Title: Real-Time Material Certification of Composites using a Digital Twin

Authors: Yuksel Yabansu, Tiffany Stewart, Andy Bauer, Gwen Gross, David Shahan

DOI:

Abstract: The exceptional structural performance of thermoset composites is worth the relatively high production costs for industries such as aerospace which are highly sensitive to material performance. Investments in automation are a key component to reducing overhead costs. As an example, the aerospace industry is investing in automated tape laying (ATL) and automated fiber placement (AFP) processes to lower the cost per pound of critical structure through automation of layup, yet overhead costs remain high. The development and qualification of manufacturing lines for high-rate production is resource-intensive and the resulting production lines incur significant recurring costs for quality assurance, reliant on destructive end-item testing. Digital twins in material processing can address the shortcomings of traditional quality assurance approaches by providing real-time tracking and prediction of material properties, improved process efficiency and predictive maintenance capabilities. In this study, we demonstrate plausibility of 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 important prepreg characteristics

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Conference: SAMPE 2026

Publication Date: 2026/04/27

SKU: 182

Pages: 15

Price: $30.00