Individual styles of problem solving and their relation to representations in the design process

The goal of the empirical study presented in this paper was to investigate the influence of individual styles of problem solving on the design process. Subjects worked on an adaptive design problem, as well as on two, complex nondesign problems. In analysing the design processes, the main focus was on the marks-on-paper which are created and used by the designers. The results demonstrate that the course of the design process in general and the use of marks-on-paper in particular depend on an individual designer's style of problem solving. Implications ;for design education and design support are discussed.     

基于TRIZ及ABD的创新设计研究

应用基于类比的设计（ABD）和发明问题解决理论（TRIZ）的设计过程均要采用已往成功的设计实例,使两种过程具有相似性; 在分析ABD及TRIZ设计过程原理的基础上,提出将TRIZ解模型及相应的设计实例作为ABD的源设计,由类比设计过程确定目标设计及新设计的方法。造纸机改进设计实例说明了该方法的应用。     

Design and other types of fixation

Design educators often comment on the difficulties that result from a premature commitment by students to a solution to a design problem. Similarly practitioners can find it difficult to move away from an idea they have developed or precedents in a field. In the psychology of problem solving this effect is called functional fixedness or fixation. It is not surprising that these effects should occur in design problem solving. However, while these types of issues have been discussed in the context of design, there has been little systematic evidence available about whether or not and under what conditions design fixation does occur. The paper reviews the results of a series of recent experiments which begin to address these issues. The results of the experiments are examined in terms of what insights they provide into the design process, what implications they have for design education and how they relate to the larger and more general area of human problem solving.     

机构设计中的空间分离方法

针对应用发明问题解决理论中的4个分离原理解决物理冲突需依靠工程技术人员经验的现状,介绍了应用空间分离原理解决技术系统物理冲突的步骤及其创新原理,并根据概念设计框架阐述了机构设计应用空间分离原理的条件,指出机构设计空间分离原理的应用条件是需求域针对功能域的实现具有相互独立的伴随性物理冲突.根据水射流喷丸强化装卡试验装置设计实例,由其设计需求定义物理冲突,确定作用力为冲突参数,作用力需求满足应用空间分离原理的条件而选择空间分离原理,按照空间分离原理解决物理冲突的步骤进行了非交叉空间的选择,应用空间分离原理的10个创新原理中的抽取原理,形成了该装置的创新方案.机构设计中应用空间分离原理解决物理冲突的条件、步骤及其创新原理构成了机构设计空间分离方法,通过水射流喷丸强化装卡试验装置设计创新方案的提出证明了机构设计空间分离方法的有效性.     

Reliability-based design optimization with progressive surrogate models

Reliability-based design optimization (RBDO) has traditionally been solved as a nested (bilevel) optimization problem, which is a computationally expensive approach. Unilevel and decoupled approaches for solving the RBDO problem have also been suggested in the past to improve the computational efficiency. However, these approaches also require a large number of response evaluations during optimization. To alleviate the computational burden, surrogate models have been used for reliability evaluation. These approaches involve construction of surrogate models for the reliability computation at each point visited by the optimizer in the design variable space. In this article, a novel approach to solving the RBDO problem is proposed based on a progressive sensitivity surrogate model. The sensitivity surrogate models are built in the design variable space outside the optimization loop using the kriging method or the moving least squares (MLS) method based on sample points generated from low-discrepancy sampling (LDS) to estimate the most probable point of failure (MPP). During the iterative deterministic optimization, the MPP is estimated from the surrogate model for each design point visited by the optimizer. The surrogate sensitivity model is also progressively updated for each new iteration of deterministic optimization by adding new points and their responses. Four example problems are presented showing the relative merits of the kriging and MLS approaches and the overall accuracy and improved efficiency of the proposed approach.     

廉租房居民生活形态研究下的洗衣机设计探索

从廉租房居民这一新生特殊群体的生活形态出发,结合相关政策,通过实地观察以及深入访谈的方法,研究人们的日常生活以及对洗衣机的需求,分析并得出了面向低端用户的设计要素以及廉租房住户在洗衣机选择上成本、空间、使用三者上的侧重点。以解决空间与成本问题为主要目的,提出了廉租房洗衣机的设计问题需要以系统化整合设计来解决,以及系统化整合设计在解决廉租房洗衣机上的重要性。     

A time-variant reliability analysis method for structural systems based on stochastic process discretization

In this paper, we propose a new method for analyzing time-variant system reliability based on stochastic process discretization, which provides an effective tool for reliability design of many relatively complex structures considering the whole lifecycle. Within a design lifetime, the stochastic process is discretized into a series of random variables, and meanwhile, we can derive a time-invariant limit-state function in each time interval; the discretized random variables from the stochastic processes and the original random variables are transformed to the independent normal space, and a conventional time-invariant system reliability problem is derived through the linearization to each discretized limit-state functions; by solving this time-invariant system reliability problem, we can obtain the structural reliability or failure probability within the design lifetime. Finally, in this paper, we provide four numerical examples to verify the effectiveness of the method.     

