Assembly accuracy and accuracy stability prediction are significant research directions for improving the reliability and efficiency of precision assembly. In this study, an improved method for assembly accuracy stability prediction, based on the contact-pressure distribution entropy, is presented. By using the contact-pressure distribution as the evaluation parameter instead of the strain-energy distribution, the improved method can not only predict the assembly accuracy of precision assembly more efficiently, but also predict the stability of the assembly accuracy with variations in the ambient temperature. The contact pressure has a clearer mechanical significance than strain energy density in the assembly process, which can be used to distinguish the actual contact area from the contact surface. Hence, the improved method is more efficient and accurate than the original. This study utilizes the same case used in the original method and an additional case from the actual production process to verify the improved method. The correctness and validity of the improved method are proved by these case studies.

The entropy evaluation method of assembly stress has become a hot topic in recent years. However, the current research can only evaluate the maximum stress magnitude and stress magnitude uniformity, and it cannot evaluate the stress position distribution. In this paper, an evaluation method of stress distribution characterized by strain energy density distribution is proposed. In this method, the relative entropy is used as the evaluation index of the stress distribution difference between the error model and the ideal model. It can evaluate not only the stress magnitude, but also the stress position. On this basis, an optimization method of the precise assembly process which takes the relative entropy as the optimization objective is proposed. The stress distributions of the optical lens are evaluated, and the assembly angle of the spacer in the process of the optical lens system assembly is optimized. By comparing the stress distribution of the optimized model and the ideal model, the validity of this method is proved.

Surface accuracy is a crucial evaluation criterion for the life cycle performance of optical components. Throughout the assembly process, the optical surface undergoes deformation due to applied assembly stresses, causing the actual surface profile to deviate from the intended design. The key to the quantitative optimization of the adhesive bonding assembly process is to elucidate the quantitative coupling mechanism between assembly stress and optical surface deformation and to establish a corresponding quantitative relationship. To address this, a comprehensive study into the optical surface deformation of multi-point adhesive bonded flat optical components is presented. Firstly, the coupled influence mechanisms governing optical surface deformation are analyzed considering both the components properties and assembly parameters, and this mechanistic analysis includes dimensionless modeling, boundary conditions analysis, and reliable design requirement analysis. Secondly, based on the understanding of mechanisms, a quantitative approximation method is developed to predict the deformation patterns within the critical central region (50% aperture) of optical components subjected to multi-point bonding. Finally, the quantitative approximation for the assembly-induced surface deformation is experimentally validated. Through this research, the surface deviation during the multi-point adhesive bonding assembly of flat optical components can be effectively approximated, holding significant importance for further imaging quality prediction and assembly parameters optimization during assembly process, and facilitating a further performance improvement for optical instruments.

The geometric error distributed on components’ contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution error modeling for precision machining surfaces based on the K-space spectrum is proposed. To determine the boundary of systematical error and random error, we used a cruciform boundary line method based on the K-space spectrum, achieving the optimal separation of the two with frequency difference. The effectiveness of the method was experimentally verified using two sets of machined surfaces. By comparing with current common random error filtering methods, the outstanding role of the proposed error separation method in separating random error and preserving processing features has been verified.