Optimization of production parameters of particle gluing on internal bonding strength of particleboards using machine learning technology

Abstract

The particleboard (PB) production is an extremely complex process, many operating parameters affecting panel quality. It is a big challenge to optimize the PB production parameters. The production parameters of particle gluing have an important influence on the internal bond (IB) strength of PB. In this study, using grey relation analysis (GRA) and support vector regression (SVR) algorithm, a prediction model was developed to accurately predict IB of PB through particle gluing processing parameters in a PB production line. GRA was used to analyze the grey relational grade between the particle gluing processing parameters and IB of PB, and the variables were screened. The SVR algorithm was used to train 724 groups of particle gluing sample data between six particle gluing processing parameters and IB. The SVR model was tested with 181 sets of experimental data. The SVR model was verified by 181 sets of experimental data, and the values of mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE), and Theil’s inequality coefficient (TIC) of the model were 0.008, 0.017, 0.013, and 0.014, respectively. The results showed that the prediction performance of the nonlinear regression prediction model based on GRA–SVR is superior, and the GRA–SVR prediction model can be used to real-time predict the IB in the PB production line.