design

Trabajo de investigación

Autores:: Guzmán-Bernal, Juan Pablo

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2022

Institución:: Universidad Católica de Colombia

Repositorio:: RIUCaC - Repositorio U. Católica

Idioma:: spa

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dc.title.spa.fl_str_mv	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design
title	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design
spellingShingle	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design INTELIGENCIA ARTIFICIAL MICRO-INJECTION INJECTION-MOLDING MOLD DESIGN POLYMER ARTIFICIAL INTELLIGENCE CAE
title_short	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design
title_full	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design
title_fullStr	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design
title_full_unstemmed	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design
title_sort	Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design
dc.creator.fl_str_mv	Guzmán-Bernal, Juan Pablo
dc.contributor.advisor.none.fl_str_mv	Chaves-Acero, Miryam Liliana
dc.contributor.author.none.fl_str_mv	Guzmán-Bernal, Juan Pablo
dc.subject.lemb.none.fl_str_mv	INTELIGENCIA ARTIFICIAL
topic	INTELIGENCIA ARTIFICIAL MICRO-INJECTION INJECTION-MOLDING MOLD DESIGN POLYMER ARTIFICIAL INTELLIGENCE CAE
dc.subject.proposal.spa.fl_str_mv	MICRO-INJECTION INJECTION-MOLDING MOLD DESIGN POLYMER ARTIFICIAL INTELLIGENCE CAE
description	Trabajo de investigación
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-02-10T20:16:00Z
dc.date.available.none.fl_str_mv	2022 2022-02-10T20:16:00Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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dc.identifier.citation.none.fl_str_mv	Guzmán-Bernal, J. P. (2021). Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design. Tesis de Grado. Universidad Católica de Colombia. Facultad de Ingeniería. Maestría en Ingeniería y Gestión de la innovación. Bogotá, Colombia
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/10983/27067
identifier_str_mv	Guzmán-Bernal, J. P. (2021). Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design. Tesis de Grado. Universidad Católica de Colombia. Facultad de Ingeniería. Maestría en Ingeniería y Gestión de la innovación. Bogotá, Colombia
url	https://hdl.handle.net/10983/27067
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abdullahi, A. A., Choudhury, I. A., & Azuddin, M. (2016). Effect of runner dimensions on cavity filling in microinjection moulding for defect-free parts. ARPN Journal of Engineering and Applied Sciences, 11(12), 7788–7793. Alfreda Campo, E. (2006). The Complete Part Design Handbook: for Injection Molding of Thermoplastics. Baruffi, F., Charalambis, A., Calaon, M., Elsborg, R., & Tosello, G. (2018). Comparison of micro and conventional injection moulding based on process precision and accuracy. Procedia CIRP, 75, 149–154. https://doi.org/10.1016/j.procir.2018.04.046 Bellantone, V., Surace, R., Modica, F., & Fassi, I. (2018). Evaluation of mold roughness influence on injected thin micro-cavities. International Journal of Advanced Manufacturing Technology, 94(9–12), 4565–4575. https://doi.org/10.1007/s00170-017-1178-0 Boden, M. A. (1996). Artificial Intelligence (Handbook of Perception and Cognition). Burke, E. K., & Graham, K. (2014). Search methodologies: Introductory tutorials in optimization and decision support techniques, second edition. In Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Second Edition. https://doi.org/10.1007/978-1-4614-6940-7 Cabrera, E., Castro, J. M., Yi, A. Y., & Lee, L. J. (n.d.). Microinjection Molding. In Advanced Injection Molding Technologies (First Edit). Carl Hanser Verlag GmbH & Co. KG. https://doi.org/10.1016/B978-1-56990-603-3.50010-9 