{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T03:01:05Z","timestamp":1774580465715,"version":"3.50.1"},"reference-count":41,"publisher":"ASME International","issue":"7","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/100008582","name":"McGill University","doi-asserted-by":"publisher","award":["Grant# 00157"],"award-info":[{"award-number":["Grant# 00157"]}],"id":[{"id":"10.13039\/100008582","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004489","name":"Mitacs","doi-asserted-by":"publisher","award":["Grant# IT13369"],"award-info":[{"award-number":["Grant# IT13369"]}],"id":[{"id":"10.13039\/501100004489","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000046","name":"National Research Council Canada","doi-asserted-by":"publisher","award":["Grant# NRC INT-015-1"],"award-info":[{"award-number":["Grant# NRC INT-015-1"]}],"id":[{"id":"10.13039\/501100000046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Data-driven research in additive manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and artificial intelligence (AI) contexts. It requires substantial effort and time to extract scientific information from these works. AM domain experts have contributed over two dozen review articles to summarize these works. However, information specific to AM and AI contexts still requires manual effort to extract. The recent success of foundation models such as bidirectional encoder representations for transformers or generative pre-trained transformers on text sequences has opened the possibility of expediting scientific information extraction. We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature. A demonstration tool is implemented based on the proposed framework and a case study is conducted to extract information relevant to the datasets, modeling, sensing, and AM system categories. We show the ability of large language models to expedite the extraction of relevant information from data-driven AM literature. In the future, the framework can be used to extract information from the broader design and manufacturing literature in the engineering discipline.<\/jats:p>","DOI":"10.1115\/1.4068275","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T17:49:39Z","timestamp":1742579379000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":3,"title":["Human\u2013Artificial Intelligence Teaming for Scientific Information Extraction From Data-Driven Additive Manufacturing Literature Using Large Language Models"],"prefix":"10.1115","volume":"25","author":[{"given":"Mutahar","family":"Safdar","sequence":"first","affiliation":[{"name":"McGill University Department of Mechanical Engineering, , , \u00a0 ,","place":["Montreal, QC, Canada, H3A 0C3"]}]},{"given":"Jiarui","family":"Xie","sequence":"additional","affiliation":[{"name":"McGill University Department of Mechanical Engineering, , , \u00a0 ,","place":["Montreal, QC, Canada, H3A 0C3"]}]},{"given":"Andrei","family":"Mircea","sequence":"additional","affiliation":[{"name":"University of Montreal & Mila Department of Computer Science and Operations Research, , , \u00a0 ,","place":["Montreal, QC, Canada, H3C 3J7"]}]},{"given":"Yaoyao Fiona","family":"Zhao","sequence":"additional","affiliation":[{"name":"McGill University Department of Mechanical Engineering, , , \u00a0 ,","place":["Montreal, QC, Canada, H3A 0C3"]}]}],"member":"33","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"issue":"1","key":"2025051513142903000_CIT0001","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/s10462-020-09876-9","article-title":"A Review on Machine Learning in 3D Printing: Applications, Potential, and Challenges","volume":"54","author":"Goh","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"2025051513142903000_CIT0002","doi-asserted-by":"publisher","first-page":"101641","DOI":"10.1016\/j.addma.2020.101641","article-title":"Invited Review: Machine Learning for Materials Developments in Metals Additive Manufacturing","volume":"36","author":"Johnson","year":"2020","journal-title":"Addit. Manuf."},{"issue":"6","key":"2025051513142903000_CIT0003","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10845-022-01957-6","article-title":"A Systematic Literature Review on Recent Trends of Machine Learning Applications in Additive Manufacturing","volume":"34","author":"Xames","year":"2022","journal-title":"J. Intell. Manuf."},{"issue":"4","key":"2025051513142903000_CIT0004","doi-asserted-by":"publisher","first-page":"416","DOI":"10.3163\/1536-5050.95.4.416","article-title":"The Development of the Medical Literature Analysis and Retrieval System (MEDLARS)","volume":"95","author":"Dee","year":"2007","journal-title":"J. Med. Libr. Assoc."