刚性机构与柔性机构性质的探讨

简单介绍了柔性机构及其性质,分析和研究了由拓扑优化得到的力和位移反向器的典型模型,发现在模型构件为刚体条件下,模型的几何增益和机械增益能达到极大即力和位移的输出效率最大。以桁架机构的位移反向器作为基本模型,分析得到有关模型的几何增益的优化目标函数,对目标函数进行了数值优化计算,得到模型的尺寸参数,用结构力学的方法对得到的机构模型进行计算分析。     

基于NSGA-Ⅱ算法的管道清灰机器人变径机构优化

管道清灰机器人变径机构尺度影响机构的运动性能及驱动性能,变径机构尺度优化可有效解决尺寸综合问题。提出了变径机构多目标尺度综合,以变径机构关键零件受力和驱动件运动范围为优化目标建立优化模型,基于快速含有精英策略的非支配排序遗传算法(Non-dominated sorting genetic algorithm Ⅱ,NSGA-Ⅱ)求解多目标优化Pareto最优解。计算结果表明:多目标优化后的变径机构在力学性能和运动范围上优于经验设计,不需重复计算可根据设计要求和工程经验权衡选取满足不同要求的优化结果。     

可调节型函数生成机构的优化综合

提出了一种可调节型连续轨迹生成机构的优化综合方法，通过改变机架杆杆长来实现可调机构运动参数的调节。在可调机构生成不同的函数时都存在驱动曲柄与不含乱支缺陷的前提下使得函数生成机构的最大结构误差达到最小，在建立了机构优化综合的模型后采用改进遗传算法获取全局最优解，优化综合实例结果表明了所提出方案的有效性。     

球面四杆机构函数综合精确点选择新方法

讨论了球面四杆机构函数综合中精确点的选择问题,以减小最大综合误差为目标,提出了一种新的精确点优化选择方法。利用球面四杆机构函数综合的封闭形式解,通过引入比例系数,推导了结构偏差的方程式,并构造了精确点选择的优化数学模型。利用数值插值的方法对结构偏差方程式进行了分析,给出了优化模型的初始解,并采用分形算法求得了全局最优解。通过对数值实例的计算和对结果的分析,验证了新方法的有效性。     

基于雅可比矩阵条件数的并联机构参数优化

提出一种基于并联机构雅可比矩阵条件数全域参数优化方法。以并联机构6-pss为研究对象,根据机构的结构形式推导出机构的速度数学模型、建立雅可比矩阵,并用范数的形式建立雅可比矩阵条件数的数学模型;对并联机构进行参数归一化处理后,以条件数的平均值与波动值的综合值作为目标建立目标函数、给出约束条件,最后通过数值计算得到机构雅可比矩阵条件数的平均值、波动值曲线和优化目标函数值的曲线图,优化出最佳参数值,为该机构尺寸优化提供重要依据。     

基于混合元胞自动机的柔顺机构多目标拓扑优化方法

采用无梯度优化方法——混合元胞自动机方法进行体积约束下柔顺机构多目标拓扑优化设计。以应变能最小化和互应变能最大化为目标,以结构体积为约束,采用标准化方法定义多目标拓扑优化的目标函数,消除不同性质目标函数在数量级上的差异。将混合元胞自动机方法用于多目标优化问题的求解,以比例控制作为局部控制规律。数值算例结果表明,该方法用于柔顺机构多目标拓扑优化设计是有效的,优化迭代次数较少,且结构不易出现单节点铰链现象。     

可调节型连续轨迹生成机构的优化综合

提出一种可调节型连续轨迹生成机构的优化综合方法。在确定了可调节型双曲柄机构轨迹柔性的基础上，基于从动杆杆长结构误差建立机构的优化综合模型，采用改进遗传算法获取综合结果。从动杆杆长结构误差不仅计算便利，且能有效反映出实际生成的轨迹曲线与预期的理想轨迹曲线间的差异，避免了在两轨迹曲线上选取对应比较点的困难。改进遗传算法保证了获得全局最优解。综合实例结果表明了本方法的有效性。     

重卡双前桥转向摇臂机构的优化设计 

针对重卡双前桥转向摇臂机构传统单目标优化方法考虑因素的不足,提出了以二轴左轮转角误差最小、转向杆系与悬架运动的最大干涉量最小、左右转向力不均匀性最小为综合优化目标的多目标优化模型。车轮转角误差模型通过将摇臂机构拆分的方法得到,悬架与转向杆系的干涉模型通过建立转向直拉杆与悬架的空间运动模型得到,左右转向力不均匀性通过计算转向传动机构传动比得到。优化结果证明,多目标优化方法优于传统优化方法,有利于减小转向轮的磨损、改善车辆的操纵稳定性及转向轻便性。     

