An Ontology-based Engineering methodology applied to aerospace Reconfigurable Manufacturing Systems design

References

• Arista, R., F. Mas, D. Morales-Palma, D. Ernadote, M. Oliva, and C. Vallellano. 2022c. “Evaluation of a Commercial Model Lifecycle Management (MLM) Tool to Support Models for Manufacturing (MfM) Methodology.” In Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies. PLM 2022. IFIP Advances in Information and Communication Technology, edited by F. Noël, F. Nyffenegger, L. Rivest, and A. Bouras, 667. doi:10.1007/978-3-031-25182-5_65.
   Google Scholar
• Arista, R., F. Mas, D. Morales-Palma, M. Oliva, and C. Vallellano. 2021. “A Preliminary Ontology-Based Engineering Application to Industrial System Reconfiguration in Conceptual Phase.” CEUR Workshop Proceedings of FOMI 2021: 11th International Workshop on Formal Ontologies Meet Industry. https://ceur-ws.org/Vol-2969/paper20-FOMI.pdf.
   Google Scholar
• Arista, R., F. Mas, D. Morales-Palma, M. Oliva, and C. Vallellano. 2022a. “The Role of Industrial Resources in Reconfigurable Aerospace Production Systems: A Preliminary Literature Review.” IFAC-PapersOnLine 55 (10): 2719–2724. doi:10.1016/j.ifacol.2022.10.124.
   Google Scholar
• Arista, R., F. Mas, D. Morales-Palma, and C. Vallellano. 2022b. “Industrial Resources in the Design of Reconfigurable Manufacturing Systems for Aerospace: A Systematic Literature Review.” Computers in Industry 142: 103719. doi:10.1016/j.compind.2022.103719.
   Web of Science ®Google Scholar
• Arista, R., F. Mas, and C. Vallellano. 2020. “Initial Approach to an Industrial Resources Ontology in Aerospace Assembly Lines.” IFIP International Conference on Product Lifecycle Management PLM20 285–294. doi:10.1007/978-3-030-62807-9_23.
   Google Scholar
• Bhardwaj, A. S., D. Veeramani, and S. Zhou. 2023. “Confidently Extracting Hierarchical Taxonomy Information from Unstructured Maintenance Records of Industrial Equipment.” International Journal of Production Research. doi:10.1080/00207543.2023.2167013.
   PubMed Web of Science ®Google Scholar
• Boldt, S., and C. Rosio. 2020. “Evaluation of Reconfigurability in Brownfield Manufacturing Development.” Advances in Transdisciplinary Engineering 13: 513–524. doi:10.3233/ATDE200190.
   Google Scholar
• Bortolini, M., F. G. Galizia, and C. Mora. 2018. “Reconfigurable Manufacturing Systems: Literature Review and Research Trend.” Journal of Manufacturing Systems 49: 93–106. doi:10.1016/j.jmsy.2018.09.005.
   Web of Science ®Google Scholar
• Bouissiere, F., C. Cuiller, P. E. Dereux, C. Malchair, C. Favi, and G. Formentini. 2019. “Conceptual Design for Assembly in Aerospace Industry: A Method to Assess Manufacturing and Assembly Aspects of Product Architectures.” Proceedings of the Design Society: International Conference on Engineering Design 1 (1): 2961–2970. doi:10.1017/dsi.2019.303.
   Google Scholar
• Caggiano, A., A. Marzano, and R. Teti. 2016. “Resource Efficient Configuration of an Aircraft Assembly Line.” Procedia CIRP 41: 236–241. doi:10.1016/j.procir.2015.12.130.
   Google Scholar
• Celek, O. E., M. Yurdakul, and T. Ic. 2019. “Axiomatic Design of a Reconfigurable Assembly System for Aircraft Fuselages.” SAE Technical Papers. doi:10.4271/2019-01-1359.
   Google Scholar
• Cooper, D., and G. LaRocca. 2007. “Knowledge-based Techniques for Developing Engineering Applications in the 21st Century.” 7th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference. doi:10.2514/6.2007-7711.
   Google Scholar
• Dassault Systèmes. 2022. “UML Profiling and DSL Guide.” MagicDraw 19.0 LTR Documentation. Accessed December 12 2022. https://docs.nomagic.com/display/MD190/UML+Profiling+and+DSL+Guide.
   Google Scholar
• Etz, D., H. Brantner, and W. Kastner. 2020. “Smart Manufacturing Retrofit for Brownfield Systems.” Procedia Manufacturing 42: 327–332. doi:10.1016/j.promfg.2020.02.085.
   Google Scholar
• Fenves, S. J., S. Foufou, C. Bock, and R. D. Sriram. 2008. “CPM2: A Core Model for Product Data.” Journal of Computing and Information Science in Engineering 8 (1): 014501–014501. doi:10.1115/1.2830842.
