Virtual Tensile Test Dataset of Stress–Strain Response in Long Discontinuous Fiber Composites with Stochastic Mesostructure

Virtual uniaxial tensile test coupons were generated with stochastic Prepreg Platelet Molded Composite (PPMC) mesostructure. Progressive Failure Analysis was performed on Abaqus Standard using Continuum Damage Mechanics (CDM) and Cohesive Zone Modelling (CZM) methods. The stochastic mesostructure information of each sample was processed such that the explicitly represented platelet geometry and fiber orientations from the Finite Element (FE) model were reduced to compact mesostructure descriptors in the form of in-plane distributions of second-order fiber orientation tensor components a11 and a12. The layerwise a11 and a12 distributions (high-resolution mesostructure descriptors) were further reduced to coarse mesostructure descriptors by locally averaging the a11 and a12 values through the thickness at each voxel location across the length and width of a coupon. The macroscopic (effective) stress-strain data for each coupon were also preprocessed to include a) the strain at peak stress, b) terminal strain (corresponding to the simulation cut-off point at 10% load drop) and c) 40 stress values at prescribed fractions of the previously mentioned reference strains a and b. Both the preprocessed (normalized) and raw (non-normalized) stress-strain data, as well as coarse and high-resolution mesostructure descriptors for 3400 unique virtual PPMC tensile test samples are included in the attachments. An accompanying dataset guide spreadsheet documents the file-naming convention, pre-processing applied on the raw data, material properties of each sample and dataset-wide summary statistics. An example python script is also provided to visualize the inputs (fiber orientation distribution at low and high resolution) and outputs (stress-strain curves) for individual samples. Research Hypothesis: This dataset enables training of data-driven surrogate models to learn mappings between fiber orientation distributions and stress–strain response of PPMCs. In a related study by the authors (to be linked upon publication), a deep learning–based surrogate model trained on this dataset demonstrated that coarse mesostructural descriptors in the form of through-thickness–averaged fiber orientations can serve as effective structural representations for multiscale analysis of stress–strain response in PPMCs. Using only these coarse descriptors, tensile stiffness was predicted with a mean absolute percentage error (MAPE) below 3% while tensile strength and failure strain were predicted with a MAPE below 10%. These results highlight the potential of reduced-order structural descriptors to lower experimental characterization and computational modeling requirements for materials with complex, spatially heterogeneous subscale morphology.

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