Packaging Machine Quality Control With Fine-Tuned Defect Recognition
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Solution Overview
Problem
Existing methods for quality control of packages produced in packaging machines are manual and tedious, leading to waste and inefficiency, and there is a need for an improved, automated system to ensure higher confidence in package quality and reduce waste.
Innovation Solution
A machine learning-based quality control apparatus and method that utilizes a pre-trained model to recognize defects in packages, allowing for continuous monitoring and fine-tuning to adapt to specific packaging machines, enabling early detection of irregularities and improving quality control efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation of batches is used to ensure package quality, then quality control is performed, but the process is tedious and leads to product waste
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated image processing system that captures images of packages and uses algorithmic analysis to detect defects, irregularities, and quality issues. This substitution eliminates the need for physical handling and visual inspection by human operators, thereby preventing product waste while maintaining quality assurance.
Solution Approach 2:
The system enables packages to be self-evaluated through automated imaging and analysis. Each package is independently inspected by the image processing system without requiring manual intervention, allowing continuous quality control without the waste associated with batch sampling by human operators.
2Reliability
If manual visual examination is used to assess package quality, then quality evaluation is performed, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces slow manual visual examination with automated image capture and processing systems that can analyze multiple packages simultaneously and instantaneously. The image processing algorithms rapidly identify defects, seal quality, and packaging irregularities, dramatically increasing productivity while maintaining or improving evaluation accuracy through consistent, objective criteria.
Solution Approach 2:
The system enables continuous quality control by processing packages as they move through the production line without interruption. Unlike manual inspection which requires stopping or sampling batches, the automated system continuously captures images and analyzes packages in real-time, maintaining uninterrupted production flow while ensuring every package is evaluated.
3Reliability
If large quantities of packages are assessed manually to ensure quality, then quality coverage is improved, but the process becomes more tedious and wasteful
Solution Approach 1:
The patent replaces tedious manual assessment of large quantities with automated imaging systems that can process entire production batches rapidly. The system captures and analyzes images of all packages or statistically significant samples without the time constraints and operator fatigue associated with manual inspection, achieving comprehensive quality coverage efficiently.
Solution Approach 2:
The system can be configured to perform either complete inspection of all packages or targeted inspection of statistically significant samples. This flexibility allows achieving sufficient quality coverage through partial action when appropriate, reducing time and resources while maintaining reliability through proper sampling methodologies embedded in the image processing algorithms.
Data Source
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AI summary
The present invention relates to a quality control apparatus for quality control of packages produced in specific packaging machine of a certain model, the quality control apparatus comprising: a memory storing a pre-trained machine learning model being trained to recognize packages produced in a generic packaging machine of the certain model to pass a quality control and to recognize a number of known defects on packages produced in the generic packaging machine; and circuitry configured to execute: an image obtaining function configured to obtain images depicting packages produced in the specific packaging machine; an irregularity identification function configured to identify an irregularity on packages produced in the specific packaging machine, by inputting the images into the pre-trained machine learning model; an irregularity classifying function configured to classify a package exhibiting the irregularity as either a package passing the quality control or a defect package; a fine-tuning function configured to fine-tune the pre-trained machine learning model to additionally recognize packages produced in the specific packaging machine of the certain model and exhibiting the irregularity as either being a package passing the quality control or being a defect package depending on a result from the irregularity classifying function, thereby generating a specific machine learning model specific to the specific packaging machine of the certain model to be used for quality control of packages produced in the specific packaging machine; and a quality control function configured to perform quality control on packages produced by the specific packaging machine using the specific machine learning model.