基于稳健性多目标优化的微客尾门轻量化设计

针对微客尾门结构的轻量化设计问题,提出了一种基于稳健性多目标优化的尾门轻量化设计方法。首先通过灵敏度分析方法筛选出了对尾门性能贡献量较大的尾门零件厚度作为设计变量,并建立了尾门各个工况响应的近似模型,其次以尾门扭转刚度、下垂刚度、尾门前三阶模态频率和抗凹分析点为约束条件,以尾门质量最小和第一阶模态频率最大和弯曲刚度位移量最小为目标,利用NSGA-Ⅱ算法对尾门进行了多目标优化,通过蒙特卡洛模拟技术对尾门进行了6σ质量水平和可靠度分析,并进行了6σ稳健性多目标优化。最终结果表明:优化设计后尾门结构在满足各性能要求的情况下实现了轻量化,质量减轻了2.28 kg,且弯曲刚度和第一阶模态得到提高;同时尾门结构各工况性能的稳健性也得到改善,达到6σ质量水平。     

机械结构分层优化技术研究进展

复杂机械结构优化设计一般表现为多目标、多约束、多参数的优化问题,所以复杂结构优化设计过程通常存在计算复杂、不易收敛等困难。分层优化技术是复杂结构优化设计的一种有效途径,通过将优化问题中复杂的约束、设计变量以及功能目标合理分解为若干子层问题进行求解,然后通过协调得到复杂机械结构整体优化问题的结果。文章对机械结构分层优化技术的理论、应用研究现状进行了总结并探讨了其关键技术环节及发展趋势。     

Timoshenko梁的变形场重构及传感器位置优化

针对KO位移理论仅适用于重构单方向位移场问题,提出一种适用于六自由度位移场重构的新方法,称之为“多维积分法”。依据Timoshenko梁的静力学平衡方程,建立了位移、转角与外载荷之间的数学模型。并针对不同的外载荷环境,推导出相应的应变场函数和位移场函数,建立了表面应变与截面应变之间的转换关系。为了提升该方法的容差性,以重构位移场的精确性和稳定性为优化目标,建立了关于应变传感器位置的多目标粒子群优化模型。以机翼框架为试验平台,对其进行有限元分析,建立优化目标模型,给出优化后的应变传感器的布置方案。并以此方案为依据,分别利用有限元分析结果和实测梁表面应变值来重构位移场。试验结果表明,提出的“多维积分法”在两种不同形式的外载荷作用下均呈现出较高的重构精度。     

Power flow response based dynamic topology optimization of bi-material plate Structures

Work on dynamic topology optimization of engineering structures for vibration suppression has mainly addressed the maximization of eigenfrequencies and gaps between consecutive eigenfrequencies of free vibration, minimization of the dynamic compliance subject to forced vibration, and minimization of the structural frequency response. A dynamic topology optimization method of bi-material plate structures is presented based on power flow analysis. Topology optimization problems formulated directly with the design objective of minimizing the power flow response are dealt with. In comparison to the displacement or velocity response, the power flow response takes not only the amplitude of force and velocity into account, but also the phase relationship of the two vector quantities. The complex expression of power flow response is derived based on time-harmonic external mechanical loading and Rayleigh damping. The mathematical formulation of topology optimization is established based on power flow response and bi-material solid isotropic material with penalization(SIMP) model. Computational optimization procedure is developed by using adjoint design sensitivity analysis and the method of moving asymptotes(MMA). Several numerical examples are presented for bi-material plate structures with different loading frequencies, which verify the feasibility and effectiveness of this method. Additionally, optimum results between topological design of minimum power flow response and minimum dynamic compliance are compared, showing that the present method has strong adaptability for structural dynamic topology optimization problems. The proposed research provides a more accurate and effective approach for dynamic topology optimization of vibrating structures.     

