| Abstract Scope |
Cold spray additive manufacturing relies on intricate toolpaths to achieve near-net-shape deposition, but developing these toolpaths often involves iterative trial-and-error processes to optimize deposition parameters. Small-scale spray trials can help refine parameters, yet errors may still emerge during full-scale part fabrication, leading to costly defects. To address these challenges, Sandia has developed high-fidelity deposition models that provide predictive insights to guide toolpath optimization. These advanced models simulate deposition behavior with precision, enabling the identification of potential defects before physical deposition occurs. By integrating these predictive tools into the manufacturing workflow, cold spray processes can achieve improved reliability, reduced material waste, and enhanced part quality. This presentation highlights the development and application of these models, showcasing their potential to shorten the cold spray additive development pipeline. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |