Teaching engineering design: Can reading a textbook make a difference?

Teaching design is an integral part of most engineering curricula. Often, students are introduced to the engineering design process through a chapter in a textbook. Does this passive approach to teaching an active process aid the students' learning? An experiment was conducted to assess what students learn about the design process when they read a text. Here, 10 students enrolled in a freshman course were asked to read aloud from a freshmen engineering textbook. Half of the subjects read the text prior to solving three open-ended engineering design problems and the other half solved the same problems before they read the text. Both the subjects' process in solving the problems, as well as the quality of their solutions (the product), are assessed. Results show that subjects that read the text before they solved the three problems spent significantly more time solving the problems and were more sophisticated in their problem solving strategies. These subjects also scored better when judged on the quality of their approach to the problem (including the number of design criteria considered, communications, assumptions, and technical accuracy). However, these subjects did not score better on a quality measure of the final solution.     

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.     

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.     

基于标注和算法生成的平庸——人工智能视觉应用

分析近年来设计领域中不断增强的人工智能应用在设计的过程和结果中所起的作用,研究判断它们将对未来的设计产生什么样的影响。通过将目前主要的一些人工智能设计工具的目标任务、系统架构、学习模式与设计师工作方式进行比对,找出人工智能设计应用的长处以及其无法克服的问题,判别其未来可能的发展方向。得出了目前的人工智能只是致力于解决设计中基础的技术性问题与较低层次上的设计需要,并无可能像人类一样提出设计上的需求并创造性地解决问题。未来的人工智能应用可能会进一步帮助设计师完成更多事务性的工作,但不可能颠覆设计并代替人类进行思考。人们需要警惕因各种智能应用泛滥而导致人的技能与想象力衰退的问题,因此开发必须要受到限制,技术伦理问题也不容忽视。     

Unexpected discoveries and S-invention of design requirements: important vehicles for a design process

Designers, during a conceptual design process, do not just synthesize solutions that satisfy initially given requirements, but also invent design issues or requirements that capture important aspects of the given problem. How do they do this? What becomes the impetus for the invention of important issues or requirements? So-called ‘unexpected discoveries', the acts of attending to visuo-spatial features in sketches which were not intended when they were drawn, are believed to contribute to it. The purpose of the present research is to verify this hypothesis. Analysing the cognitive processes of a practising architect in a design session, we found that in about a half of his entire design process there were bi-directional relations between unexpected discoveries and the invention of issues or requirements. Not only did unexpected discoveries become the driving force for the invention of issues or requirements, but also the occurrence of invention, in turn, tended to cause new unexpected discoveries. This has provided empirical evidence for two anecdotal views of designing. First, designing is a situated act; designers invent design issues or requirements in a way situated in the environment in which they design. We call inventions of this sort situated-invention (S-invention). Secondly, a design process progresses in such a way that the problem-space and the solution-space co-evolve. Further, this has brought a pedagogical implication as well as an insight about an important aspect of learning by experience in design.     

Optimal design of multi-response experiments using semi-definite programming

Optimal design of multi-response experiments for estimating the parameters of multi-response linear models is a challenging problem. The main drawback of the existing algorithms is that they require the solution of many optimization problems in the process of generating an optimal design that involve cumbersome manual operations. Furthermore, all the existing methods generate approximate design and no method for multi-response n-exact design has been cited in the literature. This paper presents a unified formulation for multi-response optimal design problem using Semi-Definite Programming (SDP) that can generate D-, A- and E-optimal designs. The proposed method alleviates the difficulties associated with the existing methods. It solves a one-shot optimization model whose solution selects the optimal design points among all possible points in the design space. We generate both approximate and n-exact designs for multi-response models by solving SDP models with integer variables. Another advantage of the proposed method lies in the amount of computation time taken to generate an optimal design for multi-response models. Several test problems have been solved using an existing interior-point based SDP solver. Numerical results show the potentials and efficiency of the proposed formulation as compared with those of other existing methods. The robustness of the generated designs with respect to the variance-covariance matrix is also investigated.     