一种2R1T远中心运动并联机构性能分析及尺度优化

与串联机构相比，并联机构具有刚度好、精度高、结构稳定紧凑等优点，适用于外科手术领域。提出一种可用于微创手术本体结构的两转动一移动远中心运动（Remote Center of Motion,RCM）(5R)OC-URR并联机构，具有部分运动解耦特性。建立机构位置分析模型，推导出机构位置逆解表达式；在此基础上，针对微创手术工作任务，定义机构期望工作空间。应用局部运动/力传递性能评价指标，给出机构期望工作空间内平均传递指标定义及计算方法。以平均传递指标最大化为目标，建立机构尺度参数约束优化模型，并应用加速灰狼优化（Accelerating Grey Wolf Optimization,AGWO）算法求解该问题。数值实例测试显示，AGWO算法求解约束优化问题的综合性能优于对比算法。机构优化设计结果表明，给出的优化模型和算法可行有效。研究结果为该机构的实际应用奠定了理论基础。     

集中柔度全柔性机构拓扑优化设计研究

根据集中柔度全柔性机构特点,基于连续体拓扑优化技术,采用SIMP人工材料密度方法.以机构的刚度和柔度要求相结合的功能函数作为优化目标,建立集中柔度全柔性机构拓扑优化设计模型.采用优化准则法进行数值计算,并推导了迭代公式.通过所建立的优化模型设计出的机构拓扑图形具有"集中柔度"特征,为集中柔度全柔性机构的设计提供了基础.数值算例证明了研究方法的有效性.     

博弈决策分析在补偿滑轮组变幅机构多目标设计中的应用

综合考虑补偿滑轮组变幅机构稳定性和经济性等多目标设计要求,提出一种基于博弈分析思想的求解方法,将多目标设计问题转化为博弈决策问题,采用影响因了指标及模糊聚类方法将整体设计变量集合分解为各博弈方拥有的战略集,建立多目标问题博弈分析的技术路线和计算步骤。具体算例的双目标博弈和三目标博弈分析结果显示,相对于多目标优化方法,博弈分析中解的收敛速度快,具有更好的健壮性。     

基于灰色系统理论的方盒件拉深稳健设计

利用灰色系统理论,以目标序列之间的关联度作为目标函数,将多目标优化转化为单目标优化问题.在板料成形的稳健设计中,通常需要满足多个目标,如不拉裂、不起皱、变形充分等质量要求,而且要满足响应波动减少的要求.对成形中可控因子进行试验设计,利用有限元分析软件获得拉深成形中各个目标对应的响应值.对计算获得的目标序列灰色关联度进行方差分析,得到各个因子各个水平对响应的影响程度,从而获得因子的最佳参数.最后利用最佳参数进行有限元分析,结果表明质量明显提高.研究表明,将灰色理论应用到板料拉深成形稳健设计中,取得较好的结果,说明该方法在多目标稳健设计中有很大的适用性.     

基于多目标遗传算法的水陆两栖可变形机器人结构参数设计方法

水陆两栖可变形机器人是一种兼具变形能力与两栖环境适应能力的新型移动机器人。在其机构设计中,结构参数直接影响该机器人在任务环境中的各项机动性能。针对水陆两栖可变形机器人工作环境复杂性和任务多变性,提出一种基于多目标遗传算法的机器人结构参数设计方法,以得到该型机器人在两栖环境中的最优的综合性能。在水陆两栖可变形机器人陆地环境和水环境中运动学和动力学模型基础上,建立两栖环境中机器人的机动性能指标函数与结构参数的映射关系,并在此基础之上构建面向水陆两栖可变形机器人的结构参数设计的多目标优化问题。利用多目标遗传算法得到该多目标机构参数设计问题的Pareto最优解集,并且通过组合赋权方法确定各目标决策属性的权重,从Pareto最优解集中得到符合设计要求的水陆两栖可变形机器人的各项机构参数最优解,进而指导机器人最终结构参数设计。根据最终得到的结构参数研制出水陆两栖可变形机器人样机Amoeba-II,并在两栖环境下进行样机的各项性能试验,最终验证了基于多目标遗传算法的机器人结构参数设计方法的有效性以及在机器人设计中的适用性。     

Comprehensive optimum and adaptable design methodology for the working mechanism of a wheel loader

