18260892. PRODUCTION OF CAMERAS WITH REDUCED REJECTION RATE simplified abstract (Robert Bosch GmbH)
PRODUCTION OF CAMERAS WITH REDUCED REJECTION RATE
Organization Name
Inventor(s)
Jens Dorfmueller of Magstadt (DE)
PRODUCTION OF CAMERAS WITH REDUCED REJECTION RATE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18260892 titled 'PRODUCTION OF CAMERAS WITH REDUCED REJECTION RATE
Simplified Explanation
The method described in the patent application involves producing a camera by adjusting prefabricated components relative to one another and bonding them together using adhesive. Machine learning is used to predict the optical performance of the camera after additional production steps.
- Prefabricated components are provided for the camera production process.
- At least two components are adjusted in accordance with specified optimality criteria.
- The adjusted components are adhesively bonded together.
- Machine learning model maps prior and measured data onto a prediction for the camera's optical performance.
- The prediction is used as feedback to influence the production process.
Potential Applications
- Camera manufacturing industry
- Automation of camera production processes
Problems Solved
- Ensuring optimal optical performance of cameras
- Streamlining camera production processes
Benefits
- Improved camera quality
- Increased efficiency in production
- Cost savings in camera manufacturing
Original Abstract Submitted
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.
