履带起重机多重臂架组合系统优化设计研究

发布时间：2018-03-20 22:35

本文选题：履带起重机　切入点：多重臂架系统　出处：《大连理工大学》2012年硕士论文　论文类型：学位论文

【摘要】：履带起重机以其优越的可带载移位特性被广泛运用于国民建设中。随着大型场馆设施等建设需求,市场上对起重机的起升高度和幅度有了进一步的要求,出现了多重臂架履带起重机。多重臂架履带起重机的臂架系统由三个或者三个以上子起重臂架(主臂、塔式副臂、固定副臂)和保证其相对几何关系的支撑构件(撑杆、拉板、变幅绳、桅杆)等组成。这些支撑构件的几何参数直接影响整机起重性能,是总体设计的重要内容。本文以具有主臂、塔式副臂和固定副臂的超起型多重臂架履带起重机(SDWF组合工况)的臂架系统为研究对象,在对整机工作原理、臂架系统力学模型、多目标优化求解方法、智能算法及MATLAB编程技术深入研究的基础上,提出了一种优化目标的建立方法,并设计智能算法对优化问题进行求解。论文的主要研究工作和成果如下： (1)分析SDWF工况的组成和工作原理,分析整机在变幅平面内的外部载荷条件,抽象出臂架系统的力学模型,考虑构件自重采用平衡原理对构件进行受力分析,分别在固定副臂系统中改变撑杆和前拉板长度、在塔式副臂系统中改变前后撑杆和前后拉板长度、在主臂系统中改变桅杆、拉板、超起桅杆和超起后拉板的长度进行构件受力影响因素分析,研究变化趋势得到一般性设计规律。 (2)参考履带起重机的一般设计过程,提出先优化主臂系统后优化副臂系统的分层优化思想。结合构件在不同影响因素下的变化趋势,提出将基于成本的经济性指标作为撑杆类复杂焊接结构件的优化目标函数。分析系统的几何模型特点,以构件不失效和不干涉为原则设计约束条件,分别建立主臂系统和副臂系统的多目标优化模型。 (3)研究多目标问题评价函数的构造方法,通过设计者输入构件极限载荷情况,定义优化目标的偏好函数区间,采用物理规划方法建立基于偏好的多目标优化问题的综合评价函数。结合遗传算法和粒子群算法的特点,设计基于Pareto支配的多目标混沌遗传粒子群算法(MCGAPSO)进行求解。最后,基于MATLAB平台编写履带起重机多重臂架组合系统优化程序。 (4)以QUY750履带起重机为优化模型,输入臂架参数、优化参数、精度参数、工况参数和算法参数,分别以构件受力和混合经济性指标为优化目标,分析优化算法在多重臂架履带起重机优化中的优劣性,并验证本文所提出建模方法的合理性和有效性。
[Abstract]:Crawler cranes have been widely used in the construction of Yu Guomin for their superior characteristics of load-shifting. With the construction demand for large-scale stadiums and facilities, there are further requirements for the lifting height and range of cranes in the market. Multiple boom crawler cranes appear. The boom system of multiple boom crawler cranes consists of three or more sub-lifting booms (main arm, tower arm, fixed accessory arm) and supporting elements (strut, plate) that guarantee their relative geometric relationship. The geometric parameters of these supporting members directly affect the lifting performance of the whole machine and are important contents of the overall design. In this paper, the working principle of the whole machine, the mechanical model of the boom system, and the optimization method of multi-objective optimization are studied, taking the arm system with the main arm, the tower arm and the fixed arm as the main arm and the multi-arm crawler crane under the SDWF combination working condition, and taking the working principle of the whole machine, the mechanical model of the boom system and the multi-objective optimization method as the research object. Based on the deep research of intelligent algorithm and MATLAB programming technology, a method of establishing optimization objectives is proposed, and an intelligent algorithm is designed to solve the optimization problem. The main research work and results are as follows:. 1) analyzing the composition and working principle of SDWF working condition, analyzing the external load condition of the whole machine in the variable amplitude plane, abstracting the mechanical model of the boom system, taking into account the self-weight of the member, using the balance principle to carry on the force analysis to the member. Changing the length of the brace and the front pull plate in the fixed accessory arm system, changing the front and back brace and the front and rear pulling plate length in the tower type accessory arm system, changing the mast and the pulling plate in the main arm system, The influence factors of the component force on the length of the overloaded mast and the overhoisting plate are analyzed, and the general design rules are obtained by studying the variation trend. 2) referring to the general design process of crawler crane, the idea of optimizing the main arm system first and then the accessory arm system is put forward. The cost based economic index is proposed as the optimization objective function of the support rod type of complex welded structural parts. The geometric model characteristics of the system are analyzed and the constraint conditions are designed based on the principle of non-failure and non-interference of the components. The multi-objective optimization models of the main arm system and the accessory arm system are established respectively. 3) the method of constructing evaluation function for multi-objective problem is studied. The interval of preference function of optimization objective is defined by the designer input the limit load of component. Based on the characteristics of genetic algorithm and particle swarm optimization, a multi-objective chaotic genetic particle swarm optimization algorithm based on Pareto is designed to solve the problem. The optimization program of multi-boom system of crawler crane is compiled based on MATLAB platform. Taking the QUY750 crawler crane as the optimization model, the parameters of boom, optimization, precision, operating condition and algorithm are input, and the optimization targets are the component bearing force and the mixed economic index, respectively. The advantages and disadvantages of the optimization algorithm in multi-boom crawler crane are analyzed, and the rationality and validity of the modeling method proposed in this paper are verified.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH213.7

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