Prediction of the surface roughness of wood for machining

Abstract

The surface quality of solid wood is very important for its effective response in manufacturing processes. The effects of feed rate, cutting depth and rake angle on surface roughness and power consumption were investigated and modeled. Neuro-fuzzy methodology was applied and shown that it could be useful, reliable and an effective tool for modeling the surface roughness of wood. Thus, the results of the present research can be successfully applied in the wood industry to reduce time, energy and high experimental costs.