Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Tao, Xu; Zhang, David; Ma, Wenzhi; Liu, Xiaoyang; Xu, De

doi:10.3390/app8091575

Cited by 362 publications

(159 citation statements)

References 40 publications

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“…Many works applied machine learning for inspection types of tasks, such as surface defect inspection, 12 AVI of machine components, 13 and AVI of microdrill bits in printed circuit board production. 14 With the rise of the interest in deep learning, this specific technology was also applied to a wide range of AVI tasks, for example, structural inspection, 15 surface defect detection and recognition, 16 defect classification system, 17 and others. [18][19][20][21] Even design tools for the creation of deep convolutional networks for AVI were developed.…”

Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

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Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated. Fig. 1 (a) Robot-based inspection system with three cameras mounted on an end effector and (b) Ensenso N35 3-D sensor.

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Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

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“…Thus far, many successful architectures have been proposed for CNN, which include DenseNet [46], ResNet [47], VGG16, RCNN, etc., and some of them have been applied to defect detection [37][38][39][40][41][42][43][44][45]. VGG16 architecture is employed for pavement crack detection in [43].…”

Section: Detection Scheme Based On Cnnmentioning

confidence: 99%

“…CNN has been used in defect detection of industry field. For example, CNN is employed to detect whether solders, chips or circuit boards have defects [37][38][39][40][41][42][43][44][45].…”

mentioning

confidence: 99%

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Xie

Zhang

2019

Applied Sciences

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To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application. Keywords: defect detection; polymer lithium-ion battery; convolutional neural network; deep learning; blister defect; flower pollination algorithm IntroductionThe application of lithium-ion batteries has changed consumer electronic products, which greatly reduce the weight and volume of mobile phones, notebooks, and other portable products. Lithium-ion batteries have been widely studied, including model, optimal design, and so on [1][2][3][4][5][6]. At present, the most commonly used lithium-ion battery is polymer lithium-ion battery (PLB). The cathode materials of PLB are commonly lithium cobalt acid, lithium manganese acid, ternary materials or lithium iron phosphate. The anode materials usually use carbon materials, such as artificial graphite, natural graphite, intermediate phase carbon microspheres, petcoke, carbon fiber, pyrolysis resin carbon and so on. C-rate is the measurement of the charge and discharge current with respect to its nominal capacity. At present, most PLB use polymer gel electrolyte instead of liquid electrolyte, which makes PLB have the advantages of thinning, arbitrary area and arbitrary shape. These characteristics improve the capacity of PLB, and PLB has the characteristics of miniaturization, thinning, and light quantification. PLB has been widely used in portable electronic equipment, and it is gradually being applied to more fields. With the application of PLB in more and more electronic products, the quality of PLB has more and more influence on the quality of electronic products. The quality of PLB is critical to the quality and reliability of electronic products.Recently, an automatic line of PLB has been developed in Dongsheng Energy Corporation, Weihai, China. The PLB has a voltage of 48 V, a charging current of 2-3 A, and nominal capacity of 16 AH. The anode of PLB is a conductive high molecular polymer, the catrode is graphite, and a colloidal polymer electrolyte is used. During the production process of the PLB, several cathode pieces, anode pieces, and separator pieces are combined to produce a cell sheet. Great battery capacity can be provided with more cell sheets combined together. Thus, the quality inspection of cell sheets in automated production lines is essential to the quality of the final battery product. The cell sheet defect needs to be detected to ens...

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“…Besides, CNN was adopted to link experimental microstructure with ionic conductivity for yttria-stabilized zirconia samples [32]. The CNN models have been applied in surface detection in bearing rollers, aluminum parts, and steel plates [33][34][35][36][37]. It was found out that CNN-based methods had better and more robust performance compared to the SVM classifiers.…”

Section: Introductionmentioning

confidence: 99%

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Cui

Zhang

et al. 2020

Applied Sciences

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Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

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Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Cited by 362 publications

References 40 publications

Classification using a three-dimensional sensor in a structured industrial environment

Classification using a three-dimensional sensor in a structured industrial environment

Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

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