SBIR-STTR Award

Additive manufacturing is steadily advancing towards fulfilling its promise of customized and on-demand production of functional parts. However, performance of as-manufactured parts can differ significantly from the as-designed parts because the as-manufactured geometry differs from the as-designed geometry and the asmanufactured material properties are not known. Attempts to predict performance of as-manufactured parts are hampered by the complexity of the as-planned geometry and the unknown as-manufactured material properties.Intact Solutions proposes to demonstrate the feasibility of performing accurate structural simulation directly from additive manufacturing plans. This will be achieved by extending Intact Solutions? moment-based interoperable simulation technology to incorporate material properties through a combination of analytical, experimental, and data-driven methods. The approach will be driven by manufacturing plans and available material information from NIST?s AMMD. A significant component of the proposed research is to utilize and improve the data fromthe AMMD in the next generation simulation technology that will support validation, planning, and optimization for additive manufacturing processes.

Additive manufacturing is steadily advancing towards fulfilling its promise of customized and on-demand production of functional parts. However, performance of as-manufactured parts can differ significantly from the as-designed parts because the as-manufactured geometry differs from the as-designed geometry and the as-manufactured material properties are unknown. Attempts to predict performance of as-manufactured parts are hampered by the complexity of the as-planned geometry and the unknown material properties. In Phase I, Intact Solutions successfully demonstrated the feasibility of performing accurate structural simulation directly from AM plans while querying material information directly from NIST’s AMMD. This was achieved by extending our moment-based interoperable simulation technology that incorporates material properties through a combination of analytical, experimental, and data-driven methods.In Phase II, we will develop a novel Part-Scale Process–Structure–Property–Performance (PSPP) architecture and implement a robust commercial grade system for performance prediction of AM parts. The developed system will support adaptive exploration of process and design spaces through direct integration with AM material databases, as well as simulated data. In addition to rigorous validation and testing, we will demonstrate how our system can be used to evaluate, compare, and optimize AM process plans, leading to significant savings and improved performance.