Post, August 7, 2026 • (University of Michigan) This research introduces a novel method to produce biomimetic shapes using low cost soluble 3D printed molds. Mesenchymal stem cells in alginate matrix cell viability was studied. The alginate stem cell structure is made in a construct that is 21 mm wide, 6 mm high, with an...
Additive Manufacturing
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Exploration of Process-Structure Linkages in Simulated Additive Manufacturing Microstructures
Post, August 7, 2026 • (Sandia National Laboratories) In this data set, we provide microstructure results from the simulation of additive manufacturing processes with the SPPARKS Monte Carlo code. The dataset will be used in our entry to the Materials Science and Engineering Data Challenge. The parameters varied during the study, and their extents are...
High-throughput EBSD of Additive Kovar over multiple working distances
Post, August 7, 2026 • (Sandia National Laboratories) This dataset includes EBSD patterns for 60 additively manufactured Kovar sample specimens, printed under 30 sets of process conditions under two orientations with respect to the build plate. EBSD was collected in two directions relative to the sample specimens (build and transverse) over three working distances, for...
In situ Visible Light and Thermal Imaging Data from a Laser Powder Bed Fusion Additive Manufacturing Process Co-Registered to X-ray Computed Tomography and Fatigue Data
Post, August 7, 2026 • (Oak Ridge National Laboratory) This dataset is comprised of in situ sensing data collected during a laser-based powder bed fusion additive manufacturing process, as well as rasterized scan path information, post-build X-ray computed tomography (XCT), and fatigue test results. A total of 64 cylinders, approximately 15 mm in diameter and...
Laser Additive Manufacturing Process (LAMP) Model Decision Support Tool
Post, August 7, 2026 • (University of Michigan) Laser Additive Manufacturing Process (LAMP) Model Decision Support Tool constructed in excel sheet to predict the environmental impact, cost and lead time associated with various manufacturing processes. A mechanistic model constructed with uncertainty for 5 different manufacturing processes: metal & polymer L-PBF, die casting, injection molding and...
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)
Post, August 7, 2026 • (Oak Ridge National Laboratory) This dataset contains layer-wise powder bed images from three different powder bed printing technologies: laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into...
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10.1)
Post, August 7, 2026 • (Oak Ridge National Laboratory) 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...
Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2022-10)
Post, August 7, 2026 • (Oak Ridge National Laboratory) 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...
Modeling of the High-Viscosity Fluid Transient Flow for Material Deposition in Direct Ink Writing
Post, August 7, 2026 • (University of Michigan) A transient flow model is developed to predict the flow of high-viscosity fluid dispensing for precision direct ink writing (DIW) in additive manufacturing. Models for pump deformation and fluid friction to accurately predict the flow of a high-viscosity non-Newtonian fluid through a progressive cavity pump, static mixer,...
Prediction of microstructure formation in laser powder bed fusion process
Post, August 7, 2026 • (Oak Ridge National Laboratory) The datasets are results analyzing the predicted microstructures in a single track during the laser powder bed fusion additive manufacturing process. They are the outputs by running the opensource code, muMatScale (The code can be cited at: Yuan, Lang, Fattebert, Jean-Luc, and Sabau, Adrian. (2023, August...
Simulated Microstructures for Laser Powder Bed Fusion Additive Manufacturing Using Myna, AdditiveFOAM, and ExaCA
Post, August 7, 2026 • (Oak Ridge National Laboratory) This dataset provides sample datasets containing voxelized, three-dimensional representations of simulated grain structures and crystallographic orientations that can result from laser powder bed fusion additive manufacturing. The six microstructure files each contain approximately 1 cubic millimeter of material (1 mm x 1 mm cross-section over 26...
Thermal images collected during large-scale 3D printing with Ingersoll MasterPrint
Post, August 7, 2026 • (Oak Ridge National Laboratory, others) This data set contains images produced to test the performance of anomaly and fault detection methods in the context of additive manufacturing. Two print jobs were executed using the Ingersoll MasterPrint with the Model 30 Strangpresse extruder. The material used was Techmer compounded polylactic acid...
Time-temperature history and input files for ExaCA v2.0 scaling, performance, and demonstration simulations
Post, August 7, 2026 • (Oak Ridge National Laboratory) The files in this data repository are used in various sections of the manuscript "ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification" by Rolchigo et al. (DOI: 10.1016/j.commatsci.2025.113734). The README file references dataset numbers as given in the manuscript's...