Development of Visual Inspection System for Assembly Machine

Jeonghong Kim

doi:10.1109/ICUFN.2018.8436961

Development of Visual Inspection System for Assembly Machine

Jeonghong Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In a factory that assembles various devices, functional inspection and visual inspection are carried out before the product is shipped out. Most functional tests are done automatically through the system, whereas visual inspection is done by the human eyes. Human remember what they have just seen before, and it is difficult to detect the failure of a similar assembly device. Even if the function of the assembling device is satisfied, there is a problem in the reliability of the quality control due to the appearance error. Various image processing techniques have been developed to solve this problem. In this paper, we proposed a method to improve the accuracy of error detection by applying the CNN model to the external inspection of the assembling device.

Original language	English
Title of host publication	ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	859-861
Number of pages	3
ISBN (Print)	9781538646465
DOIs	https://doi.org/10.1109/ICUFN.2018.8436961
State	Published - 14 Aug 2018
Event	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 - Prague, Czech Republic Duration: 3 Jul 2018 → 6 Jul 2018

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2018-July
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	10th International Conference on Ubiquitous and Future Networks, ICUFN 2018
Country/Territory	Czech Republic
City	Prague
Period	3/07/18 → 6/07/18

Keywords

Convolution Neural Network
Image classification
Image processing
Machine Learning

Access to Document

10.1109/ICUFN.2018.8436961

Cite this

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title = "Development of Visual Inspection System for Assembly Machine",

abstract = "In a factory that assembles various devices, functional inspection and visual inspection are carried out before the product is shipped out. Most functional tests are done automatically through the system, whereas visual inspection is done by the human eyes. Human remember what they have just seen before, and it is difficult to detect the failure of a similar assembly device. Even if the function of the assembling device is satisfied, there is a problem in the reliability of the quality control due to the appearance error. Various image processing techniques have been developed to solve this problem. In this paper, we proposed a method to improve the accuracy of error detection by applying the CNN model to the external inspection of the assembling device.",

keywords = "Convolution Neural Network, Image classification, Image processing, Machine Learning",

author = "Jeonghong Kim",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 ; Conference date: 03-07-2018 Through 06-07-2018",

year = "2018",

month = aug,

day = "14",

doi = "10.1109/ICUFN.2018.8436961",

language = "English",

isbn = "9781538646465",

series = "International Conference on Ubiquitous and Future Networks, ICUFN",

publisher = "IEEE Computer Society",

pages = "859--861",

booktitle = "ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks",

address = "United States",

}

Kim, J 2018, Development of Visual Inspection System for Assembly Machine. in ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks., 8436961, International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2018-July, IEEE Computer Society, pp. 859-861, 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018, Prague, Czech Republic, 3/07/18. https://doi.org/10.1109/ICUFN.2018.8436961

Development of Visual Inspection System for Assembly Machine. / Kim, Jeonghong.
ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2018. p. 859-861 8436961 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In a factory that assembles various devices, functional inspection and visual inspection are carried out before the product is shipped out. Most functional tests are done automatically through the system, whereas visual inspection is done by the human eyes. Human remember what they have just seen before, and it is difficult to detect the failure of a similar assembly device. Even if the function of the assembling device is satisfied, there is a problem in the reliability of the quality control due to the appearance error. Various image processing techniques have been developed to solve this problem. In this paper, we proposed a method to improve the accuracy of error detection by applying the CNN model to the external inspection of the assembling device.

KW - Convolution Neural Network

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KW - Image processing

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BT - ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks

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T2 - 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018

Y2 - 3 July 2018 through 6 July 2018

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Development of Visual Inspection System for Assembly Machine

Abstract

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Conference

Keywords

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