Development of an Artificial Neural Network Model to Minimize Power Consumption in the Milling of Heat-Treated and Untreated Wood
Öz
Aim of study: The power consumption of machining operations is an important part of the total production cost. Therefore, in this study, an artificial neural network (ANN) model was developed to model the effects of treatment, rotation speed, cutting depth, and feed rate on power consumption in the wood milling process.
Material and methods: A multilayer feed-forward ANN was employed for the prediction of power consumption. The accuracy of the model was assessed by performance indicators such as MAPE, RMSE, and R².
Main results: It has been observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values were obtained as 7.533, 0.027, and 0.9737 %, respectively, in the testing phase. Furthermore, it was found that power consumption decreased with decreasing of feed rate and cutting depth.
Research highlights: The findings of this study can be used effectively in the forest industry to reduce the experimental time and costs.
Anahtar Kelimeler
Kaynakça
- Aguilera, A. & Martin, P. (2001). Machining qualification of solid wood of Fagus silvatica L. and Picea excelsa L.: cutting forces, power requirements and surface roughness. Holz als Roh- und Werkstoff, 59(6), 483-488.
- Atıcı, U. (2011). Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Systems with Applications, 38(8), 9609-9618.
- Avramidis, S. & Wu, H. (2007). Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood. Holz als Roh- und Werkstoff, 65, 89-93.
- Aydın, G., Karakurt, I. & Hamzacebi, C. (2014). Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting. International Journal of Advanced Manufacturing Technology, 75(9-12), 1321-1330.
- Bakar, B. F. A., Hızıroğlu, S. & Md Tahir, P. (2013). Properties of some thermally modified wood species. Materials and Design, 43, 348-355.
- Barcík, Š., Kminiak, R., Řehák, T. & Kvietková, M. (2010). The influence of selected factors on energy requirements for plain milling of beech wood. Journal of Forest Science, 56(5), 243-250.
- Betiku, E. & Taiwo, A.E. (2015). Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network. Renewable Energy, 74, 87-94.
- Canakçı, A., Özsahin, S. & Varol, T. (2012). Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks. Powder Technology, 228, 26-35.
- Castellani, M. & Rowlands, H. (2008). Evolutionary feature selection applied to artificial neural networks for wood-veneer classification. International Journal of Production Research, 46(11), 3085-3105.
- Ceylan, İ. (2008). Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technology, 26(12), 1469-1476.