Chaubey, S. K., & Jain, N. K. (2018). State-of-art review of past research on manufacturing of meso and micro cylindrical gears. In Precision Engineering (Vol. 51, pp. 702–728). Elsevier Inc. https://doi.org/10.1016/j.precisioneng.2017.07.014 Chaves A, M. L., & Vizan, A. (n.d.). - Document - Expert system to assist in setting of micro injection machines. Retrieved May 17, 2020, from https://go.gale.com/ps/anonymous?id=GALE%7CA246014198&sid=googleScholar&v=2.1& it=r&linkaccess=abs&issn=17269679&p=AONE&sw=w Che, Z. H. (2010). PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Computers and Industrial Engineering, 58(4), 625–637. https://doi.org/10.1016/j.cie.2010.01.004 Che, Z. H., Wang, H. S., & Wang, Y. N. (2007). Cost estimation of plastic injection products through back-propagation network. https://www.researchgate.net/publication/234832207 Colombia, el séptimo país más preparado en materia tecnológica de América Latina - Cluster de Software y TI, Cámara de Comercio de Bogotá. (n.d.). Retrieved November 8, 2021, from https://www.ccb.org.co/Clusters/Cluster-de-Software-y-TI/Noticias/2018/Mayo2018/Colombia-el-septimo-pais-mas-preparado-en-materia-tecnologica-de-America-Latina Colombia entierra anualmente 2 billones de pesos en plásticos que se pueden reciclar - Cluster de Comunicación Gráfica, Cámara de Comercio de Bogotá. (n.d.). Retrieved November 8, 2021, from https://www.ccb.org.co/Clusters/Cluster-de-ComunicacionGrafica/Noticias/2019/Julio-2019/Colombia-entierra-anualmente-2-billones-de-pesos-enplasticos-que-se-pueden-reciclar Galuppo, W. de C., Magalhães, A., Ferrás, L. L., Nóbrega, J. M., & Fernandes, C. (2021). New boundary conditions for simulating the filling stage of the injection molding process. Engineering Computations (Swansea, Wales), 38(2), 762–778. https://doi.org/10.1108/EC04-2020-0190 Gao, H., Zhang, Y., Zhou, X., & Li, D. (2018). Intelligent methods for the process parameter determination of plastic injection molding. In Frontiers of Mechanical Engineering (Vol. 13, Issue 1, pp. 85–95). Higher Education Press. https://doi.org/10.1007/s11465-018-0491-0 Gülçür, M., & Whiteside, B. (2021). A study of micromanufacturing process fingerprints in microinjection moulding for machine learning and Industry 4.0 applications. International Journal of Advanced Manufacturing Technology, 115(5–6), 1943–1954. https://doi.org/10.1007/s00170-021-07252-7 Guo, Y., Hu, J., & Peng, Y. (2012). A CBR system for injection mould design based on ontology: A case study. CAD Computer Aided Design, 44(6), 496–508. https://doi.org/10.1016/j.cad.2011.12.007 INTELLIGENT SYSTEM TO SUPPORT MICRO INJECTION PROCESS (Issue June). (2020) Kazmer, D. O., & Kazmer, D. O. (2016). Injection Mold Design Engineering. In Injection Mold Design Engineering. https://doi.org/10.3139/9781569905715.fm Kim, B. R., Moon, S. N., Park, S. H., Lee, W. Il, & Kim, S. M. (2019). Simulation of Multi-cavity Micro-injection System for Reducing Cavity Filling Deviation. Fibers and Polymers, 20(2), 375–383. https://doi.org/10.1007/s12221-019-8910-3 Marhöfer, D. M., Tosello, G., Islam, A., & Hansen, H. N. (2016). Gate design in injection molding of microfluidic components process simulations. Journal of Micro and NanoManufacturing, 4(2). https://doi.org/10.1115/1.4032302 Moayyedian, M., & Mamedov, A. (2019). Multi-objective optimization of injection molding process for determination of feasible moldability index. Procedia CIRP, 84, 769–773. https://doi.org/10.1016/j.procir.2019.04.213 Peñafiel, C., & Ing. Ávila, R. (2007). Inteligencia Artificial. In Inteligencia Artificial (Vol. 2, Issue 6). https://doi.org/10.4114/ia.v2i6.614 Ponce Cruz, P. (2010). Inteligencia Artificial con aplicaciones a la ingeniería. In Alfaomega, México. