Engineering topics

Massey, Caroline E.

Publications and source records attributed to Massey, Caroline E..

3 records

DOE OSTI2024

On the Nature of Recoater Damage Powder Spreading Mechanics

Laser powder bed fusion (PBF-LB) additive manufacturing gained popularity for the creation of high mix, low volume parts for defense and commercial applications. Parts made via PBF-LB can be difficult to qualify due to their variation in performance, even within the same build. During the build process, regions of the PBF-LB recoater blade could be subject to damage by spatter or superelevation in the powder bed. As a result, the worn recoater can potentially cause spreading defects in the localized topography profile, which could in turn cause porosity or form deviations. These process concerns are not well understood for their criticality and subsequent impact on part quality. This study will investigate the mechanics of recoater damage through two experimental builds. Post-mortem inspection of the failure region will be conducted with laser line profilometry and will help propose a better understanding of the criticality of extreme recoater damage.

In-Situ Monitoring↗

DOE OSTI2023

ON PROCESS STABILITY IN WAAM-CMT OF ALUMINUM ALLOYS

Wire-arc additive manufacturing (WAAM) has become a cost-efficient metal additive manufacturing process. However, depositing aluminum with WAAM is challenging due to its sensitivity to heat input (linear energy density), which can cause undesirable surface topology waviness if not controlled. Thus, a process window is needed that can produce stable geometry and deposition conditions while minimizing production times. In this study, 5183 aluminum alloy wire is used to deposit 10-layer walls with varying wire feed speeds (WFS) and traverse speeds (TS) (at a constant WFS/TS ratio) and varying interpass temperature (IPT). In-situ process data consisting of optical contact-tip-workpiece-distance (CTWD) and current/voltage measurements are collected to determine process condition stability throughout the build. Part geometry is measured using a 3D scanner and build porosity is characterized via digital X-ray. A process window is identified that produces stable surface topology and process conditions at a minimal production time.

aluminum alloy↗

DOE OSTI2022

Understanding the role of segmentation on process-structure–property predictions made via machine learning

Here, the present study investigated the effect of porosity surface determination methods on performance of machine learning models used to predict the tensile properties of AlSi10Mg processed by laser powder bed fusion from micro-computed tomography data. Machine learning models applied in this work include support vector machines, neural networks, decision trees, and Bayesian classifiers. The effects of isosurface thresholding and local gradient approaches for porosity segmentation, as well as image filtering schemes, on model precision were evaluated for samples produced under differing levels of global energy density.

36 MATERIALS SCIENCE↗