基于神经网络和遗传算法的液压机上梁轻量化和刚度优化设计

提高刚度和轻量化是液压机设计中重点研究的内容。针对传统设计方法难以解决上梁刚度和轻量化之间的矛盾问题,提出了基于神经网络和遗传算法的液压机上梁轻量化和刚度优化设计方法。在液压机设计过程中,建立了上梁有限元分析的参数化模型。采用正交试验设计安排试验方案,获取试验数据。以试验数据为训练和检测样本,建立了设计参数与刚度和质量目标之间的非线性映射关系的神经网络模型。运用NSGA-Ⅱ遗传进化算法对神经网络模型进行优化,在指定参数区域内找出设计参数的Pareto最优解集。结果表明:该方法对于液压机上梁的多目标优化具有明显的效果。     

隧道衬砌栈桥模型重构及车-栈桥系统共振分析

针对隧道衬砌栈桥结构,以栈桥主体构架的轻量化为研究目标,通过中心组合试验设计方法获取设计点,建立多目标数值模型,采用多目标遗传算法计算数值模型的最优解。以最优解的设计参数为重构模型的尺寸参数,对重构模型进行仿真检验和车-栈桥系统共振分析,分析结果表明:栈桥结构受总承载65t时,重构模型相比原模型,最大变形增大了8mm,最大应力增大了32.4MPa,质量下降了13.86%,充分利用了强度、刚度盈余以达到轻量化的目的,且车辆运行速度避开了影响共振的危险速度。     

基于多目标遗传算法的印制电子喷印机结构轻量化设计

针对印制电子喷印机结构过重问题,基于ANSYS Workbench平台,以减轻结构重量和提高结构刚度为目标,采用多目标遗传算法对喷印机的机械结构参数进行了优化设计。优化结果表明,多目标遗传算法能在可行域内快速准确地获得喷印机结构轻量化设计问题的Pareto最优解集,并在满足结构刚度的前提下,实现了喷印机的轻量化设计,且稳定性得到了一定改善,为结构的优化设计提供了依据。     

基于NSGA-Ⅱ混合灵敏度分析的白车身轻量化优化设计

针对白车身的轻量化问题,提出了一种基于混合灵敏度分析的参数化优化方法。通过有限元仿真,标定白车身的弯扭刚度及模态性能。以试验设计分析零件厚度相对白车身弯扭刚度及模态的灵敏度,确定并筛选出对白车身刚度及模态性能影响不大的零件。将零件厚度作为多目标优化的设计变量,以白车身质量最小化、弯扭刚度最大化为优化目标,模态性能为约束条件构建多目标优化设计函数。基于NSGA-II遗传算法,进行白车身结构的轻量化优化设计。经Isight优化求解仿真,优化所选零件的厚度,轻量化设计了白车身结构。轻量化设计后的白车身性能仿真结果表明,其刚度及模态性能得到保证的前提下,白车身质量减轻了5.7%。     

机械压力机滑块结构性能分析

分析机械机械压力机滑块的结构组成与工作特点,针对设计初选参数,通过理论公式计算与有限元方法,对滑块的强度与刚度进行校核,讨论钢板尺寸对压力机滑块的刚度与强度的影响。     

基于刚度和模态的某自卸车车架轻量化设计

利用有限元模态分析理论,分析了某自卸车车架的动态特性,结合车架模态试验验证了模型的合理性。在已验证的模型基础下求解了车架的弯曲和扭转刚度。进行灵敏度分析以确定有效的设计变量。以车架总质量为目标函数,以刚度和1阶固有频率为约束条件,建立轻量化优化模型。通过拉丁方试验设计方法选取样本点,运用径向基函数构造模型各响应的近似模型,采用遗传算法求解近似模型。获得的优化结果参数代入实际模型后结果表明车架总质量在满足刚度和1阶模态频率约束要求下下降了18.37 kg。     

遗传算法在门式起重机轻量化设计中的应用

门式起重机金属结构轻量化设计涉及大量的尺寸参数,各参数之间相互关联,影响结构的承载能力。文中利用遗传算法与有限元结合,提出了一种程序化、智能化的多目标并行优化设计方法,可应用于任何参数化结构模型的轻量化设计。     

