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Increasingly, a vast variety of additive manufacturing (AM) datasets are generated through AM development lifecycles. The amount, type, and speed of the collected data are unprecedented. The datasets are created and collected for material development
Additive manufacturing (AM) creates parts layer by layer directly from three-dimensional computer-aided design data. Building in layers allows the fabrication of complex geometric shapes as well as functionally graded materials. Despite the part-quality
Saadi Habib, Steven P. Mates, Fan Zhang, Mark R. Stoudt, James Zuback, Olaf Borkiewicz
Additively-manufactured (AM) 17-4 precipitation-hardening (PH) martensitic stainless steel (SS) built from nitrogen-atomized powder often retains a large volume fraction of austenite. The retained austenite lowers the yield strength compared to both
Part quality in additive manufacturing (AM) is highly dependent on process control, but there is a lack of adequate AM control methods and standards. In particular, a standard programing language (such as G-code/M-code for traditional machine tools) is not
This document provides details on the files available in the dataset "In situ thermographic measurements of single line scans performed for the Additive Manufacturing Benchmark Test Series (AM-Bench 2018)." These measurements are performed as part of the
The number and types of sensors used to monitor additive manufacturing (AM) processes and parts in real time are growing. The emerging digital twins (DTs) associated with the data collected by those sensors and the functions that use that data as inputs
Portlandite (calcium hydroxide: CH: Ca(OH)2) suspensions aggregate spontaneously and form percolated fractal aggregate networks when dispersed in water. As a result, the yield stress and viscosity of portlandite suspensions diverge at low particle loadings
Thermoset composites are excellent candidates for material extrusion because they shear thin during extrusion but retain their shape once deposited via a yield stress. However, thermal post-curing is often required to solidify these materials, which can
Data formats play an integral role in leveraging the flexibility of additive manufacturing and achieving consistent part quality. This article compares and contrasts data formats optimized for design, materials, processes, and inspection methods. It also
Edward Garboczi, Newell Moser, Samantha Webster, Kornel Ehmann, Jian Cao, Kamel Fezzaa, Tao Sun
Material manufactured by laser, powder-blown directed energy deposition often contains residual pores, which can be detrimental to the strength and fatigue life of a part. We uncover a new kind of pore formation that is driven by powder particle impact
Minxiang Zeng, Yipu Du, Nicholas Kempf, Miles Bimrose, Jiahao Chen, Dylan Kirsch, Joshua B. Martin, Bowen Zhang, Tatsunori Hayashi, Mortaza Saeidi-Javash, Hirotaka Sakaue, Babak Anasori, Michael McMurtrey, Yanliang Zhang
The development of new materials and their compositional and microstructural optimization are essential in regard to next-generation technologies such as clean energy and environmental sustainability. However, materials discovery and optimization have been
Jordan Weaver, Alec Schlenoff, David Deisenroth, Shawn P. Moylan
Purpose: This paper aims to investigate the influence of the non-uniform gas speed across the build area on the melt pool depth during laser powder bed fusion. The study focuses on whether non-uniform gas speed is a source of process variation within an
Maxwell R. Praniewicz, Jason Fox, Lucas Fournet-Fayard, Charles Cayron, Imade Koutiri, Anne-Francoise Obaton
The geometrical qualification of additively manufactured lattice structures has largely focused on the measurement of strut diameter, form variations, and surface texture. However, the exterior surfaces of the lattice structure, defined through theoretical
Felix Kim, Sarah Robinson, Nikolai Klimov, John Henry J. Scott
Various micro and nano-manufacturing techniques were investigated to create controlled flaws for X-ray computed tomography (XCT) phantoms. We explored the use of focused ion beam (FIB), laser micromachining, and projection photolithography with deep
Hyunwoong Ko, Zhuo Yang, Yande Ndiaye, Paul Witherell, Yan Lu
Data analytics with Machine Learning (ML) and Artificial Intelligence (AI) offers high potential to continuously transform AM data to newfound knowledge of Process-Structure-Property (PSP) relationships. In AM, however, realizing the potential is still