Vision System Auto-Setup for Manufacturing Image Quality
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Solution Overview
Problem
The complexity of setting up vision systems for manufacturing machines, such as labelling machines, requires extensive testing and specialized operator intervention, leading to prolonged machine stoppages and inefficiencies when adapting to new label types or applications.
Innovation Solution
An automatic procedure for setting vision systems, which involves testing multiple operating conditions and selecting the optimal parameters for cameras, lighting devices, and image-processing algorithms to ensure clear image reproduction, allowing for quick adaptation to new applications without specialized operator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setting procedure is used for vision system, then operating parameters can be optimized based on operator experience, but machine stoppage time increases and specialized operator intervention is required
Solution Approach 1:
The vision system performs self-setting by automatically determining optimal operating parameters through test image acquisition and evaluation. The system evaluates image quality metrics and selects parameters without human intervention, enabling the system to set itself rather than requiring specialized operators.
Solution Approach 2:
The system performs preliminary test acquisitions with different operating conditions before actual production. Multiple test images are acquired with varying camera exposure, lighting intensity, and processing parameters to pre-determine the optimal configuration for the specific application.
2Measurement precision
If manual setting procedure is used for vision system, then operating parameters can be optimized based on operator experience, but device complexity increases due to specialized intervention requirements
Solution Approach 1:
The manual mechanical adjustment process by operators is replaced with an automated computational system. The control unit automatically acquires test images, evaluates image quality metrics, and determines optimal parameters through algorithmic processing rather than human judgment and manual adjustment.
Solution Approach 2:
The system incorporates feedback loops where test image quality is evaluated and used to adjust operating parameters. The control unit continuously refines camera exposure, lighting intensity, and processing parameters based on image quality feedback until optimal settings are achieved.
3Measurement precision
If extensive testing is performed for new label types, then optimal parameters can be determined, but productivity decreases due to prolonged setup time
Solution Approach 1:
The system performs periodic test acquisitions with different operating conditions systematically. Instead of continuous manual adjustment, the system cycles through predefined test parameters, evaluates results, and selects the optimal configuration, reducing overall setup time while maintaining thorough testing.
Solution Approach 2:
The system automatically varies multiple parameters including camera exposure time, lighting intensity, and image processing thresholds during test acquisitions. By systematically changing these parameters and evaluating their effect on image quality, the system determines optimal settings for new label types without manual intervention.
Data Source
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Figure 4A~4C
AI summary
Described herein is a method for operating a vision system (10) for controlling an operating process of a manufacturing machine (200) operating on articles (100), wherein said vision system (10) comprises: - at least one camera (12A, 12B, 12C); - at least one lighting device (14A, 14B, 14C); and - at least one image-processing unit (16A, 16B, 16C), the method comprising an automatic procedure for setting the vision system (10).