Real-Time Tool-Path Planning Using Deep Learning for Subtractive Manufacturing

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.