{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T09:56:26Z","timestamp":1763632586636,"version":"3.45.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032096999","type":"print"},{"value":"9783032097002","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-09700-2_11","type":"book-chapter","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T09:51:40Z","timestamp":1763632300000},"page":"109-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Discussion of Data-Driven and Knowledge-Driven Approaches in Manufacturing Process: Challenges and Opportunities for Industry 5.0"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3136-8241","authenticated-orcid":false,"given":"Matheus Herman Bernardim","family":"Andrade","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8977-1351","authenticated-orcid":false,"given":"Anderson Luis","family":"Szejka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Szejka, A.L., Canciglieri Junior, O., Mas, F.: Knowledge-based expert system to drive an informationally interoperable manufacturing system: an experimental application in the Aerospace Industry. J. Ind. Inf. Integr. 41, 100661 (2024). https:\/\/doi.org\/10.1016\/j.jii.2024.100661","DOI":"10.1016\/j.jii.2024.100661"},{"key":"11_CR2","doi-asserted-by":"publisher","unstructured":"Andrade, M.H.B., Szejka, A.L., Mas, F.: Application of models for manufacturing (MFM) methodology to aerospace sheet metal parts manufacturing. In: 10th Manufacturing Engineering Society International Conference (MESIC 2023). Adv. Sci. Technol. vol.\u00a0132, pp.\u00a0270\u2013278. Trans Tech Publications Ltd (2023). https:\/\/doi.org\/10.4028\/p-P5b5aR","DOI":"10.4028\/p-P5b5aR"},{"key":"11_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-025-15473-3","author":"MHB Andrade","year":"2025","unstructured":"Andrade, M.H.B., Szejka, A.L., Mas, F.: Intelligent systems applied to anomaly detection and diagnosis in product design and manufacturing: aerospace industry case study. Int. J. Adv. Manuf. Technol. (2025). https:\/\/doi.org\/10.1007\/s00170-025-15473-3","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"12809","DOI":"10.1007\/s00521-024-09960-z","volume":"36","author":"BP Bhuyan","year":"2024","unstructured":"Bhuyan, B.P., Ramdane-Cherif, A., Tomar, R., Singh, T.: Neuro-symbolic artificial intelligence: a survey. Neural Comput. Appl. 36, 12809\u201312844 (2024). https:\/\/doi.org\/10.1007\/s00521-024-09960-z","journal-title":"Neural Comput. Appl."},{"key":"11_CR5","doi-asserted-by":"publisher","unstructured":"Bougzime, O., Jabbar, S., Cruz, C., Demoly, F.: Unlocking the potential of generative ai through neuro-symbolic architectures: Benefits and limitations. https:\/\/doi.org\/10.48550\/arXiv.2502.11269 (2025)","DOI":"10.48550\/arXiv.2502.11269"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Chkirbene, Z., Hamila, R., Gouissem, A., Devrim, U.: Large language models (llm) in industry: A survey of applications, challenges, and trends. In: 2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET). pp.\u00a0229\u2013234 (2024). https:\/\/doi.org\/10.1109\/HONET63146.2024.10822885","DOI":"10.1109\/HONET63146.2024.10822885"},{"key":"11_CR7","doi-asserted-by":"publisher","unstructured":"Colelough, B.C., Regli, W.: Neuro-symbolic ai in 2024: A systematic review https:\/\/doi.org\/10.48550\/arXiv.2501.05435 (2025)","DOI":"10.48550\/arXiv.2501.05435"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"de Andrade, J.M.M., de M. Leite, A.F.C.S., Canciglieri, M.B., Szejka, A.L., de F. R. Loures, E., Canciglieri Junior, O.: A multi-criteria decision tool for FMEA in the context of product development and industry 4.0. Int. J. Comput. Integr. Manuf. 35, 36\u201349 (2022). https:\/\/doi.org\/10.1080\/0951192X.2021.1992664","DOI":"10.1080\/0951192X.2021.1992664"},{"issue":"4","key":"11_CR9","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1109\/TASE.2020.2983061","volume":"17","author":"SKS Fan","year":"2020","unstructured":"Fan, S.K.S., Hsu, C.Y., Tsai, D.M., He, F., Cheng, C.C.: Data-driven approach for fault detection and diagnostic in semiconductor. IEEE Trans. Autom. Sci. Eng. 17(4), 1925\u20131936 (2020). https:\/\/doi.org\/10.1109\/TASE.2020.2983061","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"1","key":"11_CR10","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s12599-023-00834-7","volume":"66","author":"S Feuerriegel","year":"2024","unstructured":"Feuerriegel, S., Hartmann, J., Janiesch, C., Zschech, P.: Generative ai. Bus. Inf. Syst. Eng. 66(1), 111\u2013126 (2024). https:\/\/doi.org\/10.1007\/s12599-023-00834-7","journal-title":"Bus. Inf. Syst. Eng."