Using Artificial Neural Networks to Model the Surface Roughness of Massive Wooden Edge-Glued Panels Made of Scotch Pine (Pinus sylvestris L.) in a Machining Process with Computer Numerical Control

Abstract

An artificial neural network (ANN) approach was employed for the prediction and control of surface roughness (Ra and Rz) in a computer numerical control (CNC) machine. Experiments were performed on a CNC machine to obtain data used for the training and testing of an ANN. Experimental studies were conducted, and a model based on the experimental results was set up. Five machining parameters (cutter type, tool clearance strategy, spindle speed, feed rate, and depth of cut) were used. One hidden layer was used for all models, while there were five neurons in the hidden layer of the Ra and Rz models. The RMSE values were calculated as 1.05 and 3.70. The mean absolute percentage error (MAPE) values were calculated as 20.18 and 15.14, which can be considered as a good prediction. The results of the ANN approach were compared with the measured values. It was shown that the ANN prediction model obtained is a useful and effective tool for modeling the Ra and Rz of wood. The results of the present research can be applied in the wood machining industry to reduce energy, time, and cost.

Anadolu kestanesi (Castenia sativa Mill.) ağaç türünün CNC ile işlenmesinde yüzey pürüzlülüğünün işleme parametreleri ile ilişkisi

“…Dönerek işleyen kesicilerde devir sayısının arttırılması ile birlikte pürüzlülük değerlerinde düşme meydana gelmekte ve daha düzgün yüzeyler elde edilebilmektedir (Davim, vd., 2009;Karagöz, 2010;Sütçü ve Karagöz, 2012;Sofuoğlu, 2015a;Sofuoğlu, 2015b;Koç, vd., 2017;Hazır, vd., 2018;Aykaç ve Sofuoğlu, 2021;Tosun, 2021). Genel olarak değerlendirildiğinde çalışmada elde edilen değerlerin literatür ile benzer eğilimler gösterdiği görülmektedir.…”

Optimization of CNC operating parameters to minimize surface roughness of Pinus sylvestris using integrated artificial neural network and genetic algorithm

“…The number of hidden neurons of the best network can vary for each problem. In the studies performed to model of surface roughness of different materials, the optimum network was found as 5-5-1 network (Sofuoglu 2015a) for massive wooden edge-glued panels and 4-9-1 network for AISI 1060 steel (Kant and Sangwan 2015). In our study, the best model was obtained from 3-7-1 network for Scotch pine wood samples.…”

“…They confirmed that prediction model obtained from ANN is fulfilling good statistically. Sofuoglu (2015a) investigated to the surface roughness of massive wooden edge-glued panels of Pinus sylvestris with ANN method. Researcher used cutter type, tool clearance strategy, spindle speed, feed rate, and depth of cut as independent variables for ANN.…”

Surface roughness of machined wood and advanced engineering materials and its prediction: A review

“…Sofuoglu 101 predicted the surface roughness using an ANN, and demonstrated that the ANN method could model the roughness of the machined wood surfaces. Tiryaki et al 102 also reported that using ANNs could predict the roughness of their machined wood surfaces.…”