Dynamic Analysis And Optimization Of Boom Lifting Mechanism Of Multifunctional Work Vehicle In Forest Land

With the rapid development of China’s economy and the process of industrialization,the demand for forestry resources is increasing,and the wide application of forestry operation machinery has become an important embodiment of the modernization of forestry development.The application of woodland multifunctional work vehicle in modern forestry production can greatly improve the efficiency and quality of woodland operation.The boom lifting mechanism is an important part of the forestry multifunctional work truck,and its reasonable design plays an important role in improving the work efficiency,increasing the operating range and reliability of the work truck.Therefore,the research on the boom lifting mechanism of the forest multifunctional work vehicle is imperative.The current research on the boom lifting mechanism only considers the analysis of the boom structure alone,and ignores the influence of the lifting cylinder on the performance of the boom,which has few experimental factors and cannot make a comprehensive and accurate evaluation of the performance of the boom lifting mechanism.At the same time,most of the optimization of the boom is only a single-objective optimization with the goal of minimizing the working pressure of the lifting cylinder,without considering the dynamic analysis of the lifting angle and angular acceleration under the actual working conditions and the multiobjective optimization of the lifting smoothness and other indicators.This makes it not a good approximation to the real surface in some cases,and the fitting accuracy is poor.In this paper,based on the Newton Euler equation,we establish the dynamics model of the boom lifting mechanism of the multifunctional work truck in-woodland,and adopt the BoxBehnken Design(BBD)experimental design method to design the experiments for the middle connecting rod and the hinge point position of the lifting cylinder of the aerial work truck,and get the experimental sample data.The BP neural network was optimized by using intelligent optimization algorithm to establish the prediction model of the dynamics of the lifting mechanism.Later,NSGA-Ⅱ genetic algorithm is used to find the optimal model and get the Pareto optimal solution set,and the subjective and objective weights of the five objectives are comprehensively assigned based on the idea of game theory to derive the optimal hinge point position.The details are as follows:(1)Firstly,the kinematics and dynamics of the aerial work vehicle are modeled:the kinematics equations of the linkage center of mass acceleration,linkage rotation angular velocity and angular acceleration are derived from the derivation of the linkage vector equations;the linkage center of mass acceleration,rotation angular velocity and rotation angular acceleration equations are established according to the composition of the boom linkage and the principle of force transfer,so as to model the dynamics equations of the woodland multifunctional work vehicle.(2)Secondly,the Box-Behnken Design(BBD)experimental design method is selected to obtain the sample points of the middle connecting rod of the boom lifting mechanism and the hinge point of the lifting cylinder of the woodland multifunctional work vehicle,and the experimental data of the sample response are obtained by using Simcenter 3D simulation software to establish the predictive agent model of the sample and response based on BP neural network.An intelligent optimization algorithm is used to optimize the initial threshold of the established BP neural network,so as to obtain the optimal neural network prediction model.(3)Then,the NSGA-Ⅱ genetic algorithm is used to find the optimal BP neural network and obtain the Pareto optimal solution set.Based on the game theory idea,the subjective weights obtained from the expert scoring and the objective weights obtained from the entropy weight evaluation model are integrated and assigned to obtain the optimal solution of the hinge point position of the boom lifting mechanism of the forest land multifunctional operation vehicle.(4)Finally,this paper builds a test platform of the multifunctional work vehicle in forest,and further applies the research results to practice.Comparing the test results with the simulation optimization results,it can be seen that the experimental results are basically consistent with the simulation results.The accuracy and effectiveness of the BP neural network prediction model and NSGA-Ⅱ genetic algorithm optimization model in the optimization of the performance of the boom lifting mechanism of the forest multifunctional work vehicle are verified.