Mohandas Reenu, Mongan Patrick, Hayes Martin
Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland.
Confirm Smart Manufacturing Research Centre, V94 T9PX Limerick, Ireland.
Sensors (Basel). 2024 Oct 11;24(20):6553. doi: 10.3390/s24206553.
Welding is an extensively used technique in manufacturing, and as for every other process, there is the potential for defects in the weld joint that could be catastrophic to the manufactured products. Different welding processes use different parameter settings, which greatly impact the quality of the final welded products. The focus of research in weld defect detection is to develop a non-destructive testing method for weld quality assessment based on observing the weld with an RGB camera. Deep learning techniques have been widely used in the domain of weld defect detection in recent times, but the majority of them use, for example, X-ray images. An RGB image-based solution is attractive, as RGB cameras are comparatively inexpensive compared to X-ray image solutions. However, the number of publicly available RGB image datasets for weld defect detection is comparatively lower than that of X-ray image datasets. This work achieves a complete weld quality assessment involving lap shear strength prediction and visual weld defect detection from an extremely limited dataset. First, a multimodal dataset is generated by the fusion of image data features extracted using a convolutional autoencoder (CAE) designed in this experiment and input parameter settings data. The fusion of the dataset reduced lap shear strength (LSS) prediction errors by 34% compared to prediction errors using only input parameter settings data. This is a promising result, considering the extremely small dataset size. This work also achieves visual weld defect detection on the same limited dataset with the help of an ultrasonic weld defect dataset generated using offline and online data augmentation. The weld defect detection achieves an accuracy of 74%, again a promising result that meets standard requirements. The combination of lap shear strength prediction and visual defect detection leads to a complete inspection to avoid premature failure of the ultrasonic weld joints. The weld defect detection was compared against the publicly available image dataset for surface defect detection.
焊接是制造业中广泛使用的技术，与其他工艺一样，焊接接头中存在缺陷的可能性，这些缺陷可能会对制成品造成灾难性影响。不同的焊接工艺使用不同的参数设置，这对最终焊接产品的质量有很大影响。焊接缺陷检测的研究重点是基于使用RGB相机观察焊缝，开发一种用于焊缝质量评估的无损检测方法。近年来，深度学习技术在焊缝缺陷检测领域得到了广泛应用，但其中大多数使用例如X射线图像。基于RGB图像的解决方案很有吸引力，因为与X射线图像解决方案相比，RGB相机相对便宜。然而，用于焊缝缺陷检测的公开可用RGB图像数据集的数量比X射线图像数据集的数量相对要少。这项工作从一个极其有限的数据集中实现了完整的焊缝质量评估，包括搭接剪切强度预测和视觉焊缝缺陷检测。首先，通过融合使用本实验设计的卷积自动编码器（CAE）提取的图像数据特征和输入参数设置数据，生成了一个多模态数据集。与仅使用输入参数设置数据的预测误差相比，该数据集的融合将搭接剪切强度（LSS）预测误差降低了34%。考虑到数据集规模极小，这是一个很有前景的结果。这项工作还借助使用离线和在线数据增强生成的超声焊缝缺陷数据集，在同一个有限数据集上实现了视觉焊缝缺陷检测。焊缝缺陷检测的准确率达到了74%，这同样是一个符合标准要求的有前景的结果。搭接剪切强度预测和视觉缺陷检测的结合实现了全面检查，以避免超声焊接接头过早失效。将焊缝缺陷检测结果与公开可用的用于表面缺陷检测的图像数据集进行了比较。
