Defect detection of gear parts in virtual manufacturing

作     者：Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao 

作者机构：School of Mechanical Engineering&AutomationBeihang UniversityBeijing 100191China State Key Laboratory of Computer ScienceInstitute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of SciencesBeijing 100049China 

出 版 物：《Visual Computing for Industry,Biomedicine,and Art》 (工医艺的可视计算（英文）)

年 卷 期：2023年第6卷第1期

页      面：76-87页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：opening fund of State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology),No.119/2017/A3 the Natural Science Foundation of China,Nos.61572056 and 61872347 the Special Plan for the Development of Distinguished Young Scientists of ISCAS,No.Y8RC535018 

主　　题：Defect detection Gear surface Gear dataset Combinational Convolution Block 

摘      要：Gears play an important role in virtual manufacturing systems for digital twins;however,the image of gear tooth defects is difficult to acquire owing to its non-convex *** this study,a deep learning network is proposed to detect gear defects based on their point cloud *** approach mainly consists of three steps:(1)Various types of gear defects are classified into four cases(fracture,pitting,glue,and wear);A 3D gear dataset was constructed with 10000 instances following the aforementioned classification.(2)Gear-PCNet++introduces a novel Combinational Convolution Block,proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology;(3)Compared with other methods,experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.