Deformation rate of engineered wood flooring with response surface methodology and adaptive network-based fuzzy inference system

Abstract

Controlling the deformation rate is the key to improving the product quality of engineered wood flooring. In this work, the changes in the deformation rate of engineered wood flooring were in focus with cold-pressing, response surface methodology, and adaptive network-based fuzzy inference system were used to explore the relationship between deformation rate and processing parameters, including adhesive spreading rate, pressing time, and pressing pressure. According to the results, the deformation rate was positively related to pressing time, while it increased first and then decreased with both the increase of adhesive spreading rate and pressing pressure. Meanwhile, a mathematical model was developed, and the significant influence of each term on the deformation rate was analyzed. This model had high feasibility and can be used to describe the relationship between the deformation rate and processing parameters. Furthermore, an adaptive network-based fuzzy inference system model was established. It has higher accuracy than that of the response surface methodology model, and it can be used for predicting deformation rate and optimizing processing parameters. Finally, an optimal processing conditions with the lowest deformation rate was determined as follows: 147 g/m2 adhesive spreading rate, 12s pressing time, and 1.2 MPa pressing pressure, and it hope to be adopted in the industrial processing of engineered wood flooring with respective of the higher product quality and lower production costs.