利用产品模型与过程模型的集成进行产品开发

建立过程链和过程网络系统在工业界变得越来越重要。计算机的始终支持,尤其在产品形成的范围内,具有特别的意义。建立过程链需要按一定方法进行的计划,或者说是以产品模型和过程模型的集成为基础的方法。     

产品可装配性评价系统建模及装配优化的研究

面向装配的设计(DFA)的研究和实践，对实施并行工程具有重要的意义，目前得到了广泛的重视。笔者依据将可装配性评价系统做为一个模块集成到整个虚拟装配支持系统中去的思路。从系统工程的观点出发，以装配单元为研究对象，提出并构造了可装配性评价的指标体系，建立了可装配性评价系统模型，并探讨了最优装配方式的求解策略和方法。     

军事冲突中战术欺骗的一个优化模型

军事冲突中实施战术欺骗已成为信息战的一个重要手段．基于军事冲突中战术欺骗所要达到的目标以及欺骗手段的资源约束和效果影响，建立了一个使军事欺骗总体效能达到最大的战术欺骗优化模型．给出的一个应用例子说明了该模型和方法的可行性．     

改进的质量功能配置在质量成本优化分析上的应用

在改进型的质量功能配置工具中,增加了质量保证成本和质量损失成本两个主要的评价指标,修正了市场竞争评价指数和技术竞争评价指数,并引入了层次分析法,对定性决策因素给以定量的测度权重.根据质量保证成本与技术重要度的关系,以及质量损失成本对质量需求的影响,提出了适合设计阶段的质量成本优化分析方法.最后的应用结果表明,利用改进型的质量功能配置工具进行质量成本分析是有效的.     

旋转剪切式MR阻尼器的性能试验与改进滞回模型

基于Bingham力学模型,设计、制作小尺寸旋转剪切式MR阻尼器,通过数字式特斯拉计测量励磁线圈的磁感应强度.设计了阻尼器试验装置,并对此MR阻尼器进行2种激励位移、11种输入电流和4种激励频率共88种工况的力学性能试验,根据MR阻尼力—速度滞回曲线,克服Bingham模型在零速度附近不能说明阻尼力—速度的关系,引入惯性力项方法,提出了改进滞回曲线非线性力学模型.采用智能粒子群算法辨识修正的滞回力学模型参数,通过阻尼器试验数据与修正滞回力学参数值进行对比,证明此滞回模型能很好地描述MR阻尼器强非线性的动力特性.     

A probabilistic solid-porous model to determine the shear strength of unsaturated soils

A probabilistic solid-porous model has been developed to determine the shear strength of unsaturated soils. The probabilistic model was built by analyzing the probability of a certain pore or group of pores of a network to fill or remain filled with water during a wetting or drying process, respectively. This model is used to determine the equivalent stress which represents the stress supported by the solid skeleton of an unsaturated soil and is related to the strength of the material. The probabilistic model is an alternative to the use of computational models and shows some important advantages. The theoretical results of the model are compared with a series of triaxial tests performed at constant suction and constant volume. These comparisons demonstrate that this model is adequate to establish the strength of unsaturated materials.     

Continuum topology optimization considering uncertainties in load locations based on the cloud model

Few researchers have paid attention to designing structures in consideration of uncertainties in the loading locations, which may significantly influence the structural performance. In this work, cloud models are employed to depict the uncertainties in the loading locations. A robust algorithm is developed in the context of minimizing the expectation of the structural compliance, while conforming to a material volume constraint. To guarantee optimal solutions, sufficient cloud drops are used, which in turn leads to low efficiency. An innovative strategy is then implemented to enormously improve the computational efficiency. A modified soft-kill bi-directional evolutionary structural optimization method using derived sensitivity numbers is used to output the robust novel configurations. Several numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed algorithm.     

企业和生产过程专用的产品模型──产品摸型是联网的产品开发过程的基础

根据面向生产过程的造型范例，提出了对产品模型的要求，发展了体系结构，讨论了具体实施的经验。     

Novel collaborative optimization framework with a negotiation model for satellite system design

This article proposes a novel collaborative optimization (NCO) framework that uses a persuasive multi-agent negotiation method for satellite system design. Satellite system design is a typical multi-disciplinary design optimization problem. The traditional collaborative optimization (CO) is a competitive method but has the disadvantage of inefficient convergence caused by the interdisciplinary compatibility constraints in system-level optimization. By introducing the multi-agent negotiation method, an NCO framework is proposed, in which the system-level and subsystem-level variable negotiations replace the system-level compatibility constraints in traditional CO. The negotiation introduces a modification for candidate values that do not satisfy the compatibility constraints, ensures that an alternative compatible candidate will be obtained and has high convergence efficiency. Two numerical benchmark functions and a multi-disciplinary satellite system design are used to analyse the performance of the proposed NCO. The simulation results show that NCO can efficiently ensure parameter interdisciplinary matching and advanced convergence performance.     

An improved version of the multiple trajectory search for real value multi-objective optimization problems

Multi-objective optimization is widely used in science, engineering and business. In this article, an improved version of the multiple trajectory search (MTS) called MTS2 is presented and successfully applied to real-value multi-objective optimization problems. In the first step, MTS2 generates M initial solutions distributed over the solution space. These solutions are called seeds. Some seeds with good objective values are selected as foreground seeds. Then, MTS2 chooses a suitable region search method for each foreground seed according to the landscape of the neighbourhood of the seed. During the search, MTS2 focuses its search on some promising areas specified by the foreground seeds. The performance of MTS2 was examined by applying it to solve the benchmark problems provided by the Competition of Performance Assessment of Constrained/Bound Constrained Multi-Objective Optimization Algorithms held at the 2009 IEEE Congress on Evolutionary Computation.     

A kriging metamodel-assisted robust optimization method based on a reverse model

The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer–inner nested optimization structures where a large amount of computational effort is required because the robustness of each candidate solution delivered from the outer level should be evaluated in the inner level. In this article, a kriging metamodel-assisted robust optimization method based on a reverse model (K-RMRO) is first proposed, in which the nested optimization structure is reduced into a single-loop optimization structure to ease the computational burden. Ignoring the interpolation uncertainties from kriging, K-RMRO may yield non-robust optima. Hence, an improved kriging-assisted robust optimization method based on a reverse model (IK-RMRO) is presented to take the interpolation uncertainty of kriging metamodel into consideration. In IK-RMRO, an objective switching criterion is introduced to determine whether the inner level robust optimization or the kriging metamodel replacement should be used to evaluate the robustness of design alternatives. The proposed criterion is developed according to whether or not the robust status of the individual can be changed because of the interpolation uncertainties from the kriging metamodel. Numerical and engineering cases are used to demonstrate the applicability and efficiency of the proposed approach.     

An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.     

Simultaneous distribution and sizing optimization for stiffeners with an improved genetic algorithm with two-level approximation

This article presents an improved genetic algorithm with two-level approximation (GATA) to optimize the distribution and size of stiffeners simultaneously. A novel optimization model of stiffeners, including two kinds of design variables, is established. The first level approximation problem transforms the original implicit problem to an explicit problem which involves the topology and size variables. Then, a genetic algorithm (GA) addresses the mixed variables. The individuals in the GA are coded by topology variables, and when calculating an individual’s fitness, the second level approximation problem is embedded to optimize the size variables. Considering the stiffeners’ optimization, several aspects of the initial GATA are updated, including the relationship between two kinds of variables, the weight and its sensitivity calculation and the GA strategy, to optimize the stiffeners’ size and distribution simultaneously. Numerical examples show that the improved GATA is effective in optimizing the stiffened shells’ topology and size variables simultaneously.     

基于用户心理模型的产品设计研究

研究了在企业实践项目中,设计师如何通过意向尺度法等科学性研究方法,去了解目标用户的心理模型,并且以目标用户的心理模型为设计的出发点进行产品设计,在一定程度上解决了设计师的心理模型与用户的心理模型难以沟通的问题,使企业设计开发的产品能够满足用户的心理需求.     

