Helical milling with the advantages of stable machining process, a well-machined surface quality, etc., is an interest of researchers and producers. Machined surface roughness (arithmetic mean deviation (Ra) and maximum height of the assessed profile (Rz)) and milling power consumption as two main machining characteristic parameters were studied and chosen as response factors to evaluate the machinability of Scots pine helical milling. Input variables included helical angle of milling cutter, rotation speed of main shaft, and depth of milling. Response surface methodology was applied for the design of experiments, data processing and analysis, and optimization of the processing parameters. The results showed that Ra and Rz decreased with an increase in helical angle and rotation speed of main shaft, though increased with an increase in depth of milling. Milling power increased when the helical angle and depth of milling increased and showed a slight downward trend as the rotational speed increased. The quadratic models were applied to predict the values of Ra, Rz, and milling power due to the high values of R² of 0.9895, 0.9905, and 0.9885, respectively. The plot of predicted and actual values also indicated that the created models had good predictability. The optimized combination of helical angle, rotation speed, and depth of milling are 64°, 7500 r/min, and 0.5 mm, respectively. The effects of input variables and the quantitative relation between input variables and response variables were revealed clearly. These achievements will be useful for guiding the selection of helical milling parameters to achieve the purposes of improving processed surface quality and saving the processing power consumption.

The cutting power consumption of milling has direct influence on the economic benefits of manufacturing particle boards. The influence of the milling parameters on the cutting power were investigated in this study. Experiments and data analyses were conducted based on the response surface methodology. The results show that the input parameters had significant effects on the cutting power. The high rake angle reduced the cutting force. Thus, the cutting power decreased with the increase in the rake angle and the cutting energy consumption was also reduced. The cutting power increased with the rotation speed of the main shaft and the depth of milling induced the impact resistance between the milling tool and particle board and the material removal rate. The p-values of the created models and input parameters were less than 0.05, which meant they were significant for cutting power and power efficiency. The depth of milling was the most important factor, followed by the rotation speed of the main shaft and then the rake angle. Due to the high values of R² of 0.9926 and 0.9946, the quadratic models were chosen for creating the relationship between the input parameters and response parameters. The predicted values of cutting power and power efficiency were close to the actual values, which meant the models could perform good predictions. To minimize the cutting power and maximize the power efficiency for the particle board, the optimized parameters obtained via the response surface methodology were 2°, 6991.7 rpm, 1.36 mm for rake angle, rotation speed of the main shaft and depth of milling, respectively. The model further predicted that the optimized parameters combination would achieve cutting power and power efficiency values of 52.4 W and 11.9%, respectively, with the desirability of 0.732. In this study, the influence of the input parameters on the cutting power and power efficiency are revealed and the created models were useful for selecting the milling parameters for particle boards, to reduce the cutting power.

Walnut (Juglans regia L.) is widely used in wood furnishings, and machinability is a key factor for improving product quality and enterprise benefits. This work focused on the influence of the rake angle, depth of cut, and cutting speed on the cutting force and machined surface roughness during the straight-tooth milling of walnut. On the basis of the experimental findings, a mathematical model was created using a response surface methodology to determine the relationship between the cutting force and the cutting conditions, as well as the relationship between the surface roughness and the cutting conditions. Variance analysis was used to study the significant contributions of the interactions of various factors and two-level interactions to the cutting force and surface roughness. The optimized combination of milling conditions, resulting in lowest cutting force and surface roughness, was determined to be a rake angle of 5°, a depth of cut of 0.6 mm, and a cutting speed of 45 m/s.

This work focused on changes in surface roughness under different cutting conditions for improving the cutting quality of beech wood during milling. A response surface methodology and an adaptive network-based fuzzy inference system were adopted to model and establish the relationship between milling conditions and surface roughness. Moreover, the significant impact of each factor and two-factor interactions on surface roughness were explored by analysis of variance. The specific objective of this work was to find milling parameters for minimum surface roughness, and the optimal milling condition was determined to be a rake angle of 15°, a spindle speed of 3357 r/min and a depth of cut of 0.62 mm. These parameters are suggested to be used in actual machining of beech wood with respect of smoothness surface.

For the sake of improving the benefit of enterprise by reducing energy waste. RSM (response surface methodology) was used to investigated the cutting power of wood–plastic composite at different cutting conditions (rake angle, cutting speed, depth of cut, and flank wear). Based on the experimental results, a cutting power model with a high degree of fitting was developed, which can be used to predict cutting power and optimal cutting conditions. Meanwhile, the effects of rake angle, cutting speed, depth of cut, and flank wear and their interaction on the cutting power were probed by analysis of variance, and the significant terms were determined. Finally, the optimal cutting condition was obtained as follows: rake angle of 10°, cutting speed of 300 m/min, depth of cut of 1.5 mm, and flank wear of 0.1 mm. This parameter combination is suggested to be used for industrial manufacturing of wood–plastic composite in terms of the incredible machining efficiency and the lowest energy consumption.