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Modeling additive manufacturing processes can be difficult due to the scale difference between the active processing point (e.g., a sub-millimeter melt pool) and the part itself. Typically, the tools used to model these processes are either too computationally intensive (due to high physical fidelity or inefficient computations) or are focused solely on either the microscale (e.g., microstructure) or macroscale (e.g., cracks). These pitfalls make the tools unsuitable for fast and efficient evaluations of additive manufacturing build files and parts. Failures in parts made by laser powder bed fusion (L-PBF) often come when there is a lack of fusion or overheating of the metal powder that causes areas of high porosity. AM-PM uses a point field-based method to model L-PBF process conditions from either the build instructions (pre-build) or in situ measurements (during the build). The AM-PM modeling technique has been tested in several builds including a Ti-6Al-4V test article that was divided into 16 parts, each with different build conditions. With AM-PM, calculations are performed faster than similar methods and the technique can be generalized to other additive manufacturing processes. The AM-PM method is at technology readiness level (TRL) 6 (system/subsystem model or prototype demonstration in a relevant environment) and is available for patent licensing.

NASA's Debris-Tolerant Valve is designed for use in machines/environments with a large quantity of airborne dust or other contaminants. Valves subjected to airborne contaminants tend to have limited lifetime due to damaged seals, bearings, and other internal components. The Debris-Tolerant Valve design addresses this problem with four core improvements over existing commercial valves that are typically used in dusty or debris-laden processes: (1) a new cylinder design that substantially decreases dust collection within the valve; (2) a rotational valve design that minimizes grinding and packing experienced by the standard ball valve; (3) the use of elastomeric seals rather than the Teflon-based seals used in existing valves which are prone to scratching and subsequent leakage; and (4) a bleed port for fluid intake that allows pressure to build slowly in the valve and eliminates the stirring of dust commonly caused by rapid inflow of air in existing valves. The operational lifetime of NASA's Debris-Tolerant Valve exceeds the lifetime of a standard commercial valve and the existing selector valve used on the ISS by 12X and 6X, respectively. NASA's valve design has fewer parts than existing valves and could be disassembled without tools, enabling easier servicing and maintenance. The Debris-Tolerant Valve is only about one-seventh (1/7) the cost of the existing ISS selector valve.

This method leverages advanced computational modeling to evaluate localized heating conditions and fusion metrics and quantify defect risks dynamically, allowing for optimized build files and process adjustments that drastically improve the final component. The model can be trained using PPF and print samples from an AM machine's prior builds to learn correlations between the machine's instructions, behavior during build, and final product quality. By integrating machine feedback metrics, model-based thermal and fusion metrics, and in-situ sensor metrics, the model learns predictive signatures that allow it to quantify localized defect probability in PBF laser beam metals. Manufacturers can employ this method to understand the reproducibility of their prints and perform defect compensation before parts are fully deployed, improving quality, reliability, and success. Particularly useful to industries that require stringent certification of safety-critical components, such as the aerospace, space, medical, and automotive sectors, this method can be flexibly deployed globally or adapted to specific AM machines. By predicting the probability of defects before and during production, 3D printing service providers and AM equipment manufacturers can save significant amounts of time and money while drastically reducing part variability. This predictive capability also allows organizations to certify parts faster and ensure consistent material properties, which is essential for meeting rigorous performance and safety standards. This is method is currently available for patent licensing (no software included).