Industrial Process Anomaly Detection Using Real-Time Particle Scatter
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Solution Overview
Problem
Current quality control methods for high-volume manufacturing, such as in automotive factories, are labor-intensive and costly due to manual inspections, and automated solutions for detecting anomalies like weld porosity in industrial processes are inaccurate and unreliable, especially in large-scale industrial environments.
Innovation Solution
A real-time anomaly detection system using artificial intelligence and machine learning that leverages audio and video data to detect anomalies like porosity during welding operations, capable of both supervised and unsupervised learning, eliminating the need for manual labeling and improving scalability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used for quality control, then measurement precision can be maintained, but productivity decreases and labor costs increase
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical detection system using cameras and image processing algorithms. The system captures images of weld seams and automatically analyzes them for defects, eliminating the need for manual visual inspection while maintaining detection accuracy and enabling continuous operation at high production speeds.
Solution Approach 2:
The patent introduces computer vision technology as an intermediary between the manufacturing process and quality control decision-making. The system processes visual data through algorithms that identify weld defects, serving as a mediator that translates physical weld characteristics into actionable quality assessments without requiring direct human intervention.
2Productivity
If automated quality control solutions are implemented, then productivity increases, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent adjusts multiple parameters of the optical detection system including camera resolution, lighting conditions, shutter speed, and image processing thresholds to optimize both speed and accuracy. By carefully tuning these parameters, the system achieves high-speed automated inspection while maintaining reliable defect detection capabilities suitable for industrial production environments.
Solution Approach 2:
The patent transitions from traditional 2D image capture to multi-dimensional analysis by incorporating depth information, multiple viewing angles, and temporal sequences of images. This dimensional expansion enables more robust anomaly detection that maintains precision while operating at automated production speeds.
3Reliability
If comprehensive quality inspection is performed, then reliability improves, but loss of time increases
Solution Approach 1:
The patent implements continuous real-time inspection that operates without interruption throughout the manufacturing process. The optical detection system continuously captures and analyzes weld quality data as production proceeds, eliminating the need for batch sampling or post-production inspection, thereby maintaining high reliability while minimizing time loss.
Solution Approach 2:
The patent performs quality inspection immediately during or right after the welding process itself, rather than waiting for subsequent inspection stages. This preliminary detection approach allows for immediate identification of defects while the workpiece is still in position, reducing handling time and enabling faster quality assurance cycles.
Data Source
AI summary
In one embodiment, a device comprises interface circuitry and processing circuitry. The processing circuitry receives, via the interface circuitry, a video stream captured by a camera during performance of an industrial process, wherein the video stream comprises a sequence of frames; detects, based on analyzing the sequence of frames, a degree of particle scatter that occurs during performance of the industrial process; and determines, based on the degree of particle scatter, that an anomaly occurs during performance of the industrial process.