基于可拓学模型的产品创新设计方法

阐述了可拓学的物元模型和菱形思维方法,并分析了可拓学应用在产品创新设计中的可行性。介绍了TRIZ方法,比较了TRIZ与可拓学矛盾解决方法的不同。将TRIZ与可拓学方法相结合,应用于产品创新设计中,形成了基于可拓学模型的产品创新设计方法,并通过创新设计实例验证了该方法。     

运用可拓理论的产品生命周期识别

针对现有产品生命周期识别方法的不足,提出了一种运用可拓理论的产品生命周期综合识别方法,通过建立产品生命周期识别的物元模型,利用关联函数对产品生命周期进行综合识别,并通过实例说明了其在实际中的应用.使用该方法不仅可以识别产品的生命周期,还能对属于同一阶段中产品的位置进行判断.     

基于相似案例的产品可拓重构设计方法

目的 为了快速构建符合客户风格需求的产品概念设计方案,本文以客车造型设计为例,提出产品可拓重构设计方法。方法 首先,在用客车案例来表达数据结构的基础上,构建整车Metadata和关键几何特征Metadata的索引机制。其次,借助意象尺度图和KNN算法在数据库中检索出相似案例。最后,将产品造型视为可拓学理论中的物元,利用物元的多种可拓变换方式,从而实现产品造型概念方案的生成。结论 客车造型设计实例的分析结果表明,这种基于相似案例的产品可拓重构设计方法能够满足客户对产品造型风格的需求,可避免在造型设计过程中对设计师主观意识的依赖过强和客观知识使用不足的缺陷。     

基于功能表面驱动与可拓工具的产品人机工程问题确定方法研究

为了准确地确定产品人机工程问题，以提升人机交互性能，运用TRIZ (Teoriya Resheniya Izobreatatelskikh Zadatc，发明问题解决理论)提出了基于功能表面驱动与可拓工具的冲突区域确定方法。首先，利用功能表面改进功能过程模型，识别人机工程问题的初始问题功能元，并结合可拓表达和相关分析完成初始问题功能元的转换及最终问题功能元的确定。然后，利用功能表面改进功能关系模型，识别产品的初始冲突区域，并结合可拓表达、相关分析、蕴含分析和优度评价完成初始冲突区域的转换及最终冲突区域的确定。最后，利用标准解对冲突区域中的不良作用进行求解，并通过优度评价完成方案的评价和筛选。通过割草机改进设计实例验证了所提出方法的可行性。研究表明，该方法有助于交互式产品冲突区域的确定，提高了其人机工程问题的解决效率。     

基于模糊Kano模型和可拓创新法的特种运载抱罐车设计

目的 结合模糊Kano模型和可拓创新法,解决特种运载抱罐车目前同质化严重、用户满意度低等问题。方法 提出基于模糊Kano模型和可拓创新法相结合的创新设计方法。首先,对特种运载抱罐车展开可拓分析,建立物元模型,进行拓展分析并利用模糊Kano模型,输出质量要素的重要程度,提取关键设计要素。其次,结合用户需求的数据分析结果,利用可拓变换方法将所构建的特种运载抱罐车物元数学表征模型进行变换,获取新的产品创意模型。最终利用优度评价法获取最优方案。结果 通过对特种运载抱罐车的优化设计,实现在外观造型、安全性能以及功能特征等方面的提升。结论 证明集成模糊Kano模型和可拓创新方法相结合的设计方法能够提高特种运载抱罐车的用户满意度和市场竞争力。     

可拓与TRIZ融合辅助设计思维过程研究

目的 构建完善的设计思维过程模型,提高设计思维方法的客观性和可操作性。方法 将可拓和TRIZ融合对设计思维过程中问题分析、求解和评价三个重要阶段进行优化。利用可拓模型建立形式化问题定义模型;集成可拓与TRIZ求解工具,构建问题求解模型;融合TRIZ理想度法则和可拓优度评价,提出理想优度评价方法。结果 构建了可拓与TRIZ融合辅助的设计思维过程模型,并通过智能购物车的设计验证了该模型的合理性和可行性。结论 所构建的基于可拓与TRIZ融合的设计思维过程模型更具客观性和可操作性,对于支持产品创新设计实践具有重要意义。     

