Digital Twin Driven Mixed Reality Assembly Inspection Method
Xiangkun Guo, Guang-ming Yang, Ruifeng Guo · 2022
Aiming at the low recognition rate of assembly features caused by angle, light and other factors in mixed reality assembly, this paper proposes a digital twin driven intelligent assembly detection method, which enhances the original data set from image enhancement, geometric enhancement and digital twin space virtual data enhancement, effectively increasing the diversity of samples. At the same time, it solves the problem of large tasks of Sample labeling. In this paper, seven features of three auto parts are selected for experiments, The experimental results show that this method can effectively expand the data set and improve the feature recognition rate.