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Figli\u00e8, R., Turchi, T., Baldi, G., Mazzei, D.: Towards an LLM-based intelligent assistant for industry 5.0. In: Proceedings of the 1st International Workshop on Designing and Building Hybrid Human\u2013AI Systems (SYNERGY 2024). vol.\u00a03701 (2024)","DOI":"10.1145\/3656650.3660537"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Gaffinet, B., Al Haj Ali, J., Naudet, Y., Panetto, H.: Human digital twins: A systematic literature review and concept disambiguation for industry 5.0. Computers in Industry, 166, 104230 (2025). https:\/\/doi.org\/10.1016\/j.compind.2024.104230","DOI":"10.1016\/j.compind.2024.104230"},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Garcia, C.I., DiBattista, M.A., Letelier, T.A., Halloran, H.D., Camelio, J.A.: Framework for llm applications in manufacturing. Manufacturing Letters, 41, 253\u2013263 (2024). https:\/\/doi.org\/10.1016\/j.mfglet.2024.09.030","DOI":"10.1016\/j.mfglet.2024.09.030"},{"key":"11_CR14","unstructured":"IBM: Large language models (2025). https:\/\/www.ibm.com\/brpt\/think\/topics\/large-language-models. Acessed 27 Mar 2025"},{"key":"11_CR15","doi-asserted-by":"publisher","unstructured":"Kernan Freire, Wang, C., Foosherian, M., Wellsandt, S., Ruiz-Arenas, S., Niforatos, E.: Knowledge sharing in manufacturing using llm-powered tools: user study and model benchmarking. Frontiers in Artificial Intelligence 7 (2024). https:\/\/doi.org\/10.3389\/frai.2024.1293084","DOI":"10.3389\/frai.2024.1293084"},{"key":"11_CR16","doi-asserted-by":"publisher","unstructured":"Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., Wang, L.: Industry 5.0: prospect and retrospect. J. Manuf. Syst. 65, 279\u2013295 (2022). https:\/\/doi.org\/10.1016\/j.jmsy.2022.09.017","DOI":"10.1016\/j.jmsy.2022.09.017"},{"key":"11_CR17","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1016\/j.jmsy.2024.10.007","volume":"77","author":"Q Li","year":"2024","unstructured":"Li, Q., Liu, Y., Sun, S., Qin, Z., Chu, F.: Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI. J. Manuf. Syst. 77, 652\u2013661 (2024). https:\/\/doi.org\/10.1016\/j.jmsy.2024.10.007","journal-title":"J. Manuf. Syst."},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.jmatprotec.2006.11.087","volume":"187","author":"W Liu","year":"2007","unstructured":"Liu, W., Liu, Q., Ruan, F., Liang, Z., Qiu, H.: Springback prediction for sheet metal forming based on GA-ANN technology. J. Mater. Process. Technol. 187, 227\u2013231 (2007). https:\/\/doi.org\/10.1016\/j.jmatprotec.2006.11.087","journal-title":"J. Mater. Process. Technol."},{"key":"11_CR19","unstructured":"My\u00f6h\u00e4nen, J.: Improving industrial performance with language models: a review of predictive maintenance and process optimization (2023)"},{"key":"11_CR20","doi-asserted-by":"publisher","unstructured":"Nawaz, U, Annes-ur-Rahaman, M., Saeed, Z.: A review of neuro-symbolic ai integrating reasoning and learning for advanced cognitive systems. Intell. Syst. Appl. 200541 (2025). https:\/\/doi.org\/10.1016\/j.iswa.2025.200541,","DOI":"10.1016\/j.iswa.2025.200541"},{"key":"11_CR21","unstructured":"NVIDIA: Introdu\u00e7\u00e3o a grandes modelos de linguagem para solu\u00e7\u00f5es corporativas (2023). https:\/\/blog.nvidia.com.br\/blog\/introducao-a-grandes-modelos-de-linguagem-para-solucoes-corporativas\/. Acessed 27 Mar 2025"},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.promfg.2017.08.017","volume":"12","author":"S Ramakrishna","year":"2017","unstructured":"Ramakrishna, S., Khong, T.C., Leong, T.K.: Smart manufacturing. Procedia Manuf. 12, 128\u2013131 (2017). https:\/\/doi.org\/10.1016\/j.promfg.2017.08.017","journal-title":"Procedia Manuf."},{"key":"11_CR23","doi-asserted-by":"publisher","unstructured":"Santos, L.M.A.L.D., Costa, M.B. da, Kothe, J.V., Benitez, G.B., Schaefer, J.L., Baierle, I.C., Nara, E.O.B.: Industry 4.0 collaborative networks for industrial performance. J. Manuf. Technol. Manage. 32, 245\u2013265 (2020). https:\/\/doi.org\/10.1108\/JMTM-04-2020-0156","DOI":"10.1108\/JMTM-04-2020-0156"},{"key":"11_CR24","doi-asserted-by":"publisher","unstructured":"Shukla, B., Fan, I.S., Jennions, I.: Opportunities for explainable artificial intelligence in aerospace predictive maintenance. In: PHM Society European Conference. vol.\u00a05, p.