Machine Vision Tray Integrity Check for Optical Filter Selection
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Solution Overview
Problem
The existing manual and semi-automated processes for selecting high-quality thin-film filters in optical communication systems often result in human errors, leading to incorrect separation of 'pass' and 'fail' quality devices, which increases manufacturing costs and risks the assembly of faulty optical equipment.
Innovation Solution
A machine-vision based method and apparatus that uses a combination of light sources, including white and single-color lights, to capture images of devices in a tray, compare the locations of remaining devices with memorized positions, and take corrective action to ensure accurate separation of 'pass' and 'fail' quality devices, thereby improving the integrity of the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-automated processes are used for device selection, then operation simplicity is maintained, but selection accuracy deteriorates due to human errors
Solution Approach 1:
The patent replaces manual mechanical picking operations with an automated machine vision system that uses optical imaging and image processing to identify and guide robotic pickers. This substitution eliminates human error in device selection while maintaining operational simplicity through centralized control software.
Solution Approach 2:
The system creates a digital copy (image) of the device tray and its contents, then processes this copy through image recognition algorithms to identify device locations and qualities. This allows the system to make selection decisions based on accurate visual data without physical contact until the final picking stage.
2Productivity
If automated machine picking is implemented, then selection accuracy improves, but error rate worsens due to machine mistakes
Solution Approach 1:
The patent implements a feedback mechanism where the machine vision system continuously monitors the device tray, verifies device locations, and confirms successful picking operations. If an error is detected (such as a device being missed or incorrectly picked), the system can issue corrective actions to retrieve or reposition devices, thereby reducing the error rate while maintaining high productivity.
3Productivity
If devices are processed in large batches, then manufacturing efficiency improves, but error detection difficulty worsens
Solution Approach 1:
The patent divides the large batch of devices into individually addressable positions within the device tray, with each position tracked and monitored separately by the machine vision system. This segmentation allows the system to process large batches efficiently while maintaining the ability to detect and correct errors at the individual device level through systematic verification of each position.
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 reduces human intervention and errors, enhancing the efficiency of the manufacturing process by ensuring accurate separation and reducing waste and potential equipment failures.
Implementation Method 1
A machine-vision based method and apparatus that uses a combination of light sources, including white and single-color lights, to capture images of devices in a tray
Data Source
AI summary
Embodiments of present invention provide a method for checking integrity of a device selection process. The method includes placing multiple devices in a device tray that has multiple cells arranged in a matrix of M-rows and N-columns; separating the multiple devices into a first group and a second group; causing a machine to memorize locations of at least the first group; removing the second group from the device tray; after the removing, causing the machine to capture an image of devices remaining in the device tray and identify locations of the remaining devices based upon the image; comparing locations so identified with locations of the first group of devices memorized by the machine; and taking a corrective action when a discrepancy is found between the locations identified and locations memorized. An apparatus for performing the above method is also provided.