A Workflow-Centric Approach to Generating FAIR Data Objects for Computationally Generated Microstructure-Sensitive Mechanical Data

Abstract

From a data perspective, the materials mechanics field is characterized by sparsity of available data, mainly due to the strong microstructure-sensitivity of properties like strength, fracture toughness, and fatigue limit. This requires testing specimens with different thermo-mechanical histories, even when the composition is similar. Experimental data on mechanical behavior is rare, as mechanical testing is destructive and requires significant material and effort. Furthermore, mechanical behavior is typically characterized in simplified tests under uniaxial loading conditions, whereas a complete characterization requires multiaxial testing. To address this data sparsity, simulation methods like micromechanical modeling can contribute to microstructure-sensitive data collections. This work introduces a novel data schema integrating both metadata and mechanical data, following the workflows of the material modeling processes by which the data has been generated. Each workflow run produces unique data objects by incorporating user, system, and job-specific information correlated with mechanical properties. This approach can be applied to any type of workflow as long as it is well-defined. This integrated format provides a sustainable way of generating Findable, Accessible, Interoperable, and Reusable (FAIR) data objects. The metadata elements focus on key features required to characterize microstructure-specific data, simplifying the collection of purpose-specific datasets by search algorithms.