基于支腿形状优化的带悬臂门机轻量化研究

悬臂端的静刚度通常对带悬臂的门式起重机轻量化设计起控制作用。这种情形下,使用常规的仅以各结构件截面尺寸为设计变量的优化方法,轻量化的效果通常不理想。以32t-26m带悬臂的L型门式起重机为例,针对悬臂端的静刚度不足问题,在主梁截面尺寸为优化参数的基础上,增加主梁和支腿连接宽度作为优化设计变量,并提出一种非对称的支腿结构形式。分析结果表明:采用改变支腿的连接宽度或支腿形状的方法,门机的质量分别减少10.47%和12.85%,可明显提高轻量化的效果。     

基于响应面法的挖掘机三油缸结构动臂轻量化设计

考虑到三油缸结构动臂的结构特殊性,以及传统经验方法在结构设计上的保守性因素,提出了基于响应面法的变厚度轻量化设计方法,并搭建了基于多学科优化软件Isight、有限元软件Nastran以及利用Python语言二次开发的集成优化环境。基于板壳理论与测试数据建立了三油缸结构动臂危险工况的有限元参数化模型,并以其主要板厚尺寸为初始设计变量,以结构强度、刚度及重量为模型响应,结合最优拉丁超立方设计法对初始变量进行了灵敏度分析,使用筛选后的设计变量拟合响应面模型(RSM);以结构强度和刚度为约束条件,动臂质量最小为目标函数,采用多岛遗传算法对响应面模型进行优化。结果表明,在保证结构性能的前提下,该方法轻量化设计后的动臂减重14.7%,优化效率较有限元模型提高84%左右,优化效果显著且大幅度缩短了设计周期。     

基于多目标优化算法的并联机构综合设计

针对并联机构尺寸综合设计参数多、关系复杂的问题,以少自由度并联机构为研究对象进行尺度综合。首先,基于雅可比矩阵的性能指标条件数、刚度、速度极值作为目标函数。其次,采用罚函数法处理约束条件,提出性能分类的方式来作为多目标优化的准则,最后利用遗传算法进行参数优化。分别构建了以条件数、刚度性能指标最优为目标的双目标优化模型和以条件数、刚度性能和速度极值指标最优为目标的三目标优化模型,对这2个方案基于提出的尺寸综合方法进行优化,得到符合设计要求和约束条件的尺寸参数。     

车身动态刚度链建模及正向概念设计研究

提出了车身正向概念设计流程,由车身A级面及整车总布置方案建立了车身简化几何模型,确定了22个车身主断面位置,并以主断面属性为设计变量,基于梁单元理论与传递矩阵法得到模态坐标系下的车身动态刚度链数学模型,建立了主断面属性与车身固有频率的关系。以车身轻量化为目标函数,以车身静刚度与固有频率为约束条件对车身进行优化,采用遗传算法求解,确定优化后的各主断面属性参数。通过对比近似标杆车的静刚度与模态分析结果验证了动态刚度链设计方法的可行性。     

大型结构地面模态试验测点选择与优化

为了解决一般传统的优化算法对于大型结构测点的优化配置的失效问题,针对传统优化算法的缺点,对大型结构的模型缩聚技术做了进一步的改进和完善。引入了一个反映高阶模态贡献比例的权重系数,由此提出了一种基于有效独立法的混合优化算法:基于有效独立法的加权平均模态应变能系数法。经火箭仿真实例对上述方法及Guyan缩聚基于模态应变能系数有效独立法、改进后Guyan缩聚基于模态应变能系数有效独立法得到的测点配置效果进行了比较。结果表明:提出的测点优化算法有效地防止了测点分布聚集现象,而且都最大可能保证了所有模态应变能的贡献和较优布置测点具有较大能量的要求,并用实际的GARTEUR飞机模型对该方法进行了模态试验验证。试验结果表明,该算法保证了监测模态的完整性和线性无关性。     

高速立式加工中心滑座的结构分析及优化

为满足高速加工中心整体性能的需要,利用Pro/E软件,建立了四种滑座结构的三维模型,分别进行了滑座的力学分析和静刚度计算,得出普筋模型为最优方案并对其进行了合理的结构优化。在满足滑座刚度的前提下,减少了重量,降低了成本。为滑座动态性能的分析研究奠定了基础。     

基于拓扑优化的天线座叉臂结构设计

针对某雷达天线座叉臂结构刚度不满足设计要求的问题,提出了将连续体结构拓扑优化方法应用到叉臂结构优化设计中,通过优化,确定满足刚度要求、重量最小化的叉臂结构中材料的最佳分布形式,并以此为依据对原设计方案进行了改进设计,仿真结果表明,改进后的叉臂结构刚度得到明显提高.     

