Applied Research on Prediction Methods of Properties of Particleboard Based on Data-Driven Methods

Abstract

As a kind of wood-based panel, particleboard is widely used in production and daily life. The physical and mechanical properties (PMPs) of particleboard play a decisive role in its practical application. At present, destructive methods are primarily used to measure the actual properties of particleboard on the production line, which is a waste of resources and time-consuming method. In order to solve these problems, this paper uses several data-driven methods to predict the PMPs of particleboard. Firstly, the data set is constructed based on the parameters of particleboard production process. Secondly, seven commonly used data-driven methods are used to build models to predict the PMPs. Finally, three different assessment indexes are used to determine the most suitable method for property prediction. The results showed that the random forest method is better for predicting the PMPs of particleboard.

Multi-objective optimization of particle gluing operating parameters in particleboard production based on improved machine learning algorithms

“…2 College of Materials Science and Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu, China. 3 Nanjing University of Information Science and Technology, 333 Xishan Rd., Avenue, Wuxi 214105, China.…”

“…Particleboards (PB) have advantages of low cost, good processing performance, renewable raw materials, and reasonable mechanical properties, which have become one of the commonly used panels for furniture and indoor decoration [1]. In the PB production process, the particle gluing process operation has a great influence on the mechanical properties of PB [2], so operators often adjust the operating parameters of particle gluing according to the mechanical properties test results of PB [3]. However, depending only on the actual production experience of operators, it is difficult to accurately control the particle gluing operating parameters.…”