The natural world provides numerous cases for inspiration in engineering design. Biological organisms, phenomena, and strategies, which we refer to as biological systems, provide a rich set of analogies. These systems provide insight into sustainable and adaptable design and offer engineers billions of years of valuable experience, which can be used to inspire engineering innovation. This research presents a general method for functionally representing biological systems through systematic design techniques, leading to the conceptualization of biologically inspired engineering designs. Functional representation and abstraction techniques are used to translate biological systems into an engineering context. The goal is to make the biological information accessible to engineering designers who possess varying levels of biological knowledge but have a common understanding of engineering design. Creative or novel engineering designs may then be discovered through connections made between biology and engineering. To assist with making connections between the two domains concept generation techniques that use biological information, engineering knowledge, and automatic concept generation software are employed. Two concept generation approaches are presented that use a biological model to discover corresponding engineering components that mimic the biological system and use a repository of engineering and biological information to discover which biological components inspire functional solutions to fulfill engineering requirements. Discussion includes general guidelines for modeling biological systems at varying levels of fidelity, advantages, limitations, and applications of this research. The modeling methodology and the first approach for concept generation are illustrated by a continuous example of lichen.

With increased pressures coming from global competition and requirements for greater innovation in product development, designers are hard pressed to deliver designs of higher quality and variety using a repertoire of technological options from different disciplines. This interdisciplinary product development approach has not only removed many of the traditional constraints to design but has now given designers a much wider freedom of choice as to the best solution to a design problem. The focus of this paper is a knowledge-based design environment called Schemebuilder, which is a comprehensive and integrated suite of software tools aimed at supporting the designer in the rapid development of product design models in the conceptual, through embodiment stages of design. Illustrated is the use of the software tools in the qualitative generation of alternative schemes, by application of stored working and decomposition principles in the development of a function-means tree-like information structure. With mechatronic product development as the main theme, this paper describes a closely integrated methodology that incorporates a bond graph approach to continuous-time energetic systems and high-level Petri nets for the rigorous description of discrete-time information systems. Additionally, a technique is suggested for the decomposition of free format statements of need into the rigorously defined design context and required functions, which form the starting point of the function-means development process.

The term design fixation is often used interchangeably to refer to situations where designers limit their creative output because of an overreliance on features of preexisting designs, or more generally, an overreliance on a specific body of knowledge directly associated with a problem. In this paper, we argue that interdisciplinary interest in design fixation has led to increasingly broad definitions of the phenomenon that may be undermining empirical research efforts, educational efforts to minimize fixation, and the acquisition and dissemination of transdisciplinary knowledge about fixation effects. To address these issues, we recommend that researchers consider categorizing fixation phenomena into one of three classifications: unconscious adherence to the influence of prior designs, conscious blocks to change, and intentional resistance to new ideas. Next, we distinguish between concept-based design fixation, fixation to a specific class of known design concepts, and knowledge-based design fixation, fixation to a problem-specific knowledge base. With these distinctions in place, we propose a system of orders of design fixation, recommend methods for reducing fixation in inventive design, and recommend areas that are in need of further research within the field of design science.

A common way of dynamically scheduling jobs in a manufacturing system is by implementing dispatching rules. The issues with this method are that the performance of these rules depends on the state the system is in at each moment and also that no “ideal” single rule exists for all the possible states that the system may be in. Therefore, it would be interesting to use the most appropriate dispatching rule for each instance. To achieve this goal, a scheduling approach that uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a literature review of the main machine learning based scheduling approaches from the last decade is presented.

Creativity of designers can be enhanced by the application of appropriate triggers. The presence of triggers helps designers to search solution spaces. The searching of a solution space increases the possibility of finding creative solutions. Both representation and content of the triggers or stimulus to which the designers are exposed are believed to play a vital role in the representation and content of the outcome of the designers during problem solving. We studied the effect of representation of triggers on ideas generated by six design engineers while trying to solve a given problem. A variety of representations (video/animation and audio, text, explanation, and others) that are potentially useful to designers for five prespecified triggers were administered to each designer, who generated ideas in response to each trigger–representation combination individually. The effect of representations of these triggers on the content and representation of the solutions generated by the design engineers was studied. The results showed significant influence of the representation of the triggers on the representations, number, and quality of the resulting ideas that were generated.

