Structure Optimization Design Of Large Mining Excavator Bucket

Large mining excavator is one of the core machines in the open-pit mining system,and its overall performance directly determines the mining efficiency,safety and energy saving.The working process of excavator is actually the interaction process between the bucket part and the excavated material.The structure and status of the bucket directly affect the load on the excavator machine.At present,the bucket has problems such as large resistance,large impact and low full bucket rate,which greatly limits the working efficiency of the excavator.Therefore,new methods are needed to analyze and optimize the structural parameters of the bucket.The acquisition and prediction of work process load spectrum has always been the focus and difficulty of excavator analysis and optimization.However,due to its large size,high downtime costs and difficulty in sensor installation,the real mining resistance data is difficult to collect.Therefore,in this paper,discrete element method was used to obtain the load spectrum for the mining process simulation,and the bucket's structural parameters were optimized through surrogate model and genetic algorithm.The main research work is as follows:The influence of various discrete material parameters on the resistance to excavation was explored.Based on the fixed excavation trajectory and initial position,in this paper,the discrete element simulation model of the bucket excavation process was established,and by converting the excavation resistance into the energy consumption,the influence of particle size on the stability of simulation results and the influence of particle density,shear modulus,restitution coefficient,friction coefficient and surface energy on the excavation resistance were reflected intuitively.The study has shown that reducing the particle size and the material shear modulus can effectively increase the simulation stability and efficiency.Based on this result,the discrete element simulation model was improved.By inversely deriving the material parameters from the load spectrum,the discreteness of the results is reduced while ensuring the accuracy of the simulation results,and the efficiency of the simulation is greatly improved.Surrogate model technology and genetic algorithm are used to optimize the structural parameters of excavator bucket.Latin Hypercube test design method was used to obtain training points and test points,and discrete element simulation was used to build surrogate models,the precision of the model was evaluated based on the determination coefficient,root mean squared error,and maximum absolute error.The model with the highest precision wasthen substituted into the genetic algorithm for optimization.Compared with the original model,the optimized bucket structure model improved the excavation weight by 22.75%,and the energy consumption of unit's effective excavation weight reduced by 13.88%.The excavator's working efficiency was greatly improved and the operating costs was reduced.Finally,the global sensitivity of each design variable was obtained by Sobol method,and the degree of influence of sensitive factors on the optimization goal was obtained.Finally,it was determined that the connection angle of the bucket had the greatest influence on the excavation resistance and the excavation weight.