Optimization of the machining of metallic additive manufacturing supports: first methodological approach

Abstract

Metal additive manufacturing is an active field of innovation. However, for laser power bed fusion (LPBF), supports removal is a major constraint. In this technology, supports are strongly welded to the part to tightly maintain it, avoid distortion, and evacuate thermal load. Although supports are usually optimized for manual removal, machining is often necessary, which can affect post-processing productivity. This paper proposes a comprehensive methodological approach to optimize the selection of cutting parameters, cutting tools, and support structure for LPBF. The aim is to help additive manufacturers find supports that reduce machining costs in terms of time and cutting tool degradation, from among the numerous support designs available. This approach can also optimize the design of lattice structures used inside parts. Our results show that among the 11 designs tested, honeycomb and squared pattern grid supports are the most efficiently machined using the 8-teeth tangential milling of the 3 tools tested, with a good post-machined surface roughness and tools’ health. The method considers low magnification optical analysis and an accelerometer sensor, which is easy to use even for small- and medium-sized enterprises. This paper also proposes and analyzes a new kind of porous support using this method.