The meta-defect-detection system for gear pitting based on digital twin

Gear pitting is a typical surface defect, and its accurate three-dimensional (3D) detection is significant for the operation and maintenance of equipment. Therefore, this paper develops a 3D gear pitting detection method based on digital twin. Firstly, a geometric model of gear pitting is built to expand the samples which are helpful improving the detection accuracy. Secondly, based on the gear pitting model and Unity, a virtual fringe projection profilometry (FPP) system is established in the metaverse for generating the deformation fringe patterns and retrieving their phases. To improve the accuracy of pitting detection, a gear pitting detection network (GPDNet) is proposed. It can not only extract local and global characteristics from different spaces and scales but also fuse these features via the proposed attention-based conditional random field modules. Thus GPD-Net can retrieve the high-precision wrapped phase by a single fringe pattern. Meanwhile, an FPP system is built in the physical world, and the reconstructed actual point cloud images are imported into the virtual FPP system for the precise measurement of gear pitting. The developed measurement system can be regarded as a meta-defectdetection system. The experimental results show the superiority of GPD-Net over the state-of-the-art phase retrieval algorithms and the effectiveness of the proposed three-dimensional gear pitting detection method. The proposed 3D detection method based on FPP is not limited by the fault type, and it can be effectively applied to the 3D measurement of various surface defects.

Automated summary

This paper develops a 3D gear pitting detection method based on digital twin, which includes building a geometric model and using a virtual FPP system for phase detection. An actual FPP system is also built for precise measurement. The experimental results demonstrate the superiority and effectiveness of this method.