Polarization Image Inspection for Molded Product Quality
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Solution Overview
Problem
Existing injection molding systems face challenges in determining the quality of molded products with minimal load, as they often require extensive manual inspection and adjustment of setting values, which can be time-consuming and inefficient.
Innovation Solution
An inspection apparatus and method that utilizes an imaging device to acquire polarization images of molded products, generating pseudo images through a machine-learned learning model to determine product quality, allowing for accurate and efficient quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection and adjustment methods are used, then quality determination can be performed, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical inspection system that uses polarization imaging and machine learning algorithms. The imaging device captures polarization images, and a determination unit with a learning model automatically analyzes these images to determine quality, eliminating the need for manual inspection and significantly improving efficiency while reducing time loss.
2Measurement precision
If extensive manual inspection is performed, then quality can be assessed, but the complexity of the inspection process increases
Solution Approach 1:
The patent extracts the essential quality determination function from complex manual inspection processes. By isolating the key task of quality assessment and implementing it through a specialized determination unit with a learning model that analyzes polarization images, the system achieves high measurement precision while simplifying the overall inspection process complexity.
3Reliability
If traditional inspection methods are used, then quality determination is possible, but the load on the inspection system becomes excessive
Solution Approach 1:
The patent creates a virtual model or representation of the quality assessment process through machine learning. The determination unit uses a learning model that has been trained on polarization images to predict quality outcomes, effectively copying the inspection function in a computationally efficient manner that maintains reliability while reducing the operational load on the inspection system.
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
Enables high-accuracy, low-load determination of molded product quality by generating pseudo images that reflect internal stress distribution, facilitating rapid parameter adjustments for improved molding processes.
Implementation Method 1
an imaging device acquiring a polarization image of a molding product
Data Source
AI summary
An inspection apparatus includes an imaging device acquiring a polarization image of a molding product, and a determination unit determining quality of the molding product. The determination unit includes an image generating unit generating a pseudo image from an input image in accordance with a machine-learned learning model, and the determination unit inputs the polarization image or a calculation image obtained by calculation from the polarization image to the image generating unit as the input image and determines the quality of the molding product based on the input image and the pseudo image generated by the image generating unit.