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Inteligencia+artificial+co n+aplicaciones+a+la+ingenier�a#0 Ponce Cruz, P. (2011). Inteligencia Artificial con aplicaciones a la ingeniería. In Alfaomega, México. Poszwa, P., Brzek, P., Muszynski, P., & Szostak, M. (2019). Influence of fill imbalance on pressure drop in injection molding. In Lecture Notes in Mechanical Engineering. Springer International Publishing. https://doi.org/10.1007/978-3-319-99353-9_58 Principles-of-CADCAMCAE-by-Kunwoo-Lee-_z-lib.org_.pdf. (n.d.) Processing, P. (2015). การข ◌ึ◌้นร ◌ูปแบบฉ ◌ีด ( Injection Molding ) ส ◌่ วนประกอบเคร ◌ือ ◌่ ง Injection Molding ส ◌่ วนประกอบเคร ◌ือ ◌่ ง Injection Molding ◌่ ง Injection Molding. Plastic Product Material and Process Selection Handbook, 1–5. https://doi.org/10.1016/B978- 185617431-2/50007-4 Rojas, A., Chaves, M. L., Bolivar, H., & Vizan, A. (2019, October 1). Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts. 2019 Congreso Internacional de Innovacion y Tendencias En Ingenieria, CONIITI 2019 - Conference Proceedings. https://doi.org/10.1109/CONIITI48476.2019.8960825 S.S. Quek, G. R. L. (2003). The finite element method. 368. Stewart, R. H., Palmer, T. S., & DuPont, B. (2021). A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers. Progress in Nuclear Energy, 138(June). https://doi.org/10.1016/j.pnucene.2021.103830 Surace, R., Sorgato, M., Bellantone, V., Modica, F., Lucchetta, G., & Fassi, I. (2019). Effect of cavity surface roughness and wettability on the filling flow in micro injection molding. Journal of Manufacturing Processes, 43, 105–111. https://doi.org/10.1016/j.jmapro.2019.04.032 Surace, Rossella, Trotta, G., Bellantone, V., & Fassi, I. (2012). the Micro Injection Moulding Process for Polymeric Components Manufacturing. New Technologies - Trends, Innovations and Research, May 2014. https://doi.org/10.5772/35299 Tosello, G. (2019). Product / Process Fingerprint in Micro Manufacturing. 1, 1–3. Trotta, G., Stampone, B., Fassi, I., & Tricarico, L. (2021). Study of rheological behaviour of polymer melt in micro injection moulding with a miniaturized parallel plate rheometer. Polymer Testing, 96, 107068. https://doi.org/10.1016/j.polymertesting.2021.107068 Van De Ven, T. G. M. (1985). The flow of suspensions. In Polymer Composites (Vol. 6, Issue 4). https://doi.org/10.1002/pc.750060405 Vera, J., Brulez, A. C., Contraires, E., Larochette, M., Trannoy-Orban, N., Pignon, M., Mauclair, C., Valette, S., & Benayoun, S. (2018). Factors influencing microinjection molding replication quality. Journal of Micromechanics and Microengineering, 28(1). https://doi.org/10.1088/1361-6439/aa9a4e Warwick, K. (2013). Artificial intelligence: The basics. In Artificial Intelligence: The Basics. https://doi.org/10.4324/9780203802878 Wu, T., Jahan, S. A., Zhang, Y., Zhang, J., Elmounayri, H., & Tovar, A. (2017). Design Optimization of Plastic Injection Tooling for Additive Manufacturing. Procedia Manufacturing, 10, 923– 934. https://doi.org/10.1016/j.promfg.2017.07.082 Yao, D., & Kim, B. (2002). Simulation of the filling process in micro channels for polymeric materials. Journal of Micromechanics and Microengineering, 12(5), 604–610. https://doi.org/10.1088/0960-1317/12/5/314 Zhang, H., Fang, F., Gilchrist, M. D., & Zhang, N. (2019). Precision replication of micro features using micro injection moulding: Process simulation and validation. Materials and Design, 177, 107829. https://doi.org/10.1016/j.matdes.2019.107829 Zhang, N., Su, Q., Choi, S. Y., & Gilchrist, M. D. (2015). Effects of gate design and cavity thickness on filling, morphology and mechanical properties of microinjection mouldings. Materials and Design, 83, 835–847. https://doi.org/10.1016/j.matdes.2015.06.012 Zhao, X., Korey, M., Li, K., Copenhaver, K., Tekinalp, H., Celik, S., Kalaitzidou, K., Ruan, R., Ragauskas, A. J., & Ozcan, S. (2022). Plastic waste upcycling toward a circular economy. Chemical Engineering Journal, 428(August 2021), 131928. https://doi.org/10.1016/j.cej.2021.131928 Zhijun, Y., Wang, H., Wei, X., Yan, K., & Gao, C. (2019). Multiobjective optimization method for polymer injection molding based on a genetic algorithm. Advances in Polymer Technology, 2019. https://doi.org/10.1155/2019/9012085
