Lens Module Assembly Optimization Using Genetic Algorithms
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Solution Overview
Problem
The mass production of high-performance camera modules with complex lens module assemblies is hindered by the inefficiency of manual selection of assembly conditions, which consumes time and resources and limits the exploration of optimal conditions.
Innovation Solution
A method utilizing a computing system to optimize lens module assembly by processing characteristic information of lenses and cavities, employing a genetic algorithm to select optimal cavity combinations, and updating a fitness function based on machine learning algorithms to improve assembly efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of assembly conditions is used by field workers, then assembly experience and knowledge can be applied, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical selection process with an automated computer-based system that uses algorithms to evaluate cavity combinations. The computing system automatically processes lens characteristic information and determines optimal cavity assignments without requiring manual trial and error, thereby reducing time consumption while maintaining or improving assembly quality.
Solution Approach 2:
The system enables self-service by allowing the computer to autonomously evaluate cavity combinations based on lens characteristics and automatically determine optimal assembly conditions without human intervention. The algorithm independently processes the information and generates assembly recommendations, freeing workers from time-consuming manual evaluation tasks.
2Reliability
If manual trial and error method is used to find optimal assembly conditions, then worker knowledge can guide the process, but the number of attemptable conditions is limited
Solution Approach 1:
The patent replaces limited human trial-and-error capability with an automated computer system that can evaluate a vastly larger number of cavity combinations. The computing system systematically processes different assembly conditions algorithmically, enabling exploration of conditions that would be impractical or impossible for workers to test manually, thereby expanding adaptability while maintaining reliability through structured evaluation.
Solution Approach 2:
The system introduces dynamics by enabling flexible and adaptive evaluation of cavity combinations based on real-time lens characteristic information. The algorithm can dynamically adjust its evaluation criteria and explore different assembly conditions systematically, allowing the system to adapt to various lens configurations and optimize performance across diverse scenarios beyond static worker knowledge.
3Manufacturing precision
If more cavity combinations are evaluated to find optimal assembly, then assembly quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and storing lens characteristic information in advance, organizing data structures that facilitate efficient evaluation. The system prepares the computational framework beforehand, including pre-defining evaluation criteria and cavity combination structures, so that when optimization is needed, the system can quickly process through pre-organized information rather than starting from scratch, thus reducing actual processing time while maintaining comprehensive evaluation.
Solution Approach 2:
The system segments the evaluation process into manageable components: first processing individual lens characteristic information, then evaluating cavity combinations in structured groups, and finally synthesizing optimal assembly conditions. This segmentation allows the system to handle complex multi-lens assemblies by breaking down the computational task into smaller, more efficient steps, reducing overall processing time while maintaining comprehensive optimization.
Data Source
AI summary
A lens module assembly optimization method includes: in preparing a lens module including assembled N lenses respectively formed in cavities: receiving characteristic information of at least N lenses respectively formed in N cavity groups each including a respective plurality of cavities; and processing information for selecting N cavities from the N cavity groups, based on the characteristic information. A past cavity selection result, a fitness function configured based on data of the assembled N lenses or data of the prepared lens module according to the past cavity selection result, and a genetic algorithm are received or stored. The processing of the information includes updating chromosome entity information based on the fitness function and output chromosome information crossed or mutated based on the genetic algorithm from input chromosome information corresponding to the past cavity selection result, and processing the information based on the chromosome entity information and the characteristic information.