|About this Abstract
|2023 TMS Annual Meeting & Exhibition
|Quantifying Microstructure Heterogeneity for Qualification of Additively Manufactured Materials
|X-ray Diffraction Peak Estimation Using In-Situ Melt-pool Sensors
|Anant Raj, Benjamin Stegman, Charles Reynolds Owen, Hany Abdel-Khalik, Xinghang Zhang, John W Sutherland
|On-Site Speaker (Planned)
Anisotropy inherent in the laser powder bed fusion (LPBF) process can lead to diverse microstructures with preferred grain orientations or texture. The texture impacts the properties of the printed parts and can be observed as the relative dominance of the different peaks in the x-ray diffraction. This work studies the x-ray diffraction profile across 100 IN718 tensile bars printed using a wide range of volumetric energy densities, based on a factorial design of experiment. The samples exhibited either (111) dominant, (200) dominant, or a mixed texture. The modulus of the samples was observed to be correlated to the relative intensity of the (111) and (200) peaks. Machine learning models are developed to predict the relative intensity of the peaks using co-axial melt pool area and intensity signatures. The models are expected to aid in part qualification, reducing the load on post-build testing.
|Additive Manufacturing, Machine Learning, Copper / Nickel / Cobalt