Real-Time Detection of Weld Defects for Automated Welding Process Base on Deep Neural Network

Shin, Seungmin; Jin, Chengnan; Yu, Jiyoung; Rhee, Sehun

doi:10.3390/met10030389

Cited by 45 publications

(23 citation statements)

References 10 publications

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“…Deep learning can obtain high-dimensional image features from the input data, fit the features and finally utilize the learning method of weights to increase the accuracy of classification prediction. However, while analyzing welds of industrial steel plates, deep learning cannot learn due to the lack of complete datasets; in addition, current research primarily focuses on improving the weld recognition ability; however, a complete automatic detection system has never been built, increasing the difficulty in actual industrial applications [45]. Image processing is the basis for effective weld detection.…”

Section: Deep Learning Detection Technologymentioning

confidence: 99%

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Deng

Feng

et al. 2021

Symmetry

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Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

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Section: Deep Learning Detection Technologymentioning

confidence: 99%

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Deng

Feng

et al. 2021

Symmetry

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show abstract

“…Deep learning can obtain high-dimensional image features from the input data, fit the features, and finally, utilize the learning method of weights to increase the accuracy of classification prediction. However, while analyzing welds of industrial steel plates, deep learning cannot learn due to the lack of complete datasets; in addition, current research mostly focuses on improving the weld recognition ability; however, a complete automatic detection system is never built, increasing the difficulty in actual industrial applications [25].…”

Section: A Analysis Of Steel Plates Surface Defectsmentioning

confidence: 99%

Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

Deng

Feng

et al. 2021

Preprint

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Welding defects not only bring several economic losses to enterprises and individuals but also threatens peoples lives. We propose a deep learning model, where the data-trained deep learning algorithm is employed to detect the weld defects, and the Convolutional Neural Networks (CNNs) are utilized to recognize the image features. The Transfer Learning (TL) is adopted to reduce the training time via simple adjustments and hyperparameter regulations. The designed deep learning-based model is compared with other classic models to prove its effectiveness in weld defect detection and image recognition further. The results show this model can accurately identify weld defects and eliminates the complexity of manually extracting features, reaching a recognition accuracy of 92.54%. Hence, the reliability and automation of detection and recognition is improved signifificantly. Actual application also verififies the effectiveness of TL in weld defect detection and image defect recognition. Therefore, our research results can provide theoretical and practical references for effificient automatic detection of steel plates, cost reduction, and the high-quality development of iron and steel enterprises.Index Terms - convolutional neural network, deep learning, image detect recognition, transfer learning, weld defect detection

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“…Recently, research on welding condition monitoring using artificial intelligence and camera vision devices has gained increasing attention because of the increasing demand for manufacturing intelligence, cost reduction, efficiency, and quality. This research can be classified into three areas of application: weld defect prediction [10][11][12], weld bead shape prediction [13,14], and weld seam tracking [15][16][17]. For instance, Zhang et al [10] developed a convolutional neural network (CNN) algorithm based on a multi-sensor system, including an auxiliary illumination (AI) visual sensor system, UVV band visual sensor system, spectrometer, and two photodiodes to detect three different welding defects during highpower disk laser welding.…”

Section: Introductionmentioning

confidence: 99%

“…Yu et al [11] proposed a deep neural network (DNN)-based quality assessment method based on a spectrometer in the laser beam welding (LBW) process. Shin et al [12] proposed a DNN-based nondestructive testing method for the detection and prediction of porosity defects in real time based on welding voltage signals. Nagesh and Datta [13] used back-propagation neural networks to associate welding process variables with the features of bead geometry and penetration.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

Jin

Rhee

2021

Metals

Self Cite

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In the flux-cored arc welding process, which is most widely used in shipbuilding, a constant external weld bead shape is an important factor in determining proper weld quality; however, the size of the weld gap is generally not constant, owing to errors generated during the shell forming process; moreover, a constant external bead shape for the welding joint is difficult to obtain when the weld gap changes. Therefore, this paper presents a method for monitoring the weld gap and controlling the weld deposition rate based on a deep neural network (DNN) for the automation of the hull block welding process. Welding experiments were performed with a welding robot synchronized with the welding machine, and the welding quality was classified according to the experimental results. Welding current and voltage signals, as the robot passed through the weld seam, were measured using a trigger device and analyzed in the time domain and frequency domain, respectively. From the analyzed data, 24 feature variables were extracted and used as input for the proposed DNN model. Consequently, the offline and online performance verification results for new experimental data using the proposed DNN model were 93% and 85%, respectively.

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Real-Time Detection of Weld Defects for Automated Welding Process Base on Deep Neural Network

Cited by 45 publications

References 10 publications

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

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