Integrated quality control of the rotor discs of gas turbines of gas-compressor units by eddy-current and dye penetrant nondestructive testing

Kryukov, I. I.; Shmelev, N. G.; Levchenko, A. I.; Rybnikov, A. I.; Andreev, M. P.; Moshnikov, A. V.

doi:10.1134/s1061830916020030

Cited by 4 publications

(2 citation statements)

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“…To assess the quality and correctness of the production process, both at the production stage and in the operational control phase, preference is given to non-invasive diagnostic methods -the so-called non-destructive testing. Such methods include, among others tomography (electric, ultrasound, magnetic) Rymarczyk, Kozłowski, Kłosowski, and Niderla, 2019;Kłosowski et al, 2020), electromagnetic testing or eddy current method (Li et al, 2021), visual research method (Deng et al, 2018), penetration method (Kryukov et al, 2016) or magnetic method (Tönshoff, Karpuschewski, and Regent, 1999). The main directions of the use of the eddy current method are the detection of material defects -defectoscopy, testing of material properties -structroscopy and the determination of dimensions (mainly thickness measurements).…”

Section: Introductionmentioning

confidence: 99%

“…The penetration method is used in ferromagnetic and nonferromagnetic materials and for testing non-metallic materials (e.g. ceramics) (Kryukov et al, 2016). The megnetic method uses the effect of magnetic flux forces on ferromagnetic particles applied to the surface of the tested object and can only be applied to this type of material (Tönshoff, Karpuschewski, and Regent, 1999).…”

Section: Introductionmentioning

confidence: 99%

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The Use of Transfer Learning with Very Deep Convolutional Neural Network in Quality Management

Kłosowski¹,

Kulisz²,

Lipski³

et al. 2021

ERSJ

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The aim of the article is to develop an algorithm for classifying cracks in the analyzed images using modern methods of deep machine learning and transfer learning based on pretrained convolutional neural network -Inception-ResNet-v2. Design/Methodology/Approach: Transfer learning based on the pretrained convolutional neural network was used to categorize the images. The fully conected layer of the Inception-ResNet-v2 network has been modified. The last layer was trained using a two-class (binary) linear SVM (Support Vector Machine). In total, 20,000 training cases (images) were used to train the fully connected layer within transfer learning process. The research analyzed the possibility of using the deep neural networks for quick and fully automatic identification of cracks / defects on the surface of analyzed parts. Findings:The results indicate that pretrained convolutional neural network using SVM to train a fully connected layer is a very effective solution for visual crack / fault detection. In the analyzed model, a positive classification was obtained at the level of 99.89%. Practical Implications: The model presented in the article can be used in quality control carried out by process monitoring. An effective model for identifying defective parts can be used in both logistics and production processes. Originality/Value: A novelty is the use of a freely available, deep neural network trained to classify 1000 categories of various images for binary categorization of faults (cracks). The algorithm was adjusted by replacing the primary, 1000-output fully connected layer in the Inception-ResNet-v2 network with a binary layer (2 categories). The fully connected layer has been trained using the classification version of the popular SVM learner, but thanks to the combination of this layer with the sophisticated fearure extraction ability of the pre-trained Inception-ResNet-v2 deep network, the resulting predictive model enables the classification of defects with a very high level of accuracy.

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