Many applications rely on scanned data, which can come from a variety of sources: optical scanners, coordinate measuring machines, or medical imaging. We assume that the data input to these applications is an unorganized point cloud or mesh of vertices. The objective may be to find particular features (medical diagnostics or reverse engineering) or comparison to some reference geometry (e.g. dimensional metrology). This paper focuses on the feature fitting of a segmented point cloud, specifically for branched, organic structures or structural frames, and targets non-monolithic geometries. In this paper, a methodology is presented for the automated recovery of cross-sectional shapes using centrally located curves. We assume a triangulated surface mesh is generated from the scanned point cloud. This surface mesh is the starting point for our methodology. We then find the curve skeleton of the part which abstractly describes the global geometry and topology. Next after segmenting the curve skeleton into non-branching segments, orthogonal planes to the curve skeleton segments, at preset or adaptive intervals, make slices through the surface mesh edges. The intersection points are extracted creating a 2D point cloud of the cross section. A number of application specific post-processing operations can be performed after obtaining the 2D point cloud cross sections and the curve skeleton paths including: calculations such as area or area moments of inertia, feature fitting or recognition, and digital shape reconstruction. Case studies are presented to demonstrate capabilities and limitations, and to provide insight into appropriate uses and adaptations for the presented methodology. Highlights Automated cross-sectional extraction for branching structures is presented. Methodology utilized skeletonization of object as reference for sampling planes. Surface mesh is sliced to extract a 2D point cloud. Filter algorithm for exclusion of peripheral slicing is presented. Several case studies demonstrate capabilities and limitations of the method.

Topology optimization provides optimized solutions with complex geometries which are often not suitable for direct manufacturing without further steps or post-processing by the designer. There has been a recent progression towards linking topology optimization with additive manufacturing, which is less restrictive than traditional manufacturing methods, but the technology is still in its infancy being costly, time-consuming, and energy inefficient. For applications in automotive or aerospace industries, the traditional manufacturing processes are still preferred and utilized to a far greater extent. Adding manufacturing constraints within the topology optimization framework eliminates the additional design steps of interpreting the topology optimization result and converting it to viable manufacturable parts. Furthermore, unintended but inevitable deviations that occur during manual conversion from the topology optimized result can be avoided. In this paper, we review recent advances to integrate (traditional) manufacturing constraints in the topology optimization process. The focus is on the methods that can create manufacturable and well-defined geometries. The survey will discuss the advantages, limitations, and related challenges of manufacturability in topology optimization.