This paper presents how the function–behavior–structure (FBS) ontology can be used to represent processes despite its original focus on representing objects. The FBS ontology provides a uniform framework for classifying processes, and includes higher level semantics in their representation. We show that this ontology supports a situated view of processes based on a model of three interacting worlds. The situated FBS framework is then used to describe the situated design of processes.

How can we prepare engineering students to work collectively on innovative design issues, involving ill-defined, “wicked” problems? Recent works have emphasized the need for students to learn to combine divergent and convergent thinking in a collaborative, controlled manner. From this perspective, teaching must help them overcome four types of obstacles or “fixation effects” (FEs) that are found in the generation of alternatives, knowledge acquisition, collaborative creativity, and creativity processes. We begin by showing that teaching based on concept–knowledge (C-K) theory can help to manage FEs because it helps to clarify them and then to overcome them by providing means of action. We show that C-K theory can provide scaffolding to improve project-based learning (PBL), in what we call project-based critical learning (PBCL). PBCL helps students be critical and give due thought to the main issues in innovative design education: FEs. We illustrate the PBCL process with several cases and show precisely where the FEs appear and how students are able to overcome them. We conclude by discussing two main criteria of any teaching method, both of which are usually difficult to address in situations of innovative design teaching. First, can the method be evaluated? Second, is the chosen case “realistic” enough? We show that C-K-based PBCL can be rigorously evaluated by teachers, and we discuss the circumstances in which a C-K-based PBCL may or may not be realistic.

The problem of finding optimal values in complex parameter optimization problems has often been solved with success by evolutionary algorithms (EAs). In many cases, these algorithms are employed as black-box methods over imprecisely known domains. Such problems arise frequently in engineering design. The principal barrier to the general use of EAs for those problems is the huge number of function evaluations that is often required. This makes EAs an impractical approach when the function evaluation depends on numerically heavy design analysis tools, for example, finite elements methods. This paper presents the use of kriging interpolation as a function approximation method for the construction of an internal model of the fitness landscape. This model is intended to guide the search process with a reduced number of fitness function evaluations.

Because of the increasing complexity of products and the design process, as well as the popularity of computer-aided documentation tools, the number of electronic and textual design documents being generated has exploded. The availability of such extensive document resources has created new challenges and opportunities for research. These include improving design information retrieval to achieve a more coherent environment for design exploration, learning, and reuse. One critical issue is related to the construction of a structured representation for indexing design documents that record engineers' ideas and reasoning processes for a specific design. This representation should explicitly and accurately capture the important design concepts as well as the relationships between these concepts so that engineers can locate their documents of interest with less effort. For design information retrieval, we propose to use shallow natural language processing and domain-specific design ontology to automatically construct a structured and semantics-based representation from unstructured design documents. The design concepts and relationships of the representation are recognized from the document based on the identified linguistic patterns. The recognized concepts and relationships are joined to form a concept graph. The integration of these concept graphs builds an application-specific design ontology, which can be seen as the structured representation of the content of the corporate document repository, as well as an automatically populated knowledge base from previous designs. To improve the performance of design information retrieval, we have developed ontology-based query processing, where users' requests are interpreted based on their domain-specific meanings. Our approach contrasts with the traditionally used keyword-based search. An experiment to test the retrieval performance is conducted by using the design documents from a product design scenario. The results demonstrate that our method outperforms the keyword-based search techniques. This research contributes to the development and use of engineering ontology for design information retrieval.

In this position paper, the ambiguity of functional descriptions in engineering is considered from a methodological point of view. Four responses to this ambiguity are discussed, ranging from defining a single meaning of function and rejecting the different meanings that are currently used in engineering to accepting these meanings as coexisting in engineering and taking function as a family resemblance concept. Rejecting the different meanings is described as the straightforward response to resolving the ambiguity of functional descriptions, yet in engineering research and design methodology it rather seems to be accepted that engineers do use the coexisting meanings side by side. In this paper, explanations are given of why this practice is beneficial to engineering. Then it is explored how the particular meaning that engineers attach to function depends on the tasks for which functional descriptions are used. Finally, the methodological implications of the four responses to the ambiguity of functional descriptions are discussed.

