{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T02:48:46Z","timestamp":1789181326983,"version":"build-2803163510"},"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RSP2024R164"],"award-info":[{"award-number":["RSP2024R164"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approaches. However, correlations between process parameters, thermal profiles, and resulting microstructure in AFSD still need to be better understood. This hinders process optimization for properties. This work employs a framework combining supervised machine learning (SML) and physics-informed neural networks (PINNs) to predict peak temperature distribution in AFSD from process parameters. Eight regression algorithms were implemented for SML modeling, while four PINNs leveraged governing equations for transport, wave propagation, heat transfer, and quantum mechanics. Across multiple statistical measures, ensemble techniques like gradient boosting proved superior for SML, with the lowest MSE of 165.78. The integrated ML approach was also applied to classify deposition quality from process factors, with logistic regression delivering robust accuracy. By fusing data-driven learning and fundamental physics, this dual methodology provides comprehensive insights into tailoring microstructure through thermal management in AFSD. The work demonstrates the power of bridging statistical and physics-based modeling for elucidating AM process-property relationships.<\/jats:p>","DOI":"10.1371\/journal.pone.0309751","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T13:44:52Z","timestamp":1743774292000},"page":"e0309751","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4939-359X","authenticated-orcid":true,"given":"Akshansh","family":"Mishra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijaykumar","family":"Jatt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyob Messele","family":"Sefene","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6542-2050","authenticated-orcid":true,"given":"Sachin","family":"Salunkhe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9610-4215","authenticated-orcid":true,"given":"Robert","family":"Cep","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emad","family":"Abouel Nasr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"pone.0309751.ref001","doi-asserted-by":"crossref","first-page":"101619","DOI":"10.1016\/j.addma.2020.101619","article-title":"Surface post-treatments for metal additive manufacturing: Progress, challenges, and opportunities","volume":"37","author":"E Maleki","year":"2021","journal-title":"Additive Manufacturing"},{"key":"pone.0309751.ref002","doi-asserted-by":"crossref","first-page":"116249","DOI":"10.1016\/j.engstruct.2023.116249","article-title":"Additive manufactured triply periodic minimal surface lattice structures with modulated hybrid topology","volume":"289","author":"Z Xu","year":"2023","journal-title":"Engineering Structures"},{"key":"pone.0309751.ref003","doi-asserted-by":"crossref","first-page":"145397","DOI":"10.1016\/j.msea.2023.145397","article-title":"Application of gradient severe shot peening as a novel mechanical surface treatment on fatigue behavior of additively manufactured AlSi10Mg","volume":"881","author":"E Maleki","year":"2023","journal-title":"Materials Science and Engineering: A"},{"issue":"4","key":"pone.0309751.ref004","doi-asserted-by":"crossref","first-page":"040801","DOI":"10.1115\/1.4040131","article-title":"The design process of additively manufactured mesoscale lattice structures: a review","volume":"18","author":"F Tamburrino","year":"2018","journal-title":"Journal of Computing and Information Science in Engineering"},{"issue":"1","key":"pone.0309751.ref005","doi-asserted-by":"crossref","first-page":"012001","DOI":"10.1088\/1757-899X\/1275\/1\/012001","article-title":"Additive manufacturing of AISI 316L specimens with distributed inner bone-type cavities: processability and characterization","author":"C Bregoli","year":"2023","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"pone.0309751.ref006","doi-asserted-by":"crossref","first-page":"102259","DOI":"10.1016\/j.addma.2021.102259","article-title":"Processing-structure-property correlation in additive friction stir deposited Ti-6Al-4V alloy from recycled metal chips","volume":"47","author":"P Agrawal","year":"2021","journal-title":"Additive Manufacturing"},{"key":"pone.0309751.ref007","doi-asserted-by":"crossref","first-page":"103282","DOI":"10.1016\/j.addma.2022.103282","article-title":"Deformation-based additive manufacturing of a metastable high entropy alloy via Additive friction stir deposition","volume":"60","author":"P Agrawal","year":"2022","journal-title":"Additive Manufacturing"},{"key":"pone.0309751.ref008","doi-asserted-by":"crossref","unstructured":"Martin LP, Luccitti A, Walluk M. Repair of aluminum 6061 plates by additive friction stir deposition. The International Journal of Advanced Manufacturing Technology. 2022:1-15.","DOI":"10.21203\/rs.3.rs-452633\/v1"},{"key":"pone.0309751.ref009","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jmapro.2018.09.030","article-title":"Potentials and strategies of solid-state additive friction-stir manufacturing technology: A critical review","volume":"36","author":"F Khodabakhshi","year":"2018","journal-title":"Journal of Manufacturing Processes"},{"key":"pone.0309751.ref010","first-page":"100217","article-title":"Potential and challenges for large-scale near-net-shaping of 7xxx aerospace grade aluminum via additive friction stir deposition","volume":"19","author":"HZ Yu","year":"2023","journal-title":"Materials Letters: X"},{"key":"pone.0309751.ref011","doi-asserted-by":"crossref","first-page":"117983","DOI":"10.1016\/j.jmatprotec.2023.117983","article-title":"Improving grain structure and dispersoid distribution of nanodiamond reinforced AA6061 matrix composite coatings via high-speed additive friction stir deposition","volume":"317","author":"B Wu","year":"2023","journal-title":"Journal of Materials Processing Technology"},{"key":"pone.0309751.ref012","doi-asserted-by":"crossref","unstructured":"Li Y, Yang B, Zhang M, Wang H, Gong W, Lai R, et al. The corrosion behavior and mechanical properties of 5083 Al-Mg alloy manufactured by additive friction stir deposition. Corrosion Science. 2023;213:110972.","DOI":"10.1016\/j.corsci.2023.110972"},{"key":"pone.0309751.ref013","doi-asserted-by":"crossref","unstructured":"Mukhopadhyay A, Saha P, Singh PK, Verma M. Development and analysis of a powder bed friction stir (PBFS) additive manufacturing process for aluminum alloys: A study on friction-stirring pitch (\u03c9\/v) and print location. 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