AI Process Defect Detection With Real-Time Image Feedback
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Solution Overview
Problem
Existing process defect detection systems fail to detect defects in real-time and require manual inspection, making it difficult to optimize processes and prevent defective products, as they rely on post-process analysis and are not easily adaptable to on-site conditions.
Innovation Solution
An artificial intelligence-based system that uses a photographing module, machine learning model, and detection module to capture and analyze image data, recognize process changes, and generate defect information in real-time, allowing for immediate correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sampling and inspection are performed after all work is completed, then defect detection is possible, but production time is lost and mass production of defective products cannot be prevented
Solution Approach 1:
The system performs preliminary defect detection during the manufacturing process itself, not after completion. The photographing module captures images at each unit process stage, and the detection module identifies defects in real-time, allowing immediate correction before mass production of defective products occurs.
Solution Approach 2:
The system implements continuous feedback by comparing work data with instruction data in real-time. When deviations are detected, the system provides immediate feedback to operators, enabling corrective actions during the process rather than after completion, thus preventing further defective production.
2Reliability
If manual inspection and data collection are required, then defect analysis can be performed, but productivity decreases and real-time optimization is difficult
Solution Approach 1:
The system performs automatic defect detection and analysis without requiring manual intervention. The photographing module automatically captures images, the machine learning model automatically processes and analyzes the images, and the detection module automatically generates defect information, eliminating the need for manual inspection while maintaining high reliability.
Solution Approach 2:
The system replaces manual mechanical inspection with an automated optical and computational system. Instead of workers manually collecting data and analyzing defects, the system uses photographing modules, machine learning models, and automated detection algorithms to perform the same function with higher speed and accuracy.
3Manufacturing precision
If work orders are established with many items, then comprehensive process control is achieved, but errors and defects increase and adaptability to on-site conditions decreases
Solution Approach 1:
The system dynamically adapts to on-site conditions by using machine learning models that can learn from actual production data. The system compares work data with instruction data in real-time and can identify deviations that require adaptive responses, allowing the manufacturing process to adjust to actual conditions while maintaining comprehensive control.
Solution Approach 2:
The system monitors and detects changes in process parameters by comparing actual work data with instructed parameters. When deviations are detected, the system can trigger parameter adjustments to maintain manufacturing precision while adapting to actual production conditions.
4Loss of information
If post-process analysis is used for defect detection, then comprehensive defect information can be obtained, but real-time correction is impossible and defective products are already produced
Solution Approach 1:
The system performs defect detection at each unit process stage before the entire manufacturing process is completed. By capturing images and analyzing defects in real-time during the process, the system enables preliminary detection that allows correction before defective products are fully produced.
Solution Approach 2:
The system maintains continuous defect detection throughout the manufacturing process rather than performing discrete post-process analysis. The photographing module continuously captures images, and the detection module continuously analyzes them, ensuring that defects are detected as they occur and can be corrected immediately, maintaining continuous useful action for quality control.
Data Source
AI summary
An artificial intelligence-based process defect detection system may include: a photographing module that collects image data by capturing a process that progresses on an object; a machine learning model that generates work data that is a result of recognizing and reading the object based on the image data; and a detection module that receives instruction data recorded regarding a process for an object optimized for product production, detects a defect or a non-defect by comparing the work data with the instruction data, and generates defect information when the process is defective.