The growing movement of biologically inspired design is driven in part by the need for sustainable development and in part by the recognition that nature could be a source of innovation. Biologically inspired design by definition entails cross-domain analogies from biological systems to problems in engineering and other design domains. However, the practice of biologically inspired design at present typically is ad hoc, with little systemization of either biological knowledge for the purposes of engineering design or the processes of transferring knowledge of biological designs to engineering problems. In this paper we present an intricate episode of biologically inspired engineering design that unfolded over an extended period of time. We then analyze our observations in terms of why, what, how, and when questions of analogy. This analysis contributes toward a content theory of creative analogies in the context of biologically inspired design.

This paper explores the use of heuristics as cognitive strategies invoked during the process of design. Heuristics are reasoning processes that do not guarantee the best solution, but often lead to potential solutions by providing a simple cognitive “shortcut.” We propose that designers use specific design heuristics to explore the problem space of potential designs, leading to the generation of creative solutions. We test whether design heuristics can be taught to novices, and suggest their use will facilitate the design process at multiple levels of instruction. In the present empirical study, we evaluate a set of six instructional heuristics and validate their effectiveness with product concepts generated by novice designers. Six hundred seventy-three drawings were created by 120 first-year college students under four instructional conditions. Drawings were coded according to their content, use of heuristics, creativity, and practicality. The most creative concepts emerged from the experimental conditions where heuristics were introduced. Heuristics appeared to help the participants “jump” to a new problem space, resulting in more varied designs, and a greater frequency of designs judged as more creative. Our findings suggest that simple demonstration of design heuristics may, at times, be sufficient to stimulate divergent thinking, perhaps because these heuristics are readily grasped and contextual application is not required. Based on these findings, a conceptual model for design education emphasizing the importance of using a variety of heuristics is proposed. This model suggests that learning can be enhanced through exposure to a variety of design heuristics, and can supplement formal education and foster personal development in design learning.

Conceptual design is an early stage in the design process that involves the generation of solution concepts to satisfy the functional requirements of a design problem. Usually, there are many solutions to a design problem; therefore, there is scope for producing improved designs if one could explore a solution space larger than is presently possible. An approach would be to use the computer to synthesize a wide variety of concepts for a given problem, and allow designers to explore these before developing the most promising ones. Adopting a research approach based on developing basic representations, knowledge base, and reasoning procedures adequate for synthesizing concepts of existing devices and mechanisms, a computer program for synthesis of solutions to a class of mechanical design problems has been developed. For a given design problem, the program can produce an exhaustive set of solution concepts, in terms of their topological and spatial configurations, which can then be explored by designers. The program has been tested in two ways: (1) by comparing the candidate solutions produced by the program with those produced by designers in two real design case studies, and (2) by using three experienced designers to evaluate the solutions, generated by the program, for their novelty and usefulness. This paper presents the theoretical basis, research method, the theory and implementation of the synthesis approach. Also, the results of the above case studies and evaluations, and a discussion of further issues highlighted by the evaluations are presented.

Climate change, resource depletion, and worldwide urbanization feed the demand for more energy and resource-efficient buildings. Increasingly, architectural designers and consultants analyze building designs with easy-to-use simulation tools. To identify design alternatives with good performance, designers often turn to optimization methods. Randomized, metaheuristic methods such as genetic algorithms are popular in the architectural design field. However, are metaheuristics the best approach for architectural design problems that often are complex and ill defined? Metaheuristics may find solutions for well-defined problems, but they do not contribute to a better understanding of a complex design problem. This paper proposes surrogate-based optimization as a method that promotes understanding of the design problem. The surrogate method interpolates a mathematical model from data that relate design parameters to performance criteria. Designers can interact with this model to explore the approximate impact of changing design variables. We apply the radial basis function method, a specific type of surrogate model, to two architectural daylight optimization problems. These case studies, along with results from computational experiments, serve to discuss several advantages of surrogate models. First, surrogate models not only propose good solutions but also allow designers to address issues outside of the formulation of the optimization problem. Instead of accepting a solution presented by the optimization process, designers can improve their understanding of the design problem by interacting with the model. Second, a related advantage is that designers can quickly construct surrogate models from existing simulation results and other knowledge they might possess about the design problem. Designers can thus explore the impact of different evaluation criteria by constructing several models from the same set of data. They also can create models from approximate data and later refine them with more precise simulations. Third, surrogate-based methods typically find global optima orders of magnitude faster than genetic algorithms, especially when the evaluation of design variants requires time-intensive simulations.

