Lens System Alignment via Machine Learning Refraction Analysis
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Solution Overview
Problem
The alignment of lens systems in optical systems, such as optical sensors or telescopes, is challenging due to manufacturing tolerances, requiring a time-consuming grid search method to find suitable alignments, which is inefficient for high-volume production.
Innovation Solution
A method utilizing a machine learning system to align lens systems by training on attribute values from refracted optical signals, allowing for a significant reduction in the number of alignments to be checked, with iterative refinement and optimization techniques to quickly converge on a suitable alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a grid search method is used to align the lens system by checking all possible alignments in equidistant steps, then the alignment can be found with sufficient precision, but the time required for alignment increases significantly
Solution Approach 1:
The patent applies preliminary action by using a machine learning model to predict alignment outcomes before actually performing the alignment. The model is trained on previous alignment data and optical signal characteristics, allowing it to predict which alignments are likely to be suitable without exhaustively checking all possibilities. This preliminary prediction step reduces the number of alignments that need to be physically checked while maintaining sufficient precision.
Solution Approach 2:
The patent uses a machine learning model that creates a virtual copy or representation of the alignment problem. Instead of physically testing every possible alignment, the model creates a computational representation that simulates the optical system's behavior under different alignments. This virtual copying allows rapid evaluation of many alignment scenarios without the time cost of physical testing for each one.
2Measurement precision
If the quantification of alignments is made very fine-grained to avoid skipping suitable alignments, then the mapping of suitable alignments is accurate, but the number of alignments to be checked increases significantly
Solution Approach 1:
The patent implements feedback by using the machine learning model to evaluate alignment candidates and adjust the search strategy accordingly. The model provides feedback on which alignments are likely to be suitable based on training data and optical signal analysis. This feedback mechanism allows the system to focus computational resources on promising alignment regions rather than uniformly checking all possibilities, maintaining accuracy while improving efficiency.
Solution Approach 2:
The patent changes the parameter of alignment evaluation from physical/experimental to computational/predictive. By using machine learning models to predict alignment outcomes, the system can evaluate many more alignment scenarios without increasing physical testing time. This parameter change from empirical to computational evaluation maintains mapping accuracy while significantly improving productivity.
3Reliability
If manufacturing tolerances are accounted for in alignment, then the alignment is reliable across production batches, but the alignment process becomes more complex
Solution Approach 1:
The patent applies universality by using a machine learning model that can handle multiple alignment scenarios and tolerance variations within a single framework. The model is trained on data that incorporates manufacturing tolerances and optical signal characteristics, allowing it to provide reliable alignment predictions across different production batches without requiring separate complex procedures for each variation. This multi-functional approach maintains reliability while reducing process complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly accelerates the alignment process by evaluating fewer alignments, allowing for faster and more efficient alignment of lens systems, reducing the time required to achieve a suitable alignment.
Implementation Method 1
ascertaining a first refracted optical signal, the first refracted optical signal being ascertained by a refraction of a first emitted optical signal at the lens system aligned according to the first alignment
Data Source
AI summary
A method for ascertaining an alignment of a lens system. The method include: aligning the lens system according to a provided first alignment; ascertaining a first refracted optical signal, the first refracted optical signal being ascertained by a refraction of a first emitted optical signal at the lens system (aligned according to the first alignment; ascertaining a first attribute value, the first attribute value characterizing an attribute of the first refracted optical signal; training a first machine learning system as a function of the first alignment and the ascertained first attribute value, the machine learning system being designed to ascertain an output for an alignment that characterizes the attribute of the alignment; ascertaining the alignment of the lens system based on an output of the first machine learning system.