FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile

Xie, Luofeng; Xi, Xiang; Xu, Hanyue; Wang, Ling; Lin, Lijun; Yin, Guofu

doi:10.1109/tie.2020.2982115

Cited by 67 publications

(16 citation statements)

References 28 publications

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“…The use case concerns a known rootcause for specific defect patterns in wafer maps. For automatic defect identification, an end-to-end CNN architecture is also proposed in (Xie et al, 2021), termed fusion feature CNN (FFCNN), consisting of three modules: feature extraction, feature fusion and decision-making. This intelligent machinevision-based system was developed to detect surface defects on magnetic tiles during the production stage.…”

Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

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The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine learning; thus, it attempts to fill this gap. This work also presents a set of open challenges to determine future developments.

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Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

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“…In (Y. M. , the soft attention template from the attention module is used as the weight of the feature map of the backbone network with convolution modules to improve the accuracy of defect detection. In (Xie et al 2020;D. F. Zhang et al 2019), dual-attention modules were introduced, i.e.…”

Section: Attention Mechanismmentioning

confidence: 99%

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu

Bao

Wang

et al. 2021

International Journal of Computer Integrated Manufacturing

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Deep learning (DL) is an important enabling technology for intelligent manufacturing. The DLbased industrial image pattern recognition (DLBIIPR) plays a vital role in the improvement of product quality and production efficiency. Although DL technology has been widely used in the field of natural image, industrial image often has some mixed characteristics, such as small sample, imbalance, small target, strong interference, fine-grained, temporality and semantical, which reduce the feasibility and generalization of DLBIIPR. To solve this problem, this paper provides an overview of approaches commonly used in industry by enriching the sample space and limiting the hypothesis space. In order to improve the confidence of front-line workers in using DL models, the explainable deep learning (XDL) methods are reviewed, and a case study is used to verify the effectiveness of XDL.

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“…Many previous defect inspection techniques are based on traditional computer vision algorithms 5,6 but techniques using deep learning algorithms have become predominant in recent years. 4,[7][8][9] End-to-end deep neural networks have achieved state-of-the-art performance on image classification and object detection tasks. Thus, deep learning techniques are a viable solution to the problem of automatic surface defect inspection.…”

Section: Introductionmentioning

confidence: 99%

“…1 Therefore, establishing an automatic assembly line is crucial for increasing productivity of magnetic tile manufacturers. To relieve human labor, a number of image processing techniques [2][3][4] have been proposed to automatically perform such inspection tasks and several machine vision systems have been built and applied in actual production process. Many previous defect inspection techniques are based on traditional computer vision algorithms 5,6 but techniques using deep learning algorithms have become predominant in recent years.…”

Section: Introductionmentioning

confidence: 99%

A feature-based transfer-YOLOv5 model for rapid defect inspection in large mass magnetic tile manufacturing

Yan

Zhu³

et al. 2023

Optoelectronic Imaging and Multimedia Technology IX

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Image super-resolution technology successfully overcomes the limitation of excessively large pixel size in infrared detectors and meets the increasing demand for high-resolution infrared image information. In this paper, the superresolution reconstruction of infrared images based on a convolutional neural network with a priori for high frequency information is reported. The main network structure is based on residual blocks, BN blocks that are not suitable for the super-resolution task are removed. The introduction of residual learning reduces computational complexity and accelerates network convergence. Multiple convolution layers and deconvolution layers respectively implement the extraction and restoration of the features in infrared images. images are divided into high frequency and low frequency parts. The low frequency part is the image of down-sampling, while the high frequency information is obeyed a simple case-agnostic distribution, which is equivalent to having a prior of high frequency information for the super-resolution network, Which is captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Compared with the other previously proposed methods for infrared information restoration, our proposed method shows obvious advantages in the ability of high-resolution details acquisition.

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FFCNN: A Deep Neural Network for Surface Defect Detection of Magnetic Tile

Cited by 67 publications

References 28 publications

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

A feature-based transfer-YOLOv5 model for rapid defect inspection in large mass magnetic tile manufacturing

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