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Optimization Of Hinge Position About Luffing System For The Hybrid Arm-Type Aerial Work Vehicle

Posted on:2019-05-17Degree:MasterType:Thesis
Country:ChinaCandidate:D SunFull Text:PDF
GTID:2382330566976220Subject:(degree of mechanical engineering)
Abstract/Summary:PDF Full Text Request
Aerial work vehicle is a kind of machine that lifts people from the ground to high altitudes and enables people to perform high-altitude operations.It is widely used in the fields of municipal administration and gardening in China.The main work of the aerial work vehicle is luffing movements,The driving force that supports luffing movement comes from extending and retracting of the driving cylinder.So the role of the hinge position directly affects the various performances during the boom luffing process.And it is very important to study the position of hinge point of boom luffing.The existing researches on the optimization of the hinge point position mostly based on the static balance theory to establish the relationship between the hinge point force and the amplitude angle.It was the single or the multi-objective optimization and it belonged to the category of statics.In view of the complicated boom structure,this paper fully considered the influence of inertial force on the luffing performance during boom luffing.The platform lifting to the fastest,height to the maximum,lifting acceleration to the minimum,and each stress of luffing cylinder hinge point to the minimum were regarded as the goal.Based on the traditional theory of statics,the response surface methodology,the theory of dynamics,the finite element and return analysis were applied to the multi-objective optimization.It was improved that the existing optimization methods was not enough in the dynamic performance and the working conditions of the aerial work platform’s boom luffing hinge.The main contents of this paper were as follows:(1)The mathematical model of the boom system of hybrid boom-type aerial work platform was established by the statics and analytic geometry.The functional relationship between the amplitude of each section of the cylinder force and the angle of each amplitude was obtained.Then the pose of the booms during each luffing cylinder force reaching a relative maximum was determined.(2)In this arm position,as the boom luffing cylinder hinge point size the design variables,taking the boom luffing cylinder forces relatively minimum asthe optimization goal,determining the boundary conditions,the hinge position was optimized using genetic algorithm.(3)The modal analysis was performed on the optimized model,and the whole process of boom lifting was simulated by using Workbench.The changes of the stress at the hinge point of the boom cylinders and the kinematics information of the working platform were recorded.(4)Based on the results of statics optimization,advanced optimization was done.A complete quadratic response surface model of optimization objective about design variables was established by the response surface method.The multi-objective optimization was carried out with this model,and the optimization result was substituted into the finite element model verification analysis with the new design parameters.The results showed that when considering the influence of inertia on the luffing of the boom,the maximum stress of the boom at the hinge point of the first boom was reduced by 6.2%,the maximum stress of the boom at the hinge point of the second boom was reduced by 10.5%,the maximum stress of the boom at the hinge point of the first boom was reduced by 4.6%,work platform vertical lifting rate increased by 4%,the average acceleration decreased by 28.2%,the maximum acceleration decreased by 8.3%,the maximum stress on the hinge point of the flying boom reduced by26.2%,the maximum stress on the hinge point of the leveling cylinder reduced by 11.5%,the platform of the height increased 2.1% during the simulation time.It was convinced that this method of optimization was effective and feasible.
Keywords/Search Tags:Aerial work platform, hinge point, Response surface method, Multi-objective optimization
PDF Full Text Request
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