Title: Real-Time Material Certification of Composites Using a Digital Twin
Authors: Tiffany A. Stewart, David W. Shahan, Yuksel C. Yabansu
DOI: 10.33599/nasampe/s.24.0136
Abstract: Qualification of manufacturing lines for new materials requires expensive and labor intensive approaches. These approaches lead to a substantial amount of qualification time and testing required for introduction of new materials and optimized processes to industry. Digital twins in material processing can address the shortcomings of traditional approaches by providing real-time tracking and prediction of material properties, improved process efficiency, and predictive maintenance capabilities. In this study, we demonstrate the ability to perform real time composite material certification using a digital twin on a research-scale prepreg line coupled with online sensing and physics-informed models. Specifically, machine learning algorithms based on Bayesian methods and Kalman filters fed by online sensor data will be demonstrated for prediction and tracking of material key characteristics.
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Conference: SAMPE 2024
Publication Date: 2024/05/20
SKU: TP24-0000000136
Pages: 14
Price: $28.00
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