Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects

Park, Je-Kang; An, Woo-Hyun; Kang, Dong-Joong

doi:10.1007/s12541-019-00074-4

Cited by 48 publications

(11 citation statements)

References 20 publications

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“…However, Masci et al [12] showed that CNNs can be effective in a similar setup, reporting a 7% error rate on their data. In another work by Park et al [13], the authors show that even strong class imbalances can be overcome with the use of sampling strategies and data augmentation. One aspect that we did not find discussed in related literature, is the evaluation of a proposed model towards its practical use for industrial quality assurance, which usually mandates a near-faultless requirement.…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of XCT Images in Manufacturing

Sabrowsky-Hirsch

Holom

Gusenbauer

et al. 2021

IFIP Advances in Information and Communication Technology

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X-ray computed tomography (XCT) is an established nondestructive testing (NDT) method that, in combination with automatic evaluation routines, can be successfully used to establish a reliable 100% inline inspection system for defect detection of cast parts. While these systems are robust in automatically localizing suspected defects, human know-how in a secondary assessment and decision-making step remains indispensable to avoid an excess of rejected parts. Rather than changing the existing defect detection system and risking difficult to anticipate changes to a solid evaluation process, we propose the integration of human know-how in a subsequent support system through end-to-end learning. Using XCT data and the corresponding decisions performed by the XCT operator, we aim to support and possibly automate the secondary quality assessment process. In our paper we present a Convolutional Neural Network (CNN) architecture to predict both, the final decision of the XCT operator and a defect class indication, for cast parts rejected by the defect detection system based on XCT slice images. On a dataset of 19,459 defect records categorized in 7 classes, we achieved an accuracy of 92% for the decision and 93% for the defect class indication on the testing split. We further show that, by binding decisions to the reliability of the predicted defect class, our model has the potential to enhance also a production process with a near-faultless condition. Based on production-line data, we estimate that our model can reliably relabel 11% of defects reported during production and provide a defect class indication for another 57%.

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Section: Related Workmentioning

confidence: 99%

Automatic Classification of XCT Images in Manufacturing

Sabrowsky-Hirsch

Holom

Gusenbauer

et al. 2021

IFIP Advances in Information and Communication Technology

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show abstract

“…Fu et al imaged vibration signals and fed them into the constructed convolutional neural network model, obtaining better comprehensive performance in the drilling process [18]. Park et al proposed a convolutional neural network-based method to inspect nonpatterned welding defects on the surface of the engine transmission, which is more effective confirmed by experimental studies [19]. However, they only took DCNNs as classifiers for state diagnosis, which are unable to make the real-time continuous prediction of tool wear.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling

Huang

Lei

et al. 2021

Mathematical Problems in Engineering

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Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.

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“…Fang et al [20] propose an SLIC head of object instance segmentation in proposal regions (Mask R-CNN) containing a network block to learn the quality of the predict masks. Park et al [21] propose a convolutional neural network (CNN) based method that inspects nonpatterned welding defects (craters, pores, foreign substances, and fissures) on the surface of the engine transmission using a single RGB camera. Ming et al [22] propose a combined classifier with dynamic weights (CCDW) to classify the LPG samples considering both feature extraction diversity and base classifiers diversity after image segmentation and enhancement.…”

Section: Introductionmentioning

confidence: 99%

Bearing Defect Detection with Unsupervised Neural Networks

Zuo

Danchao³

et al. 2021

Shock and Vibration

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Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.

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Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects

Cited by 48 publications

References 20 publications

Automatic Classification of XCT Images in Manufacturing

Automatic Classification of XCT Images in Manufacturing

Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling

Bearing Defect Detection with Unsupervised Neural Networks

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