The digital twin will take inputs like spatial and temporal characteristics of the process and material properties of the composite components to guide the manufacturing process by providing the optimal spatiotemporal temperature profiles needed for achieving the design target mechanical properties. Real-time data from sensors are collected as the blade manufacturing process advances. Thermal loads in multiple heating zones are then adjusted based off optimal cure trajectory identified by the surrogate digital twin in the background.
Dr. Shaghayegh Rezazadeh, TPI Lead Engineer, said “the application of statistical physics-informed AI models bridges the gap between deterministic Multiphysics simulations and kinetics of cure as happening on the shopfloor. This process leverages different heating zones integrated in TPI molds to achieve the desired mechanical properties while optimising the cure cycle time to ensure consistent quality and enhanced productivity for the blades manufactured by TPI. The data collection from sensors enables reinforced learning and model refinement to improve the accuracy of the models and adaptation to environmental conditions and variabilities in labor-intensive manufacturing processes.”
Dr. Dong Qian, the principal investigator on the project, commented, “We are very excited to have this opportunity to work with TPI on the project. Our collaborative research will lay an important foundation for smart composite manufacturing and provide a significant competitive advantage for industries adopting these technologies, in terms of both cost savings and performance improvement.”
Cover photo: picture by Yves Bernardi from Pixabay.
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