Multiobjective Optimization of Roadheader Shovel-Plate Parameters Using Gray Weight and Particle Swarm Optimization

作     者：Li, Qiang Liu, Songyong Gao, Mengdi 

作者机构：China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Suzhou Univ Sch Mech & Elect Engn Suzhou 234000 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2022年第10卷

页      面：104555-104566页

核心收录：

基　　金：Scienti~c Research Project of Anhui Universities [KJ2021A1106] Natural Science Foundation of Anhui Province [2008085QE265] Suzhou Science and Technology Project Scienti~c Research Platform Open Project of Suzhou University [2020ykf12] 

主　　题：Optimization Linear programming Particle swarm optimization Vibrations Heuristic algorithms Coal mining Excavation Multiobjective optimization particle swarm algorithm roadheader shovel plate 

摘      要：As the working efficiency and life span of the shovel plate of a roadheader directly influence its performance, optimization of the shovel-plate parameters is crucial. For optimizing the shovel-plate parameters, the variations in the loading capacity and shovel grubbing force with respect to the shovel-plate parameters are determined in this study. Moreover, the ideal point method and gray weight method are proposed for multiobjective optimization. The gray weights of the loading capacity and shovel resistance are determined by investigating existing molded products and through the gray decision method. Thereafter, the particle swarm optimization (PSO) algorithm is applied for multiobjective optimization of the shovel-plate parameters. Considering the EBZ230-type roadheader shovel plate as an example, parameter optimization through multiobjective optimization decreases the mass of the shovel plate by 14.3% and the shovel resistance by 7.3%, while increasing the loading capacity by 1.4%. To demonstrate the influence of optimization, coal and rock excavation with a shovel is simulated using the optimized parameters in an ANSYS-Workbench environment. The results indicate that the maximum stress at the front of the shovel plate decreases by 22.1%, minimum fatigue life increases by 139.6%, and minimum safety factor increases by 30.3%. The obtained results establish that, in multiobjective optimization based on PSO, the ideal point method and gray weight method optimize the shovel-plate parameters. This optimization can provide a theoretical basis and reference values for the design of roadheader shovel plates and can be applied for multiobjective optimization in engineering as well.