},{"issue":"7612","key":"2025051513142903000_CIT0005","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/nj7612-457a","article-title":"Scientific Literature: Information Overload","volume":"535","author":"Landhuis","year":"2016","journal-title":"Nature"},{"issue":"2","key":"2025051513142903000_CIT0006","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1002\/asi.4630260204","article-title":"Structure, Effectiveness and Benefits of LEXtractor, an Operational Computer Program for Automatic Extraction of Case Summaries and Dispositions From Court Decisions","volume":"26","author":"Borkowski","year":"1975","journal-title":"J. Am. Soc. Inf. Sci."},{"issue":"11","key":"2025051513142903000_CIT0007","doi-asserted-by":"publisher","first-page":"3383","DOI":"10.1007\/s11837-021-04902-9","article-title":"Challenges and Advances in Information Extraction From Scientific Literature: A Review","volume":"73","author":"Hong","year":"2021","journal-title":"JOM"},{"key":"2025051513142903000_CIT0008","article-title":"Zero-Shot Information Extraction Via Chatting With Chatgpt","author":"Wei","year":"2023"},{"key":"2025051513142903000_CIT0009","first-page":"6000","article-title":"Attention is All You Need","author":"Vaswani","year":"2017"},{"key":"2025051513142903000_CIT0010","doi-asserted-by":"publisher","first-page":"102691","DOI":"10.1016\/j.addma.2022.102691","article-title":"Research and Application of Machine Learning for Additive Manufacturing","volume":"52","author":"Qin","year":"2022","journal-title":"Addit. Manuf."},{"key":"2025051513142903000_CIT0011","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-32154-2","volume-title":"Engineering of Additive Manufacturing Features for Data-Driven Solutions: Sources, Techniques, Pipelines, and Applications","author":"Safdar","year":"2023"},{"issue":"4","key":"2025051513142903000_CIT0012","doi-asserted-by":"publisher","first-page":"21","DOI":"10.18063\/msam.v1i4.21","article-title":"A Survey of Additive Manufacturing Reviews","volume":"1","author":"Zhai","year":"2022","journal-title":"Mater. Sci. Addit. Manuf."},{"issue":"2","key":"2025051513142903000_CIT0013","doi-asserted-by":"publisher","first-page":"021009","DOI":"10.1115\/1.4039455","article-title":"A Knowledge-Based Method for Innovative Design for Additive Manufacturing Supported by Modular Ontologies","volume":"18","author":"Hagedorn","year":"2018","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"issue":"2","key":"2025051513142903000_CIT0014","doi-asserted-by":"publisher","first-page":"021013","DOI":"10.1115\/1.4035787","article-title":"A Design for Additive Manufacturing Ontology","volume":"17","author":"Dinar","year":"2017","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"key":"2025051513142903000_CIT0015","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.rcim.2018.03.003","article-title":"An Ontology-Oriented Knowledge Methodology for Process Planning in Additive Layer Manufacturing","volume":"53","author":"Liang","year":"2018","journal-title":"Rob. Comput. Integr. Manuf."},{"key":"2025051513142903000_CIT0016","first-page":"648","article-title":"Machine Learning Based Continuous Knowledge Engineering for Additive Manufacturing","author":"Ko","year":"2019"},{"key":"2025051513142903000_CIT0017","first-page":"V002T02A008","article-title":"Knowledge Extraction Method to Support Domain Integrated Design Methodology","author":"Sun","year":"2022"},{"key":"2025051513142903000_CIT0018","doi-asserted-by":"publisher","first-page":"109382","DOI":"10.1016\/j.knosys.2022.109382","article-title":"Transformer-Based Highlights Extraction From Scientific Papers","volume":"252","author":"La Quatra","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"2025051513142903000_CIT0019","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/D19-1371","article-title":"SciBERT: A Pretrained Language Model for Scientific Text","author":"Beltagy","year":"2019"},{"key":"2025051513142903000_CIT0020","article-title":"From Text to Insight: Large Language Models for Materials Science Data Extraction","author":"Schilling-Wilhelmi","year":"2024"},{"key":"2025051513142903000_CIT0021","article-title":"S2ORC: The Semantic Scholar Open Research Corpus","author":"Lo","year":"2019"},{"issue":"1","key":"2025051513142903000_CIT0022","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1145\/234173.234209","article-title":"Information Extraction","volume":"39","author":"Cowie","year":"1996","journal-title":"Commun. ACM"},{"key":"2025051513142903000_CIT0023","first-page":"1090","article-title":"Vector Search With OpenAI Embeddings: Lucene Is All You Need","author":"Xian","year":"2024"},{"key":"2025051513142903000_CIT0024","article-title":"Structured Information Extraction From Complex Scientific Text With Fine-Tuned Large Language Models","author":"Dunn","year":"2022"},{"key":"2025051513142903000_CIT0025","volume-title":"Human-Centered Data Discovery","author":"Gregory","year":"2023"},{"key":"2025051513142903000_CIT0026","first-page":"354","article-title":"User-Centered MT Development and Implementation","author":"Egan","year":"2008"},{"key":"2025051513142903000_CIT0027","article-title":"Human-Centric Research for NLP: Towards a Definition and Guiding Questions","author":"Kotnis","year":"2022"},{"key":"2025051513142903000_CIT0028","author":"Schleith","year":"2022"},{"issue":"3","key":"2025051513142903000_CIT0029","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s11740-021-01020-y","article-title":"Modeling Fused Filament Fabrication using Artificial Neural Networks","volume":"15","author":"Oehlmann","year":"2021","journal-title":"Prod. Eng."},{"key":"2025051513142903000_CIT0030","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2022.naacl-main.33","article-title":"Mapping the Design Space of Human-AI Interaction in Text Summarization","author":"Cheng","year":"2022"},{"key":"2025051513142903000_CIT0031","first-page":"1","article-title":"\u201cEveryone Wants to Do the Model Work, Not the Data Work\u201d: Data Cascades in High-Stakes AI","author":"Sambasivan","year":"2021"},{"key":"2025051513142903000_CIT0032","first-page":"9459","article-title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","volume":"33","author":"Lewis","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2025051513142903000_CIT0033","first-page":"1264","article-title":"Information Extraction With Humans in the Loop","author":"Gentile","year":"2019"},{"key":"2025051513142903000_CIT0034","first-page":"27","article-title":"Guiding Principles for Participatory Design-Inspired Natural Language Processing","author":"Caselli","year":"2021"},{"key":"2025051513142903000_CIT0035","first-page":"1","article-title":"Power to the People? Opportunities and Challenges for Participatory AI","author":"Birhane","year":"2022"},{"key":"2025051513142903000_CIT0036","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.isatra.2018.07.021","article-title":"In Situ Monitoring of Selective Laser Melting Using Plume and Spatter Signatures by Deep Belief Networks","volume":"81","author":"Ye","year":"2018","journal-title":"ISA Trans."},{"issue":"5","key":"2025051513142903000_CIT0037","doi-asserted-by":"publisher","first-page":"051010","DOI":"10.1115\/1.4065090","article-title":"Transferability Analysis of Data-Driven Additive Manufacturing Knowledge: A Case Study Between Powder Bed Fusion and Directed Energy Deposition","volume":"24","author":"Safdar","year":"2024","journal-title":"ASME J. Comput. Inf. Sci. Eng."},{"key":"2025051513142903000_CIT0038","first-page":"V002T02A078","article-title":"Transferability Analysis of Data-Driven Additive Manufacturing Knowledge: A Case Study Between Powder Bed Fusion and Directed Energy Deposition","author":"Safdar","year":"2023"},{"key":"2025051513142903000_CIT0039","first-page":"V02AT02A033","article-title":"Towards Reproducible Machine Learning-Based Process Monitoring and Quality Prediction Research for Additive Manufacturing","author":"Xie","year":"2024"},{"issue":"8","key":"2025051513142903000_CIT0040","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10845-022-02017-9","article-title":"A Systematic Review on Data of Additive Manufacturing for Machine Learning Applications: The Data Quality, Type, Preprocessing, and Management","volume":"34","author":"Zhang","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"2025051513142903000_CIT0041","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-3-031-32154-2_2","volume-title":"Engineering of Additive Manufacturing Features for Data-Driven Solutions: Sources, Techniques, Pipelines, and Applications","author":"Safdar","year":"2023"}],"container-title":["Journal of Computing and Information Science in Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/25\/7\/074501\/7448399\/jcise-24-1432.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article-pdf\/25\/7\/074501\/7448399\/jcise-24-1432.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T17:14:37Z","timestamp":1747329277000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/computingengineering\/article\/25\/7\/074501\/1213942\/Human-Artificial-Intelligence-Teaming-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"references-count":41,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7,1]]}},"URL":"https:\/\/doi.org\/10.1115\/1.4068275","relation":{},"ISSN":["1530-9827","1944-7078"],"issn-type":[{"value":"1530-9827","type":"print"},{"value":"1944-7078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]},"article-number":"074501"}}