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Historically, the teaching of design theory in an engineering curriculum has been relegated to a senior capstone design experience. Presently, however, engineering design concepts and courses can be found through the entirety of most engineering programs. Educators have recognized that engineering design provides a foundational platform that can be used to develop educational strategies for a wide array of engineering science principles. More recently, educators have found that product archaeology provides an effective platform to develop scalable learning materials, strategies, and educational innovations across these design courses. This paper presents and discusses how product archaeology has been incorporated at a large research university in two design-related courses for mechanical engineering students: (1) a sophomore-level course and (2) a senior-level class. More specifically, details are reported regarding how and how easily global, societal, economic, and environmental factors were emphasized in the curricula of these courses. Next, the paper shares the qualitative and quantitative assessment tools and methods used to determine the impact of incorporating a product archaeology paradigm in the courses. Finally, the results are reported which demonstrate a significant increase in the students' perceptions across a number of skill and knowledge areas related to ABET-required Outcome h without negatively impacting other important academic areas. Results demonstrate a significant increase in student perception across a number of skill and knowledge areas critical to the next generation of engineers.

As college students enter engineering, they face challenges when they navigate across various transitions. These challenges impact whether a student can successfully adapt to the rigorous curricular requirements of an engineering degree and to the norms and expectations that are particular to engineering. This article focuses on the transitions between disciplinary literacies, primarily addressing how ideas are communicated and which types of semiotic representations are valued, required, and ignored by the different cultures that come into play both prior and during a student's engineering education. Specifically, it addresses navigation: (1) Across secondary to postsecondary institutional expectations, contrasting common curricular experiences of secondary students and postsecondary curricular expectations; (2) Between and within disciplines (including mathematics and science), which often have different ways of communicating and representing a single concept, especially in theoretical applications and practical calculations; and (3) From the traditional pedagogies of classrooms to the different methods and expectations of laboratory settings (for example, collaborative engagement in planning, experimentation, an understanding of experimental error, and an understanding of how all three are communicated and represented).

The design of large scale complex engineering systems requires interaction and communication between multiple disciplines and decentralized subsystems. Game theory has been used previously to model interactions between distributed multidisciplinary design subsystems and predict convergence and equilibrium solutions. These game theoretic models assume that designers make perfectly rational decisions by selecting solutions from their rational reaction set. For convergent decentralized design problems, the intersection of the designers' rational reaction set results in a Nash equilibrium solution where designers may converge if they were making rational choices. However, this equilibrium solution is rarely optimal from a multi-objective optimization perspective. Further, empirical studies reject the claim that decision makers always make rational choices and the concept of bounded rationality is used to explain such behavior. In this paper, a framework is proposed that uses the idea of bounded rationality in conjunction with set-based design, metamodeling, and multi-objective optimization techniques to improve solutions for convergent decentralized design problems. Through the use of this framework convergent decentralized design problems converge to solutions that are superior to the Nash equilibrium.

One perspective of a design process in the engineering design community is that it is largely a process marked and defined by a series of decisions. The fundamental assumption in most developed design decision support methodologies is that decision makers make rational choices; that is. choices that maximize the payoff for the predicted outcome. Decisions that do not maximize the predicted payoff are termed as mistakes or irrational choices and discarded. However, research in behavioral economics, psychology, and cognitive science has studied the human mind and suggested the notion of 'bounded rationality' to explain decision errors. Bounded rationality refers to the intrinsic inability of human beings to accurately choose 'rational' options prescribed by decision models such as expected utility. This paper extends the notion of bounded rationality within engineering design. Specifically, this paper studies the design of complex systems that require interaction among several different subsystems contributing to the overall product design. For convergent decentralized design problems, rational designers converge to equilibrium solutions that lie at the intersection of their individual rational reaction sets. These equilibrium solutions are usually not Pareto optimal and due to the dynamics of the designers' interaction in collaborative design, it is rarely possible for them to converge to Pareto optimal solutions. However, when models for bounded rationality are introduced into individual designer behavior, it is seen that the converged solutions can improve the resulting solution. Bounded rational decisions within decentralized design are modeled, and the effects of propagating such decisions within a design process are studied. [DOI: 10.1115/1.2988479] Keywords: bounded rationality, multi-objective optimization, decentralized design, game theory