SIMULATED ANNEALING AND PARALLEL PROCESSING: AN IMPLEMENTATION FOR CONSTRAINED GLOBAL DESIGN OPTIMIZATION

Global optimization becomes important as more and more complex designs are evaluated and optimized for superior performance. Often parametric designs are highly constrained, adding complexity to the design problem. In this work simulated annealing (SA), a stochastic global optimization technique, is implemented by augmenting it with a feasibility improvement scheme (FIS) that makes it possible to formulate and solve a constrained optimization problem without resorting to artificially modifying the objective function. The FIS is also found to help recover from the infeasible design space rapidly. The effectiveness of the improved algorithm is demonstrated by solving a welded beam design problem and a two part stamping optimization problem. Large scale practical design problems may prohibit the efficient use of computationally intensive iterative algorithms such as SA. Hence the FIS augmented SA algorithm is implemented on an Intel iPSC/860 parallel super-computer using a data parallel structure of the algorithm for the solution of large scale optimization problems. The numerical results demonstrate the effectiveness of the FIS as well as the parallel version of the SA algorithm. Expressions are developed for the estimation of the speedup of iterative algorithms running on a parallel computer with hyper-cube interconnection topology. Computational speedup in excess of 8 is achieved using 16 processors. The timing results given for the example problems provide guidelines to designers in the use of parallel computers for iterative processes.     

A highly optimised tolerance-based approach for multi-stage,multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.     

Reliable design space and complete single-loop reliability-based design optimization

Reliability-based design optimization (RBDO) has been intensively studied due to its significance and its conceptual and mathematical complexity. This paper proposes a new method for RBDO on the basis of the concept of reliable design space (RDS), within which any design satisfies the reliability requirements. Therefore, a RBDO problem becomes a simple, deterministic optimization problem constrained by RDS rather than its deterministic feasible space. The RDS is found in this work by using the partial derivatives at the current design point as an approximation of the derivatives at its corresponding most probable point (MPP) on the limit state function. This work completely resolves the double loop in RBDO and turns RBDO into a simple optimization problem. Well-known problems from the literature are selected to illustrate the steps of the approach and for result comparison. Discussions will also be given on the limitation of the proposed method, which is shown to be a common limitation overlooked by the research community on RBDO.     

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。     

Co-evolution as a computational and cognitive model of design

Co-evolutionary design has been developed as a computational model that assumes two parallel search spaces: the problem space and the solution space. The design process iteratively searches each space using the other space as the basis for a fitness function when evaluating the alternatives. Co-evolutionary design can also be developed as a cognitive model of design by characterizing the way in which designers iteratively search for a design solution, making revisions to the problem specification. This paper presents the computational model of co-evolutionary design and then describes a protocol study of human designers looking for evidence of co-evolution of problem specifications and design solutions. The study shows that co-evolutionary design is a good cognitive model of design and highlights the similarities and differences between the computational model and the cognitive model. The results show that the two kinds of co-evolutionary design complement each other, having strengths in different aspects of the design process. Electronic Publication     

Stochastic optimization using a sparse grid collocation scheme

In computational sciences, optimization problems are frequently encountered in solving inverse problems for computing system parameters based on data measurements at specific sensor locations, or to perform design of system parameters. This task becomes increasingly complicated in the presence of uncertainties in boundary conditions or material properties. The task of computing the optimal probability density function (PDF) of parameters based on measurements of physical fields of interest in the form of a PDF, is posed as a stochastic optimization problem. This stochastic optimization problem is solved by dividing it into two problems—an auxiliary optimization problem to construct stochastic space representations from the PDF of measurement data, and a stochastic optimization problem to compute the PDF of problem parameters. The auxiliary optimization problem is solved using a downhill simplex method, whilst a gradient based approach is employed for solving the stochastic optimization problem. The gradients required for stochastic optimization are defined, using appropriate stochastic sensitivity problems. A computationally efficient sparse grid collocation scheme is utilized to compute the solution of these stochastic sensitivity problems. The implementation discussed, requires minimum intrusion into existing deterministic solvers, and it is thus applicable to a variety of problems. Numerical examples involving stochastic inverse heat conduction problems, contamination source identification problems and large deformation robust design problems are discussed.     

Design space optimization using a numerical design continuation method

A generalized optimization problem in which design space is also a design to be found is defined and a numerical implementation method is proposed. In conventional optimization, only a portion of structural parameters is designated as design variables while the remaining set of other parameters related to the design space are often taken for granted. A design space is specified by the number of design variables, and the layout or configuration. To solve this type of design space problems, a simple initial design space is selected and gradually improved while the usual design variables are being optimized. To make the design space evolve into a better one, one may increase the number of design variables, but, in this transition, there are discontinuities in the objective and constraint functions. Accordingly, the sensitivity analysis methods based on continuity will not apply to this discontinuous stage. To overcome the difficulties, a numerical continuation scheme is proposed based on a new concept of a pivot phase design space. Two new categories of structural optimization problems are formulated and concrete examples shown. Copyright © 2001 John Wiley & Sons, Ltd.     

