Liquid Food Packaging Deviation Detection with Basis Function Grading
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Solution Overview
Problem
Existing methods for detecting deviations in packaging containers for liquid food are inefficient and unreliable, lacking automated and reliable tools for quality control and deviation grading.
Innovation Solution
A method utilizing basis functions to analyze image data of packaging containers, determining a set of weights representing the deviation, and mapping these weights to a grading database for precise deviation classification, combined with a system for real-time feedback and control adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection methods are implemented, then productivity and reliability of quality control are improved, but device complexity increases
Solution Approach 1:
The patent replaces manual visual inspection with an automated imaging system that captures images of packaging containers and uses image processing algorithms to detect deviations. The system substitutes mechanical/optical inspection methods with computational analysis, achieving automated quality control without requiring physical contact or manual intervention.
Solution Approach 2:
The patent introduces an intermediary processing layer between image capture and quality assessment. Basis functions serve as mathematical intermediaries that decompose complex deviation patterns into manageable components, enabling the system to handle diverse deviation types through a unified detection framework.
2Measurement precision
If multiple deviation types are detected with high precision, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the deviation detection problem by introducing basis functions that decompose complex deviation patterns into distinct computational components. Each basis function targets specific deviation characteristics, allowing the system to accurately detect and classify multiple deviation types (wrinkles, dents, tears, delamination) through separate, specialized analysis channels.
Solution Approach 2:
The patent transforms the detection problem from direct image analysis to parameter space analysis. By projecting image data onto basis functions and extracting weight parameters, the system converts complex visual patterns into simplified numerical representations that are easier to analyze and compare against quality thresholds.
3Adaptability or versatility
If basis functions are used to represent deviations, then adaptability to different deviation types is improved, but computation time increases
Solution Approach 1:
The patent performs preliminary action by pre-defining a library of basis functions that represent common deviation types before actual inspection begins. These basis functions are prepared in advance and stored for rapid retrieval during production, eliminating the need to generate them in real-time and reducing processing delays.
Solution Approach 2:
The patent uses basis functions as simplified copies or representations of actual deviation patterns. Instead of analyzing complete high-resolution images for every defect, the system compares extracted features against pre-computed basis function models, significantly reducing computation time while maintaining detection accuracy.
Data Source
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AI summary
A monitoring system implements a method for versatile and efficient detection and grading of deviations in packaging containers for liquid food in a manufacturing plant. The method comprises obtaining (31) image data of a packaging container, or a starting material for use in producing the packaging container; analyzing (32) the image data for detection of a current deviation; processing (33) the current deviation in relation to a set of basis functions, which is associated with a deviation type of the current deviation, to obtain a current set of weights that represent the current deviation; and determining (35) a current grading of the current deviation based on the current set of weights. The set of basis function may be pre-computed based on reproductions of packaging containers or starting material comprising different magnitudes of the deviation type.