{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:15:28Z","timestamp":1775470528955,"version":"3.50.1"},"reference-count":80,"publisher":"ASME International","issue":"1","license":[{"start":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:00:00Z","timestamp":1687910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/100000147","name":"Division of Civil, Mechanical and Manufacturing Innovation","doi-asserted-by":"publisher","award":["CMMI-1825535"],"award-info":[{"award-number":["CMMI-1825535"]}],"id":[{"id":"10.13039\/100000147","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The temperature history of an additively manufactured part plays a critical role in determining process\u2013structure\u2013property relationships in fusion-based additive manufacturing (AM) processes. Therefore, fast thermal simulation methods are needed for a variety of AM tasks, from temperature history prediction for part design and process planning to in situ temperature monitoring and control during manufacturing. However, conventional numerical simulation methods fall short in satisfying the strict requirements of time efficiency in these applications due to the large space and time scales of the required multiscale simulation. While data-driven surrogate models are of interest for their rapid computation capabilities, the performance of these models relies on the size and quality of the training data, which is often prohibitively expensive to create. Physics-informed neural networks (PINNs) mitigate the need for large datasets by imposing physical principles during the training process. This work investigates the use of a PINN to predict the time-varying temperature distribution in a part during manufacturing with laser powder bed fusion (L-PBF). Notably, the use of the PINN in this study enables the model to be trained solely on randomly synthesized data. These training data are both inexpensive to obtain, and the presence of stochasticity in the dataset improves the generalizability of the trained model. Results show that the PINN model achieves higher accuracy than a comparable artificial neural network trained on labeled data. Further, the PINN model trained in this work maintains high accuracy in predicting temperature for laser path scanning strategies unseen in the training data.<\/jats:p>","DOI":"10.1115\/1.4062852","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T08:02:57Z","timestamp":1687939377000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":21,"title":["Accelerating Thermal Simulations in Additive Manufacturing by Training Physics-Informed Neural Networks With Randomly Synthesized Data"],"prefix":"10.1115","volume":"24","author":[{"given":"Jiangce","family":"Chen","sequence":"first","affiliation":[{"name":"Carnegie Mellon University Department of Mechanical Engineering, , Pittsburgh, PA 15213"}]},{"given":"Justin","family":"Pierce","sequence":"additional","affiliation":[{"name":"Fabric8Labs , San Diego, CA 92121"}]},{"given":"Glen","family":"Williams","sequence":"additional","affiliation":[{"name":"Re:Build Manufacturing , Framingham, MA 01701"}]},{"given":"Timothy W.","family":"Simpson","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University Department of Mechanical Engineering, , State College, PA 16801"}]},{"given":"Nicholas","family":"Meisel","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University School of Engineering Design and Innovation, , State College, PA 16801"}]},{"given":"Sneha","family":"Prabha Narra","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University Department of Mechanical Engineering, , Pittsburgh, PA 15213"}]},{"given":"Christopher","family":"McComb","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University Department of Mechanical Engineering, , Pittsburgh, PA 15213"}]}],"member":"33","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"2024072000022402800_CIT0001","doi-asserted-by":"publisher","first-page":"110008","DOI":"10.1016\/j.matdes.2021.110008","article-title":"Metal Additive Manufacturing in Aerospace: A Review","volume":"209","author":"Blakey-Milner","year":"2021","journal-title":"Mater. 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