基于可拓学的球墨铸铁石墨形态识别

根据石墨的形态特征，运用物元分析方法建立起关于石墨形态识别的物元模型，提出了可拓分类树方法，讨论了可拓分类树的构造和实现途径，并将其应用于石墨形态的类型判别；给出了典型的实验结果，并对该方法与现有识别方法作出比较。     

市场智能经济控制中的统一集方法初探

将统一集与物元变换结合用于市场智能经济控制，建立了统一集智能控制模型，在此模型中把企业的始末状态抽象为可拓学中的物元，应用统一集、界壳论、模糊控制以及可拓学的方法将经济体的初始物元进行物元变换，最后得到满意的初始物元。由于统一集包容了Fuzzy set, Vague set, Extension set,SPA,FEEC等学科，将这些学科中的方法融合起来，提出统一集市场智能经济控制的初步设想，并给出几个应用实例。     

面向专利产品创新设计的可拓变换方法研究

目的 结合可拓变换方法与TRIZ理论,运用到专利产品创新设计中。方法 对目标专利产品进行分析,将系统组件分层并建立物元,通过功能分析判断组件功能的效果,确定组件正常、不足、有害和过剩功能,并用事元和关系元表达功能信息。通过功能分析和基元模型建立起该专利产品的功能模型,将问题功能的相关组件作为规避对象,运用可拓变换中的删变换和置换变换进行组件或组件参数的删减与替换,获得规避模型,再通过TRIZ理论求解工具解决规避后产生的问题,获得解决方案,最后进行侵权判断。结论 将可拓变换方法与TRIZ理论相结合运用到专利产品的创新设计中,可结合两者的优势,更加深入地分析问题与解决问题。     

基于改进关联函数的跑道侵入风险可拓评价

为了量化民航机场跑道侵入风险,本文利用可拓评判方法对跑道侵入风险进行评价。首先,从人-机-环境系统工程的角度建立了跑道侵入风险评估指标体系。其次,利用改进的AHP法和D-S证据理论确定指标权重。针对传统关联函数的不足,提出了改进的关联函数计算式,并在此基础上建立了跑道侵入风险可拓评价物元模型。最后,通过具体实例的应用,检验了该方法的可行性。     

基于可拓学的产品功能创新模型

产品功能是概念设计的出发点,创新是概念设计的灵魂.在分析现有产品功能模型功率键图的基础上,借鉴其思想,以物元形式描述功能,引入量值属性,建立创新设计的物元模型.从功能组合角度定义了功能之间的关系,以功能重组为出发点为设计者提供创新思路.     

Using geometric mean to compute robust mixture designs

Mixture experiments involve developing a dedicated formulation for specific applications. We propose the weighted optimality criterion using the geometric mean as the objective function for the genetic algorithms. We generate a robust mixture design using genetic algorithms (GAs) of which the region of interest is an irregularly shaped polyhedral region formed by constraints on proportions of the mixture component. When specific terms in the initial model display unimportant effects, it is assumed that they are removed. The design generation objective requires model robustness across the set of the reduced models of the design. Proposing an alternative way to tackle the problem, we find that the proposed GA designs based on G- or/and IV-efficiency are robust to model misspecification.     

A multi-objective model for risk mitigating in supply chain design

The goal of this study is to recognise various factors for responsive SCs that affect supply risk and model their impact on SC design and operation. We propose a conceptual model for SC responsiveness that encompasses practices such as flexibility, agility, internal integration, and visibility. This conceptual model is utilised to build up a multi-objective, multi-period SC design and operation model. A heuristic algorithm is developed to find the supplier, product, period, and production rate for the numerical problem. The improved genetic algorithm (GA) produces solutions with more accuracy in considerably less time than a traditional GA. Finally, an approach to prioritise the objective functions is developed that allows managers to focus on specific objective functions more than optimum values. This approach provides risk-averse, responsiveness-oriented, cost-effective managers the capability to set priorities based on their policies.     

