Machine Vision Defect Detection for Beverage Bottle Quality Control
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Solution Overview
Problem
Existing beverage bottle detection systems are not accurate in assessing cleanliness before use and beverage quantity after use, leading to inefficiencies in quality control.
Innovation Solution
A machine vision-based defect detection system comprising a monitoring module, detection module, early warning module, and screening module, utilizing high-definition cameras to inspect empty and filled bottles for cleanliness, specifications, and foreign matter, with real-time data display and alarm systems to ensure compliance with standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing detection systems are used for beverage bottles, then the detection process is simple, but the detection accuracy for cleanliness and beverage quantity is insufficient
Solution Approach 1:
The detection system is divided into multiple specialized modules: a detection module for inspecting empty bottles (cleanliness, specifications, foreign matter), a monitoring module for filled bottles (beverage quantity, foreign matter), an early warning module, and a screening module. Each module focuses on specific detection tasks, improving overall measurement precision while organizing complexity into manageable segments.
Solution Approach 2:
The machine vision system serves multiple detection functions simultaneously: it detects cleanliness, inspects specifications, identifies foreign matter, and monitors beverage quantity. This multi-functional approach achieves high detection accuracy across various parameters without requiring separate systems for each function.
2Productivity
If manual detection methods are used, then the system complexity is low, but the detection efficiency and accuracy for cleanliness and beverage amount are insufficient
Solution Approach 1:
The patent replaces manual mechanical detection with an automated machine vision system using cameras, image processing algorithms, and automated screening mechanisms. This substitution dramatically improves detection efficiency and accuracy for cleanliness and beverage quantity while reducing human labor requirements.
Solution Approach 2:
The system performs self-detection and self-screening through automated image capture, processing, and analysis. The detection module automatically identifies unqualified empty bottles, and the monitoring module automatically screens unqualified filled bottles, enabling the system to service itself without continuous human intervention.
3Measurement precision
If high-definition cameras and multiple modules are deployed, then the detection accuracy improves, but the device complexity increases
Solution Approach 1:
The complex system is segmented into distinct functional modules (detection module with third camera, monitoring module with first and second cameras, early warning module, screening module), each handling specific detection tasks. This segmentation allows high-definition cameras and multiple sensors to be organized systematically, improving measurement precision while managing complexity through modular architecture.
Data Source
AI summary
A defect detection system based on machine vision includes a monitoring module, a detection module, an early warning module, and a screening module. A method and the system are implemented by timely screening out an empty bottle having a different specification in detection, monitoring whether a content in each of empty bottles meets requirement, monitoring whether foreign matter is contained in each of filled bottles, and displaying a monitored image on a display device. Bottles that are normal in the detection are filled, filled bottles are monitored through the monitoring module. The monitoring module is configured to monitor whether the content in the filled bottles meets requirement, whether foreign matter is contained in the filled bottles.