Return to search

DIMENSIONAL ACCURACY AND SURFACE ROUGHNESS IN SELECTIVE LASER MELTING OF ALUMINUM ALLOYS / QUALITY IN SELECTIVE LASER MELTING OF ALUMINUM ALLOYS

Additive manufacturing (AM) has the ability to fabricate components of high geometric complexity that are difficult or near impossible to be produced by traditional manufacturing technologies. Selective laser melting (SLM) is a commonly used AM technology for metallic fabrications. SLM offers the opportunities to customize the characteristics of the as-build part produced, by adjusting the laser settings. However, high strength aluminum (Al) alloys presents an obstacle for SLM production due to the low alloying content, which increases the alloys’ probabilities to form cracks due to thermal stress induced by the SLM build process. The current study focuses on the study of surface roughness and dimensional accuracy of SLM fabrication of Al6061 and AlSi10Mg. Using design of experiment (DOE), wide ranges SLM process parameters were experimented with, and their individual effect along with their interactive effects on the fabricated parts’ quality were evaluated. The quality characteristics studied are: microstructures, microhardness, tensile strength (ultimate tensile strength, and yield strength), density, surface roughness, and dimensional accuracy. Regression models were created for each quality characteristics, and the combination of density, surface roughness, and dimensional accuracy results was used to create processing window for SLM that ensures the production of high-quality parts. The work aims to not only be used as-is, to help with the selection of SLM process parameters for Al6061 and AlSi10Mg that will reduce the post- processing time, but also to set a foundation for future development for numerical models that could better predict and describe the relations between SLM process parameters and the part’s fundamental qualities. / Thesis / Master of Applied Science (MASc)

Identiferoai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24473
Date January 2019
CreatorsXUE, YI FU
ContributorsELBESTAWI, MO, Mechanical Engineering
Source SetsMcMaster University
LanguageEnglish
Detected LanguageEnglish
TypeThesis

Page generated in 0.0018 seconds