A heuristic approach on the facility layout problem based on game theory

Facility layout problems are related to the location of all facilities in a plant. Numerous research works related to facility layout have been published. The applicability of these various existing models may be limited by the fact that they all ignore competitive reactions to one's actions. In addition to external competitors, some internal problems of system such as material handling system design affect layout designs. For considering these effects, some researchers have investigated multi-objective approaches that in most cases lead to the optimisation of a weighted sum of score functions. The poor practicability of such an approach is due to the difficulty of normalising these functions and quantifying the weights. To the extent that competitors do react to a firm's actions and also the facility layout problem considers several conflicting objectives by distinct decision makers in the firm, the existing models may be oversimplifications of reality. In this paper, we modelled such a facility layout problem with conflicting objectives under a duopoly Bertrand competition as a game and solved it with a proposed simulated annealing meta-heuristic. Results obtained from solving some numerical examples confirm the effectiveness of the proposed model for the layout design.     

Solving coupled task assignment and capacity planning problems for a job shop by using a concurrent genetic algorithm

The problems of task assignment and capacity planning of manufacturing systems have been researched for many years. However, in the existing literature, these two types of problems are researched independently. Namely, when solving the task assignment problem, it is usually assumed that the production capacity of the manufacturing systems has been determined. On the other hand, when solving the capacity planning problem, the production tasks assigned to the workstations in the manufacturing system have also been determined. Actually, the task assignment problem and the capacity planning problem are coupled with each other. When we assign production tasks to workstations, production capacities of these workstations should be regulated so that they are enough for completing the tasks. At the same time, when planning the production capacity, we must know what production tasks are assigned to what workstations. This research focuses on the coupling relations between the two problems for a closed job shop, in which the total work-in-process (WIP) is assumed to be constant. The objective of the task assignment problem is to balance the workloads of the workstations and the objectives of the capacity planning problem are maximising the throughput and minimising total costs of machine purchasing and WIP inventory. We construct the fundamental system architecture for controlling the two coupled optimisation processes, and propose a concurrent genetic algorithm (CGA) to solve the two coupled optimisation problems. The influences of the decision variables of one problem on the objective function of the other problem are taken into consideration when the fitness functions of the CGA are constructed. Numerical experiments are done to verify the effectiveness of the algorithm.     

A deterministic global approach for mixed-discrete structural optimization

This study proposes a novel approach for finding the exact global optimum of a mixed-discrete structural optimization problem. Although many approaches have been developed to solve the mixed-discrete structural optimization problem, they cannot guarantee finding a global solution or they adopt too many extra binary variables and constraints in reformulating the problem. The proposed deterministic method uses convexification strategies and linearization techniques to convert a structural optimization problem into a convex mixed-integer nonlinear programming problem solvable to obtain a global optimum. To enhance the computational efficiency in treating complicated problems, the range reduction technique is also applied to tighten variable bounds. Several numerical experiments drawn from practical structural design problems are presented to demonstrate the effectiveness of the proposed method.     

A new, innovative and marketable IP diagnosis to evaluate, qualify and find insights for the development of SMEs IP practices and use, based on the AIDA approach

As in our knowledge economy, intellectual assets represented by intellectual and industrial property and associated rights are of growing importance for companies, it is essential for them to be aware of what they own, how to manage these assets, what are their strengths and weaknesses related to those issues.This is why, through a European project co-financed by the European Commission, an action aimed at solving such issues has been taken.The first part of the work undertaken by this action is presented in this article.It consists in the development of an IP questionnaire, based on the adaptation of the marketing and sales AIDA model, allowing classifying IP practices and uses within a progressive scale. In order to get rapid understanding from companies, graphics that can be easily analyzed have been introduced.The originality of the methodology is that the AIDA classification used, gives the opportunity to classify a set of tools or services to be delivered in order to increase the IP practices and performance in companies.     

