Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test

Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test

Blog Article

This study introduces a deep learning engine designed for the non-destructive automatic detection of defects within weld beads.A 1D waveform ultrasound signal was collected using an A-scan pulser receiver to gather defect signals from inside the weld bead.We established 5,108 training datasets and 500 test datasets for five pass/fail labels in this study.We developed a multi-branch deep afck benchmade fusion network (MBDFN) model that independently trains 1D-CNN for local pattern learning within a sequence and 2D-CNN for spatial feature extraction and then combines them in an ensemble method, achieving a classification accuracy of 92.

2%.The resulting deep learning engine has 6.5 x 18.5 soccer goal potential applications in automatic welding robots or welding inspection systems, allowing for rapid determination of internal defects without compromising the integrity of the finished product.