Deep learning based gasket fault detection: a CNN approach
Abstract Gasket inspection is a critical step in the quality control of a product. The proposed method automates the detection of misaligned or incorrectly fitting gaskets, ensuring timely repair action. The suggested method uses deep learning approaches to recognize and evaluate radiator images, wi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85223-8 |
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| author | S. Arumai Shiney R. Seetharaman V. J. Sharmila S. Prathiba |
| author_facet | S. Arumai Shiney R. Seetharaman V. J. Sharmila S. Prathiba |
| author_sort | S. Arumai Shiney |
| collection | DOAJ |
| description | Abstract Gasket inspection is a critical step in the quality control of a product. The proposed method automates the detection of misaligned or incorrectly fitting gaskets, ensuring timely repair action. The suggested method uses deep learning approaches to recognize and evaluate radiator images, with a focus on identifying misaligned or incorrectly installed gaskets. Deep learning algorithms are specific for feature extraction and classification together with a convolutional neural network (CNN) module that allows for seamless connection. A gasket inspection system based on a CNN architecture is developed in this work. The system consists of two sets of convolution layers, followed by two sets of batch normalization layer, two sets of RELU layer, max pooling layer and finally fully connected layer for classification of gasket images. The obtained results indicate that our system has great potential for practical applications in the manufacturing industry. Moreover, our system provides a reliable and efficient mechanism for quality control, which can help reduce the risk of defects and ensure product reliability. |
| format | Article |
| id | doaj-art-513b7a0f41434381b9a4782aea204e61 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-513b7a0f41434381b9a4782aea204e612025-02-09T12:37:25ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-025-85223-8Deep learning based gasket fault detection: a CNN approachS. Arumai Shiney0R. Seetharaman1V. J. Sharmila2S. Prathiba3Department of Computer Science and Engineering, S.A. Engineering CollegeDepartment of Electronics and Communication Engineering, College of Engineering Guindy Campus, Anna UniversityDepartment of Computer Science and Engineering, Loyola-ICAM College of Engineering and TechnologyDepartment of Electrical and Electronics Engineering, Loyola-ICAM College of Engineering and TechnologyAbstract Gasket inspection is a critical step in the quality control of a product. The proposed method automates the detection of misaligned or incorrectly fitting gaskets, ensuring timely repair action. The suggested method uses deep learning approaches to recognize and evaluate radiator images, with a focus on identifying misaligned or incorrectly installed gaskets. Deep learning algorithms are specific for feature extraction and classification together with a convolutional neural network (CNN) module that allows for seamless connection. A gasket inspection system based on a CNN architecture is developed in this work. The system consists of two sets of convolution layers, followed by two sets of batch normalization layer, two sets of RELU layer, max pooling layer and finally fully connected layer for classification of gasket images. The obtained results indicate that our system has great potential for practical applications in the manufacturing industry. Moreover, our system provides a reliable and efficient mechanism for quality control, which can help reduce the risk of defects and ensure product reliability.https://doi.org/10.1038/s41598-025-85223-8Gasket inspectionDeep learningCNNQuality ControlGasketRadiator |
| spellingShingle | S. Arumai Shiney R. Seetharaman V. J. Sharmila S. Prathiba Deep learning based gasket fault detection: a CNN approach Scientific Reports Gasket inspection Deep learning CNN Quality Control Gasket Radiator |
| title | Deep learning based gasket fault detection: a CNN approach |
| title_full | Deep learning based gasket fault detection: a CNN approach |
| title_fullStr | Deep learning based gasket fault detection: a CNN approach |
| title_full_unstemmed | Deep learning based gasket fault detection: a CNN approach |
| title_short | Deep learning based gasket fault detection: a CNN approach |
| title_sort | deep learning based gasket fault detection a cnn approach |
| topic | Gasket inspection Deep learning CNN Quality Control Gasket Radiator |
| url | https://doi.org/10.1038/s41598-025-85223-8 |
| work_keys_str_mv | AT sarumaishiney deeplearningbasedgasketfaultdetectionacnnapproach AT rseetharaman deeplearningbasedgasketfaultdetectionacnnapproach AT vjsharmila deeplearningbasedgasketfaultdetectionacnnapproach AT sprathiba deeplearningbasedgasketfaultdetectionacnnapproach |