Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks

Wen, Shuguang; Chen, Zhihong; Li, Chaoxian

doi:10.3390/app8122565

Cited by 30 publications

(17 citation statements)

References 31 publications

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“…Currently, many micro-motor armatures are manually placed under the microscope by the operator to adjust the armature position through the observation of the staff, according to the experience to achieve defect detection, which shows various disadvantages, such as time-consuming processes [9][10][11] and the lack of real standardization. Therefore, there is an urgent need to bring a related defect inspection system into the production process of micro-armatures.…”

Section: Related Work and Foundationsmentioning

confidence: 99%

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

et al. 2019

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Automatic vision inspection technology shows a high potential for quality inspection, and has drawn great interest in micro-armature manufacturing. Given that the inspection process is highly influenced by the lack of real standardization and efficiency performed with the human eye, thus, it is necessary to develop an automatic defect detection process. In this work, an elaborated vision system for the defect inspection of micro-armatures used in smartphones was developed. It consists of two parts, the front-end module and the deep convolution neural networks (DCNNs) module, which are responsible for different areas. The front-end module runs first and the DCNNs module will not run if the output of the front-end module is negative. To verify the application of this system, an apparatus consisting of an objective table, control panel, and a camera connected to a Personal Computer (PC) was used to simulate an industrial position of production. The results indicate that the developed vision system is capable of defect detection of micro-armatures.Recently, deep convolutional neural networks (DCNNs) have been proved to be important methods in visual detection. However, some classical methods should still be considered. Literature [6] mentioned above is an example. For instance, in order to get better parameters, a suitable smart manufacturing strategy for real industrial conditions was proposed. The results of this dataset showed that the Adaboost ensembles provided the highest accuracy and were more easily optimized than ANNs [7]. Obviously, for now, there are some limitations with using classical methods alone or using DCNNs directly. For classical methods, for example, they usually come with high complexity of programming and less tolerance to data variability. With regard to the DCNNs, a large number of samples are needed for network training and the iterative optimization of parameters is partly a black-box operation [8].In the industrial applications, part defects in the samples can be easily identified with a picture by the classical computer vision method. Therefore, we do not need to feed the whole picture into the network for discrimination, which can reduce the number of features to be identified. As a result, the network is easier to converge. Therefore, by combining the classical image recognition method and DCNNs, the landing speed of deep learning technology in industrial applications can be effectively improved.In this work, an intelligent detection system that combined the classical computer vision method and DCNNs was designed to automatically detect the quality defects of micro-motor armatures. Firstly, the quality, excluding the region of copper wire crossing (ROC), is decided based on the classical computer vision technology. If the first result is positive, the ROC will be extracted and sent to the DCNNs for identification. If the result is still positive, the image is a defect-free sample, otherwise the whole image is labeled defective. In the experiments, this system works very fast and presen...

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Section: Related Work and Foundationsmentioning

confidence: 99%

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

et al. 2019

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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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“…In order to explore multitudinous crack damages, one binary-tree network using a SVM method has been proposed in [13]. An artificial neural network (ANN) is a computing framework, inspired by biological learning, which has been applied in fatigue life prediction [17], surface inspection [18], and in many other areas. In [19], the back propagation (BP) based neural network classification method has been presented for detecting possible crack regions.…”

Section: Introductionmentioning

confidence: 99%

Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

Wang

Zhao

et al. 2019

Applied Sciences

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Detecting cracks within reinforced concrete is still a challenging problem, owing to the complex disturbances from the background noise. In this work, we advocate a new concrete crack damage detection model, based upon multilayer sparse feature representation and an incremental extreme learning machine (ELM), which has both favorable feature learning and classification capabilities. Specifically, by cropping and using a sliding window operation and image rotation, a large number of crack and non-crack patches are obtained from the collected concrete images. With the existing image patches, the defect region features can be quickly calculated by the multilayer sparse ELM autoencoder networks. Then, the online incremental ELM classified network is used to recognize the crack defect features. Unlike the commonly-used deep learning-based methods, the presented ELM-based crack detection model can be trained efficiently without tediously fine-tuning the entire-network parameters. Moreover, according to the ELM theory, the proposed crack detector works universally for defect feature extraction and detection. In the experiments, when compared with other recently developed crack detectors, the proposed concrete crack detection model can offer outstanding training efficiency and favorable crack detecting accuracy.

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Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks

Cited by 30 publications

References 31 publications

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

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