DED-IM: A Novel Method for Mapping and Path Planning in Wire Arc Directed Energy Deposition

This paper introduces DED-IM (<uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/machipanski/DED-IM</uri>), a novel image-based mapping and tool-path planning method for Wire Arc Directed Energy Deposition (WA-DED). The method automatically segments 3D models into distinct regions such as thin walls, contours, bottlenecks, and large internal areas by processing binary images. These segments enable precise, geometry-tailored tool-path generation, significantly improving control over material deposition and reducing arc interruptions. Key innovations include the application of medial axis transforms to accurately map thin walls, the introduction of oscillatory paths for bottleneck areas, and an adaptive weaving pattern that minimizes voids while enhancing geometric precision. The method also integrates user-defined parameters, offering flexibility in region mapping and filling strategies to address specific manufacturing requirements. Experimental results demonstrate the efficacy of DED-IM, showing substantial reductions in common defects such as voids, insufficient material filling, and arc interruptions. This project, provides a flexible and scalable foundation baseline for automated WA-DED processes, enabling the integration of diverse path-planning techniques, fostering innovation and customization in tool-path generation for different manufacturing requirements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i>—This paper introduces DED-IM, a novel path-planning method for Wire Arc Directed Energy Deposition (WA-DED) processes. Developed in Python, the method takes a 3D model as input, along with machine-specific parameters, and generates G-code instructions that can be customized for different WA-DED machines. DED-IM automatically segments 3D models into regions like thin walls, contours, bottlenecks, and large internal areas by processing binary images. These parameters are fully adjustable, allowing users to tailor the tool-path strategy to their specific needs. The modular design also makes it easy to integrate new strategies, such as custom zigzag patterns. To use DED-IM in a production workflow, practitioners will need to adjust the software according to their machine specifications.