Integrated Computational Material Engineering Approach to Additive Manufacturing for Stainless Steel (316L)

Additive manufacturing (AM) will reduce the delay times required in producing Naval parts that are no longer stocked. However, rapid qualification of parts is still a challenge when a limited number of components are required. To fully exploit the potential advantages of AM, a means of accurately addressing the reliability of AM components is required. By simulating the entire design-build-operation lifecycle of a part (e.g. design optimization, AM build process, operation, and repair/replacement) and enabling the outputs and their uncertainties of prior analyses to be propagated as inputs to later analyses, the entire probabilistic lifetime reliability of an AM component can be predicted. Phase I of this proposal will focus on the development of ICME tools effected through machine learning predictive capabilities trained on the high fidelity data included in the Senvol Indexes. The target application under consideration is to qualify flight critical stainless steel (316L) aerospace parts, where fatigue is the primary mode of failure. Data-driven techniques will be developed to predict part quality and performance based on input process parameters and to quantify allowable bounds on those parameters. Components will be fabricated and fatigue tested to validate the analysis and identify additional data to reduce performance uncertainties.