Image-Based Sensor Trace Analysis for Manufacturing Defects
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Solution Overview
Problem
Conventional signal trace analysis methods in manufacturing equipment lack sensitivity, are prone to error, and fail to capture complex, nonlinear relationships and noise, leading to inadequate defect detection and classification in manufacturing processes.
Innovation Solution
Implementing image-based signal trace analysis that generates images with dimensions corresponding to signal traces and time values, allowing for defect detection and classification using trained machine learning models, enhancing sensitivity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal trace analysis methods are used, then the analysis process is simple, but the sensitivity and accuracy of defect detection are insufficient
Solution Approach 1:
The patent transforms one-dimensional signal trace data into two-dimensional image representations, where the x-axis represents time and the y-axis represents signal amplitude. This dimensional transformation enables the application of image processing techniques and machine learning models to enhance defect detection accuracy while maintaining manageable system complexity.
Solution Approach 2:
The patent replaces conventional signal processing methods with machine learning-based image analysis. By substituting traditional analytical approaches with trained neural networks and image processing algorithms, the system achieves superior sensitivity and accuracy in detecting subtle defects in manufacturing equipment signal traces.
2Reliability
If conventional signal trace analysis methods are used, then the system is easy to operate, but the ability to capture complex nonlinear relationships and noise is insufficient
Solution Approach 1:
The patent performs preliminary actions by pre-processing signal trace data into image format and training machine learning models in advance. This preparation work captures complex nonlinear relationships and noise patterns beforehand, enabling the system to reliably detect defects during actual operation without requiring complex real-time processing by the user.
Solution Approach 2:
The patent introduces an intermediary image representation layer between the raw signal traces and the defect detection process. This intermediary format enables complex pattern recognition while shielding the user from complexity, maintaining ease of operation while significantly improving detection reliability.
3Measurement precision
If signal traces are grouped and transformed into images, then defect detection sensitivity is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent performs the computationally intensive image transformation and machine learning model training as preliminary actions before actual defect detection. By pre-processing the signal traces into images and training models in advance, the system reduces real-time processing requirements, enhancing sensitivity without significantly increasing operational processing time.
Data Source
AI summary
A method includes grouping signal traces based on signal trace characteristics. The method includes generating an image, a first dimension of the image corresponding to the signal traces, and a second dimension of the image corresponding to time values, where a visual indicator corresponds to a signal trace characteristic of a signal trace at a time value, the signal trace corresponds to a row or column of the first dimension, and the time value corresponds the second dimension. The method includes detecting a defect in operation of components of manufacturing equipment associated with the signal traces based on a deviation of visual indicators in a row or column from a visual indicator of a respective group of visual indicators associated with signal traces with similar signal trace characteristics. The method includes classifying the defect based on a signal trace corresponding to the row or column of the image.