Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)
- Scime, Luke | Oak Ridge National Laboratory
- Joslin, Chase | Oak Ridge National Laboratory
- Duncan, Ryan | Oak Ridge National Laboratory
- Brinkley, Frank | Oak Ridge National Laboratory
- Ledford, Christopher | Oak Ridge National Laboratory
- Siddel, Derek | Oak Ridge National Laboratory
- Paquit, Vincent | Oak Ridge National Laboratory
Overview
Description
This release consists of six datasets which together include multi-modal layer-wise powder bed images from two different powder bed printing technologies. These datasets are designed primarily to facilitate the development and testing of new computer vision and machine learning based anomaly and defect detection algorithms. The authors provide both training data with corresponding ground truth pixel masks and evaluation data with corresponding baseline prediction pixel masks made by a trained neural network. The laser powder bed fusion (L-PBF) datasets are sourced from EOS M290 and AddUp FormUp 350 printers and the binder jet (BJ) dataset is sourced from an ExOne M-Flex printer. The materials represented in these datasets include 17-4 PH Stainless Steel, DMREF, Inconel 718, Maraging Steel, and H13 Steel. The sensor imaging modalities represented include visible-light (VL), temporally-integrated (i.e., long duration exposure) near-infrared (TI-NIR), and wide-band infrared (IR).
Funding Resources
DOE Contract Number
DE-AC05-00OR22725Originating Research Organization
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)Sponsoring Organization
Office of Energy Efficiency and Renewable Energy (EERE);Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office (EE-5A);Office of Nuclear Energy (NE)Related Resources
- IsSupplementedBy (DOI): https://doi.org/10.1016/j.addma.2020.101453
- IsSupplementTo (DOI): https://doi.org/10.13139/ORNLNCCS/1779073
- IsPreviousVersionOf (DOI): https://doi.org/10.13139/ORNLNCCS/1923043
Details
DOI
10.13139/ORNLNCCS/1896716Release Date
November 10, 2022Dataset
Dataset Type
SM Specialized MixAcknowledgements
Users should acknowledge the OLCF in all publications and presentations that speak to work performed on OLCF resources:
This work was carried out [in part] at Oak Ridge National Laboratory, managed by UT-Battelle, LLC for the U.S. Department of Energy under contract DE-AC05-00OR22725.
Category
- 36 MATERIALS SCIENCE,
- 97 MATHEMATICS AND COMPUTING
Keywords
- Powder Bed Additive Manufacturing,
- Image Segmentation,
- In-Situ Process Monitoring,
- Machine Learning