Prediction of noise emission in the machining of wood materials by means of an artificial neural network
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Scion
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Background: Noise produced during machining of wood materials can be a source of harm to workers and an environmental hazard. Understanding the factors that contribute to this noise will aid the development of mitigation strategies. In this study, an artificial neural network (ANN) model was developed to model the effects of wood species, cutting width, number of blades, and cutting depth on noise emission in the machining process.
Methods: A custom application created with MATLAB codes was used for the development of the multilayer feed-forward ANN model. Model performance was evaluated by numerical indicators such as MAPE, RMSE, and R-2.
Results: The ANN model performed well with acceptable deviations. The MANE, RMSE, and R-2 values were 0.553%, 0.600, and 0.9824, respectively, in the testing phase. Furthermore, this study predicted the intermediate values not provided from the experimental study. The model predicted that lower noise emissions would occur with decreased cutting width and cutting depth.
Conclusions: ANNs are quite effective in predicting the noise emission. Practitioners relying on the ANN approach for investigating the effects of various factors on noise emission can save time and costs by reducing the number of experimental combinations studied to generate predictive models.
Açıklama
Anahtar Kelimeler
Artificial Neural Network, Noise Emission, Machining, Wood, Prediction, Saw Blade
Kaynak
New Zealand Journal of Forestry Science
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
52
Sayı
Künye
Özşahin, Ş., & Singer, H. (2022). Prediction of noise emission in the machining of wood materials by means of an artificial neural network. New Zealand Journal of Forestry Science, 52.