\u00a011 (2020). https:\/\/doi.org\/10.36001\/phme.2020.v5i1.1231","DOI":"10.36001\/phme.2020.v5i1.1231"},{"key":"11_CR25","unstructured":"Skrzek, M., Andrade, M.H.B., Cavalcanti, L.H., Szejka, A.L., Mas, F.: Intelligent system for anomaly detection and decision-making support based on semantic web technologies in manufacturing processes in aerospace industry. In: FOMI 2024: 13th International Workshop on Formal Ontologies Meet Industry, held at JOWO 2024: Episode X The Tukker Zomer of Ontology, 15\u201319 July 2024, Enschede, Netherlands (2024)"},{"key":"11_CR26","unstructured":"Szejka, A.L., Canciglieri, O., Jr., Loures, E.R., Panetto, H., Aubry, A.: Requirements interoperability method to support integrated product development. Presented at the Proceedings - CIE 45: 2015 International Conference on Computers and Industrial Engineering (2015)"},{"key":"11_CR27","doi-asserted-by":"publisher","unstructured":"Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manuf. Syst. 48, 157\u2013169. (2018). https:\/\/doi.org\/10.1016\/j.jmsy.2018.01.006","DOI":"10.1016\/j.jmsy.2018.01.006"},{"key":"11_CR28","doi-asserted-by":"publisher","unstructured":"Ugur, E., Ahmetoglu, A., Nagai, Y., Taniguchi, T., Saveriano, M., Oztop, E.: Neuro-symbolic robotics (2025). https:\/\/doi.org\/10.13140\/RG.2.2.25854.09283","DOI":"10.13140\/RG.2.2.25854.09283"},{"key":"11_CR29","doi-asserted-by":"publisher","unstructured":"Wan, Z., Liu, C.K., Yang, H., et al.: Towards cognitive ai systems: a survey and prospective on neuro-symbolic ai. (2024) https:\/\/doi.org\/10.48550\/arXiv.2401.01040","DOI":"10.48550\/arXiv.2401.01040"},{"key":"11_CR30","doi-asserted-by":"publisher","unstructured":"Wang, B., Tao, F., Fang, X., Liu, C., Liu, Y., Freiheit, T.: Smart manufacturing and intelligent manufacturing: A comparative review. Engineering 7(6), 738\u2013757 (2021). https:\/\/doi.org\/10.1016\/j.eng.2020.07.017","DOI":"10.1016\/j.eng.2020.07.017"},{"key":"11_CR31","doi-asserted-by":"publisher","unstructured":"Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: Methods and applications. J. Manuf. Syst. 48, 144\u2013156 (2018). https:\/\/doi.org\/10.1016\/j.jmsy.2018.01.003","DOI":"10.1016\/j.jmsy.2018.01.003"},{"key":"11_CR32","doi-asserted-by":"publisher","unstructured":"Wang, T., Fan, J., Zheng, P.: An llm-based vision and language cobot navigation approach for human-centric smart manufacturing. J. Manuf. Syst. 75, 299\u2013305 (2024). https:\/\/doi.org\/10.1016\/j.jmsy.2024.04.020","DOI":"10.1016\/j.jmsy.2024.04.020"},{"issue":"8","key":"11_CR33","doi-asserted-by":"publisher","DOI":"10.1115\/1.4043798","volume":"141","author":"Z Wang","year":"2019","unstructured":"Wang, Z., Liu, P., Xiao, Y., Cui, X., Hu, Z., Chen, L.: A data-driven approach for process optimization of metallic additive manufacturing under uncertainty. J. Manuf. Sci. Eng. 141(8), 081004 (2019). https:\/\/doi.org\/10.1115\/1.4043798","journal-title":"J. Manuf. Sci. Eng."},{"issue":"1","key":"11_CR34","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/21693277.2016.1192517","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23\u201345 (2016). https:\/\/doi.org\/10.1080\/21693277.2016.1192517","journal-title":"Prod. Manuf. Res."},{"issue":"11","key":"11_CR35","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.1080\/24725854.2018.1555383","volume":"51","author":"H Yang","year":"2019","unstructured":"Yang, H., Kumara, S., Bukkapatnam, S.T.S., Tsung, F.: The internet of things for smart manufacturing: A review. IISE Trans. 51(11), 1190\u20131216 (2019). https:\/\/doi.org\/10.1080\/24725854.2018.1555383","journal-title":"IISE Trans."},{"key":"11_CR36","doi-asserted-by":"publisher","unstructured":"Zhang, X., Sheng, V.S.: Neuro-symbolic ai: Explainability, challenges, and future trends https:\/\/doi.org\/10.48550\/arXiv.2411.04383(2024)","DOI":"10.48550\/arXiv.2411.04383"}],"container-title":["IFIP Advances in Information and Communication Technology","Product Lifecycle Management. PLM in the Age of Model-Based Engineering in Industry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09700-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T09:51:43Z","timestamp":1763632303000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09700-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"ISBN":["9783032096999","9783032097002"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09700-2_11","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,21]]},"assertion":[{"value":"21 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"PLM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Product Lifecycle Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Seville","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"plm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.plm-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}