Reliability Centered Additive Manufacturing Design Framework
Navy SBIR FY2015.2


Sol No.: Navy SBIR FY2015.2
Topic No.: N152-109
Topic Title: Reliability Centered Additive Manufacturing Design Framework
Proposal No.: N152-109-0462
Firm: Sentient Corporation
850 Energy Drive
Suite 307
Idaho Falls, Idaho 83401
Contact: Behrooz Jalalahmadi
Phone: (888) 522-8560
Abstract: To address U.S. Navy needs, Sentient proposes to establish a Design Framework for reliability assurance of additive manufactured (AM) parts using their DigitalCloneTM Component (DCC) software. The framework will be tailored to metal components built through AM processes with complex geometries. Sensors are embedded during AM component build to allow for exploitation of sensor data, physics-based material models, and Bayesian-based algorithms for measurement and operational uncertainty management. Computational material model(s) virtually serialized to each AM part are regularly updated to best capture current health state and adaptively assess reliability. Sentient�s Design Framework allows for inclusion of life-cycle forecasts during the creation and exploration of the AM design space. In the Phase I, coupon samples with embedded sensors are built and modeled in the DCC framework for validation against physical testing results. A Bayesian-based algorithm for information fusion will be used to validate dynamic fatigue assessment capability for AM part reliability assurance. In Phase II, the framework will be expanded to different AM material processes with more complex geometries and loadings. It will include Multi-Objective Optimization tools for use in design, and reliability monitoring of a small air-to-air heat exchanger with forecasting of life-cycle and performance metrics compared to costs.
Benefits: Sentient�s Design Framework will allow the additive manufacturing companies and related industries to design their components more efficiently and perform more accurate performance and life analysis. This specially is more significant when they use new materials in their design. This will significantly reduce the uncertainty and conservatism in design of new components and required expensive and time-consuming experimental testing, thereby improving design process, increasing performance, reliability and durability, and reducing cost of operation. The physical nature and computational strength of the developed predictive tool will help testing more geometries, materials and design concept resulting in better final products manufactured using AM processes.

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