Multi-haul quasi network flow model for vertical alignment optimization

The vertical alignment optimization problem for road design aims to generate a vertical alignment of a new road with a minimum cost, while satisfying safety and design constraints. A new model called multi-haul quasi network flow (MH-QNF) for vertical alignment optimization is presented with the goal of improving the accuracy and reliability of previous mixed integer linear programming models. The performance of the new model is compared with two state-of-the-art models in the field: the complete transportation graph (CTG) and the quasi network flow (QNF) models. The numerical results show that, within a 1% relative error, the proposed model is robust and solves more than 93% of test problems compared to 82% for the CTG and none for the QNF. Moreover, the MH-QNF model solves the problems approximately eight times faster than the CTG model.     

Uncertainty analysis and optimization of automotive driveline torsional vibration with a driveline and rear axle coupled model

The driveline torsional vibration issue is one of the most significant Noise, Vibration and Harshness (NVH) problems, especially in rear-wheel drive vehicles with manual transmission. In this article, a new driveline and rear axle coupled torsional vibration model (DRCTVM) is developed that considers the relationship between the driveline and the rear axle. The experiments show that the DRCTVM can provide much better results than the traditional model. In addition, for the first time, uncertainty theory is introduced to the analysis and optimization of driveline torsional vibration based on the DRCTVM. A truncated normal distribution is used to describe the uncertainty of DRCTVM, which considers both the probability distribution and the bounds of uncertain variables. Furthermore, robustness of the driveline torsional vibration was analysed using the Monte Carlo (MC) process and optimized using the Multi-Island Genetic Algorithm. The optimization results show that the proposed model and method are effective and improve the robustness of driveline torsional vibration performance.     

基于能量优化模型的无线传感器网络分簇算法

考虑到无线传感器分簇网络中簇的规模、簇头数量和节点剩余能量是能量有效型分簇路由算法关注的重要指标,提出了一种基于能量优化模型(EOM)的分布式分簇算法——EOMC,该算法通过建立网络能耗优化模型,以最优簇头数构建分簇通信规模,并结合功率控制将候选簇头限制在一定宽度的选举环带,使得簇头分布均衡,同时兼顾到节点剩余能量进行分簇,以达到均衡节点能耗,延长网络生存期的目的。与低能耗自适应分簇分层(LEACH)协议的对比仿真的结果表明,该算法能够达到预期指标,算法的开销相对较小。     

Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm

Constrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.     

On the association between a random parameter and an observable

The association between an observable and a random parameter characterizes their joint distribution given the marginal distributions. It has been shown to be incorporated in the (log-)odds ratio function. The association is inherent in each of the conditional distributions and hence determines the learning process formalized in Bayes' theorem. The paper focuses on two applications. Commonly used measures of dependence, especially Kullback-Leibler distances between densities of interest are identified and interpreted as expected valuves of the log-odds ratio function. Frequently Bayesian inference is based on the maximization of an expected utility. If the utility of a probability density is defined by the logarithmic score function, the expected utility can often be decomposed approximately into a term of “fit” and a term of “model complexity”. The log-odds ratio parameterization of probability densities reveals that “model complexity” again can be defined as an expected value of the log-odds ratio function, i.e., as a measure of dependence between the observable and the random parameter. The ideas are illustrated throught with examples from the class of conjugate exponential families.     

在人机学思想中关注产品愉悦功能的设计

论述了在较长一段时间里,设计师遵照人机思想,将研究重心投射到产品的可用性研究上,关注安全、方便、舒适等因素.然而到现在,从人机学本身的发展、产品设计在企业产品研发过程中的现状、消费者心理特点等多方面看,这种人机设计准则已显露出不能适应人们新需求的一面.通过分析得出初步结论:基于可用性的人机研究应继续探讨产品的愉悦功能设计,以更好符合用户需求,支持产品设计行业的发展.