DOI: 10.5281/zenodo.17856002
title: A Fully Annotated Synthetic Dataset of Fiber Microstructures for Machine Learning–Based Orientation Estimation

description: This repository contains a large-scale synthetic dataset of fiber microstructures designed for training, validating, and benchmarking machine-learning models for orientation tensor estimation in short-fiber reinforced polymers. The dataset consists of tens of thousands of microscopy-like binary images generated using a physics-informed simulation pipeline that replicates the morphological characteristics of composites processed via Large-Format Additive Manufacturing (LFAM).

Each synthetic image is paired with a CSV file containing per-fiber geometric descriptors and the analytically computed second-order orientation tensor for the entire microstructure. Because the underlying fiber geometry is known exactly, the dataset provides noise-free ground-truth annotations, enabling rigorous and reproducible machine-learning research.

The dataset is intended to support applications in materials science, computer vision, composite characterization, digital twins, and data-driven manufacturing.
description: This repository contains a complete synthetic dataset of 55,092 microscopy-like images of short-fiber reinforced polymer microstructures. Each image is paired with a metadata file that provides geometric descriptors of all individual fibers and the analytically computed orientation tensor. The dataset is organized into three main components, described below.

1. images_try/ — Main Dataset of 55,092 Synthetic Fiber Images

This folder contains the full collection of synthetic microstructure images and their corresponding per-image CSV metadata files.

Image files





Format: JPG




Each filename encodes the orientation tensor of the image, e.g.:



 

0.0003,0.9969,0.0028.jpg



where the elements correspond to(A_xx, A_yy, A_xy) of the second-order orientation tensor.

 



Metadata CSV files





Each image has a CSV file with the same base filename, containing one row per fiber, e.g.:







 



0.0003,0.9969,0.0028.csv






These files include all geometric parameters required for analysis:





Fiber centroid (x, y)




Ellipse axes (a, b)




Orientation (phi)




Aspect ratio and area




Visible intersection area




Image-level configuration (target area fraction, angle limits, generation parameters)




Orientation tensor components: a_xx, a_yy, a_zz





A full description of every column in these CSV files is provided incsv_columns_description.txt.

2. resultados_microestructura_v2/ — Example Statistical Analyses

This folder contains a set of statistical results computed from the main dataset in images_try/.These files demonstrate how the dataset can be used to extract morphological, spatial, and orientation statistics from the synthetic fibers.

Typical results included:





Global fiber morphology distributions




Orientation tensor statistics




Area-fraction distributions




Fiber count per image




Spatial arrangement metrics




Fractional anisotropy distributions



These examples serve as a reference for users who wish to perform similar analyses.

3. csv_columns_description.txt — Documentation of Metadata Fields

This text file provides an item-by-item description of all columns appearing in the CSV files associated with each image. It explains:





Fiber-level geometric attributes




Image-level simulation parameters




Generation diagnostics




All components of the orientation tensor



It is the main reference for interpreting the metadata.

4. data.parquet — Consolidated Metadata File (Optional)

This file contains the complete dataset metadata in a single Parquet table, useful for users who prefer to load the entire dataset efficiently with Python, R, or PySpark.

It aggregates:





Per-image orientation tensors




Global statistics




Aggregated fiber morphology descriptors




Any additional metadata used during analysis



Summary

In total, the repository provides:





55,092 synthetic fiber microstructure images




55,092 corresponding CSV metadata files




Complete documentation of all variables




Example statistical analyses




An aggregated metadata table (data.parquet) for large-scale processing



This structure allows full reproducibility and facilitates machine-learning applications, statistical microstructure analysis, and orientation-tensor research.