Dataset of digital microscopy images of gelatin/siloxane 3D-printed lattice constructs for image-based quality assessment

The printing speeds were 5, 10, 15, 20, and 25 mm/s. This 2 × 5 × 5 design resulted in 50 base printing conditions, with 20 repetitions per condition, producing a total of 1000 microscopy images. The dataset includes the raw image files, an image-level metadata table, a 50-condition experimental design table, a data dictionary, and documentation for reuse.

The metadata file includes image identifiers, file names, image dimensions, file format, color mode, file size, SHA-256 checksum, descriptive intensity metrics, base run order, repetition number, nozzle diameter, extrusion pressure, and printing speed. These data can be reused for automated inspection of printed lattice constructs, image preprocessing, segmentation, filament continuity analysis, geometric fidelity assessment, similarity learning using Siamese neural networks, and convolutional neural network workflows such as ResNet50-based classification.

The dataset is also intended to support related research articles focused on machine learning-assisted evaluation of gelatin/siloxane 3D-printed constructs.