Journal article
Temporal convolutional networks for data-driven thermal modeling of directed energy deposition
Journal of manufacturing processes, v 85, pp 405-416
06 Jan 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Metal additive manufacturing (AM) involves complex multiscale and multiphysics processes. Physics-based modeling approaches to simulate such processes face challenges in their predictions due to the several time and length scales involved in the thermomechanical effects that are inherent in AM. Deep learning-based approaches have been recently explored to address this issue, as they have been shown to be capable of capturing highly nonlinear relations between input and output features. This investigation proposes the use of temporal convolutional networks (TCNs) for fast inferencing of thermal histories in AM processes. TCNs have been previously shown to be superior to other deep learning approaches while requiring less training time. A methodology, therefore, of using TCNs in thermal history predictions for the case of directed energy deposition (DED) is presented herein. The results were found to be of comparable accuracy to other deep learning methods that have been proposed for similar predictions but at a fraction of their compute and training times.
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Details
- Title
- Temporal convolutional networks for data-driven thermal modeling of directed energy deposition
- Creators
- V. Perumal - Theoretical and Applied Mechanics Group, Department of Mechanical Engineering & Mechanics, Drexel University, Philadelphia, PA 19104, USAD. Abueidda - National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USAS. Koric - National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USAA. Kontsos - Theoretical and Applied Mechanics Group, Department of Mechanical Engineering & Mechanics, Drexel University, Philadelphia, PA 19104, USA
- Publication Details
- Journal of manufacturing processes, v 85, pp 405-416
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000899072600005
- Scopus ID
- 2-s2.0-85143499194
- Other Identifier
- 991019364220504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Manufacturing