Term of Award

Summer 2024

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Manufacturing Engineering

Committee Chair

Hossein Taheri

Committee Member 1

Haijun Gong

Committee Member 2

Kamran kardel

Abstract

Additive manufacturing (AM) has revolutionized the manufacturing industry by offering flexibility, customization, and rapid prototyping capabilities. However, ensuring the quality and reliability of AM parts remains a significant challenge due to variations in material properties and process parameters. The NDE method used in this study is Resonant Ultrasound Spectroscopy (RUS). This method was used to evaluate and understand its ability to detect internal voids and defects. Dog-bone samples were made using the 316L stainless steel alloy which were fabricated by powder bed fusion (PBF) AM technique at different processing conditions and post processing conditions. These samples were tested to find their mechanical properties and how the process parameters affected these properties. A correlation study was done using Pearson’s and scan speed had been shown to have the most influence of the mechanical properties. Next, RUS was used to see if it could distinguish the three different groups from one another. Based on the Z-Scores RUS was able to distinguish the groups from one another and based on the Z-score plot there is a clear separation between the groups. A correlation was established between the RUS and the mechanical properties and based on these results RUS data correlates well with mechanical properties tested except for the fatigue testing. This r value was below .5. Lastly, FEM was used to compare the actual change in frequency measured by RAM to the change in frequency spectrum using FEM. Based on the numerical results FEM was also able to distinguish between the groups.

OCLC Number

1450356738

Research Data and Supplementary Material

No

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