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A new metric was developed to quantify the impact of surface-connected defects and internal pores of different morphologies, namely irregular lack of fusion (LoF) pores and spherical keyhole pores, on the mechanical properties and fracture location of AlSi10Mg tensile samples fabricated using laser powder bed fusion additive manufacturing. As defect volume alone has been shown to be insufficient to predict fracture location, the proposed defect impact metric (DIM) incorporates contributions from additional defect features, including proximity to the surface, interaction with neighboring defects, morphology, and reduction in load-bearing cross-sectional area to better assess a defect’s propensity for corresponding to fracture location. The fracture location of keyhole samples was captured by large surface-connected defects with numerous neighboring defects and resulted in increased losses in load-bearing area. In contrast, LoF samples fractured at regions with either large surface-connected defects or large internal pores with many defects in close proximity, high curvatures, and large projected areas. The proposed DIM outperformed existing defect-based frameworks in identifying fracture locations in both LoF and keyhole samples by incorporating surface roughness, defect projected area, and interactions between defects based on distance, volume, and configuration. Additionally, the maximum DIM value within the fracture range was more strongly correlated to strength and ductility than porosity or defect size for LoF samples, demonstrating the potential of the DIM to non-destructively assess the effects of defects on mechanical behavior. The broader applicability of the DIM framework was demonstrated in its ability to capture fracture in both PBF-LB AlSi10Mg and Alloy 718. 

Abstract Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.