{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:05:56Z","timestamp":1770296756375,"version":"3.49.0"},"reference-count":190,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Department of Education","award":["P116S210005"],"award-info":[{"award-number":["P116S210005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In the fourth industrial revolution, artificial intelligence and machine learning (ML) have increasingly been applied to manufacturing, particularly additive manufacturing (AM), to enhance processes and production. This study provides a comprehensive review of the state-of-the-art achievements in this domain, highlighting not only the widely discussed supervised learning but also the emerging applications of semi-supervised learning and reinforcement learning. These advanced ML techniques have recently gained significant attention for their potential to further optimize and automate AM processes. The review aims to offer insights into various ML technologies employed in current research projects and to promote the diverse applications of ML in AM. By exploring the latest advancements and trends, this study seeks to foster a deeper understanding of ML\u2019s transformative role in AM, paving the way for future innovations and improvements in manufacturing practices.<\/jats:p>","DOI":"10.3390\/fi16110419","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T06:10:48Z","timestamp":1731478248000},"page":"419","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9658-7149","authenticated-orcid":false,"given":"Shaoping","family":"Xiao","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3432-8827","authenticated-orcid":false,"given":"Junchao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA"}]},{"given":"Zhaoan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4360-4928","authenticated-orcid":false,"given":"Yingbin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA"}]},{"given":"Soheyla","family":"Tofighi","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.eng.2020.07.017","article-title":"Smart Manufacturing and Intelligent Manufacturing: A Comparative Review","volume":"7","author":"Wang","year":"2021","journal-title":"Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10845-021-01885-x","article-title":"Trends in intelligent manufacturing research: A keyword co-occurrence network based review","volume":"33","author":"Yuan","year":"2022","journal-title":"J. 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