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.

This study investigates the relationship between metallic powders and their flowability behavior (captured in terms of Hall flow rates using Hall flowmeters). Due to the many trait dependencies of powder flowability, which have made the formulation of a physical and mechanistic generalizable model difficult to resolve, this study seeks to develop an alternative data-driven framework based on powder size and shape characteristics for Hall-flow-rate predictions. A multiple-instance regression framework was both developed for processing multiple-instance powder data and compared with standard machine learning models. Data augmentation was found to improve the overall performance of the framework, although the limited dataset was a constraint. Still, the study contributes to ongoing efforts to identify traditional, associative, and generalizable patterns between powder properties and resultant flowability behaviors. The findings show promise for real-world applications with a larger dataset, such that this initial application of multiple instance regression frameworks for metal powder Hall-flow-rate predictions as a function of powder particle size and shape data can be scrutinized in full.

Powder properties, particularly their morphology (which includes size, shape, surface roughness, etc.), play a critical role in the quality of cold spray additively manufactured materials. A change in feedstock powder morphology can impact flowability and deposition quality, deposition efficiency, and porosity. Image analysis can be used to quantify a powder's morphology, but performing this analysis with manual visual inspection can be laborious and time-consuming. Alternatively, computer vision techniques have shown promise in automating powder morphology analysis, reducing the work and time required to quantify a powder's morphology. However, the capabilities of these models are limited to the quality of their training data, which can be equally difficult and expensive to collect and annotate. Thus, this work presents DualSight, a novel multi-stage computer vision framework that improves metallic powder segmentation quality without requiring additional data or model training. With this framework, powder morphology can be extracted more accurately from scanning electron microscope images, enabling a more informed manufacturing process.

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.