## 7 Conclusions and Future Work  
This paper proposed the methods of assembly error modeling and tolerance dynamic allocation for the LSDM. The assembly error model is based on gravity compensation during the assembly process, and the different environments between the ground assembly and service in space are considered. Gravity variation is regarded as the core influencing factor. The changes in the hinge clearance and truss rod length caused by gravity variation are accounted for in the assembly error model to reduce gravity's impact on the service performance. The tolerance dynamic allocation method uses the as-built data measured in the actual assembly process as the input. Compared with the previous fixed tolerance allocation methods, it can relax the tolerance while ensuring the quality, which can reduce the assembly difficulty and cost. In addition, if the as-built data exceed the tolerance, it can evaluate whether the current process should be reworked or the subsequent processes' tolerances should be tightened to ensure the final quality.  
Specifically, the differences between the ground assembly and service environments in space for the LSDM were analyzed, and the truss rod deformation and hinge clearance caused by gravity were regarded as factors affecting the satellite's service performance; thus, an assembly error model to improve the service performance was built. The LSDM assembly based on the assembly error model proposed in this paper can improve the antenna pointing accuracy from 0.068\({}^{\circ}\) to 0.045\({}^{\circ}\). To reduce the assembly cost and to improve the collaboration of the tolerance allocation between each process, the dynamic tolerance allocation method is proposed, which uses GA as the optimization algorithm and the as-built data as the input. In GA's fitness function, the assembly error model built in this paper was constructed as the restrictive condition for the satellite's service performance, and the assembly cost function of each assembly process was considered. The verification results show that this method can reduce the assembly cost by 13.5% while guaranteeing assembly quality.  
Although this study achieved good verification results, some work remains to achieve more accurate satellite performance in space. For instance, this paper only considers the differences in gravity between the ground and space, which are the important influencing factors for assembly. However, there are also many other factors that affect the satellite's final service performance, such as air pressure, radiation, and temperature. In the future, analyzing multi-physical coupling factors' influences on the assembly quality usingmulti-physical simulation technology will have great potential. Additionally, optimization algorithms are of great significant for the tolerance allocation. Metaheuristic methods besides GA, such as the simulated annealing algorithm (SAA), ant colony optimization (ACO), particle swarm optimization (PSO), etc., can also be used as an optimization algorithm to improve the dynamic tolerance allocation method proposed in this paper.  
Conceptualization, L.Z. and Y.W.; Methodology, X.L.; Resources, B.L.; Software, W.Y.; Validation, B.L.; Writing--original draft, X.L.; Writing--review and editing, B.W. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Defense Fundamental Research Foundation China (No. JCKY2018601C011), the Beijing Key Laboratory of Digital Design and Manufacturing Project, and the Ministry of Industry and Information Technology (MIIT) Key Laboratory of Intelligent Manufacturing Technology for Aeronautics Advanced Equipment.  
Not applicable.  
The authors declare no conflict of interest.