Construction Slurry Spread Imaging for Real-Time Quality Control
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Solution Overview
Problem
Existing methods for analyzing stucco slurry spread on plasterboard production lines are offline, qualitative, and operator-dependent, failing to account for shear stress and resulting in inconsistent slurry quality due to unnoticed quality drifts.
Innovation Solution
A construction slurry spread quantification system using image analysis and a processor to identify slurry characteristics, controlling mixer and forming table vibration systems to adjust operations for consistent slurry spread, and incorporating machine learning for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline slump tests are used to analyze slurry rheology, then some indication of rheological properties is obtained, but real-time process monitoring is not achieved and quality drift goes unnoticed
Solution Approach 1:
The patent replaces the mechanical offline slump test system with an optical imaging system and image analysis processing. Cameras capture images of the slurry spread on the forming table, and image processing algorithms automatically analyze the spread characteristics, eliminating the need for manual mechanical testing and enabling continuous real-time monitoring of slurry rheology during production
Solution Approach 2:
The imaging system operates continuously throughout the production process, capturing images at regular intervals as slurry is dispensed on the forming table. This continuous monitoring provides ongoing rheological assessment rather than periodic offline testing, enabling immediate detection of quality variations and real-time process optimization
2Reliability
If visual estimation by operators is used to assess slurry spread quality, then quality assessment is performed, but small changes are difficult to notice and operator dependence causes inconsistencies
Solution Approach 1:
The patent replaces human visual estimation with automated optical imaging and computer-based image analysis. The system uses cameras to capture high-resolution images and algorithms to objectively measure slurry spread characteristics, eliminating operator subjectivity and improving both precision and consistency of quality assessment
Solution Approach 2:
The system transitions from subjective human visual perception to multi-dimensional digital image data analysis. By capturing images with high-resolution sensors and analyzing multiple image parameters (area, shape, intensity distribution), the system achieves superior measurement precision and objectivity compared to human visual estimation
3Measurement precision
If offline slump tests are conducted on static slurry under no shear stress, then rheological properties are measured, but the influence of shear stress during actual production is not accounted for
Solution Approach 1:
The system performs preliminary characterization of slurry rheology by analyzing the spread pattern immediately after dispensing, before the slurry is disturbed by subsequent processing operations. The image analysis captures the initial spread characteristics that reflect the slurry's rheological properties under production-relevant conditions, including the shear history from the dispenser
Solution Approach 2:
The system measures slurry spread characteristics (area, shape, intensity distribution) that are directly influenced by the shear stress history experienced during dispensing. These spread parameters serve as proxies for rheological properties under actual production conditions, rather than measuring static properties under idealized laboratory conditions
Data Source
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AI summary
The application describes a slurry spread quantification system (200) comprising a forming table including a forming area and at least one slurry outlet (120) configured to, in use, dispense a slurry (130) into the forming area, a sensor (210) trained on the forming area and configured to capture an image of the forming area; and a processor (220) operable to: analyse the image to identify the slurry (130); determine at least one characteristic of the slurry (130); and output at least one indication of the at least one characteristic of the slurry (130). A method, a computer implemented method and a non-transitory computer readable storage medium comprising instructions, that when executed on a computing device cause the computing device to control a system (200) as described are also discussed.