| Yazarlar (3) |
Öğr. Gör. Merve ÖZKAN
Kastamonu Üniversitesi, Türkiye |
Caner Ozcan
Karabük Üniversitesi, Türkiye |
|
Mahmut Selman Gokmen
Kastamonu Üniversitesi, Amerika Birleşik Devletleri |
| Özet |
| In the solid wood panel industry, combining lamella pieces of consistent quality is critical for final product performance. However, most manufacturers still rely on manual classification by quality control teams, a process that is labor-intensive, error-prone, and inefficient. This reliance often results in production delays, inconsistent quality, and even financial losses due to panels being sold below cost, ultimately eroding customer trust. To address these challenges, this study proposes an expert decision support system based on deep learning to automate lamella quality classification. A Mask R-CNN model with a ResNet-101 backbone was trained on 2972 beech wood images to detect key defects knots, ray cells, and cracks that determine lamella grading. The system then classifies lamellas into AA, BB, CC, and Crack categories, achieving an overall classification accuracy of 90.90%. This work introduces a novel ... |
| Anahtar Kelimeler |
| Artificial intelligence network | Decision support system | Lamella dataset segmentation | Lamella quality classification | Mask R-CNN | Solid wood panel production |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SCOPUS dergilerinde yayınlanan tam makale |
| Dergi Adı | Operations Research Forum |
| Dergi ISSN | 2662-2556 Scopus Dergi |
| Dergi Tarandığı Indeksler | SCOPUS |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2025 |
| Cilt No | 6 |
| Sayı | 4 |
| Sayfalar | 1 / 26 |
| Doi Numarası | 10.1007/s43069-025-00534-w |
| Makale Linki | https://rdcu.be/eMil8 |
| Atıf Sayıları |
