Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network
The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire pro...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6856 |
| _version_ | 1797552201668755456 |
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| author | Leandro Ruiz Manuel Torres Alejandro Gómez Sebastián Díaz José M. González Francisco Cavas |
| author_facet | Leandro Ruiz Manuel Torres Alejandro Gómez Sebastián Díaz José M. González Francisco Cavas |
| author_sort | Leandro Ruiz |
| collection | DOAJ |
| description | The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility. |
| first_indexed | 2024-03-10T15:57:38Z |
| format | Article |
| id | doaj.art-2ef6f9e4714f473e923accf20dc72ab5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:57:38Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2ef6f9e4714f473e923accf20dc72ab52023-11-20T15:33:03ZengMDPI AGApplied Sciences2076-34172020-09-011019685610.3390/app10196856Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural NetworkLeandro Ruiz0Manuel Torres1Alejandro Gómez2Sebastián Díaz3José M. González4Francisco Cavas5Doctorate Program in Industrial Technologies, International School of Doctorate, Technical University of Cartagena, 30202 Cartagena, SpainInnovation Division, MTorres Diseños Industriales SAU, Ctra. El Estrecho-Lobosillo, Km 2, Fuente Álamo, 30320 Murcia, SpainInnovation Division, MTorres Diseños Industriales SAU, Ctra. El Estrecho-Lobosillo, Km 2, Fuente Álamo, 30320 Murcia, SpainInnovation Division, MTorres Diseños Industriales SAU, Ctra. El Estrecho-Lobosillo, Km 2, Fuente Álamo, 30320 Murcia, SpainInnovation Division, MTorres Diseños Industriales SAU, Ctra. El Estrecho-Lobosillo, Km 2, Fuente Álamo, 30320 Murcia, SpainDepartment of Structures, Construction and Graphical Expression, Technical University of Cartagena, 30202 Cartagena, SpainThe aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.https://www.mdpi.com/2076-3417/10/19/6856advanced manufacturingIndustry 4.0product developmentproduct designdesign for X methodstolerancing |
| spellingShingle | Leandro Ruiz Manuel Torres Alejandro Gómez Sebastián Díaz José M. González Francisco Cavas Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network Applied Sciences advanced manufacturing Industry 4.0 product development product design design for X methods tolerancing |
| title | Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network |
| title_full | Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network |
| title_fullStr | Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network |
| title_full_unstemmed | Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network |
| title_short | Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network |
| title_sort | detection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural network |
| topic | advanced manufacturing Industry 4.0 product development product design design for X methods tolerancing |
| url | https://www.mdpi.com/2076-3417/10/19/6856 |
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