A methodology for planning experiments in robust product and process design

Robust design is an important method for improving product manufacturability and life, and for increasing manufacturing process stability and yield. In 1980 Genichi Taguchi introduced his approach to using statistically planned experiments in robust product and process design to U.S. industry. Since then, the robust design problem and Taguchi's approach to solving it has received much attention from product designers, manufacturers, statisticians and quality professionals. Although most agree on the importance of the robust design problem, controversy over some of the specific methods used to solve the problem has made this an active research area. Although the answers are not all in yet, the importance of the problem has led to development of a four-step methodology for implementing robust design. The steps are (1) formulate the problem by stating objectives and then listing and classifying product or process variables, (2) plan an experiment to study these variables, (3) identify improved settings of controllable variables from the experiment's results and (4) confirm the improvement in a small follow-up experiment. This paper presents a methodology for the problem formulation and experiment planning steps. We give practical guidelines for making key decisions in these two steps, including choice of response characteristics, and specification of interactions and test levels for variables. We describe how orthogonal arrays and interaction graphs can be used to simplify the process of planning an experiment. We also compare the experiment planning strategies we are recommending to those of Taguchi and to more traditional approaches.     

Training for design of experiments using a catapult

Design of experiments (DOE) is a powerful approach for discovering a set of process (or design) variables which are most important to the process and then determine at what levels these variables must be kept to optimize the response (or quality characteristic) of interest. This paper presents two catapult experiments which can be easily taught to engineers and managers in organizations to train for design of experiments. The results of this experiment have been taken from a real live catapult experiment performed by a group of engineers in a company during the training program on DOE. The first experiment was conducted to separate out the key factors (or variables) from the trivial and the second experiment was carried out using the key factors to understand the nature of interactions among the key factors. The results of the experiment were analysed using simple but powerful graphical tools for rapid and easier understanding of the results to engineers with limited statistical competency. Copyright © 2002 John Wiley & Sons, Ltd.     

Using Graphical Diagnostics to Deal With Bad Data

This article deals with thorny issues that confront every experimenter, i.e., how to handle individual results that do not appear to fit with the rest of the data. It provides graphical tools that make it easy to diagnose what is wrong with response data—damaging outliers and/or a need for transformation. The trick is to maintain a reasonable balance between two types of errors: (1) deleting data that vary only due to common causes, thus introducing bias to the conclusions. (2) not detecting true outliers that occur due to special causes. Such outliers can obscure real effects or lead to false conclusions. Furthermore, an opportunity may be lost to learn about preventable causes for failure or reproducible conditions leading to breakthrough improvements (making discoveries more or less by accident).

Two real life data sets are reviewed. Neither reveals its secrets at first glance. However, with the aid of various diagnostic plots (readily available in off-the-shelf statistical software), it becomes much clearer what needs to be done. Armed with this knowledge, quality engineers will be much more likely to draw the proper conclusions from experiments that produce bad (discrepant) data.     

LAGRANGEAN RELAXATION AND SUBGRADIENT OPTIMIZATION APPLIED TO OPTIMAL DESIGN WITH DISCRETE SIZING

The discrete sizing problem in optimal design is adressed. Lagrangean dual approaches earlier published are briefly reviewed and it is noted that quite sophisticated procedures have been used to solve the dual problems. The simple concept of Lagrangean relaxation combined with subgradient optimization and Lagrangean heuristics has, however, not been applied to the discrete sizing problem. In this paper a scheme based on this concept is described and tested on some small problems. The results indicate that subgradient optimization is completely capable of solving the dual problem. Moreover it is possible to devise heuristics that construct feasible solutions to the original problem, using the Lagrangean subproblem solution.     

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.     

Design as a socially significant activity: An introduction

In understanding design-society relationships its crucial that we grasp what we are talking about when we use the word ‘design’. Too often the term is used not to denote the activity of designing but either the results of that activity (designed products) or the problems which originate that activity or both. But considered in this way the design process disappears from conceptual view. This lack of reference to design activity as such leads to ambiguities in formulations such as design-and-society as to what is meant in a particular formulation of the term. At the same time the related use of analogical models to explain or account for design prevents real understanding of what design is. The significance of design can only be understood, it is argued, by focusing on design activity. But once we do focus on design activity we find it has a wider social significance than has previously been thought.     

