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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.

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Cutting force and temperature are critical indicators for improving cutting performance and productivity. This study used an up-milling experiment to ascertain the effect of tool tooth number, cutting speed, and depth on the machinability of bamboo–plastic composite. We focused on the changes in the resultant force and cutting temperature under different milling conditions. A response surface methodology was used to build prediction models for the resultant force and temperature. A verification test was conducted to prove the model’s reliability. The empirical findings suggested that the number of tool teeth had the most significant impacts on both the resultant force and the cutting temperature, followed by the depth of cut and the cutting speed. Moreover, the resultant force and cutting temperature showed increasing trends with decreasing numbers of tool teeth and increasing cut depths. However, cutting speed had a negative relationship with the resultant force and a positive relationship with temperature. We also determined the optimal milling conditions with the lowest force and temperature: four tool teeth, 300 m/min cutting speed, and 0.5 mm depth. This parameter combination can be used in the industrial manufacture of bamboo–plastic composite to improve tool life and manufacturing productivity.