# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 3 Digital Simulation Prediction and Analysis Experiment of Assembly Deviation
### Precision Measurement and Data Processing of Key Components
#### 3.2.2 3D Scanning Point Cloud Data Processing  
Figure 6 shows the process of 3D point cloud data processing for those key parts obtained via the 3D laser scanner, which mainly involves the collocation of multi-angle 3D scanned point cloud data, point cloud data denoising and the segmentation and feature extraction of point cloud data according to the needs for assembly features. In the processing of point cloud data, it is also a common practice to repair the holes of the point cloud. For the 3D point cloud needed to examine the actual processing quality of the parts, point cloud repair can lead to data distortion and affect the outcome of assembly simulation.  
To extract the key part feature parameters, conversion of the measured coordinate system into the design coordinate system of the part is usually required. For the parts produced with design models, the best fit can be aligned with the CAD model to create a complete picture of the error between the machined part and the CAD design model. The data of dimensions, form, deformation and contour of key features are collected by extracting the actual point cloud measurement data at the corresponding location of the CAD model, creating features based on the drawings of the part, extracting the key features.  
To extract the parameters of key features from 3D scanned point cloud data, there are different methods available, such as probing, fitting, CAD model-based extraction, etc. The probing method employed in this study bears resemblance to a virtual CMM technique. Within the software, the scanned point cloud is treated as a physical entity, and a specific feature to be measured is selected. Subsequently, a virtual probe is employed to detect the point cloud, thereby determining the corresponding feature and its associated measurement value. On the other hand, the fitting method utilized in this research involves fitting the scanned data to standard features. To accomplish this, the point cloud is subjected to a least squares fitting approach using Polyworks software. Finally, the CAD model extraction method is an automated technique that leverages a designated region surrounding the corresponding feature in the CAD model as the actual measurement data area. This enables the direct extraction of the measurement data. The accuracy achieved with different methods varies. Take the mounting hole of the core machine studied in this paper as an  
Figure 6: Point cloud data-processing flowchart.  
example. In design, the diameter of the mounting hole is 170.43 mm, and the actual CMM value of the part is 170.593 mm. With the CMM data as the standard value, the data are extracted by using different methods. Then, the data obtained via different extraction methods are compared, the results of which are shown in Table 1.  
According to the findings presented in Table 1, the outcomes derived from the probing method exhibit the most minimal disparity when compared to the CMM values. This can be attributed to the enhanced capability of the probing method to comprehensively capture the intricate details of the features by utilizing a dense set of points. Thus, the presence of stray points is effectively eliminated during this process, thereby leading to an improved accuracy in the extraction of feature parameters. On the other hand, the probing method relies on sparsely distributed points, which fails to provide a holistic representation of the entire feature, resulting in substantial deviations in the measured values. Similarly, the CAD method, despite its automated extraction capability, is susceptible to erroneous selection of point clouds and extraction ranges, thereby leading to significant deviations in the measured values.