Multisource system for NDT of welded elements exploited in aircraft industry

Chady, Tomasz; Sikora, R.; Szwagiel, Mariusz; Grzywacz, Bogdan; Misztal, Leszek; Waszczuk, Paweł; Szydłowski, Michał; Szymanik, Barbara

doi:10.1108/compel-01-2016-0018

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The necessity of quick and accurate measurement and quality evaluation of components exploited in the aircraft industry is particularly important in the production process [2]. Maintaining a high standard of production translates directly into the quality of the whole structure, which must meet all the safety requirements defined by applicable regulations.…”

Section: Problem Descriptionmentioning

confidence: 99%

Automatic non-destructive system for quality assurance of welded elements in the aircraft industry

Chady

Waszczuk

Szydłowski

et al. 2018

AIP Conference Proceedings

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Abstract. Flaws that might be a result of the welding process have to be detected, in order to assure high quality thus reliability of elements exploited in aircraft industry. Currently the inspection stage is conducted manually by a qualified workforce. There are no commercially available systems that could support or replace humans in the flaw detection process. In this paper authors present a novel non-destructive system developed for quality assurance purposes of welded elements utilized in the aircraft industry.

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Section: Problem Descriptionmentioning

confidence: 99%

Automatic non-destructive system for quality assurance of welded elements in the aircraft industry

Chady

Waszczuk

Szydłowski

et al. 2018

AIP Conference Proceedings

Self Cite

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“…This method can photograph the internal welding state that cannot be seen by human eyes. 3 Early defect classification methods mainly extracted features of defect images manually or through algorithms, and then input feature sets into some traditional machine learning classifiers, such as support vector machine (SVM) 4 and artificial neural network (ANN). 5 For example, a three-step defect detection method was proposed, in which the wavelet de-noising method was used to process the ray images.…”

Section: Introductionmentioning

confidence: 99%

Image recognition of limited and imbalanced samples based on transfer learning methods for defects in welds

Zhang

Liu

Sun

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Welding quality inspection is critical to the quality control of the welding structure. Traditional manual detection requires experienced workers and the method is time-consuming. Currently, deep learning has made great progress in the field of image recognition. However, in terms of industrial defect detection, the contradiction between huge computational parameters and limited imbalanced samples still exists, which makes the deep learning method unable to play its role to the maximum extent. In this case, a combination of deep learning methods and transfer learning methods is considered to improve the performance of the model on limited and imbalanced data sets. The VGG16 model, which is pre-trained on a large number of source data sets, is fine-tuned on our target data sets. In this paper, a new DTN-VGG16 structure is proposed, in which a Global Average Pooling (GAP) layer, Batch Normalization (BN) layer, and Soft-max classifier are added on top of the frozen pre-training model, which can effectively reduce the number of parameters and accelerate the convergence speed of the model. In addition, focal loss function is used to replace the commonly used multi-class cross entropy loss function. By adjusting the parameters of the loss function, the training process has better convergence. Experimental results show that DTN-VGG16 has better performance than other traditional machine learning methods and deep learning models. And the proposed model has good robustness and generalization performance when valid on the real data of aerospace welding seams which is difficult to learn.

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“…1 summarizes implemented operations aimed at flaws detection from radiograms. The more details about consecutive operations included in radiogram processing can be found in [1]. The exemplary results of weld radiogram processing are shown in Fig.…”

Section: Outline Of Data Fusion Conceptmentioning

confidence: 99%