Camera Assembly Alignment Using ML Feedback to Cut Rejects
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Solution Overview
Problem
The production of cameras with optical components of varying tolerances faces challenges in maintaining optical performance due to deviations from the advance planning, which are difficult to predict and compensate for during the production process, leading to potential waste and inefficiencies.
Innovation Solution
A method involving prefabricated components is used, where machine learning models predict optical performance based on prior and measured data, allowing for early detection and correction of suboptimal combinations, and optimizing component pairings to ensure adherence to specified criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optical components are produced with tighter tolerances to ensure consistent optical performance, then manufacturing precision improves, but production cost increases and component availability decreases
Solution Approach 1:
The patent changes the parameter of optical component tolerance from tight to wide, accepting greater variations in component dimensions and optical properties. This is compensated by implementing active alignment procedures that adjust component positions and orientations during assembly, thereby maintaining optimal optical performance without requiring expensive tight-tolerance components
Solution Approach 2:
The patent performs preliminary characterization of optical components by measuring their actual optical properties and geometric parameters before assembly. This advance knowledge is stored and used to guide the active alignment process, allowing the system to pre-compensate for known component variations and achieve consistent optical performance across batches
2Manufacturing precision
If active alignment is performed to compensate for component variations, then optical performance consistency improves, but production time and process complexity increase
Solution Approach 1:
The patent implements a feedback-driven active alignment process where measured optical performance data from preliminary characterization and during assembly is continuously fed back to adjust component positions. The system uses this feedback to iteratively optimize the spatial arrangement of components, compensating for variations and achieving consistent optical performance across production batches
Solution Approach 2:
The patent replaces complex mechanical precision mechanisms with a combination of automated measurement systems and software-controlled adjustment procedures. Instead of relying on precision mechanical fixtures and jigs to maintain alignment, the system uses digital characterization data and automated feedback control to achieve and verify optimal optical performance
3Stability of the object's composition
If adhesive bonding is used to fix component positions after alignment, then spatial arrangement stability improves, but subsequent production steps may cause misalignment due to adhesive curing shrinkage and handling
Solution Approach 1:
The patent performs preliminary characterization of components and performs active alignment before adhesive bonding. By completing the precision alignment work before the bonding process begins, the system ensures that components are in their optimal positions while the adhesive is still adjustable, avoiding the need to re-align after the adhesive sets
Solution Approach 2:
The patent anticipates the misalignment that will occur during adhesive curing and handling by pre-compensating for these effects during the active alignment phase. The system adjusts component positions to account for expected adhesive shrinkage and handling disturbances, so that the final aligned position accounts for these future changes
4Productivity
If machine learning prediction is implemented to assess final optical performance early, then rejection rate improves, but measurement and data processing requirements increase
Solution Approach 1:
The patent performs preliminary optical performance prediction using machine learning models trained on data from preliminary component characterization. This early prediction, made before final assembly and testing, allows the system to identify and reject component combinations that are unlikely to achieve target optical performance, reducing the number of cameras that need to undergo complete production and testing cycles
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 enhances the production efficiency by reducing rejects and material waste by identifying and addressing potential performance issues early, ensuring higher yields of functional cameras.
Implementation Method 1
This adhesive bonding can be initiated, e.g., by activating a light curing adhesive using UV light.
Data Source
AI summary
A method for producing a camera. The method includes: providing prefabricated components; adjusting at least two of these prefabricated components relative to one another in accordance with at least one specified optimality criterion; and adhesively bonding the components to one another in the adjusted state; wherein prior data characterizing a specific specimen of at least one of the prefabricated components, and/or measured data in respect of the optical performance of the combination of the components adjusted with respect to one another, are mapped by a trained machine learning model onto a prediction for the optical performance that the camera will deliver once it has run through at least one additional production step after the adhesive bonding; and this prediction is used as feedback for an influencing action on the production process.