Magnetic Circuit Quality Testing With Neural Verification
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Solution Overview
Problem
Manual testing of magnetic circuits in loudspeakers is costly and inefficient, with high labor costs and low yield due to human fatigue and visual limitations, and existing visual tests have poor accuracy and robustness.
Innovation Solution
A method and device using a pre-trained neural network model to analyze product images, followed by a secondary judgment based on defective feature pixels to improve accuracy and avoid misjudgment, incorporating region division and exposure duration adjustments to enhance test efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual testing is used to inspect magnetic circuits, then testing can be performed with simple equipment, but labor cost is high and productivity is low
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical inspection system that uses image capture devices and neural network algorithms to detect defects on magnetic circuit surfaces, eliminating the need for human operators while significantly improving testing efficiency and productivity
Solution Approach 2:
The patent introduces an intermediary neural network model that acts as a bridge between the captured image and the final defect detection result, enabling automated decision-making while maintaining system simplicity and avoiding the need for complex manual inspection procedures
2Extent of automation
If manual testing is used to inspect magnetic circuits, then testing can be performed without complex automation, but testing accuracy is limited by human fatigue and visual limitations
Solution Approach 1:
The patent replaces the human visual system with an automated optical inspection system equipped with image capture devices and neural network analysis, eliminating human fatigue and visual limitations while achieving consistent high-precision defect detection across all products
Solution Approach 2:
The patent creates a digital copy of the magnetic circuit surface through high-resolution image capture, allowing the neural network to analyze the copied image data without affecting the original product, thereby achieving precise defect detection while preserving product integrity
3Productivity
If neural network model testing is used to identify defects, then productivity increases and labor cost decreases, but misjudgment of qualified products as defective may occur
Solution Approach 1:
The patent implements a feedback mechanism where the secondary judgment module receives the neural network's testing results and uses position information of defective feature pixels to verify and correct potential misjudgments, ensuring that qualified products are not incorrectly classified as defective while maintaining high productivity
Solution Approach 2:
The patent introduces a secondary judgment module as an intermediary between the neural network model and the final testing result, which uses position information analysis to verify defect detections and reduce false positives, thereby improving reliability while maintaining the high productivity gained from automated testing
Data Source
AI summary
A method and device for testing product quality are disclosed. The method for testing product quality comprises: acquiring an image of a product to be tested; testing the image by using a pre-trained neural network model to obtain a testing result output by the neural network model; when the testing result indicates that the product to be tested is a defective product, performing a secondary judgment on the testing result according to position information of defective feature pixels in the image in the testing result, and determining whether the product to be tested is qualified according to a secondary judgment result. The method has high test accuracy, ensures the quality of product and facilitates reducing the labor cost of test.