Edge Computing Welding Quality Detection Using Deep Learning

The patent replaces traditional imaging algorithms with deep learning algorithms that use convolutional neural networks to automatically extract features from welding images. This substitution eliminates the need for manual feature engineering and complex post-processing, improving robustness while reducing overall system complexity through automated pipelines.

The patent creates a digital copy of the welding process through image capture and stores it in a database for later analysis. This copying allows the system to re-examine welding defects without repeating the physical welding process, enabling robust detection through multiple analysis passes on the same digital data.

The patent implements a dynamic training system where the deep learning model is continuously updated with new welding images and defect annotations as standards change. The system can adapt to new defect types and detection criteria by retraining the neural network with updated datasets, maintaining high accuracy while remaining adaptable to evolving requirements.

The patent pre-processes welding images by capturing them during the welding process and storing them in a database before final analysis. This preliminary action allows the system to prepare and organize data in advance, enabling quick retraining when standards change without requiring complete data collection restarts.

The patent substitutes physical destructive testing with digital image analysis using deep learning algorithms. Instead of physically breaking or damaging the weld to assess quality, the system captures images and uses neural networks to identify defects, saving time and resources while maintaining assessment accuracy.

The patent creates digital copies of welds through image capture, allowing quality assessment without touching or damaging the actual weld. Multiple copies can be analyzed simultaneously through different algorithms and by different operators, dramatically reducing the time required compared to sequential physical testing.

The patent replaces human visual inspection with automated deep learning algorithms that process welding images. The convolutional neural networks automatically detect and classify defects, eliminating the need for skilled operators while maintaining or improving detection accuracy through consistent, objective analysis.

The patent implements a self-service system where the deep learning model autonomously performs quality assessment without human intervention. The system captures images, processes them through the neural network, and provides defect detection results automatically, enabling continuous operation without labor constraints.