Optimization Design Of Hydraulic Shovel Working Device Based On GA

Working device is the core part of hydraulic shovel.The movement and functioned force of working device are very complicated.The face-shovel working device is much used among them.Due to its outstanding performance,hydraulic shovel is more and more widely used in construction projects,such as open pit and water constructions.However,our country is insufficient for independent research and development ability and there is absolutely a need to develop new hydraulic shovel.Based on present sophisticated types,a new working device was designed by analyzing the movement and force of hydraulic shovel and the obtained scheme was optimized by Genetic Algorithm.The main contents of this paper covered:1.Based on the working device of the Liebherr R9 series hydraulic shovel working device parameters and according to geometric similarity law and empirical formula,the new working device structure parameters were obtained,including the angle ranges and joint pinpoints of boom,arm and bucket mechanism and working cylinder strokes.The enveloping map of new device was also got.2.A digging resistance curve in digging process was obtained after the resistance analysis of working shovel based on arm digging and horizontal crowd condition;the thrust force of working cylinders were determined by matching digging resistance with force;the hydraulic cylinder types were also obtained;3.The maximum theoretical digging force which the whole machine can exert under arm digging and bucket digging condition were calculated;differrent from the traditional digging maps analysis method,the digging maps of working device was analyzed by a new overlap map method the performance of the whole machine was estimated;it was concluded that the design scheme did not meet the requirements,thus it was necessary to optimize the scheme;4.hydraulic shovel working device optimization mathematics model was obtained and the multi-objective optimization model was transferred to single-objective model by ideal value method and index tolerance method;due to the high dimension,in-differentiability and absence of explicit function expression,the optimization was conducted by a modified Genetic Algorithm;the constraint conditions was handled by a dynamic punishment method and the operator were Tournament Operator,Arithmetic Crossover Operator and Non-uniform Mutation Operator and the control parameters was got by experiments;based on the overlaps method standard,it was concluded that the whole performance in main area was greatly improved.