A number of approaches for multi-attribute selection decisions exist, each with certain advantages and disadvantages. One method that has recently been developed, called the hypothetical equivalents and inequivalents method (HELM) supports a decision maker (DM) by implicitly determining the importances a DM places on attributes using a series of simple preference statements. In this and other multi-attribute selection methods, establishing consistent preferences is critical in order for a DM to be confident in his/her decision and its validity. In this paper, a general preference consistency method is developed, which is used to ensure that a consistent preference structure exists for a given DM. The method is demonstrated as part of HELM, but is generalizable to any cardinal or ordinal preference structure, where the preferences can be over alternatives or attributes. These structures play an important role in making selection decisions in engineering design including selecting design concepts, materials, manufacturing processes, and configurations, among others. The theoretical foundations of the method are developed and the need for consistent preferences is illustrated in the application to a drill selection case study where the decision maker expresses inconsistent preferences. [DOI: 10.1115/1.2761921]

Supporting the decision of a group in engineering design is a challenging and complicated problem when issues like consensus and compromise must be taken into account. In this paper, we present the foundations of the group hypothetical equivalents and inequivalents method and two fundamental extensions making it applicable to new classes of group decision problems. The first extension focuses on updating the formulation to place unequal importance on the preferences of the group members. The formulation presented in this paper allows team leaders to emphasize the input from certain group members based on experience or other factors. The second extension focuses on the theoretical implications of using a general class of aggregation functions. Illustration and validation of the developments are presented using a vehicle selection problem. Data from ten engineering design groups are used to demonstrate the application of the method. [DOI: 10.1115/1.2197836]

As the complexity of system design has substantially increased, so, too, has the need for incorporating tradeoffs into the design process. When such tradeoffs are incorporated, the optimal system performance of each objective is sacrificed to increase the overall range of the system's functionality. Reconfigurable systems have been defined as systems designed to maintain a high level of performance through real-time change in their configuration when operating conditions or requirements change in a predictable or unpredictable way. When effectively applied, these reconfigurable systems have the ability to achieve the optimal performance for all system objectives, while also meeting the different constraints of each objective. A method of designing effective reconfigurable systems is presented that focuses on determining how the design variables of a system change, as well as investigating the stability of a reconfigurable system through the application of a state-feedback controller. This method is then applied to a study involving the design of a reconfigurable Formula One race car traversing a predefined racetrack where the design variables change values to achieve maximum performance.

In this paper, the problem of selecting from among a set of alternatives using multiple, potentially conflicting criteria is discussed. A number of approaches are commonly used to make these types of decisions in engineering design, including pairwise comparisons, ranking methods, rating methods, weighted sum approaches, and strength of preference methods, In this paper, we first demonstrate the theoretical and practical flaws with a number of these commonly employed methods. We demonstrate the strengths and weaknesses of the various decision-making approaches using an aircraft selection problem. We then present a method based on the concept of hypothetical equivalents and expand the method to include hypothetical inequivalents. Visualization techniques, coupled with an indifference point analysis, are then used to understand the robustness of the solution obtained and determine the appropriate additional constraints necessary to identity a single robust optimal alternative. The same aircraft example is used to demonstrate the method of hypothetical equivalents and inequivalents. [DOI: 10.1115/1.1814389]

In this paper, a framework for the concept of flexibility in complex system design is presented. This is one of the first of many steps toward developing new design methods for designers that will aid them in the development of customizable systems that meet the requirements of multiple customers and multiple tasks. The hope is that this paper will provide both a starting point from which academia and industry can move forward in developing new design methods for flexible systems and a basis for establishing a standard lexicon for use when referring to flexible system design.

The design of complex systems can involve the selection of several subsystem designs. We investigate the problem of selecting discrete concepts from multiple, coupled subsystems. This problem is one where measures of merit for both subsystem (local) and system (global) levels are present. An approach is developed to obtain the sets of preferred subsystem design concepts. Graph theory is used to represent the coupled selection problem where the nodes of the graph are the subsystem design choices and the arcs connecting the nodes indicate the relationships between the subsystems. Optimization techniques from graph theory and physical programming are combined to form an approach to model and solve this problem. This approach can be used to identify a given number of successful, or feasible, subsystem combinations that represent design alternatives. Once the promising subsystem designs are obtained at the conceptual design stage, focus can be restricted to these chosen design alternatives for further testing and refinement at a later embodiment design stage. Although the examples presented in this paper involve conceptual design, the presented approach can be used with any coupled discrete selection problem.

A design methodology that combines the robust design complex with the game theoretic approach to multidisciplinary design has been developed. Ranges of solutions were derived for variables coupled between multiple disciplines using the Stackelberg leader/follower rules. This methodology provided the flexibility that proved helpful in resolving the disputes and conflicts of rationality that were present between the varying interests of multiple disciplines.

Multiple decision makers, design teams, or organizations were cited as essential components in designing multidisciplinary systems. The game theory approach was used to model decision-making processes facilitated by concurrent engineering principles and was established useful in many other decision-making sectors such as economics, politics, and strategic warfare.