Powers of observation in creative design

Being perceptive is a trait highly valued in scientific and engineering professions. What a scientist or engineer notices while considering a problem, evaluating alternatives, or interpreting data has a profound impact on how a problem is viewed and solved. This paper focuses on processes we believe underlie being perceptive: firstly, preparation—becoming attuned to salient or important features; secondly, assimilation-detection and exploration of patterns (invariants) as well as anomalies; and thirdly, strategic control-heuristic strategies for exploring the implications of what has been observed. These processes play an integral role in characteristic activities within creative design, including problem reformulation, the emergence of properties and constraints on the solution, and the ability to incorporate into the design experimental feedback from the environment and from experiences with prototypes and previous designs. The paper presents a computational model incorporating these ideas, implemented in a system called IMPROVISER.     

Analyzing experiments with degradation data for improving reliability and for achieving robust reliability

Statistically designed experiments provide a proactive means for improving reliability; moreover, they can be used to design products that are robust to noise factors which are hard or impossible to control. Traditionally, failure‐time data have been collected; for high‐reliability products, it is unlikely that failures will occur in a reasonable testing period, so the experiment will be uninformative. An alternative, however, is to collect degradation data. Take, for example, fluorescent lamps whose light intensity decreases over time. Observation of light‐intensity degradation paths, given that they are smooth, provides information about the reliability of the lamp, and does not require the lamps to fail. This paper considers experiments with such data for ‘reliability improvement’, as well as for ‘robust reliability achievement’ using Taguchi's robust design paradigm. A two‐stage maximum‐likelihood analysis based on a nonlinear random‐effects model is proposed and illustrated with data from two experiments. One experiment considers the reliability improvement of fluorescent lamps. The other experiment focuses on robust reliability improvement of light‐emitting diodes. Copyright © 2001 John Wiley & Sons, Ltd.     

Design for manufacturing: application of collaborative multidisciplinary decision-making methodology

Design for manufacturing is often difficult for mechanical parts, since significant manufacturing knowledge is required to adjust part designs for manufacturability. The traditional trial-and-error approach usually leads to expensive iterations and compromises the quality of the final design. The authors believe the appropriate way to handle product design for manufacturing problems is not to formulate a large design problem that exhaustively incorporates design and manufacturing issues, but to separate the design and manufacturing activities and provide support for collaboration between engineering teams. In this article, the Collaborative Multidisciplinary Decision-making Methodology is used to solve a product design and manufacturing problem. First, the compromise Decision Support Problem is used as a mathematical model of each engineering teams’ design decisions and as a medium for information exchange. Second, game-theoretic principles are employed to resolve couplings or interactions between the teams’ decisions. Third, design-capability indices are used to maintain design freedom at the early stages of product realization in order to accommodate unexpected downstream design changes. A plastic robot-arm design and manufacturing scenario is presented to demonstrate the application of this methodology and its effectiveness for solving a complex design for manufacturing problem in a streamlined manner, with minimal expensive iterations.     

A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design

A number of multi-objective evolutionary algorithms have been proposed in recent years and many of them have been used to solve engineering design optimization problems. However, designs need to be robust for real-life implementation, i.e. performance should not degrade substantially under expected variations in the variable values or operating conditions. Solutions of constrained robust design optimization problems should not be too close to the constraint boundaries so that they remain feasible under expected variations. A robust design optimization problem is far more computationally expensive than a design optimization problem as neighbourhood assessments of every solution are required to compute the performance variance and to ensure neighbourhood feasibility. A framework for robust design optimization using a surrogate model for neighbourhood assessments is introduced in this article. The robust design optimization problem is modelled as a multi-objective optimization problem with the aim of simultaneously maximizing performance and minimizing performance variance. A modified constraint-handling scheme is implemented to deal with neighbourhood feasibility. A radial basis function (RBF) network is used as a surrogate model and the accuracy of this model is maintained via periodic retraining. In addition to using surrogates to reduce computational time, the algorithm has been implemented on multiple processors using a master–slave topology. The preliminary results of two constrained robust design optimization problems indicate that substantial savings in the actual number of function evaluations are possible while maintaining an acceptable level of solution quality.