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.

The problem of session-aware recommendation aims to predict users’ next click based on their current session and historical sessions. Existing session-aware recommendation methods have defects in capturing complex item transition relationships. Other than that, most of them fail to explicitly distinguish the effects of different historical sessions on the current session. To this end, we propose a novel method, named Personalized Graph Neural Networks with Attention Mechanism (A-PGNN) for brevity. A-PGNN mainly consists of two components: one is Personalized Graph Neural Network (PGNN), which is used to extract the personalized structural information in each user behavior graph, compared with the traditional Graph Neural Network (GNN) model, which considers the role of the user when the node embedding is updated. The other is Dot-Product Attention mechanism, which draws on the Transformer net to explicitly model the effect of historical sessions on the current session. Extensive experiments conducted on two real-world data sets show that A-PGNN evidently outperforms the state-of-the-art personalized session-aware recommendation methods.