Research Data Leeds Repository

Citation

Masters, Louis (2025) Ceramic 3D printed parts. University of Leeds. [Dataset] https://doi.org/10.5518/1666

Dataset description

This dataset contains 5197 images, and the corresponding annotations, of ceramic 3D printed parts. The dataset was used to train a Yolov8 model for defect detection purposes, and is split into training, validation and test datasets.

Keywords:	3D printing, Additive Manufacturing, Ceramics, Computer Vision, Deep Learning
Subjects:	H000 - Engineering > H300 - Mechanical engineering
Divisions:	Faculty of Engineering and Physical Sciences > School of Mechanical Engineering
Related resources:	Location Type https://doi.org/10.1016/j.addma.2025.104752 Publication https://eprints.whiterose.ac.uk/224699/ Publication https://github.com/Advanced-Manufacturing-at-UoL Software
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	21 Mar 2025 15:10
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/1389
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Masters, Louis https://orcid.org/0009-0006-1522-560X University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: EPSRC Grant numbers: EP/T517860/1 Publication date: 2025 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: l.masters@leeds.ac.uk Last Modified: 24 Mar 2025 14:51

Files

Documentation

README_Masters_2025.txt [2kB] README_Masters_2025.txt [2kB] README_Masters_2025.txt [2kB] Security: Anyone File name: README_Masters_2025.txt Content type: Documentation Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 2kB

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Data

Image_data [660MB] Image_data [660MB] Image_data [660MB] Security: Anyone File name: Image_data.zip Content type: Data Description: Zip folder containing images and labels for all data used to develop the defect detection model. Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 660MB