Feature Points Recognition of Computerized Numerical Control Machining Tool Path Based on Deep Learning. (August 2022)

• Record Type: Journal Article
• Authors: Hu, Pengcheng; Song, Yingbo; Zhou, Huicheng; Xie, Jiejun; Zhang, Chenglei
• Is Part Of: Computer aided design. Volume 149(2022)
• Publication Date: 2022-08
• Subjects: Feature point recognition -- CNC machining tool path -- Deep learning -- Convolutional Neural Network; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2022.103273
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: In the processes of feed rate planning, interpolation and tool path optimization of Computerized Numerical Control (CNC) machining, feature point recognition of tool path is an essential operation. Accurate identification of feature points is the premise for partitioning the tool path that affects the accuracy and efficiency of CNC machining. Current research on feature point recognition of tool path mainly relies on the hand-crafted approach, of which the result is sensitive to the values of some manually defined threshold. Therefore the approach heavily relies on the human experience. In this paper, a novel deep learning-based approach is presented that can automatically and precisely recognize the feature point of the tool path. A set of geometric descriptors is first defined for each Cutter Location (CL) point and then conversed to images that feed to the deep learning pipeline. A residual learning-based Convolutional Neural Network (CNN) called Feature Point CNN (FP-CNN) is designed that takes the conversed images as input and the recognized results as output. Extensive experiments on some industrial parts are conducted to validate the effectiveness and the advantage of the proposed network. Results show that the proposed approach has good performance in identifying accuracy and recall, which is much superior to two types of benchmarks and does not involve any human intervention. Highlights: Deep learning-based feature point recognition of tool path is presented. Geometric descriptors of tool path are defined and encoded into images. Residual learning-based network FP-CNN is designed to identify feature points. Proposed approach can predict feature points with high accuracy and sensitivity.