Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System

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 degrees, 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.

Automated summary

This study focused on improving the cutting quality of beech wood during milling by investigating the changes in surface roughness under different cutting conditions. A relationship model between milling conditions and surface roughness was established using response surface methodology and adaptive network-based fuzzy inference system. The optimal milling condition, which includes a rake angle of 15 degrees, a spindle speed of 3357 r/min, and a depth of cut of 0.62 mm, was determined for achieving smooth surface in actual machining.