A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.

Design rationale is a topic that implies different things to different people. To some it implies argumentation and frameworks for argumentation. To others it implies the documentation of design, like that required for many types of industrial or government work. Still others describe design rationale as the capture and potential reuse of normal communication about design. These perspectives of design rationale use different representations, which influence their ability to capture and to retrieve and use information. We propose an integrated approach to design rationale where design communication is captured and, over time, incrementally structured into argumentation and other formalisms to enable improved retrieval and use of this information. Two systems, PHIDIAS and the Hyper-Object Substrate, are used to demonstrate: (1) how to capture and integrate a variety of design information, (2) how to support the structuring of unstructured information, and (3) how to use design information to actively support design.

This paper addresses the construction of digital twins (exact mirror images of real-world in cyberspace) using hidden Markov models for the futuristic manufacturing systems known as Industry 4.0. The proposed digital twin consists of two components namely model component and simulation component. The model component forms a Markov chain that encapsulates the dynamics underlying the phenomenon by using some discrete states and their transition probabilities. The simulation component recreates the phenomenon using a Monte Carlo simulation process. The efficacy of the proposed digital twin construction methodology is shown by a case study, where the digital twin of the surface roughness of a surface created by successive grinding operations is described. The developers of the cyber-physical systems will be benefitted from the outcomes of this study because these systems need the computable virtual abstractions of the manufacturing phenomena to address the issues related to the maturity index of futuristic manufacturing systems (i.e., understand, predict, decide, and adopt).

The thesis presented here is that the result of engineering is the design, construction, or operation of systems or their subsystems and components and that the teaching of systems must be central to engineering education. It is maintained that current undergraduate engineering curricula do not give the student adequate appreciation of this major intellectual element of their profession. Five proposals for approaches to correct this deficiency are offered: opportunities for clinical practice throughout all the undergraduate years; the use of distributed interactive simulation technology in semester-long projects; courses or course material on the phenomenology and behavior of systems; use of project management tools in engineering clinics; and encouraging engineering faculty to spend some part of their sabbaticals engaged in system design or operation. Issues of implementation are addressed, including the scaling of these ideas to universities that must meet the needs of large numbers of students.

Extending product life is one of the hopeful approaches to reduce the environmental issue, which is one of the most critical issues of today. However, many products are thrown away because of obsolescence of functions and their performance. Therefore, we should design products to be functionally upgradable. Moreover, such upgradable products may create business chances at later stages of product life cycles. The objective of this research is to propose a design methodology for upgradability. This methodology employs a functional modeling scheme, FBS modeling, because upgrade design is a distinctive application of functional design that aims at maximizing functional flexibility with minimal structural changes after the product is manufactured. Here, the functional flexibility refers to an ability of a product to adapt its functions to changes of user needs. This paper proposes and models design processes and design operations in the upgrade design. Especially, the methodology supports finding out candidates of modifications of the function structure and configuration of a platform, which is common structure of a product among several generations, and upgrade modules. One of its central issues of upgrade design is treatment of future uncertainty. For this purpose, we propose two design strategies: delayed selection of components, and expanding and shrinking platform. A prototype system and a case study of upgrade design for a vacuum cleaner are also illustrated. The case study indicates that the system succeeded in systematically supporting a designer to execute the design methodology. Regarding the functional design, as an extension of FBS modeling, this paper proposes a method to relate abstract entity concepts in FBS modeling to concrete components through a quantitative behavior model and range calculation, in addition to deployment of FBS modeling for the design methodology.

In this paper, we propose a population-based evolutionary multiobjective optimization approach to design combinational circuits. Our results indicate that the proposed approach can significantly reduce the computational effort required by a genetic algorithm (GA) to design circuits at a gate level while generating equivalent or even better solutions (i.e., circuits with a lower number of gates) than a human designer or even other GAs. Several examples taken from the literature are used to evaluate the performance of the proposed approach.