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Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks
(2021)
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.
The composition of concrete determines its resistance to various degradation mechanisms such as ingress of ions, carbonation or reinforcement corrosion. Knowledge of the composition of the hardened concrete is therefore helpful to assess the remaining service life of an existing structure or evaluate the damage observed during inspections. For example, for most existing concrete structures the type of cement originally used is not known and must therefore be determined afterwards. This paper presents a preliminary study on the application of laser-induced breakdown spectroscopy (LIBS) to identify the type of cement. For this purpose, ten different types of cement were investigated. For every type, three cement paste prisms were produced: (i) prisms dried, ground and pressed into tablets, (ii) prisms dried and (iii) prisms untreated. LIBS measurements were performed with a diode-pumped low energy laser (1064 nm, 3 mJ, 1.5 ns, 100 Hz) in combination with two compact spectrometers which cover the UV and NIR spectral range. A reduced subset of spectral features was used to build a classification model based on linear discriminant analysis. The results show that the classification of homogenized pressed cement powder samples provides a high accuracy, however, factors such as a different sample matrix and moisture content can affect the accuracy of the classification. The study demonstrates that LIBS is a promising tool to identify the type of cement.