A Sensory Material Approach for Reducing Variability in Additively Manufactured Metal Parts

Autor: Kubra Karayagiz, Jun Liu, Ibrahim Karaman, Raymundo Arroyave, Brian E. Franco, B. Loveall, Ji Ma, Alaa Elwany, Gustavo Tapia
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Scientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
Scientific Reports
ISSN: 2045-2322
Popis: Despite the recent growth in interest for metal additive manufacturing (AM) in the biomedical and aerospace industries, variability in the performance, composition, and microstructure of AM parts remains a major impediment to its widespread adoption. The underlying physical mechanisms, which cause variability, as well as the scale and nature of variability are not well understood, and current methods are ineffective at capturing these details. Here, a Nickel-Titanium alloy is used as a sensory material in order to quantitatively, and rather rapidly, observe compositional and/or microstructural variability in selective laser melting manufactured parts; thereby providing a means to evaluate the role of process parameters on the variability. We perform detailed microstructural investigations using transmission electron microscopy at various locations to reveal the origins of microstructural variability in this sensory material. This approach helped reveal how reducing the distance between adjacent laser scans below a critical value greatly reduces both the in-sample and sample-to-sample variability. Microstructural investigations revealed that when the laser scan distance is wide, there is an inhomogeneity in subgrain size, precipitate distribution, and dislocation density in the microstructure, responsible for the observed variability. These results provide an important first step towards understanding the nature of variability in additively manufactured parts.
Databáze: OpenAIRE
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