Automatic Labeling Machine Camera Calibration via Marked Test Containers
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Solution Overview
Problem
Existing automatic labeling machines face challenges in reliably detecting misalignments of cameras, leading to incorrect label application and inefficient correction processes, resulting in defective products and unnecessary machine shutdowns.
Innovation Solution
A method involving a camera-based optical detection system with image evaluation, using specially marked measuring containers to regularly check and adjust the monitoring device's position, ensuring precise label placement and preventing defective containers from further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the camera is used to monitor label placement without regular calibration checks, then the machine can operate continuously, but the detection accuracy deteriorates due to camera misalignment
Solution Approach 1:
The system performs preliminary calibration by introducing a test container with known marking structure before normal production. This preliminary action establishes the correct camera position and detection parameters, ensuring subsequent measurements maintain high accuracy throughout continuous operation without requiring frequent shutdowns for recalibration.
Solution Approach 2:
A test container with a predefined marking structure serves as an intermediary object between the camera and the actual product containers. This intermediary provides a stable reference framework that mediates the calibration process, allowing the system to verify and adjust camera positioning without directly measuring the product containers themselves.
2Measurement precision
If the camera is frequently calibrated using test containers, then detection accuracy is maintained, but machine productivity decreases due to interruption of normal operation
Solution Approach 1:
Instead of continuous calibration that would halt production, the system implements periodic calibration by introducing test containers at predetermined intervals during normal operation. This periodic action maintains camera accuracy over time while minimizing interruptions to productivity, as calibration only occurs when a test container is present in the field of view.
Solution Approach 2:
The system performs self-calibration by automatically detecting the test container's marking structure and adjusting camera parameters without requiring manual intervention or complete machine shutdown. The calibration process is integrated into the existing operational flow, allowing the system to service itself while maintaining production continuity.
3Ease of operation
If the camera position is not correctly initialized, then the machine can run without calibration procedures, but defective containers are not detected leading to quality issues
Solution Approach 1:
The system implements feedback by continuously monitoring the position and appearance of container markings against expected parameters. When deviations are detected indicating camera misalignment, the system generates feedback signals to trigger recalibration procedures, ensuring reliable defect detection is maintained without requiring complex manual setup procedures.
Solution Approach 2:
The patent replaces complex mechanical alignment procedures with an optical/electronic solution. Instead of requiring precise mechanical positioning of the camera during setup, the system uses image processing and pattern recognition to automatically determine correct camera position and orientation by analyzing the marking structure on test containers, thereby simplifying operation while maintaining reliability.
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 detection quality and accuracy of the monitoring device, reducing defective products and minimizing machine downtime by allowing for timely correction of camera misalignments and precise label application.
Implementation Method 1
at least one optical detection device, in particular a camera with downstream image evaluation
Data Source
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AI summary
The method involves feeding a specially marked measuring container (14) to an automatic labeling machine (10). A labeling process is discontinued for the measuring container based on detection of the marked measuring container. The measuring container provided with markings is scanned by an optical sensing device (21) of a monitoring device (20), where the optical sensing device is formed by a camera (22) with downstream image analysis. A radio frequency identification (RFID) identifying marking is provided for interacting with a sensor of the labeling machine.