LAUSR

Abstract Roughness is an important property of wood surface and has a significant influence on the interface bonding strength and surface coating quality. However, there are no theoretical models for basswood-veneered medium-density fiberboard (MDF) by fine sanding from existing research work. In this paper, the basswood-veneered MDF was fine sanded with an air drum. Orthogonal experiment was implemented to study the effects of abrasive granularity, feed rate, belt speed, air drum deformation and air drum pressure on the surface roughness of basswood-veneered MDF. The simulation models of the parallel-grain roughness and the vertical-grain roughness of the sanded surface were conducted based on the BP (error back propagation) neural network, which was optimized by a genetic algorithm (GA) (GA-BP neural network), and these models were verified by extensive experimental data. The results showed that the influence of sanding parameters on parallel-grain roughness was similar to that on vertical-grain roughness. The order of influence was that: abrasive granularity > belt speed > feed speed > air drum deformation and air drum pressure. Based on the work, the parallel-grain roughness and vertical-grain roughness of basswood-veneered MDF could be well predicted by the GA-BP neural network. The average relative errors on parallel-grain roughness and vertical-grain roughness were 3.4% and 1.9%, respectively.