Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products

Abstract

Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded seams. Typically, the images are examined and evaluated by experts. The evaluation process qualitatively provides the properties of the welds. Particularly in times when artificial intelligence is being used more and more in processes, the quantization of properties that could previously only be determined qualitatively is gaining importance. In this contribution, we propose to use deep learning to perform semantic segmentation of micrographs of complex weld areas to achieve the automatic detection and quantization of weld seam properties. A semantic segmentation dataset is created containing 282 labeled images. The training process is performed with DeepLabv3+. The trained model achieves a value of around 95% for weld contour detection and 76.88% of mean intersection over union (mIoU).

LoHi-WELD: A Novel Industrial Dataset for Weld Defect Detection and Classification, a Deep Learning Study, and Future Perspectives

“…They reported achieving an accuracy of 96.30% in classifying defects, even though the authors had to add transfer learning and handcrafted techniques, such as image filtering, to help overcome retraining samples' limitations due to scenario changes. Similarly, in [43], the researchers proposed a semantic segmentation of weld contours. The research includes detecting weld metal, background, and defects (cracks and pores) using a pretrained neural network.…”

GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning

“…This is a very important step in any artificial intelligence technique because datasets contain duplicates and missing values which cannot be used to train deep learning models, as they may lead to larger errors in object detection [13]. The major steps in general include gathering the dataset, removing missing data and outliers, analysing the data, and finally converting it into a suitable format for the deep learning model for further processing [14]. In this work, actual weld plates were laser cut into eight different asymmetrical seam shapes to address the industrial welding process.…”