Inspecting Method for Defective Casting Products with Convolutional Neural Network (CNN)

Nguyen, Thong; Choi, Seung-Ho; Park, Sung-Jun; Park, Sung Hyuk; Yoon, Jonghun

doi:10.1007/s40684-020-00197-4

Cited by 57 publications

(25 citation statements)

References 35 publications

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“…In previous studies on defective product classifications [14][15][16][17][18][19][20][21][22][23][24][25][26], researchers have proposed methods based on deep learning which developed and run on personal computer or server GPU. In practical aspects, installing an additional new computer or a server to running on automation system such as conveyor belts is infeasible.…”

Section: Discussionmentioning

confidence: 99%

“…A multitask convolution neural network (CNN) was proposed in [13] to integrate wire defect region detection and defective product classification. Other quality inspection tasks that use CNNs have been suggested to monitor various products such as printed circuit boards (PCBs) [14,15], metal surfaces [16], bottled wine [17], casting products [18,19], semiconductor fabrication [20], and light emitting diode (LED) cup apertures [21], mobile phone screen [22], cover glass of display panels [23], bearings [24], optical film [25], and leather defect [26]. In the aforementioned research on defect classification, authors only concentrated on developed the software model and implement on personal computer (PC) or server computer with graphics processing unit (GPU).…”

Section: Introductionmentioning

confidence: 99%

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Defective Product Classification System for Smart Factory Based on Deep Learning

Nguyễn

Shin

et al. 2021

Electronics

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Smart factories merge various technologies in a manufacturing environment in order to improve factory performance and product quality. In recent years, these smart factories have received a lot of attention from researchers. In this paper, we introduce a defective product classification system based on deep learning for application in smart factories. The key component of the proposed system is a programmable logic controller (PLC) artificial intelligence (AI) embedded board; we call this an AI Edge-PLC module. A pre-trained defective product classification model is uploaded to a cloud service from where the AI Edge-PLC can access and download it for use on a certain product, in this case, electrical wiring. Next, we setup the system to collect electrical wiring data in a real-world factory environment. Then, we applied preprocessing to the collected data in order to extract a region of interest (ROI) from the images. Due to limitations on the availability of appropriate labeled data, we used the transfer learning method to re-train a classification model for our purposes. The pre-trained models were then optimized for applications on AI Edge-PLC boards. After carrying out classification tasks, on our electrical wire dataset and on a previously published casting dataset, using various deep neural networks including VGGNet, ResNet, DenseNet, and GoogLeNet, we analyzed the results achieved by our system. The experimental results show that our system is able to classify defective products quickly with high accuracy in a real-world manufacturing environment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Defective Product Classification System for Smart Factory Based on Deep Learning

Nguyễn

Shin

et al. 2021

Electronics

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“…Their results showed that the method was effective in solving the problem of false and missing inspections. Nguyen et al [180] proposed an inspection system based on a CNN to achieve defect classification in casting products. However, the CNN deep learning model can only perform well under the condition of having a large number of highquality datasets.…”

Section: Classificationmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

284

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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“…Promising defect detection results and quantitative evaluation based on CNNs were recently reported for a variety of materials and methods of examination, including IRT in carbon fibre reinforced polymer analyses [ 48 , 49 ], ground penetrating radar in asphalt pavement examination [ 50 ], X-ray computed tomography for medical image evaluation [ 51 ], visual methods for defect size recognition in steel elements [ 52 ], as well as rails [ 53 ], and multi-sensor magnetic field measurements for steel defect evaluation [ 54 ].…”

Section: Introductionmentioning

confidence: 99%

Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks

et al. 2021

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In this paper, we propose a new method based on active infrared thermography (IRT) applied to assess the state of 3D-printed structures. The technique utilized here—active IRT—assumes the use of an external energy source to heat the tested material and to create a temperature difference between undamaged and defective areas, and this temperature difference is possible to observe with a thermal imaging camera. In the case of materials with a low value of thermal conductivity, such as the acrylonitrile butadiene styrene (ABS) plastic printout tested in the presented work, the obtained temperature differences are hardly measurable. Hence, the proposed novel IRT method is complemented by a dedicated algorithm for signal analysis and a multi-label classifier based on a deep convolutional neural network (DCNN). For the initial testing of the presented methodology, a 3D printout made in the shape of a cuboid was prepared. One type of defect was tested—surface breaking holes of various depths and diameters that were produced artificially by inclusion in the printout. As a result of examining the sample via the IRT method, a sequence of thermograms was obtained, which enabled the examination of the temporal representation of temperature variation over the examined region of the material. First, the obtained signals were analysed using a new algorithm to enhance the contrast between the background and the defect areas in the 3D print. In the second step, the DCNN was utilised to identify the chosen defect parameters. The experimental results show the high effectiveness of the proposed hybrid signal analysis method to visualise the inner structure of the sample and to determine the defect and size, including the depth and diameter.

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Inspecting Method for Defective Casting Products with Convolutional Neural Network (CNN)

Cited by 57 publications

References 35 publications

Defective Product Classification System for Smart Factory Based on Deep Learning

Defective Product Classification System for Smart Factory Based on Deep Learning

State of the Art in Defect Detection Based on Machine Vision

Detection and Identification of Defects in 3D-Printed Dielectric Structures via Thermographic Inspection and Deep Neural Networks

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