A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem

In this article, we consider the facility layout problem which combines the objective of minimization of the total material handling cost and the maximization of total closeness rating scores. Multi-objective optimization is the way to consider the two objectives at the same time. A simulated annealing (SA) algorithm is proposed to find the non-dominated solution (Pareto optimal) set approximately for the multi-objective facility layout problem we tackle. The Pareto optimal sets generated by the proposed algorithm was compared with the solutions of the previous algorithms for multi-objective facility layout problem. The results showed that the approximate Pareto optimal sets we have found include almost all the previously obtained results and many more approximate Pareto optimal solutions.     

基于粒子群算法的后桥可靠性稳健优化设计

为提高车辆零部件的安全性和稳健性,应用可靠性稳健优化设计理论和多目标决策方法,将车辆后桥的可靠性稳健优化设计转化为多目标优化问题.通过模糊多目标粒子群算法求出所有满足约束性条件的pareto解集,结合实际情况,依据pareto解集确定零部件的设计规格.实验表明,所提方法能迅速有效地获得可靠性稳健设计的信息.     

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.     

Lightweight design of an electric bus body structure with analytical target cascading

Lightweight designs of new-energy vehicles can reduce energy consumption, thereby improving driving mileage. In this study, a lightweight design of a newly developed multi-material electric bus body structure is examined in combination with analytical target cascading (ATC). By proposing an ATC-based two-level optimization strategy, the original lightweight design problem is decomposed into the system level and three subsystem levels. The system-level optimization model is related to mass minimization with all the structural modal frequency constraints, while each subsystem-level optimization model is related to the sub-structural performance objective with sub-structure mass constraints. To enhance the interaction between two-level systems, each subsystem-level objective is reformulated as a penalty-based function coordinated with the system-level objective. To guarantee the accuracy of the model-based analysis, a finite element model is validated through experimental modal test. A sequential quadratic programming algorithm is used to address the defined optimization problem for effective convergence. Compared with the initial design, the total mass is reduced by 49 kg, and the torsional stiffness is increased by 17.5%. In addition, the obtained design is also validated through strength analysis.     

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.     

基于多目标优化策略的螺旋弹簧可靠性稳健优化设计

应用可靠性稳健优化设计理论和多目标决策方法,将车辆螺旋弹簧的可靠性稳健优化设计转化为多目标问题.运用层次分析法选取粒子群算法中的全局极值和个体极值,提出基于层次分析法的多目标粒子群算法,并将该算法应用于可靠性稳健优化设计的多目标模型求解中.与传统方法相比,该方法简便、易行,并能迅速准确地得到车辆螺旋弹簧的可靠性稳健优化设计信息.     

基于支持向量机的多目标模糊稳健优化设计

为了提高模糊稳健优化设计的计算效率,探讨了基于支持向量机回归机(SVR)的多目标模糊稳健设计方法,该方法以SVR作为非线性约束函数的替代模型,并采用SVR对模糊概率进行仿真计算,可显著降低模糊稳健优化设计的机时消耗;采用字典序优先级的目标规划法,建立了多目标稳健优化设计模型;把SVR与遗传算法相结合,构建了一种混合智能优化算法;通过多目标稳健设计实例,对所提出的方法进行了验证。     

需求不确定的电子供应链多目标鲁棒动态运作模型

建立了由多个供应商和多个不确定需求的顾客构成的多阶段供应链动态运作模型。供应链中的供应商可以通过电子市场也可以直接将多种产品供应给不同的顾客。采用已知概率的情景集合描述顾客不确定需求，利用基于情景分析的鲁棒优化方法，建立了供应链的运作模型。该模型为一个多目标动态规划问题，满足诸如尽可能达到顾客需求、系统的总成本最小、供应商的开工率不低于某一指定水平、对应于不确定需求的决策的鲁棒性等多个相互冲突目标。数值仿真结果表明，模型的解是最保守的，但却能够有效地保证供应链运作的鲁棒性。     

Reliability-based robust optimization design of automobile components with non-normal distribution parameters

In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (>0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (>0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.     

Optimization of Uncertain Structures with Interval Parameters Considering Objective and Feasibility Robustness

For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.     

