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TPI Composites, Inc., (TPI) has collaborated with WindSTAR, a National Science Foundation (NSF) funded Industry-University Cooperative Research Center, to design a composite manufacturing process based on a digital twin approach, as released in the 2022 WindSTAR Annual Report. The project leveraged machine learning (ML) using big data to serve as the digital twin of the blade manufacturing process. This ML framework provides real-time feedback during fabrication, results in reduced defects, and enables more efficient production of wind blades versus the current high computational costs of the physics-based models.
Stephen Nolet, Senior Director of Innovation & Technology for TPI, worked alongside student researchers and faculty from the University of Texas at Dallas, as well as technical experts from Olin Epoxy and Westlake Epoxy to develop a framework for the digital twin of the vacuum assisted resin infusion molding (VARIM) process. By applying an ML approach, the team achieved predictive accuracy of more than 95% with 100-times faster computation than the physics-based simulations.
In the coming year, the WindSTAR research team plans to focus on scaling the technology to larger components with greater manufacturing complexity. The work will apply tools taken from Artificial Intelligence (AI) to find patterns in historical data and predict outcomes on full-scale wind blade components including blade shells.

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