GALCIT Colloquium

Composite materials are known for their customizable properties and superior performance characteristics. However, the design of these materials is inherently complex, as it involves navigating through an extensive array of possible material combinations and configurations. In this talk, I will first present novel computational approaches based on optimization algorithms and machine learning techniques to design composite materials. Attention is focused on discovering new design strategies to achieve superior mechanical properties and describing structure-property relationships. Additive manufacturing is a promising technology to create composite materials with complex architectures. However, current additive manufacturing techniques are not robust when it comes to defects. In the second part of this talk, I will discuss how to improve the robustness of additive manufacturing by incorporating sensor technologies, computer vision, and machine learning models. I will present our recent work using a real-time monitoring and autonomous correction system to diagnose the quality of parts and adjust process parameters iteratively and adaptively to ensure high printing quality. I will conclude by describing how these intelligent design and manufacturing frameworks can be advantageous for challenging and extreme environments.