Design broadly defined deals with mapping from societal wants or needs to means for satisfying these needs. Axiomatic design is a well-known approach to design that was initially proposed by Nam P. Suh in the late 1970s. Since that time, it has underpinned much academic research in engineering design; it has been taught internationally as part of engineering curricula; and it has been used across many industries. This paper presents a summary of axiomatic design and provides practical suggestions for best practices in implementation and education.
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Axiomatic design (AD) was created by N.P. Suh to create an "academic [discipline] for design and manufacturing" and detailed in three books [1-3]. The starting point for axiomatic design is that "there exists a fundamental set of principles that determines good design practice" [1] in contrast to views that good design cannot be taught, but can only be learned through experience. A primary motivation for developing axiomatic design is education. To be effective "the student must be taught to see the big picture and [be taught] the ability to conceptualize a solution, as well as how to optimize an existing product or process" [1]. The keys are "correct principles and [methods] to guide decision making in design; otherwise, the ad hoc nature of design cannot be improved" [1].
initiate constructive discussion among the community in CIRP: First it provides a comprehensive, current review and summary of key work that has been done in the field of Axiomatic Design. Second, based on this review, the authors provide their conclusion on whether Axiomatic Design research has achieved Suh's vision of providing a means to teach and practice good design.
At its most basic, axiomatic design is composed of five concepts. These concepts are domains, hierarchies, zigzagging, and the two design axioms. The theory was later expanded by Suh to include concepts of time-varying large systems, complexity in terms of uncertainty and strategies for reducing complexity [3, 4].
Axiomatic Design Process. A design process is a sequence of activities in which engineers or designers develop and/or select the means to satisfy a set of objectives subject to constraints. The way that AD summarizes this is that designers map from "what do they want to do?" to "how do they choose to do this?" [1]. The AD design process consists of at least three activities: "problem formulation," "synthesis" (concept
Domains and mapping. During the design process, the task which is being addressed can be divided into four domains [6]. The four domains are generalized as the customer domain, the functional domain, the physical domain, and the process domain. Associated with each domain are the design elements it contains. AD terms these customer attributes (CAs), functional requirements (FRs), design parameters (DPs), and process variables (PVs). The design axioms are applied as designers map between domains [1, 2]. In addition to these elements, constraints on the design task are not restricted to a particular domain, but limit the choice of acceptable solutions [1].
Functional Requirements. Functional requirements are "defined to be the minimum set of independent requirements that completely characterize the design objectives for a specific need" [1]. A key observation by Suh is that these FRs must be specified in a "solution-neutral environment" in terms of the functions to be achieved, not in terms of particular solutions. Related to the solution neutrality requirement is the inherent independence of FRs. That is, when FRs are defined in the functional domain, there is no pre-existing interdependence between the FRs, and in principle it is possible to satisfy the FRs independently.
Design Parameters. Design parameters are defined as "the set of elements of the design object that have been chosen to satisfy the FRs" [1]. These can be items used in product design: geometric parameters, material properties, part features, assemblies, and so on. Beyond this, they can consist of intangible items: strategies, methods, software classes, etc.
Process Variables. Process Variables include fabrication methods, resources, and implementation plans to materialize the design parameters. In the axiomatic design process, a directed relationship exists between domains: CAs to FRs, FRs to DPs, and DPs to PVs. This directed relationship is referred to as design mapping, in which the objectives (what) are mapped to means to achieve them (how).
The first fundamental principle in the axiomatic design theory is that a design task must begin with carefully defining the goals and objectives of design. Only after they are clearly and explicitly stated, can the designers proceed to conceive appropriate solutions to achieve them. While it sounds simple, our experiences and observations abound with examples where a design project suffers due to poorly and ambiguously defined requirements or requirements that are constantly shifting during the design process. Also, many bad designs come about when designers mix "what" and "how" in the same domain.
Hierarchies. The design process progresses from a system level, or a high level of abstraction, to levels of more detail. The decisions about the design object are represented in three of the domains with design hierarchies: an FR hierarchy, a DP hierarchy, and a PV hierarchy.
Zigzagging. The designers go through a process in which they zigzag between domains in decomposing the design problem. At a given level of the design hierarchy, a set of functional requirements exists. Before these FRs can be decomposed, the corresponding design parameters must be
selected. Once a functional requirement can be satisfied by a corresponding design parameter, that FR can be decomposed into a set of sub-requirements, and the process is repeated. The designers follow the zigzag approach until they have decomposed the problem to a point where the solutions to the remaining sub-problems are known.
Decision Making in Axiomatic Design. Axiomatic design provides guidelines consisting of axioms, theorems, and corollaries that specify the relationships that should exist between the FRs and the DPs of a design.
These axioms were generalized from observations of good design decisions. They establish the minimum acceptability for a design solution, and enable the identification of the best among several proposed. In addition to the axioms, AD has many theorems and corollaries that follow from the two axioms.
System Architecture and Modularity. In addition to hierarchies, Suh has proposed definition of system modules according to the design hierarchies combined with the relationships within the design matrices [8, 9]. AD approach to modularity contrasts sharply with other approaches that focus on defining modules based on DPs, rather than based on design matrices.
In generating an FR, the designers define a desired target value for the FR. They also specify an appropriate tolerance region about this target value; this region is known as the design range. Each available design alternative is able to provide the desired FR within its system range. This system range is the region in which the design alternative performs relative to the design range. The intersection of the system range and the design range is called the common range.
The probability of success, labeled Ps to indicate its basis on the tolerance of the FR is defined as the ratio of the common range to the system range, and the information content, I, is the natural log of this. For uncoupled designs the FRs may be considered independent variables. Thus the total information content for a set of n FRs in an uncoupled design is equal to sum of information contents for each of the n FRs.
The principled nature of axiomatic design differentiates it from many existing design methodologies that study design processes and aim to extract descriptive and prescriptive design rules and guidelines for successful designs. AD teaches a very insightful thinking process, especially useful for the very early stage of design.
Time-Independent Complexity. Time-independent complexity can be either real or imaginary. Real complexity describes uncertainty due to non-zero information content. Part of the system range is outside the design range. Imaginary complexity is due to lack of knowledge of the system, for example, a design matrix that is decoupled, but FRs that cannot be met because the sequence of setting the DPs is not known.
conceptual nature of the subject" [2]. Initial interest in the topic of teaching axiomatic design was low in the first two axiomatic design conferences with only one paper in this focus area of "teaching and learning methods" [18]. More recently, the numbers of papers on axiomatic design education have grown [19-23].
Graduate Level. Axiomatic design was originally taught as a graduate-level course at MIT by Prof. Suh starting in the 1987-88 academic year using a draft of his book The Principles of Design [1]. Next it was taught as a summer course at MIT during the 1990s [24]. A more recent iteration of the course from 2005 that incorporates Suh's complexity theory [3] can be found on MIT's Open CourseWare.
Undergraduate Level. The Korea Advanced Institute for Science and Technology (KAIST) conducted a bold initiative for all freshman students to study design [25-28] as part of efforts towards achieving the university's goals and creating a campus-wide culture of design thinking [29]. At KAIST, the goals were to effect a deep change in the students' thinking, view of their role in the world, and mode of working [28]:
Precollege and Community College Level. The axiomatic approach to design has also been applied to community college education in automatic technology [30], and in inspiring a the FRAME design process model for primary through grade 12 (P-12) engineering education [31, 32].
Industrial Workshops. Industrial workshops or short courses have been used to introduce companies to axiomatic design and to work on solutions to particular design tasks. An example of a similar workshop can be found at the website for ICAD2013, see AxiomaticDesign.com. 2ff7e9595c
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