Numerical Simulation and Bayesian Optimization CatBoost Prediction Method for Characteristic Parameters of Veneer Roller Pressing and Defibering

Abstract

Defibering equipment is employed in the production of scrimber for the purpose of wood veneer rolling, cutting, and directional fiber separation. However, the current defibering equipment exhibits a notable degree of automation deficiency, relying more on manual operation and empirical methods for process control, which impedes the stability of the defibering equipment and the defibering quality. This study presented an in-depth finite element analysis of the roller-pressing process for veneer defibering equipment, and a prediction method incorporating numerical simulation and ensemble learning was proposed through data collection and feature selection. The objective is to integrate this method into the intelligent decision-making system of the equipment, with the aim of improving the productivity of the equipment and effectively stabilizing the product quality. The simulation process and the analysis of the results in ABAQUS 2020 revealed that the roller gap and roller velocity of the defibering equipment, as well as the geometrical parameters of the veneer, have a significant influence on the defibering effect. Combining these factors, 702 simulation experiments were devised and executed, and a database was constructed based on the model-building parameters and simulation outcomes. The strain and stress observed in the simulation results served to represent the veneer force and veneer deformation. The CatBoost algorithm was used to establish prediction models for the key parameters of the defibering effect, and the Bayesian Optimization and 5-fold cross-validation techniques enabled the strain and stress prediction models to achieve coefficients of determination of 0.98 and 0.97 for the training and test datasets, respectively. Shapley Additive Explanation was used to provide insight into the contribution of each feature, thereby guiding the selection of feature parameters and simplifying the model. The results show that the scheme can effectively determine the core process parameters of the defibering equipment and then provide a practical control strategy for intelligent online control.