{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T03:16:31Z","timestamp":1774322191356,"version":"3.50.1"},"reference-count":208,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Deep learning and additive manufacturing have progressed together in the previous couple of decades. Despite being one of the most promising technologies, they have several flaws that a collaborative effort may address. However, digital manufacturing has established itself in the current industrial revolution and it has slowed down quality control and inspection due to the different defects linked with it. Industry 4.0, the most recent industrial revolution, emphasizes the integration of intelligent production systems and current information technologies. As a result, deep learning has received a lot of attention and has been shown to be quite effective at understanding image data. This review aims to provide a cutting-edge deep learning application of the AM approach and application. This article also addresses the current issues of data privacy and security and potential solutions to provide a more significant dimension to future studies.<\/jats:p>","DOI":"10.3390\/a15120466","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T02:50:51Z","timestamp":1670554251000},"page":"466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Synergic Approach of Deep Learning towards Digital Additive Manufacturing: A Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3852-7054","authenticated-orcid":false,"given":"Ayush","family":"Pratap","sequence":"first","affiliation":[{"name":"Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Ropar 140001, Punjab, India"}]},{"given":"Neha","family":"Sardana","sequence":"additional","affiliation":[{"name":"Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Ropar 140001, Punjab, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5623-2183","authenticated-orcid":false,"given":"Sapdo","family":"Utomo","sequence":"additional","affiliation":[{"name":"Graduate Institute of Ambient Intelligence and Smart Systems, National Chung Cheng University, Chiayi 621301, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3889-0112","authenticated-orcid":false,"given":"John","family":"Ayeelyan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621301, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8977-5520","authenticated-orcid":false,"given":"P.","family":"Karthikeyan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621301, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3639-1467","authenticated-orcid":false,"given":"Pao-Ann","family":"Hsiung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621301, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","first-page":"12","article-title":"An overview of Direct Laser Deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and control","volume":"8","author":"Shamsaei","year":"2015","journal-title":"Addit. 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