AI Process Monitoring for Real-Time Defect Detection in Unit Processes
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Solution Overview
Problem
Existing process management systems struggle to optimize unit processes in real-time during manufacturing, leading to frequent errors and defects, as they fail to directly apply efficient process sequences known by experienced workers and require post-production analysis for defect identification.
Innovation Solution
An artificial intelligence-based process optimization management system that includes a reading module using machine learning to generate work data from image data, a detection module to identify defects, and an output module to notify workers of defects in real-time, allowing for immediate correction and optimization of the process sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sampling and inspection are performed after all work is completed, then defect detection is possible, but production time is lost and cause analysis is delayed
Solution Approach 1:
The system performs preliminary actions by capturing images and detecting defects during the manufacturing process itself, rather than waiting until completion. The imaging device continuously monitors the workpiece, and the defect detection device identifies issues in real-time, allowing for immediate intervention before production is fully completed.
Solution Approach 2:
The system implements continuous feedback by detecting defects during manufacturing and immediately notifying workers through the notification device. This real-time feedback loop allows workers to correct issues as they occur, preventing defective products from being completed and enabling immediate cause analysis without stopping production.
2Reliability
If workers manually collect and analyze internal data from inspection equipment, then defect detection is possible, but productivity decreases due to manual intervention
Solution Approach 1:
The system enables self-service by automatically capturing images, detecting defects, and notifying workers without requiring manual data collection or analysis. The imaging device and defect detection device work autonomously to monitor the manufacturing process, freeing workers from manual inspection tasks while maintaining high defect detection accuracy.
Solution Approach 2:
The system replaces manual mechanical inspection with automated imaging and defect detection technology. Instead of workers physically examining workpieces and analyzing data from inspection equipment, the system uses imaging devices to capture visual data and automated defect detection algorithms to identify issues, significantly improving productivity while maintaining reliability.
3Productivity
If an optimized work order is derived in real-time without interruption, then productivity improves, but system complexity increases
Solution Approach 1:
The system divides the complex manufacturing process into manageable segments by monitoring individual workpieces and their specific defects separately. The imaging device captures images of specific workpieces, and the defect detection device analyzes each workpiece independently, allowing for targeted real-time optimization without requiring complete system redesign or excessive complexity.
Data Source
AI summary
An artificial intelligence-based process optimization management system may include: a reading module that, when receiving instruction data recorded for an optimal execution determined among a plurality of executions that are performed in different progress sequences of unit processes, which are part of a process for manufacturing a product, generates work data, which is a result of reading image data for a process that progresses on an object, so as to correspond to the instruction data; a detection module that receives the work data from the reading module and generates defect information for the process by comparing the work data with the instruction data; and an output module that outputs the defect information.