Automatic Support Removal for Additive Manufacturing Post Processing

Abstract

An additive manufacturing (AM) process often produces a near-net shape that closely conforms to the intended design to be manufactured. It sometimes contains additional support structure (also called scaffolding), which has to be removed in post-processing. We describe an approach to automatically generate process plans for support removal using a multi-axis machining instrument. The goal is to fracture the contact regions between each support component and the part, and to do it in the most cost-effective order while avoiding collisions with evolving near-net shape, including the remaining support components. A recursive algorithm identifies a maximal collection of support components whose connection regions to the part are accessible as well as the orientations at which they can be removed at a given round. For every such region, the accessible orientations appear as a 'fiber' in the collision-free space of the evolving near-net shape and the tool assembly. To order the removal of accessible supports, the algorithm constructs a search graph whose edges are weighted by the Riemannian distance between the fibers. The least expensive process plan is obtained by solving a traveling salesman problem (TSP) over the search graph. The sequence of configurations obtained by solving TSP is used as the input to a motion planner that finds collision free paths to visit all accessible features. The resulting part without the support structure can then be finished using traditional machining to produce the intended design. The effectiveness of the method is demonstrated through benchmark examples in 3D.

Optimizing Build Orientation for Support Removal using Multi-Axis Machining

“…The underlying assumption is that the entirety of the support region (of arbitrary shape) need to be machined. This is in contrast with a previous approach presented in [22], where we made restrictive assumptions on the shape of the support structure; namely, a finite collection of support beams (e.g., vertical beams) attached at small 'dislocation features' modeled as singular attachment points. The main drawback of this approach is the restrictive assumptions on both tool and support column shapes, and the inability to cut through the middle of columns to reach deeper columns, leading to unnecessary spirals around the part to remove support in "rings" of support columns.…”

“…Once the near-net shape is fabricated for an optimal build orientation, further post-process planning is required to specify a sequence of actions to remove the support volume either by manually detaching them or by machining them out using CNC milling. An automated support removal planning algorithm was proposed in [22], where removability of supports was formulated based on accessibility of support beam "dislocation features" for columnar supports, defined by attachment points whose machining is sufficient to peel the columns off. The supports are recursively peeled off according to an optimal tool path obtained by solving a traveling salesman problem [23].…”

An Integrative Experimental Approach to Design Optimization and Removal Strategies of Supporting Structures Used during L-PBF of SS316L Aortic Stents

“…As a conceivable support structure, a variety of geometrical patterns are feasible. In comparison to a complicated design, a simple design that meets the essential criteria is always favored [37]. The support structure optimization followed in the present study was based on a holistic approach combining a stable support structure design and its easy removal.…”

Exploring feasible design spaces for heterogeneous constraints

“…We show in Section 4 that the pointwise predicate defines a point membership classification (PMC) that implicitly determines the entire feasibility halfspace H i = c −1 i (1) using its "representative" maximal/minimal feasible design Ω * i := c * i −1 (1) using (16) or (17), respectively. In this paper, we restrict our attention to the former ("for all" quantifier and maximal designs), since it lends itself to material reducing downstream solvers such as TO.…”