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.

In the intelligent manufacturing of furniture, the power signal has the characteristics of low cost and high accuracy and is often used as a tool wear condition monitoring signal. However, the power signal is not very sensitive to tool wear conditions. The present work addresses this issue by proposing a novel woodworking tool wear condition monitoring method that employs a limiting arithmetic average filtering method and particle swarm optimization (PSO)-back propagation (BP) neural network algorithm. The limiting arithmetic average filtering method was used to process the power signal and extracted the features of the woodworking tool wear conditions. The spindle speed, depths of milling, features and tool wear conditions were used as sample vectors. The PSO-BP neural network algorithm was used to establish the monitoring model of the woodworking tool wear condition. Experiments show that the proposed limiting arithmetic average filtering method and PSO-BP neural network algorithm can accurately monitor the woodworking tool wear conditions under different milling parameters.