Unsupervised Welding Defect Detection Using Audio And Video
In this work we explore the application of AI to robotic welding. Robotic welding is a widely used technology in many industries, but robots currently do not have the capability to detect welding defects which get introduced due to various reasons in the welding process. We describe how deep-learning methods can be applied to detect weld defects in real-time by recording the welding process with microphones and a camera. Our findings are based on a large database with more than 4000 welding samples we collected which covers different weld types, materials and various defect categories. All deep learning models are trained in an unsupervised fashion because the space of possible defects is large and the defects in our data may contain biases. We demonstrate that a reliable real-time detection of most categories of weld defects is feasible both from audio and video, with improvements achieved by combining both modalities. Specifically, the multi-modal approach achieves an average Area-under-ROC-Curve (AUC) of 0.92 over all eleven defect types in our data. We conclude the paper with an analysis of the results by defect type and a discussion of future work.
Code (0)
등록된 구현이 없습니다.
Tasks
Defect DetectionSimilar Papers 제목 키워드 기반
Automated Detection of Welding Defects without Segmentation
Abstract Substantial research has been performed on automated detection and classification of welding defects in continuous welds using X-ray imaging. Typically, the detection follows a pattern recognition schema (seg…
ClassificationHuman Detectionobject-detectionObject DetectionAdvancing Welding Defect Detection in Maritime Operations via Adapt-WeldNet and Defect Detection Interpretability Analysis
Weld defect detection is crucial for ensuring the safety and reliability of piping systems in the oil and gas industry, especially in challenging marine and offshore environments. Traditional non-destructive testing (NDT…
Transfer LearningInvestigating the ability of deep learning to predict Welding Depth and Pore Volume in Hairpin Welding
To advance quality assurance in the welding process, this study presents a deep learning DL model that enables the prediction of two critical welds' Key Performance Characteristics (KPCs): welding depth and average pore …
Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework
New production techniques have emerged that have made it possible to produce metal parts with more complex shapes, making the quality control process more difficult. This implies that the visual and superficial analysis …
Defect Detectionobject-detectionObject DetectionSegmentationDefect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11
This study proposes an advanced method for surface defect detection in printed circuit boards (PCBs) using an improved YOLOv11 model enhanced with a generative adversarial network (GAN). The approach focuses on identifyi…
Data AugmentationDefect DetectionGenerative Adversarial Network