Optimizing Wood Composite Drilling with Artificial Neural Network and Response Surface Methodology

Abstract

Many factors (material properties, drill bit type and size, drill bit wear, drilling parameters used, and machine-tool characteristics) affect the efficiency of the drilling process, which could be quantified through the delamination factor, thrust force, and drilling torque. To find the optimal combination among the factors that affect the desired responses during drilling of wood-based composites, various modelling techniques could be applied. In this work, an artificial neural network (ANN) and response surface methodology (RSM) were applied to predict and optimize the delamination factor at the inlet and outlet, thrust force, and drilling torque during drilling of prelaminated particleboards, medium- density fiberboard (MDF), and plywood. The artificial neural networks were used to design four models—one for each analyzed response. The coefficient of determination (R2) during the validation phase of designed ANN models was among 0.39 and 0.96. The response surface methodology was involved to reveal the individual influence of analyzed factors on the drilling process and also to figure out the optimum combination of factors. The regression equations obtained an R2 among 0.88 and 0.99. The material type affects mostly the delamination factor. The thrust force is mostly influenced by the drill type. The chipload has a significant effect on the drilling torque. A twist drill with a tip angle equal to 30° and a chipload of 0.1 mm/rev. could be used to efficiently drill the analyzed wood-based composites.