Artificially Intelligent Manufacturing (AIM) Paradigm for Composites EFRC Center
@aimforcomposites
Newark, Delawarehttps://www.aimforcomposites.com/ Higher EducationOverview
About Artificially Intelligent Manufacturing (AIM) Paradigm for Composites EFRC Center
The mission of AIM EFRC is to build an AI-enabled inverse design approach for fundamental understanding and integrated material-manufacturing design of advanced polymer composites.
While uncovering these fundamental insights, this EFRC also aims to build Inverse Design Software (InDeS) tools that accelerate the discovery of advanced polymer composites for improved performance and energy-efficient manufacturing, thereby enabling a smaller carbon footprint, lower structural weight, and lower cost.
Research Goals
Unravel the fundamental underpinnings of the material-process-microstructure-performance (MP2) relationship via constructing an “Digital Life Cycle” (DLC) that represents a suite of seamlessly linked, experimentally converged, high-fidelity models embracing all stages of a composite component’s life cycle, linking perceived risk from energy consumption to carbon footprint;
Leverage physics-informed AI models and build microservice-based cloud tools to enable inverse composites material architecture and manufacturing process design;
Inform and validate the DLC and AI models and implement new material and process designs by exploiting innovative material engineering, characterization, and testing methods.
While uncovering these fundamental insights, this EFRC also aims to build Inverse Design Software (InDeS) tools that accelerate the discovery of advanced polymer composites for improved performance and energy-efficient manufacturing, thereby enabling a smaller carbon footprint, lower structural weight, and lower cost.
Research Goals
Unravel the fundamental underpinnings of the material-process-microstructure-performance (MP2) relationship via constructing an “Digital Life Cycle” (DLC) that represents a suite of seamlessly linked, experimentally converged, high-fidelity models embracing all stages of a composite component’s life cycle, linking perceived risk from energy consumption to carbon footprint;
Leverage physics-informed AI models and build microservice-based cloud tools to enable inverse composites material architecture and manufacturing process design;
Inform and validate the DLC and AI models and implement new material and process designs by exploiting innovative material engineering, characterization, and testing methods.