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dc.publisher.spa.fl_str_mv	Universidad Católica de Colombia
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spelling	Chaves-Acero, Miryam Liliana8ea8ec68-068c-41bb-a237-3eb5fd95ff15-1Guzmán-Bernal, Juan Pablo74647ead-0918-45bd-ad5c-6c6b5f98c8b3-12022-02-10T20:16:00Z20222022-02-10T20:16:00Z2022Trabajo de investigaciónContain the process to develop a smart decision support system for microinjection mold design, from the definition of parameters and standard micro parts, simulation process using CAE, selection, and application of AI to data obtained finally with analysis and validation of the results provided by the smart system.MaestríaMagister en Ingeniería y Gestión de la Innovación1. INTRODUCTION 2. PROBLEM STATEMENT 3. OBJECTIVES 4. CONCEPTUAL FRAMEWORK 5. THEORICAL FRAMEWORKS 6. STATE OF THE ART 7. METHODOLOGY 8. DESCRIPTION OF PROJECT 9. SYSTEM VALIDATION THROUGHT FEM AND FVM RESULTS 10. CONCLUSIONS AND FUTURE WORK REFERENCES ANNEXES138 páginasapplication/pdfGuzmán-Bernal, J. P. (2021). Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design. Tesis de Grado. Universidad Católica de Colombia. Facultad de Ingeniería. Maestría en Ingeniería y Gestión de la innovación. Bogotá, Colombiahttps://hdl.handle.net/10983/27067spaUniversidad Católica de ColombiaFacultad de IngenieríaBogotáMaestría en Ingeniería y Gestión de la InnovaciónAbdullahi, A. A., Choudhury, I. A., & Azuddin, M. (2016). Effect of runner dimensions on cavity filling in microinjection moulding for defect-free parts. ARPN Journal of Engineering and Applied Sciences, 11(12), 7788–7793.Alfreda Campo, E. (2006). The Complete Part Design Handbook: for Injection Molding of Thermoplastics.Baruffi, F., Charalambis, A., Calaon, M., Elsborg, R., & Tosello, G. (2018). Comparison of micro and conventional injection moulding based on process precision and accuracy. Procedia CIRP, 75, 149–154. https://doi.org/10.1016/j.procir.2018.04.046Bellantone, V., Surace, R., Modica, F., & Fassi, I. (2018). Evaluation of mold roughness influence on injected thin micro-cavities. International Journal of Advanced Manufacturing Technology, 94(9–12), 4565–4575. https://doi.org/10.1007/s00170-017-1178-0Boden, M. A. (1996). Artificial Intelligence (Handbook of Perception and Cognition).Burke, E. K., & Graham, K. (2014). Search methodologies: Introductory tutorials in optimization and decision support techniques, second edition. In Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Second Edition. https://doi.org/10.1007/978-1-4614-6940-7Cabrera, E., Castro, J. M., Yi, A. Y., & Lee, L. J. (n.d.). Microinjection Molding. In Advanced Injection Molding Technologies (First Edit). Carl Hanser Verlag GmbH & Co. KG. https://doi.org/10.1016/B978-1-56990-603-3.50010-9Chaubey, S. K., & Jain, N. K. (2018). State-of-art review of past research on manufacturing of meso and micro cylindrical gears. In Precision Engineering (Vol. 51, pp. 702–728). Elsevier Inc. https://doi.org/10.1016/j.precisioneng.2017.07.014Chaves A, M. L., & Vizan, A. (n.d.). - Document - Expert system to assist in setting of micro injection machines. Retrieved May 17, 2020, from https://go.gale.com/ps/anonymous?id=GALE%7CA246014198&sid=googleScholar&v=2.1& it=r&linkaccess=abs&issn=17269679&p=AONE&sw=wChe, Z. H. (2010). PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Computers