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). To download the dataset: 1. Create a Globus account. 2. Create a Globus Endpoint on your computer. 3. Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Common troubleshooting steps: 1. Confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. 2. Create an exception for Globus in your antivirus software so that it can create an Endpoint. 3. Manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab.

This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Arcam Q10 Electron Beam Powder Bed Fusion (EB-PBF) Inconel 738 build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise Near Infrared (NIR) in-situ imaging data, in-situ temporal sensor data, ex-situ X-Ray Computed Tomography (X-CT) scans, and the target part geometries. Additionally, anomaly detections produced by a trained Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided. To download the dataset: (1) Create a Globus account. (2) Create a Globus Endpoint on your computer. You may need to create an exception for Globus in your antivirus software so that it can create an Endpoint. (3) Transfer the dataset from the OLCF DOI-DOWNLOADS Collection to your Collection. Be sure to confirm that the transfer is going from OLCF DOI-DOWNLOADS to your Collection. (4) Sometimes users will need to manually create a Globus access directory (where the data will be downloaded) by going to the Preferences > Access tab before the download will begin.

This dataset contains micro-computed tomography images of selected additively manufactured (powder bed fusion of metals using a laser beam, PBF-LB/M) downskin surface test specimens in accordance with DIN EN ISO/ASTM 52902:2020-05. Individual scans of flat tabs with downskin angles of 15° were acquired per material to ensure comparability.

• Materials: 316L (Carpenter Additive) and CuCrZr (Eckart TLS GmbH)
• Downskin Angle: 15°
• PBF-LB Machine: Aconity MIDI+ equipped with an integrated Aerosint SPD Recoater V1.0
• µCT Scanner: Bruker SkyScan 1275

https://data.uni-hannover.de/dataset/415667ec-e70b-46c3-b815-ce3a058203a2/resource/09c26a6e-b898-4f6d-9ef4-203771c7f315/download/downskin_52902_316l_15deg_volumetric.png" alt="Exemplary volumetric representation of the 316L 15° test specimen"> Exemplary volumetric representation of the 316L 15° test specimen

References

The acquired data was used as part of the following publications, which can be referenced for further details:

• Meyer, I., Oel, M., Ehlers, T., & Lachmayer, R. (2023). Additive manufacturing of multi-material parts – Design guidelines for manufacturing of 316L/CuCrZr in laser powder bed fusion. Heliyon, 9(8), e18301. https://doi.org/10.1016/j.heliyon.2023.e18301
• Meyer, I. (2022). Belichtungsprozess für die Multimaterialfertigung von Testkörpern im Laser powder bed fusion. https://doi.org/10.5446/58540

Acknowledgments

• This research has been funded by the Ministry for Science and Culture of Lower Saxony (MWK) – School for Additive Manufacturing SAM.
• The project “Major Research Instrumentation for integration of efficient effects in multi-material structural components” was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project number 445707542.
• The project “Computer tomograph for optomechatronic systems” was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project number 432176896.

In-Process Porosity Detection Dataset by Means of Optical Measurement Technology in PBF-LB/M

Please see provided Jupyter Notebook "README" for further information.

In-Process Porosity Detection Dataset by Means of Structure-Borne Acoustic Emission Measurement Technology in PBF-LB/M by wbk Institute of Production Science and ZEISS

Please see provided Jupyter Notebook "README" for further information. This dataset is part 2 of 2.