Predictive damage analytics of additive manufactured materials

Additive Manufacturing (AM) has a large potential in adapting simultaneously functionality, processing, and materials, but it currently lacks repeatability and reliability of parts. Several initiatives were recently launched to benchmark and mitigate the uncertainty of structural properties in AM parts, but there is a notable lack of research on fatigue failure. This research will develop, apply, and assess the effectiveness of microstructure sensitive fatigue prognosis approaches applied to AM metallic materials. While the methods have been validated for traditionally manufactured materials, this research will investigate which microstructural characteristics of AM should be considered to make blind assessments that quantify fatigue cracking in AM. Results from this research will pioneer data-driven holistic approaches that incorporate microstructural attributes, AM processing settings, crack size distributions, and computational variables to create effective predictive analytics for fatigue prediction. We will demonstrate a basis for future multiscale algorithms that can predict fatigue failure in AM materials from first principles. Furthermore, the outputs will contribute to cheaper fatigue testing procedures for AM components by means of high-throughput fatigue testing methods. The success of this procedure is likely to reduce the cost and improve the fit-for-purpose certification of AM components undergoing fatigue damage in a wide range of industries.