A cross-disciplinary technology transfer for search-based evolutionary computing: from engineering design to software engineering design

Although object-oriented conceptual software design is difficult to learn and perform, computational tool support for the conceptual software designer is limited. In conceptual engineering design, however, computational tools exploiting interactive evolutionary computation (EC) have shown significant utility. This article investigates the cross-disciplinary technology transfer of search-based EC from engineering design to software engineering design in an attempt to provide support for the conceptual software designer. Firstly, genetic operators inspired by genetic algorithms (GAs) and evolutionary programming are evaluated for their effectiveness against a conceptual software design representation using structural cohesion as an objective fitness function. Building on this evaluation, a multi-objective GA inspired by a non-dominated Pareto sorting approach is investigated for an industrial-scale conceptual design problem. Results obtained reveal a mass of interesting and useful conceptual software design solution variants of equivalent optimality—a typical characteristic of successful multi-objective evolutionary search techniques employed in conceptual engineering design. The mass of software design solution variants produced suggests that transferring search-based technology across disciplines has significant potential to provide computationally intelligent tool support for the conceptual software designer.     

飞机总体设计中的一种多目标鲁棒优化方法

 提出一种基于灵敏度的多目标鲁棒优化方法。针对各维设计变量存在扰动的情况,在原约束多目标优化模型上,附加偏差目标函数,并采用最差估计法对约束条件进行鲁棒可行性调整。采用全局敏度方程方法来计算目标函数和约束函数对设计变量的敏度,进而采用Pareto遗传算法搜索约束多目标优化问题的非劣解集,设计者可以根据不同的设计准则从中选择合适的设计点。将上述方法用于飞机总体参数优化设计,并与采用常规优化方法所得的优化结果进行了分析和比较。     

Genetic algorithm attributes for component selection

This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful for component selection are explored. A comparison of these attributes is performed using two industrial design problems. A set of genetic algorithm attributes including integer coding, uniform crossover, anti-incest mating, variable mating and mutation rates, retention of population members from generation to generation, and an attention shifted penalty function are suggested for a more efficient search in component selection problems.     

Hybrid neural network model for the design of beam subjected to bending and shear

There is no direct method for design of beams. In general the dimensions of the beam and reinforcement are initially assumed and then the interaction formula is used to verify the suitability of chosen dimensions. This approach necessitates few trials for coming up with an economical and safe design. This paper demonstrates the applicability of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for the design of beams subjected to moment and shear. A hybrid neural network model which combines the features of feed forward neural networks and genetic algorithms has been developed for the design of beam subjected to moment and shear. The network has been trained with design data obtained from design experts in the field. The hybrid neural network model learned the design of beam in just 1000 training cycles. After successful learning, the model predicted the depth of the beam, area of steel, spacing of stirrups required for new problems with accuracy satisfying all design constraints. The various stages involved in the development of a genetic algorithm based neural network model are addressed at length in this paper.     

OPTIMUM DESIGN OF FIBRE COMPOSITE STIFFENED PANELS USING GENETIC ALGORITHMS

A procedure is derived for a generalized optimum design of stiffened panels made up of laminated fibre reinforced plastics (FRP) and subjected to longitudinal membrane compression (in the direction of stiffeners) and in plane shear loading. The strength and stability (local and overall) are the main design constraints. The size, configuration, fibre orientation and topology optimization are done simultaneously. The optimum design procedure is based on genetic algorithms and a simplified set of design equations. A few example problems of optimum design are solved and the results are discussed.     

基于功能模式的概念结构生长型设计研究

为解决功能-结构映射问题,首先提出了功能表面映射模型。功能表面作为功能-结构映射的信息载体,可分为定位表面和使定表面,进而提出了基于功能表面的功能传递链。通过对生物个体与产品的类比,提出了概念结构生长型设计方法,依据广义定位原理和分解重构理论,建立了基于功能表面的功能模式,具体包括执行模式、传动模式和定位模式,得到了基于功能模式的概念结构生长型设计过程模型。最后,以偶件滚字夹具设计为例,说明了基于功能模式的生长型设计过程。