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Journals / Düzce Üniversitesi Bilim ve Teknoloji Dergisi / 2019 / Cilt: 7 - Sayı: 3

Utilizing an Artificial Neural Network Model in Wood Surface Roughness Prediction

Odun Yüzey Pürüzlülüğü Tahmininde Bir Yapay Sinir Ağı Modelinin Kullanılması

Şükrü ÖZŞAHİN
Hilal SİNGER
Pages
1764–1777
Publication Date
2022-10-12
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Consequently, ANN is an effective tool in predicting the surface roughness of planed wood and quite useful instead of costly and time-consuming investigations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The surface roughness of wood materials is very important in terms of assessing the quality of final products.
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Key Findings & Data: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.
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Conclusion & Impact:Consequently, ANN is an effective tool in predicting the surface roughness of planed wood and quite useful instead of costly and time-consuming investigations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay sinir ağı · Yüzey pürüzlülüğü · Bıçak sayısı

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.

Özet

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.

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Cite This Article

ÖZŞAHİN, Ş., SİNGER, H. (2022). Utilizing an Artificial Neural Network Model in Wood Surface Roughness Prediction. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Cilt: 7 - Sayı: 3, 1764–1777. https://ajindex.com/articles/494432

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References (12)

  1. C. Söğütlü, P. Nzokou, I. Koc, R. Tutgun and N. Döngel, “The effects of surface roughness on varnish adhesion strength of wood materials,” Journal of Coatings Technology and Research, vol. 13, no. 5, pp. 863–870, 2016
  2. S. D. Sofuoğlu and A. Kurtoğlu, “Effects of machining conditions on surface roughness in planing and sanding of solid wood,” Drvna Industrija, vol. 66, no. 4, pp. 265–272, 2015
  3. L. Gurau and M. Irle, “Surface roughness evaluation methods for wood products: a review,” Current Forestry Reports, vol. 3, no. 2, pp. 119–131, 2017
  4. M. Budakci, L. Gurleyen, H. Cinar and S. Korkut, “Effect of wood finishing and planing on surface smoothness of finished wood,” Journal of Applied Sciences, vol. 7, no. 16, pp. 2300–2306, 2007
  5. B. Hendarto, E. Shayan, B. Ozarska and R. Carr, “Analysis of roughness of a sanded wood surface,” International Journal of Advanced Manufacturing Technology, vol. 28, no. 7–8, pp. 775–780, 2006
  6. İ. Aydın ve G. Çolakoğlu, “Odun yüzeylerinde pürüzlülük ve pürüzlülük ölçüm yöntemleri,” Artvin Orman Fakültesi Dergisi, c. 4, s. 1, ss. 92–102, 2003
  7. E. Csanády, E. Magoss and L. Tolvaj, Quality of machined wood surfaces, Basel: Springer International Publishing, 2015
  8. H. Efe, S. Demirci and Y. Kilic, “Effect of the cutting direction, number of cutters, feed rate and cutting depth to the surface roughness in planning beech (Fagus orientalis Lipsky) wood,” Kastamonu University Journal of Forestry Faculty, vol. 3, no. 1, pp. 77–87, 2003
  9. I. Usta, S. Demirci and Y. Kilic, “Comparison of surface roughness of locust acacia (Robinia pseudoacacia L.) and european oak (Quercus petraea (Mattu.) Lieble.) in terms of the preparative process by planing,” Building and Environment, vol. 42, no. 8, pp. 2988–2992, 2007
  10. S. Demirci, “Effect of the number of knives, feed rate and cutting depth on surface roughness of some wood species processed with planer,” Kastamonu University Journal of Forestry Faculty, vol. 13, no. 1, pp. 100–108, 2013
  11. A. Rolleri, F. Burgos and A. Aguilera, “Surface roughness and wettability variation: the effect of cutting distance during milling of pinus radiata wood,” Drvna Industrija, vol. 67, no. 3, pp. 223–228, 2016
  12. R. Haghbakhsh, H. Adib, P. Keshavarz, M. Koolivand and S. Keshtkari, “Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions,” Thermochimica Acta, vol. 551, pp. 124–130, 2013. [13]