Combining Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Operating Parameters of MDF Panels

Abstract

Most of the parts of furniture made of medium density fiberboards (MDF) require at least one hole to be assembled. The drilling technological parameters influence the quality of holes. Factors such as tip angle of the drill bit, feed rate, type and diameter of the drill bit, and spindle rotational speed could affect the drilling process. Therefore, the right choosing of drilling parameters is a mandatory condition to improve the drilling efficiency that is expressed through tool durability, cost, and quality of the drilling. Thus, in this work, we are proposed an approach that consists in combining two modelling techniques, which were successfully applied in various fields, namely artificial neural network (ANN) and response surface methodology (RSM), to analyze and optimize the drilling process of MDF boards. Four artificial neural network models with a reasonable accuracy were developed to predict the analyzed responses, namely delamination factor at inlet, delamination factor at outlet, thrust force, and drilling torque. These models were used to complete the experimental design that was requested by the RSM. The optimum values of the selected factors and their influence on the drilling process of the MDF boards were revealed. A part of optimum combinations among analyzed factors could be used both during the drilling of the MDF boards and prelaminated wood particleboards.