Automatic Toolpath Pattern Recommendation for Various Industrial Applications based on Deep Learning

Abstract

Automatic toolpath generation (ATG) systems are a class of robotic systems aimed at generating customized patterns of robotic trajectories and toolpaths to automate several industrial processes such as polishing, deburring, masking, etc. ATG systems are especially valuable in automating industrial processes that require high precision, are repetitive or labor intensive. ATG systems face challenges when CAD data for the manufacturing object/workpiece is either not available or inaccurate. We have developed an adaptive ATG system that can generate the robotic tool path based on scan data for both contact and non-contact manufacturing processes. There are total five tool path patterns designed for our ATG systems, including zigzag tool path, spiral tool path, meridian tool path, contour tool path and boundary tool path. Since the workpiece/coupon differs in shapes and sizes, the choice of a specific tool path pattern or a combination of several patters for the given process is a critical criterion for optimal manufacturing results. In this paper, we present our tool path pattern recommendation methods based on deep learning neural networks in Tensorflow. 3D point cloud segmentation and classification are deployed to identify the object features. Moreover, transfer learning is used in order to enhance performance and save training time. This collective decision making can be implemented either on the computing edge or cloud.