Odun Yüzey Pürüzlülüğü Tahmininde Bir Yapay Sinir Ağı Modelinin Kullanılması

Year 2019, Volume: 7 Issue: 3, 1764 - 1777, 31.07.2019

Şükrü Özşahin , Hilal Singer

https://doi.org/10.29130/dubited.554419

Cited By: 2

Abstract

Ağaç malzemelerin yüzey pürüzlülüğü, nihai
ürünlerin kalitesinin değerlendirilmesi açısından çok önemlidir. Bu nedenle bu
çalışmada, odun türü, bıçak sayısı, besleme hızı ve kesme derinliğinin planyalama
işleminde yüzey pürüzlülüğü üzerindeki etkisini modellemek için bir yapay sinir
ağı (YSA) modeli geliştirilmiştir. Farklı YSA modelleri oluşturulmuş ve
bunların performansı ortalama mutlak yüzde hata (MAPE), ortalama karesel
hatanın karekökü (RMSE) ve determinasyon katsayısı (R²) kullanılarak
değerlendirilmiştir. Önerilen modelin test safhasındaki MAPE, RMSE ve R²
değerleri sırasıyla %7,27, 0,57 ve 0,903 olmuştur. Sonuç olarak YSA, planyalanan
odunun yüzey pürüzlülüğünü tahmin etmede etkili bir araçtır ve maliyetli ve
zaman alıcı araştırmalar yerine oldukça yararlıdır.

Keywords

Yapay sinir ağı, Yüzey pürüzlülüğü, Bıçak sayısı, Besleme hızı, Kesme derinliği

Utilizing an Artificial Neural Network Model in Wood Surface Roughness Prediction

Abstract

The surface roughness
of wood materials is very important in terms of assessing the quality of final
products. Therefore, in this study, an artificial neural network (ANN) model
was developed to model the effect of wood species, number of knives, feed rate,
and cutting depth on surface roughness in the planing process. Different ANN
models were created and the performance of them was evaluated using the mean
absolute percentage error (MAPE), the root mean square error (RMSE), and the
coefficient of determination (R²). The MAPE, RMSE, and R² values in
the testing phase of the proposed model were 7.27%, 0.57, and 0.903,
respectively. Consequently, ANN is an effective tool in predicting the surface roughness
of planed wood and quite useful instead of costly and time-consuming
investigations.

Keywords

Artificial neural network, Surface roughness, Number of knives, Feed rate, Cutting depth

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