Sample Cartridge Seal Defect Detection Using Multi-Sensor AI
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Solution Overview
Problem
Conventional methods for detecting defects in sample cartridges, such as leaks or seal failures, are often destructive, inconsistent due to human error, and fail to identify all defects, leading to unnecessary waste and inefficiencies in manufacturing.
Innovation Solution
An automated defect detection system using external sensors like RGB and IR cameras, ultrasonic microphones, and machine learning models to identify defects in real-time during manufacturing by comparing data sets to baseline values, allowing for non-destructive and consistent identification of defects in sample cartridges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seal testing approaches and visual inspections are used, then defects can be detected, but the methods are destructive and may fail to identify all defects
Solution Approach 1:
The patent replaces manual visual inspection and mechanical seal testing with an automated machine learning-based image analysis system. The system uses trained neural networks to automatically detect defects in seal areas, eliminating human error and inconsistency while providing reliable, repeatable defect detection without destructive testing
Solution Approach 2:
The patent performs defect detection during the manufacturing process rather than after completion. By implementing automated image capture and analysis at intermediate manufacturing stages, the system identifies defects early, allowing for real-time quality control without requiring destructive final testing
2Measurement precision
If extensive testing of multiple cartridges in a lot is performed, then defect detection improves, but the entire lot must be scrapped if unacceptable defects are detected
Solution Approach 1:
The system performs automated defect detection on individual cartridges during manufacturing using image capture and machine learning analysis. By identifying and isolating only defective units rather than testing entire lots, the system prevents unnecessary scrapping of acceptable cartridges while maintaining comprehensive defect detection
Solution Approach 2:
The automated inspection system enables each cartridge to be individually evaluated and sorted based on its own quality characteristics. The machine learning model independently assesses each unit's seal integrity, allowing defective cartridges to be removed while preserving good ones, eliminating the need for lot-based scrapping
3Ease of manufacture
If manual visual inspection methods are used, then defect detection can be performed, but human error causes inconsistency
Solution Approach 1:
The patent replaces manual visual inspection with an automated system that captures images and uses machine learning algorithms to detect defects. This substitution eliminates human error and fatigue, providing consistent, repeatable inspection results while maintaining ease of implementation through automated processing
Solution Approach 2:
The system creates digital copies (images) of the seal areas for automated analysis. By working with image data rather than direct physical inspection, the system enables consistent, programmable defect detection that can be replicated across all cartridges without variation
Data Source
AI summary
Methods and systems for detecting defect in sample cartridges in real-time during manufacturing. Such systems utilize one or more external sensors that detect characteristics or parameters of the sample cartridge and/or the manufacturing process from one or more data sets. The external sensor(s) include any of: an RGB camera, IR camera, high-resolution optical camera, and ultrasonic microphone or combination thereof. An automated system obtains data sets from external sensor(s) and compares the data sets to a baseline of the sample cartridge and/or manufacturing process such that defects can be determined based on a variance from the baseline. Such methods can utilize feature extraction and spectrum analysis to identify features or characteristics for comparison with the baseline. A machine learning model can be used to determine an algorithm based on data sets of acceptable sample cartridges and data sets from the external sensor(s) that are associated with the cartridge defect.