Screw absence classification on aluminum plate via features based transfer learning models
Screw is one of the important elements in every industry. Present of screw play an important role in which it holds the product in its own position and prevent loosen or collision with the case which will cause the small components or compartment fall off from its original position and lead to produ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38077/ http://umpir.ump.edu.my/id/eprint/38077/1/Screw%20Absence%20Classification%20on%20Aluminum%20Plate%20via%20Features.pdf |
| _version_ | 1848825416709570560 |
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| author | Lim, Weng Zhen Norasmiza, Mohd Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Koon, Yin Goon |
| author_facet | Lim, Weng Zhen Norasmiza, Mohd Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Koon, Yin Goon |
| author_sort | Lim, Weng Zhen |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Screw is one of the important elements in every industry. Present of screw play an important role in which it holds the product in its own position and prevent loosen or collision with the case which will cause the small components or compartment fall off from its original position and lead to product failure. With the rise of revolution 4.0 in the industry, it helps to reduce the labor cost and human error. The main purpose of this study is to create a robust classification model used for machine vision detection – absence and present of screw, which could be adapted into respective robotics application system. 6 degree of freedom UR robot, Universal Robot is used to collect the custom dataset in TT Vision Technologies Sdn Bhd. The collected dataset is then classified into two categories, named as absent and present. Pretrained dataset, ImageNet is used to ease the training process in this research. Transfer learning model is used to extract the features which used to feed into different machine learning models. Each machine learning models undergoes hyperparameters tunning to achieve best classification accuracy. Samling ratio of 60:20:20 is used to separate the data in training, validation and testing respectively before fed into different ml models |
| first_indexed | 2025-11-15T03:28:35Z |
| format | Article |
| id | ump-38077 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:28:35Z |
| publishDate | 2023 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-380772023-07-20T01:12:37Z http://umpir.ump.edu.my/id/eprint/38077/ Screw absence classification on aluminum plate via features based transfer learning models Lim, Weng Zhen Norasmiza, Mohd Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Koon, Yin Goon TJ Mechanical engineering and machinery Screw is one of the important elements in every industry. Present of screw play an important role in which it holds the product in its own position and prevent loosen or collision with the case which will cause the small components or compartment fall off from its original position and lead to product failure. With the rise of revolution 4.0 in the industry, it helps to reduce the labor cost and human error. The main purpose of this study is to create a robust classification model used for machine vision detection – absence and present of screw, which could be adapted into respective robotics application system. 6 degree of freedom UR robot, Universal Robot is used to collect the custom dataset in TT Vision Technologies Sdn Bhd. The collected dataset is then classified into two categories, named as absent and present. Pretrained dataset, ImageNet is used to ease the training process in this research. Transfer learning model is used to extract the features which used to feed into different machine learning models. Each machine learning models undergoes hyperparameters tunning to achieve best classification accuracy. Samling ratio of 60:20:20 is used to separate the data in training, validation and testing respectively before fed into different ml models Penerbit UMP 2023-04 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/38077/1/Screw%20Absence%20Classification%20on%20Aluminum%20Plate%20via%20Features.pdf Lim, Weng Zhen and Norasmiza, Mohd and Anwar, P. P. Abdul Majeed and Mohd Azraai, Mohd Razman and Koon, Yin Goon (2023) Screw absence classification on aluminum plate via features based transfer learning models. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 5 (1). pp. 62-66. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v5i1.9408 https://doi.org/10.15282/mekatronika.v5i1.9408 |
| spellingShingle | TJ Mechanical engineering and machinery Lim, Weng Zhen Norasmiza, Mohd Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Koon, Yin Goon Screw absence classification on aluminum plate via features based transfer learning models |
| title | Screw absence classification on aluminum plate via features based transfer learning models |
| title_full | Screw absence classification on aluminum plate via features based transfer learning models |
| title_fullStr | Screw absence classification on aluminum plate via features based transfer learning models |
| title_full_unstemmed | Screw absence classification on aluminum plate via features based transfer learning models |
| title_short | Screw absence classification on aluminum plate via features based transfer learning models |
| title_sort | screw absence classification on aluminum plate via features based transfer learning models |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/38077/ http://umpir.ump.edu.my/id/eprint/38077/ http://umpir.ump.edu.my/id/eprint/38077/ http://umpir.ump.edu.my/id/eprint/38077/1/Screw%20Absence%20Classification%20on%20Aluminum%20Plate%20via%20Features.pdf |