Powder Bed Surface Quality and Particle Size Distribution for Metal Additive Manufacturing and Comparison with Discrete Element Model

<p>Metal additive manufacturing (AM) can produce complex parts that were once considered impossible or too costly to fabricate using conventional machining techniques, making AM machines an exceptional tool for rapid prototyping, one-off parts, and labor-intensive geometries. Due to the growing popularity of this technology, especially in the defense and medical industries, more researchers are looking into the physics and mechanics behind the AM process. Many factors and parameters contribute to the overall quality of a part, one of them being the powder bed itself. So far, little investigation has been dedicated to the behavior of the powder in the powder bed during the lasering process. A powder spreading machine that simulates the powder bed fusion process without the laser was designed by Lawrence Livermore National Laboratory and was built as a platform to observe powder characteristics. The focus for this project was surface roughness and particle size distribution (PSD), and how dose rate and coating speed affect the results. Images of the 316L stainless steel powder on the spreading device at multiple layers were taken and processed and analyzed in MATLAB to access surface quality of each region. Powder from nine regions of the build plate were also sampled and counted to determine regional particle size distribution. As a comparison, a simulation was developed to mimic the adhesive behavior of the powder, and to observe how powder distributes powder when spread.</p>

Name thesis:degree_name

MS in Mechanical Engineering

Discipline thesis:degree_discipline

Mechanical Engineering

Year dc:date.available

2018

Author dc:creator

• Yee, Irene

Contributors dc:contributor

• Andrew Davol

• powder bed fusion
• powder spreading
• powder bed surface roughness
• particle size distribution
• metal additive manufacturing
• Other Mechanical Engineering
• Tribology

Identifier

10.15368/theses.2018.10

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-3118