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Completed TRL 2 (started at 2, targeting 3)
Space structures should benefit from thermoplastics (TPCs) where their high toughness are desirable. TPCs are also recyclable and have the potential to retire energy-intensive thermoset autoclave manufacturing. Developing out-of-autoclave manufacturing of composites is important to fulfilling NASA’s roadmap (TA 12.2.1.1). In-situ consolidation of TPCs during Automated Fiber Placement (AFP) is, therefore, of interest for space applications (TA12.4.1 Manufacturing Processes, TA 12.4.4 Sustainable Manufacturing). Often, the resultant TPC parts are hindered by defects (e.g., porosity, wrinkles, gaps, and overlaps), part warpage and residual stresses, and weak inter-laminar bonding. To overcome these challenges, we propose a digital twin-like framework that represents defect creation in TPC-AFP and supports real-time optimal control using sensor feedback and surrogate models. The creation of this framework is the central objective of this project. This data-driven approach, using advanced computational science and engineering, will minimize defect creation in TPC-AFP. This study supports the NASA agenda for the accelerated certification of composites as shown by Advanced Composites Project (ACP) and Hi-Rate Composite Aircraft Manufacturing, Hi-CAM. The techniques will be developed in a cycle of (1) Data-driven predictive results will be validated in comparison with experimental measurements. (2) Bayesian assimilation of sensor data to update the model state variables and parameters under uncertainty as they change over time. (3) Employing surrogate models that capture the complex dynamics of the TPC-AFP process using novel scientific machine learning methods to make the assimilation and control tractable in real-time. And (4) (Not in the scope of this Ph.D. project but enabled by the first three objectives): Optimal control under uncertainty of the updated model to minimize defects in AFP parts. The team represents a marriage of the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin (UT Austin), widely regarded as the leading institute of its kind in the world, with experimental technical experts on TPC-AFP. This partnership promises to catalyze a revolution in TPC-AFP, resulting in expedited composite certification and far more reliable and precise systems. An in-house platform, Dr. Tehrani at UT Austin, for experimental testing of in-situ consolidation of thermoplastics offers a unique opportunity to develop an uncertainty model for this technology. NASA Glenn, Dr. Evan Pineda, provides a rich background in modeling of composite materials, the foundation for the study. Another visiting technologist experience NASA Langley is planned to enhance this graduate study. NASA Langley possesses an Electroimpact TPC-AFP machine, the industry standard, and relevant expertise for large-scale experimental research using AFP. Each partner provides a distinctly necessary competence to fulfilling the task. This collaboration promises to catalyze a revolution in TPC-AFP, resulting in expedited composite certification and far more reliable and precise systems. Creating an intelligent integrated manufacturing systems (TA 12.4.2) approach for TPC-AFP has a cross-cutting impact on NASA’s agenda. This study aims to use data-driven physics-based modeling, simulation, and sensory control to mature intelligent manufacturing. TPC-AFP is of specific interest for its relevance with TA 12.2.1.1 and TA 12.2.1.2, out-of-autoclave primary structure, and composite structures. This study aims to utilize computational methods to gain a further understanding of the important phenomena governing the in-situ consolidation process. The outcome of this study will be a richer understanding of the innovative manufacturing technique, TP-AFP, and a model-based approach to predicting defects.
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