{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:09:47Z","timestamp":1772773787172,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UIDB\/00481\/2020"],"award-info":[{"award-number":["UIDB\/00481\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UIDP\/00481\/2020"],"award-info":[{"award-number":["UIDP\/00481\/2020"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["CENTRO-01-0145-FEDER-022083"],"award-info":[{"award-number":["CENTRO-01-0145-FEDER-022083"]}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UI\/BD\/151258\/2021"],"award-info":[{"award-number":["UI\/BD\/151258\/2021"]}]},{"name":"Centro Portugal Regional Operational Programme (Centro2020)","award":["UIDB\/00481\/2020"],"award-info":[{"award-number":["UIDB\/00481\/2020"]}]},{"name":"Centro Portugal Regional Operational Programme (Centro2020)","award":["UIDP\/00481\/2020"],"award-info":[{"award-number":["UIDP\/00481\/2020"]}]},{"name":"Centro Portugal Regional Operational Programme (Centro2020)","award":["CENTRO-01-0145-FEDER-022083"],"award-info":[{"award-number":["CENTRO-01-0145-FEDER-022083"]}]},{"name":"Centro Portugal Regional Operational Programme (Centro2020)","award":["UI\/BD\/151258\/2021"],"award-info":[{"award-number":["UI\/BD\/151258\/2021"]}]},{"name":"European Regional Development Fund","award":["UIDB\/00481\/2020"],"award-info":[{"award-number":["UIDB\/00481\/2020"]}]},{"name":"European Regional Development Fund","award":["UIDP\/00481\/2020"],"award-info":[{"award-number":["UIDP\/00481\/2020"]}]},{"name":"European Regional Development Fund","award":["CENTRO-01-0145-FEDER-022083"],"award-info":[{"award-number":["CENTRO-01-0145-FEDER-022083"]}]},{"name":"European Regional Development Fund","award":["UI\/BD\/151258\/2021"],"award-info":[{"award-number":["UI\/BD\/151258\/2021"]}]},{"name":"FCT","award":["UIDB\/00481\/2020"],"award-info":[{"award-number":["UIDB\/00481\/2020"]}]},{"name":"FCT","award":["UIDP\/00481\/2020"],"award-info":[{"award-number":["UIDP\/00481\/2020"]}]},{"name":"FCT","award":["CENTRO-01-0145-FEDER-022083"],"award-info":[{"award-number":["CENTRO-01-0145-FEDER-022083"]}]},{"name":"FCT","award":["UI\/BD\/151258\/2021"],"award-info":[{"award-number":["UI\/BD\/151258\/2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>In the current study, the integration of finite element simulation and machine learning is used to find the optimal combination of processing parameters in the directed energy deposition of SS316L. To achieve this, the FE simulation was validated against previously implemented research, and a series of simulations were conducted. Three inputs, namely laser power, scanning speed, and laser beam radius, and two outputs, namely residual stress and displacement, were considered. To run the machine learning model, artificial neural networks and a non-dominated sorting genetic algorithm were applied to determine the optimal combination of processing parameters. In addition, the current study underscores the novelty of combining FE simulation and machine learning methods, which provides enhanced precision and efficiency in controlling residual stress and displacement (geometrical deviation) in the Directed Energy Deposition (DED) process. Then, the results obtained via machine learning were validated with confirmatory tests and reported. The findings offer a practical solution for process parameter optimization, contributing to the progression of additive manufacturing technologies.<\/jats:p>","DOI":"10.3390\/ma18051039","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T11:22:12Z","timestamp":1740568932000},"page":"1039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["On the Prediction and Optimisation of Processing Parameters in Directed Energy Deposition of SS316L via Finite Element Simulation and Machine Learning"],"prefix":"10.3390","volume":"18","author":[{"given":"Mehran","family":"Ghasempour-Mouziraji","sequence":"first","affiliation":[{"name":"TEMA\u2014Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"LASI\u2014Intelligent Systems Associate Laboratory, 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Daniel","family":"Afonso","sequence":"additional","affiliation":[{"name":"TEMA\u2014Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"LASI\u2014Intelligent Systems Associate Laboratory, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5848-6424","authenticated-orcid":false,"given":"Ricardo","family":"Alves de Sousa","sequence":"additional","affiliation":[{"name":"TEMA\u2014Centre for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"LASI\u2014Intelligent Systems Associate Laboratory, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110226","DOI":"10.1016\/j.optlastec.2023.110226","article-title":"A review study on metal powder materials and processing parameters in Laser Metal Deposition","volume":"170","author":"Lagarinhos","year":"2024","journal-title":"Opt. 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