Kim, Hyojin; Mohan, K. Aditya; Ferrucci, Massimiliano; Divin, Chuck (2024). Dynamic 4DCT Datasets for Compression of Additive Manufactured Parts. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0V69JSM

Dynamic 4D time-space computed tomography (D4DCT) refers to reconstruction of moving or deformable objects over time while x-ray projections are acquired from multiple angles, in contrast to most conventional CT where objects are static during imaging. D4DCT offers broad applicability in examining object deformation and dynamics across various industrial and clinical applications. In material science and additive manufacturing (AM), the advanced capabilities of D4DCT facilitate the exploration of damage evolution resulting from dynamic thermal loads and mechanical stresses over time. This insight provides invaluable information for assessing the performance and safety of materials and structures.

We provide a real experimental D4DCT dataset of two AM parts and their compression using the Deben stage. The first 3D-printed lattice structure sample called "log-pile" was fabricated using an AM technique called direct ink writing (DIW). The second sample called "SiC cylinder" was made using another AM technique called binder jetting. The compression of those samples was performed using the Deben stage mounted in the Zeiss Versa scanner to observe in-situ deformation under mechanical loads over time. The projection data comprises 722 views over two full rotations, with each projection image having dimensions of 1024x1024 and a pixel size of 69.16 microns. The scanner operates under cone beam geometry.

• 2023-03-01 to 2024-02-29

• 2024

• Kim, Hyojin

• Divin, Chuck
• Ferrucci, Massimiliano
• Mohan, Aditya

We provide a D4DCT dataset capturing the compression behavior of two Additive Manufacturing (AM) parts. The first sample called "log-pile" was made from polydimethylsiloxane, using direct ink writing technique. The log-pile sample is comprised of several layers of strands. The distance between adjacent layers is approximately 0.750mm. The second AM part called "SiC sample" consists of two vertically stacked Silicon Carbide (SiC) cylinders using binder jetting technique. We performed in-situ compression of these two samples using Deben CT5000 in-situ loadcell testing stage, mounted in the Zeiss Xradia 510 Versa X-ray scanner. We added plastic disk spacers to separate the samples from the steel anvils of the Deben stage. We acquired 722 X-ray projections over two full rotations of 361 degrees. Each projection image has dimensions of 1024x1024 and a pixel size of 69.16 microns. The projection data captures the dynamic behavior and compression of the samples over time under the mechanical loads of the Deben stage. This dataset will serve as a valuable resource for D4DCT reconstruction research, particularly focusing on the compression of real AM objects. The anticipated output of the reconstruction process is a sequence of 3D volumes representing the progressive deformation of the samples over time.

Python: version 3.9 or 3.10. NumPy: version 1.21. h5py: version 3.7

Laboratory Directed Research and Development (LDRD): 22-ERD-032. This research was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and is released under tracking number LLNL-MI-863667.

• Additive manufacturing
• Affine transformation
• Computed tomography
• Deben Stage
• Deformation
• Dynamic 4D CT
• Dynamic computed tomography (DCT)
• Machine learning
• Material deformation and compression
• Reconstruction

• data

• English

Identifier: Aditya Mohan: https://orcid.org/0000-0002-0921-6559

Identifier: Chuck Divin: https://orcid.org/0000-0001-6067-2288

Identifier: Hyojin Kim: https://orcid.org/0000-0001-7032-0999

Identifier: Massimiliano Ferrucci: https://orcid.org/0000-0002-8811-8681

Primary associated publication

• K. Aditya Mohan, Massimiliano Ferrucci, Garrett A. Stevenson, Chuck Divin, Hyojin Kim, Distributed Stochastic Optimization of Neural Representation Networks for Time-Space Tomography Reconstruction, Nature Machine Intelligence (paper in preparation).

Software

• Distributed Implicit Neural Representation (DINR), CP02882
• Livermore Tomography Tools (LTT): https://ipo.llnl.gov/technologies/instruments-sensors-and-electronics/livermore-tomography-tools-ltt

Related data

• Kim, Hyojin; Kang, Jingu; Champley, Kyle; Anirudh, Rushil; Mohan, Aditya (2020). LLNL D4DCT Datasets: Dynamic 4DCT Datasets using MPM-based Deformation. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative. UC San Diego Library Digital Collections. https://doi.org/10.6075/J00R9MZF

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• Lawrence Livermore National Laboratory

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2024-05-01