Welding defect classification based on convolution neural network (CNN) and Gaussian kernel

Khumaidi, Agus; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery

doi:10.1109/isitia.2017.8124091

Cited by 83 publications

(25 citation statements)

References 3 publications

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“…Dung et al [ 25 ] compared three deep learning methods for crack detection in gusset plate welded joints. Khumaidi et al [ 26 ] and Zhang et al [ 27 ] used CNN for weld inspection, reaching accuracies of 95.8% and 93.9%, respectively, for the classification of three different types of defects, while the CNN developed by Bacioiu et al [ 28 ] achieved a 93.4% accuracy during the classification of five different types of defects. The transfer learning approach was used by Yang et al [ 29 ] for optical inspection of laser welding.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Cruz

Rivas

Quiza

et al. 2020

Sensors

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One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

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Section: Introductionmentioning

confidence: 99%

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Cruz

Rivas

Quiza

et al. 2020

Sensors

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“…Hou et al [17] construct a deep convolutional neural network (DCNN) to extracted high-level features from X-ray images. Khumaidi et al [18] introduced the idea of Gaussian kernel into deep learning. The purpose is to ensure that the main information of the image is extracted while minimizing the occurrence of noise and interference and improving the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification of Weld Surface Defects

Zhu

Liu

2019

Applied Sciences

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In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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“…The weld visual inspection process performed through image processing on the image sequence to improve data accuracy is presented in [11]. The Convolution Neural Network (CNN) as an image processing technique can determine the feature automatically to classify the variation of each weld defect pattern.…”

Section: Introductionmentioning

confidence: 99%

Using Entropy for Welds Segmentation and Evaluation

Haffner

Kučera

Drahoš

et al. 2019

Entropy

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In this paper, a methodology based on weld segmentation using entropy and evaluation by conventional and convolution neural networks to evaluate quality of welds is developed. Compared to conventional neural networks, there is no use of image preprocessing (weld segmentation based on entropy) or data representation for the convolution neural networks in our experiments. The experiments are performed on 6422 weld image samples and the performance results of both types of neural network are compared to the conventional methods. In all experiments, neural networks implemented and trained using the proposed approach delivered excellent results with a success rate of nearly 100%. The best results were achieved using convolution neural networks which provided excellent results and with almost no pre-processing of image data required.

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Welding defect classification based on convolution neural network (CNN) and Gaussian kernel

Cited by 83 publications

References 3 publications

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Deep Learning-Based Classification of Weld Surface Defects

Using Entropy for Welds Segmentation and Evaluation

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