A prediction method for the radial adhesive stress applied on multipoint bonded flat optical lenses based on a quantitative characterization of stress fields

The quantitative prediction and control of assembly stress are crucial for the accuracy of precision optical instruments during their manufacturing process. As a typical optical component, the multipoint bonded flat lens is widely used in precision optical instruments, in which the main assembly stress (i.e., radial adhesive stress) and its induced nonuniform stress field in the lens vary with environmental parameters and time. These factors ultimately determine the accuracy and stability of such precision instruments. Effective prediction of the radial adhesive stress is important for bonding parameter optimization and accuracy enhancement but remains difficult to obtain due to the lack of direct measurement devices and effective prediction methods. Here, a novel prediction method is presented to estimate the radial adhesive stress applied on a multipoint bonded flat lenses. First, a quantitative characterization method (QCM) is developed to concisely describe the nonuniform stress field in a lens using quantitative characterization parameters (QCPs). Then, based on the QCM and the theoretical model established in our previous work (Xiong et al., 2022), a prediction method is developed for the radial adhesive stress applied on a lens by analyzing the stress field in the lens. In this method, a backpropagation neural network is constructed to obtain the mapping relationship between the key QCPs of the stress field and the radial adhesive stress applied on the lens. Finally, an experimental verification of the prediction method is carried out. This paper proposes a novel method to predict the radial adhesive stress for multipoint bonded optical lenses, which can be adopted to monitor the variation characteristics of assembly stress in optical instruments during the entire life cycle and then further employed to optimize the bonding parameters during the lens manufacturing process. This paper attempts to construct a direct mapping relationship between assembly quality and assembly parameters for precision instruments, supporting the quantitative optimization of assembly parameters and quantitative control and prediction of assembly quality during the manufacturing and working process.