Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data

Abstract Background Early fatigue mechanisms for various materials are yet to be unveiled the (very) high-cycle (VHCF) regime. This can ascribed a lack of available data capturing initial damage evolution, which continues adversely affect scientists and computational modeling experts attempting derive microstructural dependencies from small sample size incomplete feature representations. Objective The aim this work is address drive digital transformation such that future virtual component design rendered more reliable efficient. Achieving relies on models comprehensively capture all relevant dependencies. Methods To end, proposes combined experimental post-processing workflow establish multimodal crack initiation propagation sets efficiently. It evolves around testing mesoscale specimens increase detection sensitivity, fusion through registration heterogeneity, image-based data-driven localization. Results A with high degree automation established, links large distortion-corrected microstructure localization evolution kinetics. enables cycling up VHCF regime in comparatively short time spans, while maintaining unprecedented resolution evolution. Resulting interaction features hold potential unravel mechanistic understanding. Conclusions proposed lays foundation mining by providing statistically meaningful extendable wide range materials.