The working mechanism is an important part of a wheel loader. It is used to accomplish the shoveling, loading, lifting, and dumping tasks. Usually, the working mechanism is a Z-bar linkage and the dimensional design of the linkage directly affects the loader’s service performance, such as dumping clearance and dumping height. As there are many other performance indexes and various field requirements for the loader, comprehensive optimum, and adaptable design methodology with full-parameterized kinematic model attracts engineers’ research interests. Focusing on the multi-objective and multi-variable problems, this paper firstly establishes an accurate and simplified kinematic model of the Z-bar linkage with deduced expression of the basic parameters, and then investigates the performance indexes including mechanism transmission ratio, lifting stationary, dumping angle in any position, bucket flat setting, and extreme transmission angle, etc. With the full-parameterized kinematic model and the optimum algorithm, modified complex method, a comprehensive and adaptable design platform is built to solve the multi-objective and multi-variable problems of the Z-bar linkage. A case study with two optimization schemes as “full variables design” and “partial variable design” is conducted. The simulation results show that both schemes are better than the original one. The methodology and platform in this paper are of importance in generating user requirement oriented linkage design schemes with enough consideration and optimization of multiple performance indexes.     

大型压铸机合模机构的多目标模糊优化设计

基于多目标模糊优化设计的原理,结合大型压铸机合模机构的性能和尺寸要求,建立了符合实际工况的数学模型,并用最优水平截集法求解,用复合形法上机寻优.研究结果表明,多目标模糊优化在合模机构设计方面具有较高的实用价值.     

Multi-objective optimum design of fast tool servo based on improved differential evolution algorithm

The flexure-based mechanism is a promising realization of fast tool servo (FTS), and the optimum determination of flexure hinge parameters is one of the most important elements in the FTS design. This paper presents a multi-objective optimization approach to optimizing the dimension and position parameters of the flexure-based mechanism, which is based on the improved differential evolution algorithm embedding chaos and nonlinear simulated anneal algorithm. The results of optimum design show that the proposed algorithm has excellent performance and a well-balanced compromise is made between two conflicting objectives, the stroke and natural frequency of the FTS mechanism. The validation tests based on finite element analysis (FEA) show good agreement with the results obtained by using the proposed theoretical algorithm of this paper. Finally, a series of experimental tests are conducted to validate the design process and assess the performance of the FTS mechanism. The designed FTS reaches up to a stroke of 10.25 μm with at least 2 kHz bandwidth. Both of the FEA and experimental results demonstrate that the parameters of the flexure-based mechanism determined by the proposed approaches can achieve the specified performance and the proposed approach is suitable for the optimum design of FTS mechanism and of excellent performances.     

轮式车辆转向梯形优化方案的研究

对轮式车辆转向梯形优化方案进行了研究,用三种方案对转向梯形进行了优化,编制了参数化的优化程序,给出了各种方案的逼近误差曲线,将优化结果进行了对比。研究成果表明:采用相对误差作为目标函数是一种比较理想的优化方案,符合转向机构的设计要求,具有很高的逼近精度,对于进行车辆转向机构的设计具有指导意义和重要的应用价值。     

Parameters Matching and Control Method of Hydraulic Hybrid Vehicles with Secondary Regulation Technology

Hydraulic hybrid vehicles (HHV) with secondary regulation technology has the potential of improving fuel economy by operating the engine in the optimum efficiency range and making use of regenerative braking. Hydrostatic transmission technology has the advantage of higher power density and the ability to accept the high rates and high frequencies of charging and discharging, both of which are not favorable for batteries, but the lower energy density requires special power matching design and control strategy to coordinate all the powertrain components in an optimal manner. A multi-objective optimization method is proposed to distinguish the components size values of HHV by considering the requirements of driving cycles and technology aspects. The regenerative braking strategy and energy control strategy based on the optimized HHV is proposed to recovery the braking energy and distribute the regenerated braking energy. Simulation results show that by taking the optimized configuration of HHV, adopting the regenerative braking strategy and energy control strategy are helpful to improve the system efficiency and fuel economy of HHV under urban driving cycles.     

Multi-objective optimization and evaluation method of modular product configuration design scheme

Product configuration is one of the key technologies for mass customization. Traditional product configuration optimization targets are mostly single. In this paper, an approach based on multi-objective genetic optimization algorithm and fuzzy-based select mechanism is proposed to solve the multi-objective configuration optimization problem. Firstly, the multi-objective optimization mathematical model of product configuration is constructed, the objective functions are performance, cost, and time. Then, a method based on improved non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the configuration design optimization problem. As a result, the Pareto-optimal set is acquired by NSGA-II. Due to the imprecise nature of human decision, a fuzzy-based configuration scheme evaluation and select mechanism is proposed consequently, which helps extract the best compromise solution from the Pareto-optimal set. The proposed multi-objective genetic algorithm is compared with two other established multi-objective optimization algorithms, and the results reveal that the proposed genetic algorithm outperforms the others in terms of product configuration optimization problem. At last, an example of air compressor multi-objective configuration optimization is used to demonstrate the feasibility and validity of the proposed method.     

Multiobjective optimization of a steering linkage

In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria.