Surrogate Neural Network Model for Sensitivity Analysis and Uncertainty Quantification of the Mechanical Behavior in the Optical Lens-Barrel Assembly

TL;DR

This paper presents a surrogate neural network model trained on finite element analysis data to enable rapid sensitivity analysis and uncertainty quantification of optical lens profiles in camera assemblies, improving efficiency and design optimization.

Contribution

The study introduces a neural network surrogate model that accelerates evaluation of lens behavior, facilitating large-scale Monte Carlo simulations for uncertainty quantification in optical assembly.

Findings

Neural network surrogate enables near-instant evaluation of lens profiles.

Model accurately predicts the impact of assembly tolerances on optical performance.

The approach supports optimization of manufacturing tolerances and component matching.

Abstract

Surrogate neural network-based models have been lately trained and used in a variety of science and engineering applications where the number of evaluations of a target function is limited by execution time. In cell phone camera systems, various errors, such as interferences at the lens-barrel and lens-lens interfaces and axial, radial, and tilt misalignments, accumulate and alter profile of the lenses in a stochastic manner which ultimately changes optical focusing properties. Nonlinear finite element analysis of the stochastic mechanical behavior of lenses due to the interference fits is used on high-performance computing (HPC) to generate sufficient training and testing data for subsequent deep learning. Once properly trained and validated, the surrogate neural network model enabled accurate and almost instant evaluations of millions of function evaluations providing the final lens profiles. This computational model, enhanced by artificial intelligence, enabled us to efficiently perform Monte-Carlo analysis for sensitivity and uncertainty quantification of the final lens profile to various interferences. It can be further coupled with an optical analysis to perform ray tracing and analyze the focal properties of the lens module. Moreover, it can provide a valuable tool for optimizing tolerance design and intelligent components matching for many similar press-fit assembly processes.