Optimal design and development of a 2-DOF PKM-based machine tool

This paper presents multiobjective optimization of a typical 2-degree-of-freedom (DOF) parallel kinematic machine (PKM) tool that has only single DOF joints. Nondimensional indices, namely global stiffness index (GSI), global conditioning index (GCI), and workspace index, are considered as the objectives for optimization. The indices GSI and GCI depict the variation of stiffness and dexterity of PKM within the workspace. The leg length and distance between two rails on which actuators slide are treated as design variables as these greatly influence the characteristics of PKM. A multiobjective genetic algorithm (MOGA) approach is implemented in MATLAB to find an efficient solution to this complex optimization problem. Fitness function includes inverse kinematics equations, Jacobian and stiffness matrices to compute and optimize the nondimensional indices. First, the optimal value of each index is obtained by single-objective GA. To further improve the results, a hybrid function PATTERNSEARCH is used. This helps to select appropriate boundary conditions for MOGA. To obtain the optimal values of all the three indices, a multiobjective GA is carried out. The results are compared with a conventional exhaustive search method of optimization. The obtained results show that the use of MOGA enhances the quality of the optimization outcome. Secondly, a prototype has been designed and developed with the optimal dimensions. The actual workspace of the PKM and influence of leg collision on the workspace are studied. Finally, a preliminary experimentation was carried out. A comparison between PKM and the three-axis serial kinematic machine tool is presented.     

New knowledge-based genetic algorithm for excavator boom structural optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.     

利用聚类分析的宏细观多尺度并行拓扑优化设计方法研究

多孔材料因为具有质量轻、比刚度和比强度大、隔振和隔热效果好等优点,越来越多的应用在航空航天和制造装备等领域。为充分发挥多孔材料的性能,本文提出同时考虑结构的宏观性能和细观微结构性能的多尺度并行拓扑优化设计方法,获得性能优良的多孔结构。论文采用聚类方法有效降低了计算成本,针对并行优化难收敛的问题提出改进模型,使迭代平稳收敛。最后以经典的悬臂梁、MBB梁和Michell结构为例进行优化设计,通过对宏细观多尺度并行优化结果的分析,验证了所提方法的有效性与正确性。     

刚度和质量驱动的预紧组合框架式液压机多目标优化设计

为了满足液压机高刚度、轻量化的性能要求,提出刚度和质量驱动的液压机多目标优化设计方法。通过对98MN数控等温锻造液压机结构分析,抽取影响液压机刚度和质量的关键因素;运用正交试验设计方法进行试验,提取刚度和强度与关键因素之间的关系数据。采用一阶响应面建模方法建立上横梁、下横梁刚度和主要部件强度的数学模型,并用统计误差分析方法验证模型的有效性。根据部件结构,建立液压机整机质量的数学模型。进而建立以提高刚度和减轻质量为目标的液压机优化模型。运用非支配解排序遗传优化算法,对液压机上梁刚度与整机质量进行多目标优化,得到Pareto优化解集,可使98MN数控等温锻造液压机刚度提高5%、质量减轻10%。     

Light-Weight Design Method for Force-Performance-Structure of Complex Structural Part Based Co-operative Optimization

A light?weight design method of integrated structural topology and size co?optimization for the force?performance?structure of complex structural parts is presented in this paper. Firstly, the supporting function of a complex structural part is built to map the force transmission, where the force exerted areas and constraints are considered as connecting structure and the structural configuration, to determine the part performance as well as the force routines. Then the connecting structure design model, aiming to optimize the static and dynamic performances on connection configuration, is developed, and the optimum design of the characteristic parameters is carried out by means of the collaborative optimization method, namely, the integrated structural topology optimization and size optimization. In this design model, the objective is to maximize the connecting stiffness. Based on the relationship between the force and the structural configuration of a part, the optimal force transmission routine that can meet the performance requirements is obtained using the structural topology optimization technology. Accordingly, the light?weight design of conceptual configuration for complex parts under multi?objective and multi?condition can be realized. Finally, based on the proposed collaborative optimization design method, the optimal performance and optimal structure of the complex parts with light weight are realized, and the reasonable structural unit configuration and size charac?teristic parameters are obtained. A bed structure of gantry?type machining center is designed by using the proposed light?weight structure design method in this paper, as an illustrative example. The bed after the design optimization is lighter 8% than original one, and the rail deformation is reduced by 5%. Moreover, the lightweight design of the bed is achieved with enhanced performance to show the effectiveness of the proposed method.