A Workflow-Centric Approach to Generating FAIR Data Objects for Mechanical Properties of Materials

Abstract

From a data perspective, the field of materials mechanics is characterized by a sparsity of available data, mainly due to the strong microstructure-sensitivity of properties such as strength, fracture toughness, and fatigue limit. Consequently, individual tests are needed for specimens with various thermo-mechanical process histories, even if their chemical composition remains the same. Experimental data on the mechanical behavior of materials is usually rare, as mechanical testing is typically a destructive method requiring large amounts of material and effort for specimen preparation and testing. Furthermore, mechanical behavior is typically characterized in simplified tests under uniaxial loading conditions, whereas a complete characterization of mechanical material behavior requires multiaxial testing conditions. To address this data sparsity, different simulation methods, such as micromechanical modeling or even atomistic simulations, can contribute to microstructure-sensitive data collections. These methods cover a wide range of materials with different microstructures characterized under multiaxial loading conditions. In the present work, we describe a novel data schema that integrates metadata and mechanical data itself, following the workflows of the material modeling processes by which the data has been generated. Each run of this workflow results in unique data objects due to the incorporation of various elements such as user, system, and job-specific information in correlation with the resulting mechanical properties. Hence, this integrated data format provides a sustainable way of generating data objects that are Findable, Accessible, Interoperable, and Reusable (FAIR). The choice of metadata elements has centered on necessary features required to characterize microstructure-specific data objects, simplifying how purpose-specific datasets are collected by search algorithms.