Parts fabricated by additive manufacturing (AM) are often fabricated first as a near-net shape, a combination of intended nominal geometry and sacrificial support structures, which need to be removed in a subsequent post-processing stage using subtractive manufacturing (SM). In this paper, we present a framework for optimizing the build orientation with respect to removability of support structures. In particular, given a general multi-axis machining setup and sampled build orientations, we define a Pareto-optimality criterion based on the total support volume and the "secluded" support volume defined as the support volume that is not accessible by a given set of machining tools. Since total support volume mainly depends on the build orientation and the secluded volume is dictated by the machining setup, in many cases the two objectives are competing and their trade-off needs to be taken into account. The accessibility analysis relies on the inaccessibility measure field (IMF), which is a continuous field in the Euclidean space that quantifies the inaccessibility of each point given a collection of tools and fixturing devices. The value of IMF at each point indicates the minimum possible volumetric collision between objects in relative motion including the part, fixtures, and the tools, over all possible tool orientations and sharp points on the tool. We also propose an automated support removal planning algorithm based on IMF, where a sequence of actions are provided in terms of the fixturing devices, cutting tools, and tool orientation at each step. In our approach, each step is chosen based on the maximal removable volume to iteratively remove accessible supports. The effectiveness of the proposed approach is demonstrated through benchmark examples in 2D and realistic examples in 3D.
翻译:添加制造(AM) 所制造的零部件通常首先作为近网形状,将预期的名义几何和牺牲性支持结构组合起来,在随后的后处理阶段使用减式制造(SM)来消除这些结构。在本文中,我们提出了一个框架,以优化支持结构可拆卸的建筑方向。特别是,鉴于一般的多轴机械化设置和抽样构建方向,我们根据总选择的支持量和“封闭”支持量定义的“封闭”支持量标准,作为一套既定的机械化工具无法获取的支持量。由于总支持量主要取决于建设方向,而缩略出的数量是由机械化结构设定决定的,在许多情况下,这两个目标相互竞争,需要考虑它们的交易需要。 无障碍性分析依赖于无障碍度计量字段(IMF),这是Eucliidean空间的一个连续字段,它可以量化每个点的获取量,因为每个点都是工具的收集量级缩略式工具,并且精确的定位工具的顺序中,每个阶值都通过IMFSOL工具的每个可能的数值。