Research On Mechanical Properties Prediction Model Of Hot-pressing Mixed Material Boards Based On Support Vector Machine

21st century,the output of man-made boards has been ranked in the forefront of the world.As an important component of the man-made boards industry,the demand and output of mixed material boards are also increased.China,as a world's most populous country,has a forest coverage rate of only 21.66%,which still falls short of the world's average.According to statistics,China's annual crop straw that has been discarded and burned has reached 200-300 million tons,which can causes serious waste of natural resources and environmental pollution.So,how to efficiently economize the comprehensive utilization of wood and plant straw,alleviate the imbalance between supply and demand of timber resources,improve the utilization rate of natural resources,increase the added value,improve the environment and realize the sustainable development of natural resources is particularly urgent.Hot-pressing is one of the main processes in the production of mixed material boards,which plays a decisive role in the mechanical properties of mixed material boards.From the perspective of engineering application,taking the hot-pressing process of mixed material boards as the research object,applies the machine learning and intelligent algorithm to the prediction of the mechanical properties of mixed material boards,and uses MATLAB2015 b and open-source software LIBSVM-3.22 for analysis and research.The main research contents and research results are as follows:(1)By analyzing the hot-pressing process of mixed material boards,it was found that the mechanical properties(compressive strength,elastic modulus,internal bond strength)of mixed material boards are mainly affected by the raw material parameters(moisture content)and hot pressing parameters(hot pressing temperature,pressure and time);(2)Determine the level of orthogonal experiment to conduct the hot pressing test of mixed material boards,test its mechanical properties and sort out the experimental data,and construct the SVR prediction model of the mechanical properties of mixed material boards by using the support vector regression principle;(3)Mechanical properties of mixed material boards SVR prediction model performance is mainly affected by the penalty factor C and RBF kernel function parameters g.For the grid search method,the optimal parameters are easy to fall into local optimum.This paper adopts the globaloptimization algorithm(genetic algorithm and particle swarm optimization algorithm).Optimize and select C,g build a globally optimal PSO-SVR/GA-SVR prediction model;(4)Comparing the experimental results of SVR,PSO-SVR and GA-SVR and analyzing and discussing.The results show that the PSO-SVR prediction model can better describe the nonlinear relationship between the hot-pressing parameters and the mechanical properties of mixed material boards,Rapidly and accurately predict the mechanical properties of mixed material boards based on independent variables.Compared with SVR and GA-SVR,PSO-SVR prediction model has higher prediction accuracy,stronger stability and better generalization performance;(5)Verify the stability of the PSO-SVR prediction model.Based on Matlab GUI and PSO-SVR prediction model,the prediction UI of mechanical properties of mixed material boards is established by using the results of orthogonal test of mixed material plate hot-pressing as data set.The research on the-hot pressing process of mixed material boards provide new idea for the application of high added value of crop straw.PSO-SVR prediction model can provide theoretical reference for performance prediction and hot-pressing control parameter selection of mixed material boards.