Fiber reinforced polymer composites, by virtue of their high stiffness and high strength-to-weight ratio, are increasingly used in a wide range of industries such as transportation, marine, wind energy, aerospace, and construction. Automation of composite laminates manufacturing is instrumental in meeting the growing demand for composites and promises to revolutionize composite-dependent industries. However, the few existing layup automation systems are prohibitively expensive and are not fully optimized for product quality. This Grant Opportunity for Academic Liaison with Industry (GOALI) research aims to establish a systematic framework that will assist in solving the critical challenges in automation, monitoring and control for composite laminates manufacturing. The framework will establish the necessary fundamental understanding of relationships among composite constituents' properties, principal composite manufacturing processes and automation, and fabricated product quality. The new knowledge will help increase productivity and quality of composite laminates manufacturing. Results from this research will assist U.S. composite manufacturers and benefit the U.S. economy and society. This GOALI research employs a multi-disciplinary approach (involving solid mechanics, composites engineering, control theory and robotics) to achieve the research objectives. In addition, it incorporates research goals with industrial needs and helps broaden the participation of underrepresented groups in research and enhance engineering education.
This research will fill the knowledge gap in the complex interactions between fabrication parameters and laminas' temperature- and time- dependent properties (viscoelasticity and tackiness) and their impact on the properties of composite laminates (i.e., a stack of multi-directional laminas arranged to exhibit specific mechanical properties). The research team will (1) create multi-physics manufacturing process models to predict impacts of process parameters on manufacturing induced defects and final product quality, and conduct experiments to verify the models; (2) gain an understanding on the roles of quality inspection and optimal control in increasing productivity through manufacturing dynamics simulations; and (3) obtain the knowledge on how to control the physical process to ensure the product quality as a guideline for manufacturing practices.