   Web of Science ®Google Scholar
• Gruber, T. R. 1995. “Toward Principles for the Design of Ontologies Used for Knowledge Sharing?” International Journal of Human-Computer Studies 43 (5–6): 907–928. doi:10.1006/ijhc.1995.1081.
   Web of Science ®Google Scholar
• Haug, A. 2022. “Factors Influencing Knowledge Sharing in new Product Development in High-Tech Manufacturing Firms.” International Journal of Production Research. doi:10.1080/00207543.2022.2128918.
   Web of Science ®Google Scholar
• Jarrar, Q., F. Belkadi, R. Blanc, K. Kestaneci, and A. Bernard. 2022. “Knowledge Reuse for Decision Aid in Additive Manufacturing: Application on Cost Quotation Support.” International Journal of Production Research. doi:10.1080/00207543.2022.2142861.
   Web of Science ®Google Scholar
• Jefferson, T. G., P. Benardos, and S. Ratchev. 2016. “Reconfigurable Assembly System Design Methodology: A Wing Assembly Case Study.” SAE International Journal of Materials and Manufacturing 9 (1): 31–48. doi:10.4271/2015-01-2594.
   Google Scholar
• Jefferson, T. G., S. Ratchev, and R. Crossley. 2014. “Axiomatic Design of a Reconfigurable Assembly System for Primary Wing Structures.” SAE International Journal of Aerospace 7 (2). doi:10.4271/2014-01-2249.
   Google Scholar
• Koren, Y. 2006. “General RMS Characteristics. Comparison with Dedicated and Flexible Systems.” In Reconfigurable Manufacturing Systems and Transformable Factories, edited by Anatoli I. Dashchenko, 27–45. Berlin, Heidelberg: Springer. doi:10.1007/3-540-29397-3.
   Google Scholar
• Koren, Y., U. Heisel, F. Jovane, T. Moriwaki, G. Pritschow, and G. Ulsoy. 1999. “Reconfigurable Manufacturing Systems.” CIRP Annals 48 (2): 527–540. doi:10.1016/S0007-8506(07)63232-6
   Web of Science ®Google Scholar
• Koren, Y., and M. Shpitalni. 2010. “Design of Reconfigurable Manufacturing Systems.” Journal of Manufacturing Systems 29 (4): 130–141. doi:10.1016/j.jmsy.2011.01.001.
   Web of Science ®Google Scholar
• Kumar, S. P. L. 2019. “Knowledge-based Expert System in Manufacturing Planning: State-of-the-Art Review.” International Journal of Production Research 57 (15-16): 4766–4790. doi:10.1080/00207543.2018.1424372.
   Web of Science ®Google Scholar
• Liu, J., P. Zhao, X. Jing, X. Cao, S. Sheng, H. Zhou, X. Liu, and F. Feng. 2022. “Dynamic Design Method of Digital Twin Process Model Driven by Knowledge-Evolution Machining Features.” International Journal of Production Research 60 (7): 2312–2330. doi:10.1080/00207543.2021.1887531.
   Web of Science ®Google Scholar
• Maganha, I., C. Silva, and L. M. D. F. Ferreira. 2019. “The Layout Design in Reconfigurable Manufacturing Systems: A Literature Review.” International Journal of Advanced Manufacturing Technology 105 (1–4): 683–700. doi:10.1007/s00170-019-04190-3.
   Web of Science ®Google Scholar
• Mas, F., M. Oliva, J. Racero, and D. Morales-Palma. 2018. “A Preliminary Methodological Approach to Models for Manufacturing (MfM).” Product Lifecycle Management to Support Industry 4.0. PLM 2018. IFIP Advances in Information and Communication Technology 540. doi:10.1007/978-3-030-01614-2_25.
   Google Scholar
• Mas, F., J. Racero, M. Oliva, and D. Morales-Palma. 2019. “Preliminary Ontology Definition for Aerospace Assembly Lines in Airbus Using Models for Manufacturing Methodology.” Procedia Manufacturing 28: 207–213. doi:10.1016/j.promfg.2018.12.034.
   Google Scholar
• Mas, F., J. Rios, J. L. Menendez, and A. Gomez. 2013. “A Process-Oriented Approach to Modelling the Conceptual Design of Aircraft Assembly Lines.” The International Journal of Advanced Manufacturing Technology 67 (1-4): 771–784. doi:10.1007/s00170-012-4521-5.
   Web of Science ®Google Scholar
• McCarthy, J. 1980. “Circumscription – a Form of non-Monotonic Reasoning.” Artificial Intelligence 13 (1-2): 27–39. doi:10.1016/0004-3702(80)90011-9.
   Web of Science ®Google Scholar
• Morales-Palma, D., M. Oliva, C. Vallellano, and F. Mas. 2022. “Enhanced Metamodels Approach Supporting Models for Manufacturing (MfM) Methodology.” IFIP International Conference on Product Lifecycle Management PLM22. https://ceur-ws.org/Vol-3240/paper1.pdf.
