模糊数学方法在机械设计中的应用

本文应用模糊数学理论,针对模糊设计计算问题,提出了基于模糊数的设计计算方法,并给出了具体的设计计算实例。     

Optimisation of a multi-objective two-dimensional strip packing problem based on evolutionary algorithms

This paper considers a real-world two-dimensional strip packing problem involving specific machinery constraints and actual cutting production industry requirements. To adapt the problem to a wider range of machinery characteristics, the design objective considers the minimisation of material length and the total number of cuts for guillotinable-type patterns. The number of cuts required for the cutting process is crucial for the life of the industrial machines and is an important aspect in determining the cost and efficiency of the cutting operation. In this paper we propose the application of evolutionary algorithms to address the multi-objective problem, for which numerous approaches to its single-objective formulation exist, but for which multi-objective approaches are almost non-existent. The multi-objective evolutionary algorithms applied provide a set of solutions offering a range of trade-offs between the two objectives from which clients can choose according to their needs. By considering both the length and number of cuts, they derive solutions with wastage levels similar to most previous approximations which just seek to optimise the overall length.     

Optimum plate-fin heat sinks by using a multi-objective evolutionary algorithm

This article demonstrates the practical applications of a multi-objective evolutionary algorithm (MOEA) namely population-based incremental learning (PBIL) for an automated shape optimization of plate-fin heat sinks. The computational procedure of multi-objective PBIL is detailed. The design problem is posed to find heat sink shapes which minimize the junction temperature and fan pumping power while meeting predefined constraints. Three sets of shape design variables used in this study are defined as: vertical straight fins with fin height variation, oblique straight fins with steady fin heights, and oblique straight fins with fin height variation. The optimum results obtained from using the various sets of design variables are illustrated and compared. It can be said that, with this sophisticated design system, efficient and effective design of plate-fin heat sinks is achievable and the best design variables set is the oblique straight fins with fin height variation.     

基于多模态信息交互的智能家居设计研究

目的 针对界面、语音等单一智能家居交互模式阻碍用户自然体验的瓶颈问题,探索基于多模态信息交互的智能家居设计方法,实现智能家居更自然、友好的用户体验。方法 基于用户日常生活多模态的自然交互特点,探索一种以多模态为特征的智能家居信息交互模式,构建了面向智能家居的多模态信息交互设计框架,并对该框架中的构成要素、交互流程、模态组织方式及多模态信息的注意度、模态密度、模态结构配置进行分析讨论。结果 智能家居多模态信息交互实现了智能家居系统的主动性、交感化信息获取和情境化的信息分析与反馈,对提升智能系统自主性,降低用户在交互过程中的认知负荷,以及实现更加自然、友好的用户体验有积极价值。结论 智能家居环境需要改变传统相对单一的人机系统信息交互方式,以多模态信息交互促进智能家居使用体验的最优化。     

A multicriteria evolutionary algorithm for mechanical design optimization with expert rules

This paper addresses the problem of optimizing mechanical components during the first stage of the design process. While a previous study focused on parameterized designs with fixed configurations—which led to the development of the PAMUC (Preferences Applied to Multiobjectivity and Constraints) method, to tackle constraints and preferences in evolutionary algorithms (EAs)—, the models to be considered in this work are enriched by the presence of topological variables. In this context, in order to create optimal but also realistic designs, i.e. fulfilling not only technical requirements but also technological constraints (more naturally expressed in terms of rules), a novel approach is proposed: PAMUC II. It consists in integrating an inference engine within the EA to repair the individuals violating the user‐defined rules. PAMUC II is tested on mechanical benchmarks, and provides very satisfactory results in comparison with a weighted sum method with penalization to deal with the constraints. Copyright © 2004 John Wiley & Sons, Ltd.     

Bi-directional evolutionary structural optimization for strut-and-tie modelling of three-dimensional structural concrete

This article presents a method for the automatic generation of optimal strut-and-tie models in reinforced concrete structures using a bi-directional evolutionary structural optimization method. The methodology presented is developed for compliance minimization relying on the Abaqus finite element software package. The proposed approach deals with the generation of truss-like designs in a three-dimensional environment, addressing the design of corbels and joints as well as bridge piers and pile caps. Several three-dimensional examples are provided to show the capabilities of the proposed framework in finding optimal strut-and-tie models in reinforced concrete structures and verifying its efficiency to cope with torsional actions. Several issues relating to the use of the topology optimization for strut-and-tie modelling of structural concrete, such as chequerboard patterns, mesh-dependency and multiple load cases, are studied. In the last example, a design procedure for detailing and dimensioning of the strut-and-tie models is given according to the American Concrete Institute (ACI) 318-08 provisions.     

