I-Corps: An Intelligent Re-Design Recommender System for Additive and Hybrid Manufacturing

Award ID: 2227223

The broader impact/commercial potential of this I-Corps project is the development of a potentially novel, robust, intelligent, and automated assessment and re-design global evaluation system that provides precise feedback on components printability for advanced manufacturing. Such evaluations could reduce mistakes and printing failures, potentially avoid costly trial-and-error exercises, and remove biases and inconsistencies that arise from inefficient or manual evaluation processes. This system allows for potentially effectively organizing assessments and can avoid costly computations and modifications. This capability, therefore, could prevent the waste of energy, time, and financial resources imposed by current manual assessments of components printability. This I-Corps project is based on the development of a novel re-design recommender system that may allow companies with large inventories of existing parts to quickly assess the feasibility of employing additive manufacturing for each part and to implement necessary re-design approaches as appropriate. The technology encompasses multiple intelligent assessments and re-design methodologies, as well as a fully automated correction system that rectifies the geometries of problematic components and converts designs to be advanced manufacturing compatible. These re-design solutions can potentially provide successful geometries for 3D-printing components using different techniques. Lastly, this technology can present a novel design recommender system that intelligently analyzes an extensive database of closely related components for advanced manufacturing using machine learning techniques to provide effective design recommendations for those component clusters whose current designs are deemed inefficient for advanced manufacturing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman