## 1 Introduction  
Large-scale mechanical products like ships, automobiles, aircrafts, etc. are complex in structure, large in size, and accurate in assembly quality. The assembly workload of the manufacturing process is heavy [1]. These products often need accuracy compensation in the assembly process because of the excessive assembly deviations, which lead to inefficiency. The assembly deviations might be caused by the eventual poor machining quality of parts, or excessive tolerances set by designers. Thus, the trial assembly is often used to detect the assembly deviations in advance, and the parts are then separated to make an accuracy compensation on the bad dimensions. The assembly process takes a long time by the following steps: Trial assembly, measurement of deviations, separation of parts, and re-trial assembly. Therefore, an assemblability analysis and optimization method based on the measurement data is necessary to predict the assembly deviation and make the accuracy compensation in advance. With the development of measurement-assisted assembly (MAA) [2], measurement technology has become a bridge between the real world and the digital world. [PERSON] et al. [3] introduced a MAA application in an airbus assembly line. The least-squares method was used to calculate the optimal pose. [PERSON] et al. [4] proposed a weighted SVD algorithm to obtain the optimal pose of components,which improved the accuracy of pose evaluation. [PERSON] et al. [5] proposed a coaxial alignment method using distributed monocular vision. The iterative reweighted particle swarm optimization method was constructed to improve the measurement ability of complicated wearing holes. [PERSON] et al. [6] calculated the assembly clearance of a wing-fuselage assembly based on the optimal pose. The above methods mainly consider the measurement and calculation of the assembly pose, and then realize alignment through pose adjustment tooling. The assembly will be difficult if the quality of the parts is poor. Assemblability prediction is the first step to judge whether the assembly is qualified in the measurement-assisted assembly. [PERSON] et al. [7] evaluated the assemblability based on tolerance propagation. [PERSON] et al. [8] assessed the assemblability by the maximum likelihood problem, which was solved by the Kalman filter algorithm. The traditional assemblability evaluation methods are mainly used to find the assembly problem in the design phase, but not in the assembly phase. [PERSON] and [PERSON] [9] proposed the concept of pose feasible space to assess the assembly coordination. [PERSON] et al. [10] proposed an assembly quality assessment method based on weighted geometric constraints to calculate the optimal pose. [PERSON] et al. [11] proposed a constraint coordination index to assess the assembly quality. [PERSON] et al. [12] developed the assembly precision pre-analysis technique in the simulation of virtual assembly. [PERSON] et al. [13] proposed a pose decoupling model of the axis tolerance feature to decouple the analysis of any pose within the tolerance domain. The accuracy compensation methods are used to improve assemblability. The digital compensation method has become a research highlight to improve the assemblability. [PERSON] et al. [14] put forward the method of measuring the assembly clearance and realizing the digital manufacturing of the accuracy compensation gasket. [PERSON] et al. [15] introduced a shimming method by 3D printing technology, and the assembly clearance was measured by optical measurement. [PERSON] et al. [16] provided a shimming method based on scanned data for a wing box assembly involving non-uniform gaps. In addition, finite element analysis was taken to improve the shimming scheme. Those methods, however, need to be assembled first, followed by measurement of the deviations to be compensated, resulting in a lower efficiency. Some scholars proposed predictive shimming and predictive fettling methods to improve the assembly efficiency and quality [17]. [PERSON] et al.