Combining computer-aided design and computer numerical control (CNC) with global technical connections have become interesting topics in the manufacturing industry. A framework was implemented that includes point clouds to workpieces and consists of a mesh generation from geometric data, optimal surface segmentation for CNC, and tool path planning with a certified scallop height. The latest methods were introduced into the mesh generation with implicit geometric regularization and total generalized variation. Once the mesh model was obtained, a fast and robust optimal surface segmentation method is provided by establishing a weighted graph and searching for the minimum spanning tree of the graph for extraordinary points. This method is easy to implement, and the number of segmented patches can be controlled while preserving the sharp features of the workpiece. Finally, a contour parallel tool-path with a confined scallop height is generated on each patch based on B-spline fitting. Experimental results show that the proposed framework is effective and robust.

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Automated feature recognition (AFR) makes it possible to abstract semantic information from neutral CAD models. In an earlier work, we proposed an AFR method for aerospace sheet metal (ASM) parts. In this new work, that method's implementation as an AFR prototype is outlined and the differences between the prototype and the original proposal are pointed out. Then, streamlined data structures are described and explained. They are used to organize the B rep elements extracted from the ASM parts' STEP models, classify and enhance them, and structure the features recognized from the STEP models. Next, a few examples of the algorithms that are implemented in the prototype to manipulate the B rep elements and recognize features are represented and explained. The details of the algorithms are presented in the appendices. To validate the AFR method and verify its correct implementation, a collection of 26 real-world ASM parts was used to create CAD models that were subsequently converted to STEP models. The STEP models were processed to recognize their features, and the results show perfect accuracy. A few of the output feature files are presented in detail. Our results confirm great potential for further AFR method development for rather specialized domains of application.