Research On The Multiobjective Optimization Design Method Of Large Face Shovel Hydraulic Exacavator Working Attachment

As a representative high-end mechanical equipment in construction machinery and mining equipment,the design and development capabilities of large face shovel hydraulic excavator reflect the overall technical level of national equipment manufacturing industry.Nowadays,although the domestic large hydraulic excavator products are developing rapidly,they are still in the initial stage and there is a big gap compared to the international top-level.The research on the design theory,technology and method of large face shovel hydraulic excavator is very conducive to the design level of hydraulic excavator,the development of excavator industry and the international competitiveness of national construction machinery equipment.Based on the advanced computational intelligence method of multiobjective evolutionary algorithm,this paper studies the multiobjective optimization design of large face shovel hydraulic excavator's working attachment.The theoretical model and analysis framework of shovel attachment are established,including kinematics model,mechanics model,calculation model of digging resistance and evaluation method of digging performance.The technical indexes and design considerations of hydraulic excavator are introduced,such as the working parameters,special working conditions,digging forces,crowding force,lifting force,digging resistances and the standard of digging atlas superposition.The multiobjective optimization model of simple digging & loading device is established,which sets level crowding,stick digging force and bucket digging force as the objective functions.Based on the dynamic crowding distance sorting strategy,differential evolution operator and random mutation operator,an improved nondominated sorting genetic algorithm is proposed.The test results indicate that the convergence and diversity of the improved algorithm are better than the original algorithm,and it could deal with the optimization problems which have complex Pareto fronts.Then the improved algorithm is applied to the multiobjective optimization problem of simple digging & loading device,and the scheme of simple digging & loading device which meets the design requirements is selected by the technique for order preference by similarity to ideal solution.According to the features of TriPower shovel attachment,a many-objective optimization model with the objective functions of level crowding,translational lifting,constant boom momentum,stick digging force and bucket digging force is established.An improved decomposition-based many-objective evolutionary algorithm is proposed to solve the optimization problem of TriPower shovel attachment and compared with six multiobjective evolutionary algorithms.Then,the satisfactory solution which can fully realize the three features of TriPower shovel attachment is selected by multicriteria decision-making method.These results demonstrate the effectiveness of the proposed algorithm.Considering the features,a many-objective optimization model of TriRocker shovel attachment is builted which includes the objective functions of level crowding,translational lifting,crowding force,stick digging force and bucket digging force.An improved reference points-based evolutionary algorithm is proposed to solve it and compared with a number of multiobjective evolutionary algorithms.The result of comparison demonstrates the high performance of the proposed algorithm.Finally,an satisfactory design of TriRocker shovel attachment is obtained.Based on the cooperative research and development project with some construction machinery enterprise,a prototype of 80 t TriRocker hydraulic excavator has been developed.The performance indicators and kinematic characteristics of the prototype are in accord with the theoretical design,and the validity of the design method in this paper have been verified.