   Google Scholar
• Object Management Group. 2017. “OMG Unified Modeling Language Version 2.5.1.” Object Management Group. Accessed December 9th 2022. https://www.omg.org/spec/UML/2.5.1/PDF.
   Google Scholar
• Okorie, O., R. Subramoniam, F. Charnley, J. Patsavellas, D. Widdifield, and K. Salonitis. 2020. “Manufacturing in the Time of COVID-19: An Assessment of Barriers and Enablers.” IEEE Engineering Management Review 48 (3): 167–175. doi:10.1109/EMR.2020.3012112.
   Google Scholar
• Pansare, R., G. Yadav, and M. R. Nagare. 2021. “Reconfigurable Manufacturing System: A Systematic Review, Meta-Analysis and Future Research Directions.” Journal of Engineering, Design and Technology. doi:10.1108/JEDT-05-2021-0231.
   Web of Science ®Google Scholar
• Sanya, I. O., and E. M. Shehab. 2014. “An Ontology Framework for Developing Platform-Independent Knowledge-based Engineering Systems in the Aerospace Industry.” International Journal of Production Research 52 (20): 6192–6215. doi:10.1080/00207543.2014.919422.
   Web of Science ®Google Scholar
• Sanya, I. O., and E. M. Shehab. 2015. “A Framework for Developing Engineering Design Ontologies Within the Aerospace Industry.” International Journal of Production Research 53 (8): 2383–2409. doi:10.1080/00207543.2014.965352.
   Web of Science ®Google Scholar
• Smith, B. 2012. “Ontology.” In The Furniture of the World. Leiden: Brill. doi:10.1163/9789401207799_005.
   Google Scholar
• Taisch, M., M. L. Casidsid, G. May, T. R. Morin, V. Padelli, M. Pinzone, and T. Wuest. 2020. “World Manufacturing Forum 2020. Back to the Future: Manufacturing beyond Covid-19.” ISBN: 978-88-94386-16-5.
   Google Scholar
• Terkaj, W., and M. Urgo. 2014. “Ontology-based Modelling of Production Systems for Design and Performance Evaluation.” Proceedings 2014 12th IEEE International Conference on Industrial Informatics, INDIN 2014, 748–753.
   Google Scholar
• Trappey, A. J. C., C. V. Trappey, T. A. Chiang, and Y. H. Huang. 2013. “Ontology-based Neural Network for Patent Knowledge Management in Design Collaboration.” International Journal of Production Research 51 (7): 1992–2005. doi:10.1080/00207543.2012.701775.
   Web of Science ®Google Scholar
• Van De Ginste, L., A. De Cock, A. Van Alboom, S. Huysentruyt, H. Aghezzaf, and J. Cottyn. 2022. “A Formal Skill Model to Enable Reconfigurable Assembly Systems.” International Journal of Production Research. doi:10.1080/00207543.2022.2128922.
   Web of Science ®Google Scholar
• Wen, Y., X. Yue, J. H. Hunt, and J. Shi. 2019. “Virtual Assembly and Residual Stress Analysis for the Composite Fuselage Assembly Process.” Journal of Manufacturing Systems 52: 55–62. doi:10.1016/j.jmsy.2019.04.001.
   Web of Science ®Google Scholar
• Wu, Z., L. He, X. Ming, and M. Goh. 2022. “Service-oriented Knowledge Recommender System and Performance Evaluation in Industrial Product Development.” International Journal of Production Research 60 (20): 6226–6247. doi:10.1080/00207543.2021.1988748.
   Web of Science ®Google Scholar
• Yelles-Chaouche, A. R., E. Gurevsky, N. Brahimi, and A. Dolgui. 2020. “Reconfigurable Manufacturing Systems from an Optimisation Perspective: A Focused Review of Literature.” International Journal of Production Research 59 (21). doi:10.1080/00207543.2020.1813913.
   Web of Science ®Google Scholar
• Zheng, C., Y. An, Z. Wang, X. Qin, B. Eynard, M. Bricogne, J. Le Duigou, and Y. Zhang. 2022. “Knowledge-based Engineering Approach for Defining Robotic Manufacturing System Architectures.” International Journal of Production Research. doi:10.1080/00207543.2022.2037025.
   Web of Science ®Google Scholar
• Zheng, X., X. Hu, R. Arista, J. Lu, J. Sorvari, J. Lentes, F. Ubis, and D. Kiritsis. 2022. “A Semantic-Driven Tradespace Framework to Accelerate Aircraft Manufacturing System Design.” Journal of Intelligent Manufacturing. doi:10.1007/s10845-022-02043-7.
   PubMed Web of Science ®Google Scholar
• Zindel, A., S. Feo-Arenis, P. Helle, G. Schramm, and M. Elaasar. 2022. “Building a Semantic Layer for Early Design Trade Studies in the Development of Commercial Aircraft.” Proceedings of the 8th IEEE International Symposium on Systems Engineering (ISSE). doi:10.1109/ISSE54508.2022.10005324.
   Google Scholar