# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 3 Digital Simulation Prediction and Analysis Experiment of Assembly Deviation  
### Simulation Modeling and Analysis of Assembly Deviation Based on Design Tolerance  
In the prediction and analysis model of assembly-deviation simulation based on design tolerance, the Monte Carlo method is used to carry out assembly simulation several times by transferring the changes of each element in the chain in the form of random numbers for the key characteristic tolerances of each component affecting the outcome of the assembly. At each time of simulation, the assembly-deviation data are collected for statistical calculation and analysis of the pattern and characteristics of distribution of assembly deviation, as required to evaluate the assembly qualification rate and to analyze tolerance allocation. Thus, the key factors affecting the accuracy of assembly and the main cause of error are determined. In this paper, the assembly of the core engine used for micro gas turbine is studied through a verification experiment for the digital simulation and analysis of assembly deviation. The core engine consists of various key components such as compressor, combustion chamber and turbine, as shown in Figure 2. The turbine of the micro-combustion engine studied in this paper is a centripetal radial turbine, the assembly of which is required to ensure a sufficient clearance between the rotor blade tip and the guide of the core engine, so as to prevent the rotating parts from collision with the stationary parts during operation. All relevant dimensional features in the dimensional chain where the clearance is located are important. In this paper, the 3 DCS tolerance simulation software is used to model and simulate the assembly deviation of the design model, with the simulation results referenced to assess the reliability and rationality of the design. The process of modeling the simulation of assembly deviation based on design tolerance is as follows:  
1. Import the assembly model of the part into 3 DCS. 2. Establish part tolerances, construct feature meshes and select the appropriate feature tolerance scores according to the machining of the component, such as normal distribution, skewed distribution, Rayleigh distribution, etc. 3. Define the assembly scheme. The non-rotating parts of the motor and core machine are stacked assembly, and the rotor is a rotating part mounted on it by two bearings. The non-rotating parts are subjected mainly to stacked assembly, through flange connection, stop positioning, etc. The suitable assembly methods, such as feature assembly and two-point assembly, are adopted to construct a lightweight model for the assembly of the core machine. 4. Establish the measurement target according to the assembly requirements. As shown in Figure 3, it is difficult to perform mesh fitting for the complex curved parts such as the core machine rotor, and the measurement must be converted into the measurement of distance between discrete points. In this paper, the rotor blade tip surface is replaced by 16 blade tip key position points, and the cassette surface is changed into a mapping point for the blade tip point, with 16 measurement targets defined. In this way, the simulation error is significantly reduced. 5. Perform simulation and analysis optimization. Two thousand Monte Carlo assemblies are performed on the design model of the part to obtain its digital assembly results. Figure 4 shows the simulation results of an assembly gap-measurement target, with the probability of assembly interference reaching 28.75%. It is indicated that the reliability and rationality of the design are not satisfactory, which means the assembly must be optimized. Therefore, the assembly interference is solved in this paper by modifying and optimizing the position in which the rotor is mounted. The simulation reveals that the assembly no longer causes interference and the gap is uniform when the mounting position of the rotor is moved forward by 0.47 mm, as shown in Figure 4b. Figure 3: Discrete point distance-measurement model.