{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T03:15:39Z","timestamp":1774322139806,"version":"3.50.1"},"reference-count":0,"publisher":"American Society of Mechanical Engineers","license":[{"start":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T00:00:00Z","timestamp":1698537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Directed Energy Deposition (DED) is crucial in additive manufacturing for various industries like aerospace, automotive, and biomedical. Precise temperature control is essential due to high-power lasers and dynamic environmental changes. Employing Reinforcement Learning (RL) can help with temperature control, but challenges arise from standardization and sample efficiency. In this study, a model-based Reinforcement Learning (MBRL) approach is used to train a DED model, improving control and efficiency. Computational models evaluate melt pool geometry and temporal characteristics during the process. The study employs the Allen-Cahn phase field (AC-PF) model using the Finite Element Method (FEM) with the Multi-physics Object-Oriented Simulation Environment (MOOSE). MBRL, specifically Dyna-Q+, outperforms traditional Q-learning, requiring fewer samples. Insights from this research aid in advancing RL techniques for laser metal additive manufacturing.<\/jats:p>","DOI":"10.1115\/imece2023-113629","type":"proceedings-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T19:31:32Z","timestamp":1707161492000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Sample Efficiency for Temperature Control in DED With Reinforcement Learning and MOOSE Framework"],"prefix":"10.1115","author":[{"given":"Jo\u00e3o","family":"Sousa","sequence":"additional","affiliation":[{"name":"Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI) , Porto, Portugal"}]},{"given":"Roya","family":"Darabi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering of the University of Porto (FEUP) , Porto, Portugal"}]},{"given":"Armando","family":"Sousa","sequence":"additional","affiliation":[{"name":"Faculty of Engineering of the University of Porto (FEUP) , Porto, Portugal"}]},{"given":"Lu\u00eds P.","family":"Reis","sequence":"additional","affiliation":[{"name":"Faculty of Engineering of the University of Porto (FEUP) , Porto, Portugal"}]},{"given":"Frank","family":"Brueckner","sequence":"additional","affiliation":[{"name":"Fraunhofer IWS , Dresden, Germany"}]},{"given":"Ana","family":"Reis","sequence":"additional","affiliation":[{"name":"Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI) , Porto, Portugal"}]},{"given":"Jos\u00e9 C\u00e9sar","family":"de S\u00e1","sequence":"additional","affiliation":[{"name":"Faculty of Engineering of the University of Porto (FEUP) , Porto, Portugal"}]}],"member":"33","published-online":{"date-parts":[[2024,2,5]]},"event":{"name":"ASME 2023 International Mechanical Engineering Congress and Exposition","location":"New Orleans, Louisiana, USA","acronym":"IMECE2023","sponsor":["ASME"],"start":{"date-parts":[[2023,10,29]]},"end":{"date-parts":[[2023,11,2]]}},"container-title":["Volume 3: Advanced Manufacturing"],"original-title":[],"link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/IMECE\/proceedings-pdf\/doi\/10.1115\/IMECE2023-113629\/7238581\/v003t03a072-imece2023-113629.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/IMECE\/proceedings-pdf\/doi\/10.1115\/IMECE2023-113629\/7238581\/v003t03a072-imece2023-113629.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T19:31:33Z","timestamp":1707161493000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/IMECE\/proceedings\/IMECE2023\/87608\/V003T03A072\/1195616"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,29]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1115\/imece2023-113629","relation":{},"subject":[],"published":{"date-parts":[[2023,10,29]]},"article-number":"V003T03A072"}}