Predicting the Impacts of Various Factors on Failure Load of Screw Joints for Particleboard Using Artificial Neural Networks
Yazarlar (1)
Prof. Dr. Selahattin BARDAK Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Bioresources (Q1) | ||
| Dergi ISSN | 1930-2126 Dergi Bilgileri (2018) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2018 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 16-04-2018 |
| Cilt / Sayı / Sayfa | 13 / 2 / 3868–3879 | DOI | 10.15376/BIORES.13.2.3868-3879 |
| Makale Linki | https://bioresources.cnr.ncsu.edu/ | ||
| UAK Araştırma Alanları |
Veri Madenciliği
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| Özet |
| Innovations in the furniture industry have an important place in the global competitive environment. The use of mechanical joining techniques is rapidly increasing in the furniture industry. One of the most common mechanical joining techniques is screwing. This study investigated the impacts of screw diameter, screw length, and the distance between the screws on the failure load of screw joints in particleboard. Additionally, a model was developed on an artificial neural network model (ANN), based on experimental data, to predict the failure load of joints. The results indicated that the highest tension and compression strengths of joints were achieved when the distance is 140 mm between the screws. Joint strengths of all specimens were improved when the screw length and diameter were increased. It is necessary to estimate the effect of various factors to improve furniture joint performance. Coefficients of … |
| Anahtar Kelimeler |
| Artificial neural networks | Furniture | Joint | Screw |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 8 |
| Scopus | 7 |
| Google Scholar | 8 |