Evolutionary Algorithm Integrating Stress Heuristics for Truss Optimization

The optimal truss design using problem-oriented evolutionary algorithm is presented in the paper. The minimum weight structures subjected to stress and displacement constraints are searched. The discrete design variables are areas of members, selected from catalogues of available sections. The integration of the problem specific knowledge into the optimization procedure is proposed. The heuristic rules based on the concept of fully stressed design are introduced through special genetic operators, which use the information concerning the stress distribution of structural members. Moreover, approximated solutions obtained by deterministic, sequential discrete optimization methods are inserted into the initial population. The obtained hybrid evolutionary algorithm is specialized for truss design. Benchmark problems are calculated in numerical examples. The knowledge about the problem integrated into the evolutionary algorithm can enhance considerably the effectiveness of the approach and improve significantly the convergence rate and the quality of the results. The advantages and drawbacks of the proposed method are discussed.     

基于用户体验的新中式家具设计研究

目的从用户体验的角度探讨新中式家具的创新设计方法。方法以需求层次理论为基础,阐述用户体验理念在产品设计创新中的优势,结合新中式家具产生的时代背景,分别从感官用户体验、交互用户体验、情感用户体验3个方面,分析用户体验对新中式家具设计创新的重要性,提出新中式家具基于用户体验创新设计的思路与方法。结论新中式家具创新设计既需要表现中国传统文化,又要满足现代人的具体生活需求和心理需求,反应时代的特征。从感官、交互、情感等多个方面不断创新来适应消费者需求的变化,才能实现"以人为本"的设计。     

基于情境构建法的消防头盔设计

目的深入理解用户行为,挖掘用户需求,探究加强消防头盔安全性与高效性的设计方法。方法以消防头盔为设计研究对象,从使用者、产品及使用环境三者所构成的情境动态关系出发。首先通过信息获取,得出情境系统中有关问题情境的相关情境要素信息,接着在求解情境的对象操作层面,结合问题情境相关情境要素信息归纳使用者行为的关键性问题需求,最后在结果情境的目标状态层面对设计对象进行功能域、物理域及交互域的相关设计需求描述,并构建产品情境模型。结论将该模型运用于消防头盔方案设计中,并通过与以往方案的功能对比验证,表明了该方法的实用性及有效性。     

A hybrid cellular automaton–bi-directional evolutionary optimization algorithm for topological optimization of crashworthiness

This article proposes a new algorithm for topological optimization under dynamic loading which combines cellular automata with bi-directional evolutionary structural optimization (BESO). The local rules of cellular automata are used to update the design variables, which avoids the difficulty of obtaining gradient information under nonlinear collision conditions. The intermediate-density design problem of hybrid cellular automata is solved using the BESO concept of 0–1 binary discrete variables. Some improvement strategies are also proposed for the hybrid algorithm to solve certain problems in nonlinear topological optimization, e.g. numerical oscillation. Some typical examples of crashworthiness problems are provided to illustrate the efficiency of the proposed method and its ability to find the final optimal solution. Finally, numerical results obtained using the proposed algorithms are compared with reference examples taken from the literature. The results show that the hybrid method is computationally efficient and stable.     

Sequential interactive evolution for finding high-quality topologies

Finding a diverse set of high-quality (HQ) topologies for a single-objective optimization problem using an evolutionary computation algorithm can be difficult without a reliable measure that adequately describes the dissimilarity between competing topologies. In this article, a new approach for enhancing diversity among HQ topologies for engineering design applications is proposed. The technique initially selects one HQ solution and then searches for alternative HQ solutions by performing an optimization of the original objective and its dissimilarity with respect to the previously found solution. The proposed multi-objective optimization approach interactively amalgamates user articulated preferences with an evolutionary search so as sequentially to produce a set of diverse HQ solutions to a single-objective problem. For enhancing diversity, a new measure is suggested and an approach to reducing its computational time is studied and implemented. To illustrate the technique, a series of studies involving different topologies represented as bitmaps is presented.     

Customized evolutionary optimization procedure for generating minimum weight compliant mechanisms

In this article, a customized evolutionary optimization procedure is developed for generating minimum weight compliant mechanisms. A previously-suggested concept of multi-objectivization in which a helper objective is introduced in addition to the primary objective of the original single-objective optimization problem (SOOP) is used here. The helper objective is chosen in a way such that it is in conflict with the primary objective, thereby causing an evolutionary multi-objective optimization algorithm to maintain diversity in its population from one generation to another. The elitist non-dominated sorting genetic algorithm (NSGA-II) is customized with a domain-specific initialization strategy, a domain-specific crossover operator, and a domain-specific solution repairing strategy. To make the search process computationally tractable, the proposed methodology is made suitable for parallel computing. A local search methodology is applied on the evolved non-dominated solutions found by the above-mentioned modified NSGA-II to refine the solutions further. Two case studies for tracing curvilinear and straight-line paths are performed. Results demonstrate that solutions having smaller weight than the reference design solution obtained by SOOP are found by the proposed procedure. Interesting facts and observations brought out by the study are also narrated and conclusions of the study are made.     