公差稳健优化设计的研究

为了解决产品加工成本与质量稳健的协调性问题,提出了一种新的公差稳健优化设计数学模型.依据公差稳健设计的思想,考虑产品质量的模糊性,以封闭环误差分布概率密度函数的方差和优质品概率之比为设计目标,建立了公差优化设计产品质量稳健性损失成本目标函数,并研究了优质品率和封闭环误差分布方差的确定方法.以加工成本和产品质量稳健性损失成本为目标,以模糊装配可靠度、可取公差极限范围为约束条件,建立了公差多目标优化数学模型.举例说明了文中所述的公差稳健优化设计方法的应用,采用遗传算法实现了公差的多目标优化设计.实例表明,该方法能够协调零件的加工成本和产品质量的稳健性损失成本,使优化指标的综合性能最佳.     

基于耦合分析与加权满意度的机械系统多目标协同优化方法

针对机械系统优化中因设计变量耦合关联导致的优化结果可靠性问题,提出了基于加权满意度的耦合关联机械系统多目标协同优化方法。建立机械系统设计变量耦合关联模型,采用最大-最小满意度函数法,并考虑各子目标的相对权重,建立基于权重加权和的多目标协同优化评价函数,将多目标优化问题转变为单目标优化问题。以掩护式液压支架四连杆机构-结构多目标协同优化为对象的应用表明,提出的方法简化了耦合关联机械系统多目标优化求解的复杂度,而且提高了优化结果的可靠性。     

基于模糊贴近度法的蜗杆传动多目标优化设计

常规多目标优化设计处理方法一般只能求出问题的若干有效解,为了从若干有效解中选择出最有效解,将模糊贴近度法引入多目标优化设计.以蜗轮齿冠体积最小、传动效率最高和中心距最小为目标,在保证蜗轮和蜗杆满足承载、强度及其他要求的前提下建立蜗杆传动多目标优化设计的数学模型,用改进遗传算法求得该问题的理想解和若干个有效解,采用正态模...     

基于改进PSO的可靠性稳健优化计算方法

基于现代设计方法和可靠性设计理论,建立了多目标约束优化数学模型,根据动态加速常数和速度自适应的改进粒子群算法(PSO),用编写的程序代码实现了数学模型的数值化求解。同时研究了加速常数和粒子速度的变化对优化计算结果的影响规律。算例表明,提出的可靠性稳健优化计算方法与传统方法相比,具有简便易行、能迅速得到结构可靠性稳健优化设计信息的优点,适合工程应用。     

IMC-PID design based on model matching approach and closed-loop shaping

Motivated by the limitations of the conventional internal model control (IMC), this communication addresses the design of IMC-based PID in terms of the robust performance of the control system. The IMC controller form is obtained by solving an H-infinity problem based on the model matching approach, and the parameters are determined by closed-loop shaping. The shaping of the closed-loop transfer function is considered both for the set-point tracking and for the load disturbance rejection. The design procedure is formulated as a multi-objective optimization problem which is solved by a specific optimization algorithm. A nice feature of this design method is that it permits a clear tradeoff between robustness and performance. Simulation examples show that the proposed method is effective and has a wide applicability.     

Robust proportional-integral Kalman filter design using a convex optimization method

This paper proposes a design approach to the robust proportional-integral Kalman filter for stochastic linear systems under convex bounded parametric uncertainty, in which the filter has a proportional loop and an integral loop of the estimation error, providing a guaranteed minimum bound on the estimation error variance for all admissible uncertainties. The integral action is believed to increase steady-state estimation accuracy, improving robustness against uncertainties such as disturbances and modeling errors. In this study, the minimization problem of the upper bound of estimation error variance is converted into a convex optimization problem subject to linear matrix inequalities, and the proportional and the integral Kalman gains are optimally chosen by solving the problem. The estimation performance of the proposed filter is demonstrated through numerical examples and shows robustness against uncertainties, addressing the guaranteed performance in the mean square error sense.