Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency.With the rapid development of 3D scanning technology,high-density 3D po...Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency.With the rapid development of 3D scanning technology,high-density 3D point cloud data can be generated by 3D scanners in complex manufacturing systems.However,due to the challenges of complex surface modeling and various types,it lacks effective surface anomaly detection methods that can meet the practical requirements regarding detection accuracy and speed.This survey aims to review the surface anomaly detection methodology of manufacturing products based on 3D machine vision.Specifically,the machine learning methodologies will be systematically reviewed for 3D point cloud data modeling and anomaly detection.Related public data sets for this research are also summarized.Finally,the future research directions are pointed out.展开更多
基金upported by the National Natural Science Foundation of China under(Grant Nos.72371219,72001139,52372308 and 72371217)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011656)+3 种基金Guangzhou Funding Program(Grant No.2025A04J5288)Guangzhou-HKUST(GZ)Joint Funding Program(Grant Nos.2023A03J0651 and 2024A03J0680)Guangzhou Industrial Informatic and Intelligence Key Laboratory(No.2024A03J0628)Nansha Key Area Science and Technology(Project Nos.2023ZD003,and Project No.2021JC02X191).
文摘Surface quality monitoring of manufacturing products is critical for manufacturing industries to ensure product quality and production efficiency.With the rapid development of 3D scanning technology,high-density 3D point cloud data can be generated by 3D scanners in complex manufacturing systems.However,due to the challenges of complex surface modeling and various types,it lacks effective surface anomaly detection methods that can meet the practical requirements regarding detection accuracy and speed.This survey aims to review the surface anomaly detection methodology of manufacturing products based on 3D machine vision.Specifically,the machine learning methodologies will be systematically reviewed for 3D point cloud data modeling and anomaly detection.Related public data sets for this research are also summarized.Finally,the future research directions are pointed out.