Hybrid metamodel-based design space management method for expensive problems

Metamodel-based global optimization methods have been extensively studied for their great potential in solving expensive problems. In this work, a design space management strategy is proposed to improve the accuracy and efficiency of metamodel-based optimization methods. In this strategy, the whole design space is divided into two parts: the important region constructed using several expensive points and the other region. Combined with a previously developed hybrid metamodel strategy, a hybrid metamodel-based design space management method (HMDSM) is developed. In this method, three representative metamodels are used simultaneously in the search of the global optimum in both the important region and the other region. In the search process, the important region is iteratively reduced and the global optimum is soon captured. Tests using a series of benchmark mathematical functions and a practical expensive problem demonstrate the excellent performance of the proposed method.     

MULTIOBJECTIVE DESIGN OPTIMIZATION BASED ON SATISFACTION METRICS

A formal multiobjective optimization method based on satisfaction metrics is presented for designing an engineering system with mathematical rigour. Three satisfaction-based design models with different tradeoff strategies are developed to facilitate the incorporation of satisfaction metrics into the context of design formulations. These models are derived from different combinations of satisfaction-incorporated design objectives, enabling the conversion of the original multiple objectives appropriately to a single unified goal. This makes it easy to apply any available single-objective mathematical programming solver for the resulting problem solving. Not only does the method generate a Pareto-optimal solution, but also it allows for the generation of many design alternatives in a feasible design space. A computational procedure is also suggested to guide design implementations. For illustration, an example is worked out to show the computational details and the utility of the newly developed design models.     

Robust design optimization of nonlinear energy sink under random system parameters

Generally, in designing nonlinear energy sink (NES), only uncertainties in the ground motion parameters are considered and the unconditional expected mean of the performance metric is minimized. However, such an approach has two major limitations. First, ignoring the uncertainties in the system parameters can result in an inefficient design of the NES. Second, only minimizing the unconditional mean of the performance metric may result in large variance of the response because of the uncertainties in the system parameters. To address these issues, we focus on robust design optimization (RDO) of NES under uncertain system and hazard parameters. The RDO is solved as a bi-objective optimization problem where the mean and the standard deviation of the performance metric are simultaneously minimized. This bi-objective optimization problem has been converted into a single objective problem by using the weighted sum method. However, solving an RDO problem can be computationally expensive. We thus used a novel machine learning technique, referred to as the hybrid polynomial correlated function expansion (H-PCFE), for solving the RDO problem in an efficient manner. Moreover, we adopt an adaptive framework where H-PCFE models trained at previous iterations are reused and hence, the computational cost is less. We illustrate that H-PCFE is computationally efficient and accurate as compared to other similar methods available in the literature. A numerical study showcasing the importance of incorporating the uncertain system parameters into the optimization procedure is shown. Using the same example, we also illustrate the importance of solving an RDO problem for NES design. Overall, considering the uncertainties in the parameters have resulted in a more efficient design. Determining NES parameters by solving an RDO problem results in a less sensitive design.     

A hybrid case-based reasoning CAD system for injection mould design

Injection mould design generally lies on the critical path of a new product development. The design efficiency will have significant impact on the overall lead time of a new product. This paper presents a prototype injection mould-design system using a hybrid case-based reasoning (HCBR) approach. Case-based reasoning (CBR) is a solving paradigm that uses previous episodes on solving problems similar to the problem at hand (the new problem) as the basis for solving the new problem. In this hybrid system, CBR is incorporated with generalized design knowledge, and provides a flexible and comprehensive model of design. The knowledge base of the system would be accessed by mould designers through interactive programs so that their own intelligence and experience could also be incorporated with the total mould design. The approach provides a workable model of mould design system with CBR and knowledge-based expert system intelligent support, which could suggest good and proven design solutions to new design problems quickly, avoiding the time necessary to create those designs from scratch, for the plastic products manufacturing industry.     

Designing variations

In addition to functioning as a general design strategy and, possibly, even as a general problem solving heuristic, componentizing possesses other attributes in a class of design problems involved with diversity and variation. This article is about variation, the way in which systems behave in creating variations, and the principles of design which help achieve variation.     

The effects of experience during design problem solving

Behavioural and physiological experiments to analsye the development, influence and application of experience during design problem solving are described. The results of the behavioural experiments show, that while novices try to solve assignments through deductive reasoning, experts prefer to apply their experience directly. The electrophysiological experiments indicate, that as a manifestation of this, the regions activated in the human brain during problem solving vary according to the experience a test person has. Novices show a longer activity in the frontal regions whereas the experts seem to have longer activity in the parietal regions of the brain.