Research And Implementation Of High Precision Virtual Assembly For Specific Components

As one of the key technologies of virtual manufacturing,virtual assembly technology has received extensive attention in the academic and industrial circles in recent years,and has far-reaching influence on the implementation of advanced manufacturing modes such as high-precision device manufacturing and virtual manufacturing.With the development of the manufacturing industry,especially nowadays more and more products have higher fineness,more and more complex in space structure,and the accuracy requirements for virtual assembly are also increasing.Therefore,for the quality inspection of products,size,shape and position detection,especially for products with curved surfaces,the use of conventional detection methods is not effective.At present,most virtual assembly systems are assembled according to the assembly constraints of the device,ignoring the fit of the assembly surface,resulting in differences from the actual assembly process and increasing product development costs.Aiming at the problem of lack of assembly surface detection method in virtual assembly system,a face-based point cloud segmentation algorithm is proposed.The algorithm is based on the classical RANSAC algorithm.The parameters of the prediction model,the number of intra-site points and the intra-point distance are used as evaluation factors,and the model evaluation function is redefined to improve the accuracy of the model.After using the improved algorithm to extract the point cloud of the high-precision device point cloud data,the clustering algorithm is used to segment the remaining point cloud to obtain the extra-point cloud.Finally,by calculating the outlier point cloud,the geometric characteristics of the assembly surface of the device are obtained.Experiments show that the face-based point cloud segmentation algorithm can accurately segment point cloud data,speed up the segmentation speed and improve assembly efficiency.In order to verify the reliability of the algorithm,a high-precision virtual assembly system is designed and implemented.According to the system function,it is divided into an interactive subsystem and an assembly verification subsystem.The system assembly process consists of three phases,namely point cloud data preprocessing,device surface detection,and device non-surface point cloud detection.First,the device point cloud data preprocessing is performed to increase the proportion of the inner point to the total data set and reduce the number of algorithm iterations.Then,the pre-processed data set is calculated by the face-based point cloud segmentation algorithm,and the surface model is estimated to extract the geometric features of each surface of the device.Finally,the Euclidean clustering algorithm is used to extract the outer point set,and the surface model is used to calculate the error of each surrface of the device,so as to judge the assemblyability between the devices.Experiments show that the system can quickly and accurately detect the surface of the device and improve the assembly efficiency of the device.