Deep Learning Automated System for Thermal Defectometry of Multilayer Materials
Currently, along with growth in industrial production, the requirements for product quality testing are also increasing. In the tasks of defectoscopy and defectometry of multilayer materials, the use of thermal nondestructive testing method is promising. At the same time, interpretation of thermal testing data is complicated by a number of factors, which makes the use of traditional methods of data processing ineffective. Therefore, an urgent task is to search for new methods of thermal testing that will automate the diagnostic process and increase information content of obtained results. The purpose of article is to use the advances in deep learning for processing results of active thermal testing of products made of multilayer materials and development of an automated system for thermal defectoscopy and defectometry of such products. The proposed system consists of a heating source, an infrared camera for recording sequences of thermograms and a digital information processing unit. Three neural network modules are used for automated data processing, each of which performs one of the tasks: defects detection and classification, determination of the defect depth and thickness. The software algorithms and user interface for interacting with system are programmed in the NI LabVIEW development environment.Experimental studies on samples made of multilayer fiberglass have shown a significant advantage of the developed system over using traditional methods for analyzing thermal testing data. The defect classification (determining the type) error on the test dataset was 15.7 %. Developed system ensured determination of defect depth with a relative error of 3.2 %, as well as the defect thickness with a relative error of 3.5 %.In order to process the experimental sequence of thermograms of the test sample, neural networks of appropriate modules for detection and classification, determination of depth and thickness of defects were created and trained. To solve these tasks, it is advisable to choose the architecture of neural networks for the detection and classification of defects, which is shown in Figure 6. Architecture of networks for determining defects depth and thickness is similar. The input layer contains 20 neurons, which corresponds to the number of thermograms in sequence. The source layer contains 4 (according to the number of classes) or 1 neuron. The Levenberg-Markard algorithm was used as an optimizer. Loss function - MSE, metric - MAE. According to the training results, MAE of defects depth determination on the validation set was 0.028 mm, MAE of thickness determination was 0.019 mm. The defects map, obtained by the resultsof work of trained neural network module fordefects detection and classification, is shown inFigure 7. All 12 artificial defects were detectedand unmistakably classified on the map. Defectscolor on the map corresponds to their depth. The shape and size of the defects are close to true ones. In the image we can see some dots ofincorrectly classified temperature profiles, whichcan be filtered by a median filter. In addition,Figure 7 also shows binary defect maps obtainedusing classical methods: optimal thermogram,Fourier and wavelet analysis methods, principalcomponents analysis method (PCA), and dynamicthermal tomography (DTT). Visually it is possibleto notice the increased efficiency of offered systemon the basis of deep learning in comparison withclassical methods. Figure 8 shows the thermal tomogram obtainedby the DTT method and the image of test sampleinternal structure, which was built on the results ofproposed system. At the defects boundaries thereare negative edge effects in the form of anomalous This effect can be eliminated using median filtering. In general, image of the internal structure is reliable. Quantitative evaluation of certain parameters and comparison of results with traditional methods is given below.DiscussionAccording to the results of quantitative evaluation of effectiveness of defects detecting in test sample by different methods (Table), it is established that the best results are demonstratedby developed automated system based on deep learning. In particular, the use of neural networks is only method by which it was possible to detect all 12 artifi defects and automatically classify them. Results of thermograms sequence processing by different methodsThe neural network module for defects detection and classification allows to determine the size of defects by their thermal imprints with the highest accuracy among considered methods. Temperature profiles were automatically classified with an error of 15.7 %. The value of Tanimoto criterion [15]at 88.7 % confirms the high reliability of constructed defects map.In considered conditions of testing the use of deep learning is the only method that gives chance to define defects depth effectively. Corresponding neural network module allows to determine the depthf artifi defects of in test sample with a relative error within ± 3.2 %. DTT method in a similar task showed negative results, which makes it impossible to assess the accuracy of determining defects depth.Relative error in determining defects thickness by neural network module is ± 3.5 %. Constructed thermal tomogram of the internal structure of sample is generally reliable, but at the boundaries of some defects there are negative boundary effects. Therefore, the thickness measurement must be performed at the central points of thermal imprints of the defects.Consequently, the use of proposed automated system based on deep learning demonstrates the advantages of neural network modules over traditional methods in all testing tasks. Due to the high noise immunity and generalizing properties of neural networks, the presence of non-uniform heating has a weak effect on the efficiency of defects detection in multilayer materials and the accuracy of measuring their parameters.ConclusionIn the paper offered to use the deep learning approach for automation of thermal defectometry of products from multilayer materials. The system for implementation of this method consists of three modules based on neural networks. Modules are designed to solve tasks of defects classification by type, determining their depth and thickness. Experimentally established that developed automated system allowed to detect and classify all artificial defects embedded in the test sample, and to estimate their depth with an error within ± 3.2 % and thickness with an error up to ± 3.5 %. Defect maps constructed as a result of processing experimental data using the proposed system have a high reliability according to Tanimoto criterion (88.1 %). In addition, the results of comparative analysis show that the developed system has an advantage over traditional methods in qualitative and quantitative indicators.The main direction for further research is to optimize the architecture of neural networks of relevant system modules by using the latest advances in deep learning. In particular, it is proposed to introduce normalization and dropout layers into the network architecture, to change the training optimization algorithm and activation function of fully connected layers. An important task is also the formation of a wide training samples dataset with different defects and materials configurations. This will expand the scope of developed automated system without the need to retrain neural networks for each individual task or type of multilayer material.