and Industrial Engineering, 58(4), 625–637. https://doi.org/10.1016/j.cie.2010.01.004Che, Z. H., Wang, H. S., & Wang, Y. N. (2007). Cost estimation of plastic injection products through back-propagation network. https://www.researchgate.net/publication/234832207Colombia, el séptimo país más preparado en materia tecnológica de América Latina - Cluster de Software y TI, Cámara de Comercio de Bogotá. (n.d.). Retrieved November 8, 2021, from https://www.ccb.org.co/Clusters/Cluster-de-Software-y-TI/Noticias/2018/Mayo2018/Colombia-el-septimo-pais-mas-preparado-en-materia-tecnologica-de-America-LatinaColombia entierra anualmente 2 billones de pesos en plásticos que se pueden reciclar - Cluster de Comunicación Gráfica, Cámara de Comercio de Bogotá. (n.d.). Retrieved November 8, 2021, from https://www.ccb.org.co/Clusters/Cluster-de-ComunicacionGrafica/Noticias/2019/Julio-2019/Colombia-entierra-anualmente-2-billones-de-pesos-enplasticos-que-se-pueden-reciclarGaluppo, W. de C., Magalhães, A., Ferrás, L. L., Nóbrega, J. M., & Fernandes, C. (2021). New boundary conditions for simulating the filling stage of the injection molding process. Engineering Computations (Swansea, Wales), 38(2), 762–778. https://doi.org/10.1108/EC04-2020-0190Gao, H., Zhang, Y., Zhou, X., & Li, D. (2018). Intelligent methods for the process parameter determination of plastic injection molding. In Frontiers of Mechanical Engineering (Vol. 13, Issue 1, pp. 85–95). Higher Education Press. https://doi.org/10.1007/s11465-018-0491-0Gülçür, M., & Whiteside, B. (2021). A study of micromanufacturing process fingerprints in microinjection moulding for machine learning and Industry 4.0 applications. International Journal of Advanced Manufacturing Technology, 115(5–6), 1943–1954. https://doi.org/10.1007/s00170-021-07252-7Guo, Y., Hu, J., & Peng, Y. (2012). A CBR system for injection mould design based on ontology: A case study. CAD Computer Aided Design, 44(6), 496–508. https://doi.org/10.1016/j.cad.2011.12.007INTELLIGENT SYSTEM TO SUPPORT MICRO INJECTION PROCESS (Issue June). (2020)Kazmer, D. O., & Kazmer, D. O. (2016). Injection Mold Design Engineering. In Injection Mold Design Engineering. https://doi.org/10.3139/9781569905715.fmKim, B. R., Moon, S. N., Park, S. H., Lee, W. Il, & Kim, S. M. (2019). Simulation of Multi-cavity Micro-injection System for Reducing Cavity Filling Deviation. Fibers and Polymers, 20(2), 375–383. https://doi.org/10.1007/s12221-019-8910-3Marhöfer, D. M., Tosello, G., Islam, A., & Hansen, H. N. (2016). Gate design in injection molding of microfluidic components process simulations. Journal of Micro and NanoManufacturing, 4(2). https://doi.org/10.1115/1.4032302Moayyedian, M., & Mamedov, A. (2019). Multi-objective optimization of injection molding process for determination of feasible moldability index. Procedia CIRP, 84, 769–773. https://doi.org/10.1016/j.procir.2019.04.213Peñafiel, C., & Ing. Ávila, R. (2007). Inteligencia Artificial. In Inteligencia Artificial (Vol. 2, Issue 6). https://doi.org/10.4114/ia.v2i6.614Ponce Cruz, P. (2010). Inteligencia Artificial con aplicaciones a la ingeniería. In Alfaomega, México. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Inteligencia+artificial+co n+aplicaciones+a+la+ingenier�a#0Ponce Cruz, P. (2011). Inteligencia Artificial con aplicaciones a la ingeniería. In Alfaomega, México.Poszwa, P., Brzek, P., Muszynski, P., & Szostak, M. (2019). Influence of fill imbalance on pressure drop in injection molding. In Lecture Notes in Mechanical Engineering. Springer International Publishing. https://doi.org/10.1007/978-3-319-99353-9_58Principles-of-CADCAMCAE-by-Kunwoo-Lee-_z-lib.org_.pdf. (n.d.)Processing, P. (2015). การข ◌ึ◌้นร ◌ูปแบบฉ ◌ีด ( Injection Molding ) ส ◌่ วนประกอบเคร ◌ือ ◌่ ง Injection Molding ส ◌่ วนประกอบเคร ◌ือ ◌่ ง Injection Molding ◌่ ง Injection Molding. Plastic Product Material and Process Selection Handbook, 1–5. https://doi.org/10.1016/B978- 185617431-2/50007-4Rojas, A., Chaves, M. L., Bolivar, H., & Vizan, A. (2019, October 1). Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts. 2019 Congreso Internacional de Innovacion y Tendencias En Ingenieria, CONIITI 2019 - Conference Proceedings. https://doi.org/10.1109/CONIITI48476.2019.8960825S.S. Quek, G. R. L. (2003). The finite element method. 368.Stewart, R. H., Palmer, T. S., & DuPont, B. (2021). A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers. Progress in Nuclear Energy, 138(June). https://doi.org/10.1016/j.pnucene.2021.103830Surace, R., Sorgato, M., Bellantone, V., Modica, F., Lucchetta, G., & Fassi, I. (2019). Effect of cavity surface roughness and wettability on the filling flow in micro injection molding. Journal of Manufacturing Processes, 43, 105–111. https://doi.org/10.1016/j.jmapro.2019.04.032Surace, Rossella, Trotta, G., Bellantone, V., & Fassi, I. (2012). the Micro Injection Moulding Process for Polymeric Components Manufacturing. New Technologies - Trends, Innovations and Research, May 2014. https://doi.org/10.5772/35299Tosello, G. (2019). Product / Process Fingerprint in Micro Manufacturing. 1, 1–3.Trotta, G., Stampone, B., Fassi, I., & Tricarico, L. (2021). Study of rheological behaviour of polymer melt in micro injection moulding with a miniaturized parallel plate rheometer. Polymer Testing, 96, 107068. https://doi.org/10.1016/j.polymertesting.2021.107068Van De Ven, T. G. M. (1985). The flow of suspensions. In Polymer Composites (Vol. 6, Issue 4). https://doi.org/10.1002/pc.750060405Vera, J., Brulez, A. C., Contraires, E., Larochette, M., Trannoy-Orban, N., Pignon, M., Mauclair, C., Valette, S., & Benayoun, S. (2018). Factors influencing microinjection molding replication quality. Journal of Micromechanics and Microengineering, 28(1). https://doi.org/10.1088/1361-6439/aa9a4eWarwick, K. (2013). Artificial intelligence: The basics. In Artificial Intelligence: The Basics. https://doi.org/10.4324/9780203802878Wu, T., Jahan, S. A., Zhang, Y., Zhang, J., Elmounayri, H., & Tovar, A. (2017). Design Optimization of Plastic Injection Tooling for Additive Manufacturing. Procedia Manufacturing, 10, 923– 934. https://doi.org/10.1016/j.promfg.2017.07.082Yao, D., & Kim, B. (2002). Simulation of the filling process in micro channels for polymeric materials. Journal of Micromechanics and Microengineering, 12(5), 604–610. https://doi.org/10.1088/0960-1317/12/5/314Zhang, H., Fang, F., Gilchrist, M. D., & Zhang, N. (2019). Precision replication of micro features using micro injection moulding: Process simulation and validation. Materials and Design, 177, 107829. https://doi.org/10.1016/j.matdes.2019.107829Zhang, N., Su, Q., Choi, S. Y., & Gilchrist, M. D. (2015). Effects of gate design and cavity thickness on filling, morphology and mechanical properties of microinjection mouldings. Materials and Design, 83, 835–847. https://doi.org/10.1016/j.matdes.2015.06.012Zhao, X., Korey, M., Li, K., Copenhaver, K., Tekinalp, H., Celik, S., Kalaitzidou, K., Ruan, R., Ragauskas, A. J., & Ozcan, S. (2022). Plastic waste upcycling toward a circular economy. Chemical Engineering Journal, 428(August 2021), 131928. https://doi.org/10.1016/j.cej.2021.131928Zhijun, Y., Wang, H., Wei, X., Yan, K., & Gao, C. (2019). Multiobjective optimization method for polymer injection molding based on a genetic algorithm. 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Develop of a smart decision support system integrating computational aided engineering (CAE) and artificial intelligence (AI) for micro-injection mold design

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