• Gleason, Matthew
• Tsaknopoulos, Kyle
• Neamtu, Rodica
• Price, Stephen

Abstract

<jats:p>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.</jats:p>

Topics

• Deposition
• wire
• directed energy deposition
• machine learning