Part detection model for aerospace manufacturing quality control using convolutional neural networks
Ensuring the precision and efficiency of quality control processes in the aerospace,manufacturing industry is critical to remain competitive. This paper presents an advanced approach to address the challenge of identifying and categorising similar aerospace components quality effectively using Artif...
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| Format: | Article |
| Language: | en |
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Penerbit Akademia Baru
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29191/2/0114908082025121016.pdf http://eprints.utem.edu.my/id/eprint/29191/ https://akademiabaru.com/submit/index.php/ard/article/view/6393/6149 https://doi.org/10.37934/ard.133.1.110 |
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| author | Abdullah, Rohana Pang, Chang Tze Hafizh, Hadyan Abdul Rasib, Amir Hamzah Mansour, Mohamed |
| author_facet | Abdullah, Rohana Pang, Chang Tze Hafizh, Hadyan Abdul Rasib, Amir Hamzah Mansour, Mohamed |
| author_sort | Abdullah, Rohana |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Ensuring the precision and efficiency of quality control processes in the aerospace,manufacturing industry is critical to remain competitive. This paper presents an advanced approach to address the challenge of identifying and categorising similar aerospace components quality effectively using Artificial Intelligence’s computer vision
and convolutional neural network’s deep learning technique to resolve the issue of parts misclassification. Specifically, in the development of a comprehensive database for object detection using Phyton programming and tailored to the unique requirements of the aerospace industry under study. The methodology involved collecting a diverse dataset comprising 50 images per class, annotated with bounding boxes and class labels, covering a pilot of D232B and D233B machining parts. The dataset is partitioned into training, validation, and test sets to facilitate the model training and evaluation. Furthermore, tools for managing and accessing the dataset were introduced, including the interface for image labelling. Leveraging on TensorFlow, the effectiveness of this approach was able to be tested in training the part detection model with 94% accuracy for part D233B and 97% accuracy for part D232B, showcasing its suitability for real-world aerospace manufacturing quality inspection applications. Overall, this project represents a significant advancement in improving quality inspection process in the
aerospace industry, offering a valuable approach for enhancing efficiency and accuracy in component identification. |
| format | Article |
| id | my.utem.eprints-29191 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | Penerbit Akademia Baru |
| record_format | eprints |
| spelling | my.utem.eprints-291912025-12-11T02:42:27Z http://eprints.utem.edu.my/id/eprint/29191/ Part detection model for aerospace manufacturing quality control using convolutional neural networks Abdullah, Rohana Pang, Chang Tze Hafizh, Hadyan Abdul Rasib, Amir Hamzah Mansour, Mohamed Ensuring the precision and efficiency of quality control processes in the aerospace,manufacturing industry is critical to remain competitive. This paper presents an advanced approach to address the challenge of identifying and categorising similar aerospace components quality effectively using Artificial Intelligence’s computer vision and convolutional neural network’s deep learning technique to resolve the issue of parts misclassification. Specifically, in the development of a comprehensive database for object detection using Phyton programming and tailored to the unique requirements of the aerospace industry under study. The methodology involved collecting a diverse dataset comprising 50 images per class, annotated with bounding boxes and class labels, covering a pilot of D232B and D233B machining parts. The dataset is partitioned into training, validation, and test sets to facilitate the model training and evaluation. Furthermore, tools for managing and accessing the dataset were introduced, including the interface for image labelling. Leveraging on TensorFlow, the effectiveness of this approach was able to be tested in training the part detection model with 94% accuracy for part D233B and 97% accuracy for part D232B, showcasing its suitability for real-world aerospace manufacturing quality inspection applications. Overall, this project represents a significant advancement in improving quality inspection process in the aerospace industry, offering a valuable approach for enhancing efficiency and accuracy in component identification. Penerbit Akademia Baru 2025 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/29191/2/0114908082025121016.pdf Abdullah, Rohana and Pang, Chang Tze and Hafizh, Hadyan and Abdul Rasib, Amir Hamzah and Mansour, Mohamed (2025) Part detection model for aerospace manufacturing quality control using convolutional neural networks. Journal of Advanced Research Design, 133 (1). pp. 1-10. ISSN 2289-7984 https://akademiabaru.com/submit/index.php/ard/article/view/6393/6149 https://doi.org/10.37934/ard.133.1.110 |
| spellingShingle | Abdullah, Rohana Pang, Chang Tze Hafizh, Hadyan Abdul Rasib, Amir Hamzah Mansour, Mohamed Part detection model for aerospace manufacturing quality control using convolutional neural networks |
| title | Part detection model for aerospace manufacturing quality control using convolutional neural networks |
| title_full | Part detection model for aerospace manufacturing quality control using convolutional neural networks |
| title_fullStr | Part detection model for aerospace manufacturing quality control using convolutional neural networks |
| title_full_unstemmed | Part detection model for aerospace manufacturing quality control using convolutional neural networks |
| title_short | Part detection model for aerospace manufacturing quality control using convolutional neural networks |
| title_sort | part detection model for aerospace manufacturing quality control using convolutional neural networks |
| url | http://eprints.utem.edu.my/id/eprint/29191/2/0114908082025121016.pdf http://eprints.utem.edu.my/id/eprint/29191/ https://akademiabaru.com/submit/index.php/ard/article/view/6393/6149 https://doi.org/10.37934/ard.133.1.110 |
| url_provider | http://eprints.utem.edu.my/ |