基于TRIZ进化树的滚筒洗衣机柔顺剂供给装置创新设计

利用进化树可以进行产品预测,并作出专利规避.进化树的画法是以物体的分割路线为主线,找出其中需要改进的阶段,进一步寻找可用的进化路线,最终形成进化树,从而在进化树中找出解决问题的方案.在立式滚筒洗衣机柔顺剂供给装置的改进创新中,利用进化树,根据系统组件裁剪进化路线和物体的几何进化路线的提示,找到了新的解决方案,对原来的四槽供给装置改进为单个三角棱柱供给装置,有效规避了现有的专利.     

Bi-directional evolutionary structural optimization for design-dependent fluid pressure loading problems

This article presents an evolutionary topology optimization method for compliance minimization of structures under design-dependent pressure loads. In traditional density based topology optimization methods, intermediate values of densities for the solid elements arise along the iterations. Extra boundary parametrization schemes are demanded when these methods are applied to pressure loading problems. An alternative methodology is suggested in this article for handling this type of load. With an extended bi-directional evolutionary structural optimization method associated with a partially coupled fluid–structure formulation, pressure loads are modelled with hydrostatic fluid finite elements. Due to the discrete nature of the method, the problem is solved without any need of pressure load surfaces parametrization. Furthermore, the introduction of a separate fluid domain allows the algorithm to model non-constant pressure fields with Laplace's equation. Three benchmark examples are explored in order to show the achievements of the proposed method.     

A particle swarm optimizer for constrained multi-objective engineering design problems

This article presents a particle swarm optimizer (PSO) capable of handling constrained multi-objective optimization problems. The latter occur frequently in engineering design, especially when cost and performance are simultaneously optimized. The proposed algorithm combines the swarm intelligence fundamentals with elements from bio-inspired algorithms. A distinctive feature of the algorithm is the utilization of an arithmetic recombination operator, which allows interaction between non-dominated particles. Furthermore, there is no utilization of an external archive to store optimal solutions. The PSO algorithm is applied to multi-objective optimization benchmark problems and also to constrained multi-objective engineering design problems. The algorithmic effectiveness is demonstrated through comparisons of the PSO results with those obtained from other evolutionary optimization algorithms. The proposed particle swarm optimizer was able to perform in a very satisfactory manner in problems with multiple constraints and/or high dimensionality. Promising results were also obtained for a multi-objective engineering design problem with mixed variables.     

Bi-directional evolutionary level set method for topology optimization

A bi-directional evolutionary level set method for solving topology optimization problems is presented in this article. The proposed method has three main advantages over the standard level set method. First, new holes can be automatically generated in the design domain during the optimization process. Second, the dependency of the obtained optimized configurations upon the initial configurations is eliminated. Optimized configurations can be obtained even being started from a minimum possible initial guess. Third, the method can be easily implemented and is computationally more efficient. The validity of the proposed method is tested on the mean compliance minimization problem and the compliant mechanisms topology optimization problem.     

互联网产品的情感温度提升策略研究

目的 研究互联网产品中的情感温度的设计触点及其提升策略,丰富用户体验设计的相关理论。方法 从用户的感知层面出发,分析互联网产品在视觉、触觉、意觉三个层面的情感交互内容,并以此为设计点,提出设计策略,提升用户体验。结论 指出了提升互联网产品情感温度的重要性。从用户的感知层面将情感温度分为视觉层面、触觉层面和意觉层面,不仅有针对性地探索了优化视觉层面的导流设计、扁平化触觉层面的信息层级、简化触觉层面的行为路径,而且深入地解读了用户在意觉层面的需求等。通过使用这些方面的策略,能有效地提高产品实用性,更好地满足用户情感需求,进而提高产品的竞争力。     

Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.     

Trajectory planning for multiple unmanned vehicles under reciprocal constraints

In this article, a special class of trajectory optimization problems is formalized and solved. It involves the optimization of different unmanned vehicle (UMV) trajectories that are coupled through reciprocal constraints. It is shown in the article that searching for a solution to the problem at hand may stipulate not just planning a longer than the shortest possible path for each UMV, but also choosing slower travel speeds in order to co-ordinate between the UMVs. Although it seems that solving the problem possesses merits, it has been only partially treated before. Here it is solved by utilizing an evolutionary approach which involves a new algorithmic feature that allows striving towards the desired optimality. The approach is demonstrated and studied through solving and simulating several trajectory planning problems. It is shown that a wide range of problems might be related to that class of problems.     

A framework for the analysis of cognitive reliability in complex systems: a recovery centred approach

Managing complex industrial systems requires reliable performance of cognitive tasks undertaken by operating crews. The infrequent practice of cognitive skills and the reliance on operator performance for novel situations raised cognitive reliability into an urgent and essential aspect in system design and risk analysis. The aim of this article is to contribute to the development of methods for the analysis of cognitive tasks in complex man-machine interactions. A practical framework is proposed for analysing cognitive errors and enhancing error recovery through interface design. Cognitive errors are viewed as failures in problem solving which are difficult to recover under the task constrains imposed by complex systems. In this sense, the interaction between context and cognition, on the one hand, and the process of error recovery, on the other hand, become the focal points of the proposed framework which is illustrated in an analysis of a simulated emergency.