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Direct Energy Deposition (DED) is one of the Metal Additive Manufacturing processes, which can be used to print near-net shapes and repair volumes using five-axis CNC or six-axis serial manipulator-based robotic systems. For such a system, robust algorithms are needed to decompose faceted solid models into programmable tool paths for multi-directional printing using multi-axis systems. In the past, many algorithms for volume decomposition have been developed for nesting parts inside the build envelope, improving the mechanical properties, and maintaining the printed part surface quality. This paper presents the use of a Volume Decomposition strategy to identify and decompose the overhang features in faceted models, which can be built using multi-axis DED systems. The Improved Convex Volume Decomposition algorithm presented in this work makes use of down-facing surface normal in the tessellated model as a reference for decomposing the overhang features, and also provides an opportunity to identify and decompose the presence of overhang features in the particular build direction of those overhang features. This methodology could eliminate the need for intrinsic mass property evaluations such as centroids, and silhouette edges for simple overhanging features. In addition, a test part was built in a multi-axis DED system to exemplify the benefits of overhang-angle-driven volume decomposition, which decomposes the overhang features in the decomposed Overhang Sub-Volumes. Furthermore, the post-processing time of multi-directional printed parts is reduced, and this multi-directional part printing may overcome the poor surface finish generally found in unidirectional printed parts.

Novel process planning approach for support-free additive manufacturing using multi-axis deposition systems