Tool-path planning is a crucial factor of computer-aided design (CAD) and computer-aided manufacturing (CAM). Previous path generation methods often transform the problem into local or global optimization methods to solve it, leading to a long computational time. With the development of modern industry, real-time path planning is becoming an urgent issue in advanced manufacturing. This article proposes an efficient neural network-based direct tool-path generation method on B-spline surface for subtractive end milling. In order to build the first corresponding dataset, adaptive iso-scallop height method is proposed, which can effectively avoid generating breakpoints at the boundary. B-Spline reparameterization is used to fit discrete tool paths to obtain regular control points data structure for further deep learning. After that, an intelligent neural network is proposed to learn the relationship between the input B-Spline surface and the reparameterized tool paths. Finally, experimental results and case study are provided to illustrate and clarify our method, which only needs a few microseconds of planning time while ensuring the quality of the generated paths. Due to its simple structure and low computational burden, this method can be easily applied to CAD/CAM software.

Smooth surfaces from an arbitrary topological control grid have been widely studied, which are mostly generalized from splines with uniform knot intervals. These methods fail to work well on extraordinary points (EPs) whose edges have varying knot intervals. This article presents a patching solution for arbitrary topological 2-manifold control grid with non-uniform knots that defines one bi-cubic Bézier patch per control grid face except those faces with EPs. Experimental results demonstrate that the new solution can improve the surface quality for non-uniform parameterization. Applications in surface reconstruction, arbitrary sharp features on the complex surface and tool path planning for the new surface representation are also provided in the paper.

Reconstructing a spline surface from a given arbitrary topological triangle mesh is a fundamental and challenging problem in computer-aided design and engineering. This article introduces a novel surface fitting method utilizing G-NURBS capable of handling control meshes with arbitrary topologies. This method employs adaptive control point adjustment, guided by the geometric attributes of the input model, ensuring precise representation of sharp features such as edges and corners. Two primary strategies are employed: A parameter correspondence approach designed for sharp features and a control mesh iterative refinement technique that incorporates geometrical feature information. The proposed method has been tested and evaluated on various CAD models to demonstrate its effectiveness. This method can achieve higher fitting accuracy while faithfully preserving the geometrical features with fewer control points.

We present a novel implicit neural representation to reconstruct CAD models from point clouds with high quality. Our method first extracts edge points from input points by an edge detection network and then recovers implicit surfaces while preserving the sharp features of ground-truth models. The edge detection network uses a U-Net structure for feature encoding, and the attention module is introduced to improve the accuracy in the CAD models. This detection network is light weighted and runs fast. Afterward, we propose an MLP-based network to train an implicit representation from input points with its extracted edge points. A two-stage training process is proposed, and loss functions are designed for each stage to ensure that the sharp features of the input points are learned while the surface details are fitted. Comparing our method with other SOTA methods in the ABC dataset, our method is significantly superior to the existing nonlearning and learning 3D reconstruction methods in terms of surface approximation quality and sharp feature preservation. Moreover, we can gain spline representations from learned shapes for CAM as the application of our method.