Investigations into Multi Axis and Multi Robotic Additive Manufacturing Processes for Improved Part Quality

Abstract

Additive Manufacturing (AM) is the process of fabricating parts by slicing three- dimensional Computer-Aided Design (CAD) model data. Optimized process planning is necessary for AM to overcome issues such as poor surface quality, lower process speed, and higher anisotropy. The use of support structures in AM causes poor surface quality in the contact areas of the support with the part and increases the part printing time. Therefore, this support structure usage is a critical issue that needs to be controlled to minimize the printing time and its post-processing challenges. To alleviate the issues associated with the support structures and surface roughness, these faceted models can be deposited in multiple build directions using multi-axis systems. Directed Energy Deposition (DED) can support the use of multi-axis AM systems such as five- axis Computer Numerical Control (CNC) or six-axis serial manipulator for printing parts and repair volumes in multiple orientations. For such a system, robust algorithms are needed to decompose the faceted models into programmable tool paths for multidirectional printing using multi-axis systems. Thus, Improved Convex Volume Decomposition (ICVD) algorithm was developed to decompose the overhang features in the faceted models, which can be built using multi-axis DED systems. This algorithm can cumulatively decompose the presence of overhang features in a particular build direction, when the threshold angle is above 18 degrees. This algorithm presented in this work 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 with multi-axis DED system to exemplify the benefits of overhang-angle-driven volume decomposition. Furthermore, post-processing time is reduced and it overcome the poor surface finish generally found in uni-directional printed parts.