Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood
Plywood is a wood-based composite with many applications in construction, shipbuilding, and furniture production. One of the basic plywood processing and mandatory operations is drilling. Up to now, considerable and very diverse thematic research has been recently carried out on drilling, but little...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/20/11343 |
| _version_ | 1797574888143192064 |
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| author | Bogdan Bedelean Mihai Ispas Sergiu Răcășan |
| author_facet | Bogdan Bedelean Mihai Ispas Sergiu Răcășan |
| author_sort | Bogdan Bedelean |
| collection | DOAJ |
| description | Plywood is a wood-based composite with many applications in construction, shipbuilding, and furniture production. One of the basic plywood processing and mandatory operations is drilling. Up to now, considerable and very diverse thematic research has been recently carried out on drilling, but little of that deals with modeling of the drilling process of plywood. Therefore, in this work, the artificial neural network modeling technique and response surface methodology were applied to model and optimize the drilling process of plywood. Two artificial neural network models were developed to predict the thrust force and the drilling torque based on drill tip angle, tooth bite, and drill type. The developed ANN models were used to complete the value of responses in the experimental design, which was requested by the response surface methodology. The trust force during the drilling of plywood is significantly influenced by the drill type (helical or flat). The most significant factor that affects the drilling torque during the drilling of plywood is the tooth bite. A helical drill assures a lower minimum thrust force and drilling torque than a flat drill. The proposed method could be used as an optimization tool during the design phase of the furniture manufacturing process. |
| first_indexed | 2024-03-10T21:28:25Z |
| format | Article |
| id | doaj.art-75fb0c2364ec48f2bbe831b2848e7a36 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:28:25Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-75fb0c2364ec48f2bbe831b2848e7a362023-11-19T15:30:54ZengMDPI AGApplied Sciences2076-34172023-10-0113201134310.3390/app132011343Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of PlywoodBogdan Bedelean0Mihai Ispas1Sergiu Răcășan2Faculty of Furniture Design and Wood Engineering, Transilvania University of Brasov, Bd-ul Eroilor nr. 29, 500036 Brasov, RomaniaFaculty of Furniture Design and Wood Engineering, Transilvania University of Brasov, Bd-ul Eroilor nr. 29, 500036 Brasov, RomaniaFaculty of Furniture Design and Wood Engineering, Transilvania University of Brasov, Bd-ul Eroilor nr. 29, 500036 Brasov, RomaniaPlywood is a wood-based composite with many applications in construction, shipbuilding, and furniture production. One of the basic plywood processing and mandatory operations is drilling. Up to now, considerable and very diverse thematic research has been recently carried out on drilling, but little of that deals with modeling of the drilling process of plywood. Therefore, in this work, the artificial neural network modeling technique and response surface methodology were applied to model and optimize the drilling process of plywood. Two artificial neural network models were developed to predict the thrust force and the drilling torque based on drill tip angle, tooth bite, and drill type. The developed ANN models were used to complete the value of responses in the experimental design, which was requested by the response surface methodology. The trust force during the drilling of plywood is significantly influenced by the drill type (helical or flat). The most significant factor that affects the drilling torque during the drilling of plywood is the tooth bite. A helical drill assures a lower minimum thrust force and drilling torque than a flat drill. The proposed method could be used as an optimization tool during the design phase of the furniture manufacturing process.https://www.mdpi.com/2076-3417/13/20/11343plywood drillingmodelingoptimizationneural networksresponse surface methodologythrust force |
| spellingShingle | Bogdan Bedelean Mihai Ispas Sergiu Răcășan Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood Applied Sciences plywood drilling modeling optimization neural networks response surface methodology thrust force |
| title | Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood |
| title_full | Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood |
| title_fullStr | Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood |
| title_full_unstemmed | Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood |
| title_short | Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood |
| title_sort | applying the artificial neural network and response surface methodology to optimize the drilling process of plywood |
| topic | plywood drilling modeling optimization neural networks response surface methodology thrust force |
| url | https://www.mdpi.com/2076-3417/13/20/11343 |
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