Kiran Judd

Wire arc-directed energy deposit (wire-arc DED) enables the efficient manufacturing of large-scale metal parts. Many factors can impact overall part quality during manufacturing, including processing parameters such as feed rate, travel speed, and various material properties. Previous works have sought to use machine learning to evaluate and predict these impacts, but they have primarily focused on the width and height of single-layer deposits. Building upon these studies, this work offers a novel technique to characterize and evaluate the asymmetry of deposited beads to better understand the impact these parameters have. Specifically, leveraging time-series analysis techniques, the surface profiles of beads can be compared and analyzed to identify the degree of asymmetry. Additionally, this work relates these factors to the extent to which substrates warp during the depositing of material. With a better understanding of these impacts, manufacturing processes can be optimized for improved quality and reduced waste. These findings highlight that, while material selection and processing parameters do not strongly correlate with bead asymmetry, beads are deposited with varying degrees of asymmetry, requiring further analysis to identify the source. In contrast, substrate warping is significantly influenced by the thermal properties of the materials used. Of the properties analyzed, heat capacity, thermal diffusivity and thermal conductivity were found to be most relevant to substrate warping. Additionally, while to a lesser extent, material properties of the wire were found to be similarly correlated to warping as their substrate counterparts. These insights can inform the optimization of manufacturing processes, leading to improved part quality and reduced material waste. This study also underscores the need for further research into the interplay between processing conditions and material characteristics in wire-arc DED.

Gas-atomization is extensively used to produce metallic feedstock powders for additive manufacturing processes, including gas dynamic cold spray processing. This work explores the potential utility of on-demand recycled titanium scrap feedstock powder as a viable substitute for virgin powder sources. Three recycled titanium powders were atomized from different battlefield scrap sources using a mobile foundry developed by MolyWorks Materials Corporation. Recycled titanium alloy powders were compared against virgin Ti-6Al-4V powder to verify there were no significant variations between the recycled and virgin materials. Powder characterization methods included chemical analysis, particle size distribution analysis, scanning electron microscopy (SEM), Karl Fischer (KF) titration moisture content analysis, X-ray diffraction (XRD) phase analysis, microparticle compression testing (MCT), and nanoindentation. Results indicate that recycled titanium powder provides a viable alternative to virgin titanium alloy powders without compromising mechanical capabilities, microstructural features, or ASTM-specified composition and impurity standards. The results of this work will be used to aid future research efforts that will focus on optimizing cold spray parameters to maximize coating density, mechanical strength, and hardness of recycled titanium feedstock powders. “Cold spray” presents opportunities to enhance the sustainability of titanium component production through the utilization of recycled feedstock powder, mitigating issues of long lead times and high waste associated with the use of conventional virgin feedstock.

This study advances foundational knowledge regarding the impact of processing parameters and material selection on bead shape in Wire Arc directed energy deposition (Wire Arc DED) additive manufacturing. Through the collection and analysis of the largest Wire Arc DED bead shape dataset to date, this work confirms the dominant roles of the feed rate and travel speed on bead shape. Specifically, an increasing feed rate correlates with an increased bead size, while increasing the travel speed decreases the bead size. Furthermore, as the first dataset to directly compare bead shape across different wire–substrate combinations, this research identified that material selection has a smaller, but still relevant, impact on bead shape compared to the feed rate and travel speed. These insights into the roles of parameters and materials are critical for improving large-scale manufacturing efficiency and quality with Wire Arc DED. By generating a robust, multi-material dataset, this work enables applications of machine learning to optimize Wire Arc DED through quicker manufacturing, reduced material waste, and improved structural integrity.