Machine learning–based automated image processing for quality management in industrial Internet of Things
The aim of this article is to automate quality control once a product, essentially a central processing unit system, is manufactured. Creating a model that helps in quality control, increases efficiency and speed of production by rejecting abnormal products automatically is vital. A widely used tech...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2019-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719883551 |
| _version_ | 1797708939234639872 |
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| author | Nematullo Rahmatov Anand Paul Faisal Saeed Won-Hwa Hong HyunCheol Seo Jeonghong Kim |
| author_facet | Nematullo Rahmatov Anand Paul Faisal Saeed Won-Hwa Hong HyunCheol Seo Jeonghong Kim |
| author_sort | Nematullo Rahmatov |
| collection | DOAJ |
| description | The aim of this article is to automate quality control once a product, essentially a central processing unit system, is manufactured. Creating a model that helps in quality control, increases efficiency and speed of production by rejecting abnormal products automatically is vital. A widely used technology for this is to use industrial image processing that is based on the use of special cameras or imaging systems installed within the production line. In this article, we propose a highly efficient model to automate central processing unit system production lines in an industry such that images of the production lines are scanned and any abnormalities in their assembly are pointed out by the model and information about this is transferred to the system administrator via a cyber-physical cloud system network. A machine learning–based approach is used for proper classification. This model not only focuses on just the abnormalities but also helps in configuring the angles from which images of the production are taken, and our methods show 92% accuracy. |
| first_indexed | 2024-03-12T06:30:02Z |
| format | Article |
| id | doaj.art-66ce31fec3bd4b028a6d61592929146b |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T06:30:02Z |
| publishDate | 2019-10-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-66ce31fec3bd4b028a6d61592929146b2023-09-03T01:42:31ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772019-10-011510.1177/1550147719883551Machine learning–based automated image processing for quality management in industrial Internet of ThingsNematullo Rahmatov0Anand Paul1Faisal Saeed2Won-Hwa Hong3HyunCheol Seo4Jeonghong Kim5The School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaThe School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaThe School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaThe School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, South KoreaThe School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, South KoreaThe School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaThe aim of this article is to automate quality control once a product, essentially a central processing unit system, is manufactured. Creating a model that helps in quality control, increases efficiency and speed of production by rejecting abnormal products automatically is vital. A widely used technology for this is to use industrial image processing that is based on the use of special cameras or imaging systems installed within the production line. In this article, we propose a highly efficient model to automate central processing unit system production lines in an industry such that images of the production lines are scanned and any abnormalities in their assembly are pointed out by the model and information about this is transferred to the system administrator via a cyber-physical cloud system network. A machine learning–based approach is used for proper classification. This model not only focuses on just the abnormalities but also helps in configuring the angles from which images of the production are taken, and our methods show 92% accuracy.https://doi.org/10.1177/1550147719883551 |
| spellingShingle | Nematullo Rahmatov Anand Paul Faisal Saeed Won-Hwa Hong HyunCheol Seo Jeonghong Kim Machine learning–based automated image processing for quality management in industrial Internet of Things International Journal of Distributed Sensor Networks |
| title | Machine learning–based automated image processing for quality management in industrial Internet of Things |
| title_full | Machine learning–based automated image processing for quality management in industrial Internet of Things |
| title_fullStr | Machine learning–based automated image processing for quality management in industrial Internet of Things |
| title_full_unstemmed | Machine learning–based automated image processing for quality management in industrial Internet of Things |
| title_short | Machine learning–based automated image processing for quality management in industrial Internet of Things |
| title_sort | machine learning based automated image processing for quality management in industrial internet of things |
| url | https://doi.org/10.1177/1550147719883551 |
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