Affordance-based Design vs Algorithmic Design

In classical design theory, algorithms and scoring mechanisms are typically used to identify the ideal path for design decisions. Though excellent at identifying functional requirement conformance to customer requirements, these algorithms fail to identify human factors associated with the design. Systems such as House of Quality and Axiomatic Design do not capture non-functional aesthetics, human-machine interaction quality, and other fuzzy concepts. While not directly advancing the product’s adherence to functional requirements, these metrics are still critical to ensure the product achieves economic success in the marketplace.

Maier et al. write that design theory while making great strides in the previous 50 years, has reached a point where there is difficulty making advances using existing design methodologies [1]. As discussed, existing methods lack sufficient rigor in human-desired characteristics. As a result, developing a system that encourages the use of relational design while maintaining a high priority on design affordances is necessary. This system may either be used to complement or instead of traditional algorithmic design systems.

Maier et al. write that modern design science involves algorithmic and empirical design methodologies. Systems such as axiomatic design use mathematical calculations to achieve an engineering result. Other methods, such as Quality Function Deployment, use empirically determined concepts applied to design problems to reach a solution. A third category of design methodologies uses guidelines as in Design for Assembly, Design for Casting, and any other Design for Manufacturing-style systems. Maier et al. conclude that these systems are inherently flawed by the lack of consideration of design affordances [2].

Design affordances are described in five primary ways. Design affordances may be complementary because they represent the interaction and interface between two subsystems. This may be visualized in terms of an object in a design that cannot independently perform its function without interacting with another thing in the design external to itself. An example of this could be a gear-transferring power or a combustion chamber without a piston to drive [2].

Design affordances may be either positive or negative. In this way, they may be considered to possess polarity. This describes the impact of the design on involved or uninvolved stakeholders. A combustion system providing motive force is a positive affordance to the customer using the combustion system as a transportation system; however, the pollutants injected into the atmosphere would be considered a negative affordance by individuals in the environment [2].

These affordances are not all equal. Some affordances meet design criteria better than others. For example, a high-end system may better meet the designer’s goals than a low-end system. In this way, the system may be regarded as having a higher quality of affordance. Additionally, the impacted system may possess multiple affordances giving it the aspect of multiplicity [2].

Design affordances may possess form dependence. This impacts the overall ability of the system to function based on the form. For example, a door may not work normally without custom shaping and framing in a particular car with a novel gull-wing style door system [2].

Since product designs are to be reviewed and purchased by a human customer, incorporating affordances into the design is an often-underserved area of design methodology development. Many design systems developed over the past fifty years have dealt primarily with algorithmic design. Formal algorithmic design can be proven to be imperfect when system parameters outside of those being analyzed are present.

Existing algorithmic-style systems provide a meaningful way to score designs based on metrics. Using standardized terminology and a defined scoring system, designs can be reconstituted as functional models and graded accordingly [3 4]. This allows for designs to be scored based on aspects of their operation in the context of the necessary functions desired and their interconnections. These interconnections are scored so that design equivalency may be determined [5].

Alternatively, other Quality Function Deployment opportunities exist to use algorithmic grading of designs. In the House of Quality system, algorithms drive the grading of functional requirements regarding how they relate to customer requirements [6 7]. This allows for efficient mapping of functions to customer requirements to prevent wasted design energy on non-value-added components and subassemblies.

In another system, design axioms contribute to a method by which designs are scored. In Suh’s Axiomatic Design system, systems modeling combines with linear algebra to allow for a matrix-style scoring system. By preserving independence between functional requirements and ensuring a minimalistic approach to design, Suh’s Axiomatic design system promotes a mathematical approach to design thinking [8 9].

Despite the benefits, these three mathematical systems do not offer designers a viable method of tracking affordances that do not directly contribute to functional requirements. As a result, designers are left without vital information that might otherwise contribute to product development. To prevent this situation, designs should consider using affordance-based design systems.

To establish affordance-based design as a robust design methodology, designers need to look no further than the field of perceptual psychology. Gibson’s work may be adapted to incorporate environmental constraints and human interactions with said environment. These interactions can be related in such a way as to build an understanding of improving the resulting affordances [10].

Understanding the future-state framework of an affordance-based design is the next step in achieving a holistic design solution that incorporates fuzzy human desires in the design structure. Defined relationships between design artifacts and the user must be created for each design in question.

Each relationship should be categorized as an affordance appropriately and entered into the tracking system used by the designer. Artifact-artifact affordances should similarly be recorded [1]. By performing these steps and optimizing for the recorded affordances, designers ensure that the user experience is improved in terms of easily qualifiable functions and less apparent aesthetic decisions. As a result, product marketability is improved, resulting in a greater return on investment.

REFERENCES

[1] Maier, Jonathan RA, and Georges M. Fadel. “Affordance based design: a relational theory for design.” Research in Engineering Design 20.1 (2009): 13-27

[2] Maier, Jonathan RA, and Georges M. Fadel. “Affordance-based design methods for innovative design, redesign and reverse engineering.” Research in Engineering Design 20.4 (2009): 225-239.

[3] Pahl, G., Beitz, W., Feldhusen, J., Grote, K., 2007, Engineering Design: A Systematic Approach, 3rd ed., Springer Science & Business Media, London.

[4] Stone, R. B. and Wood, K. L., 2000, “Development of a functional basis for design, “Journal of Mechanical Design, Vol. 122, p. 359. - 370

[5] Kurfman, M., Stone, R., VanWie, M., Wood, K., and Otto, K., “Theoretical Underpinnings of Functional Modeling: Preliminary Experimental Studies,” ASME Design Engineering Technical Conferences, 2000, Baltimore, Maryland.

[6] Hauser, J. and Clausing, D., 1988, “The House of Quality,” Harvard Business Review, May/June pp. 63-73.

[7] Olewnik, A. and Lewis, K., 2005, “Can a House Without a Foundation Support Design?,” ASME 2005 Design Engineering Technical Conference. Paper No. DETC2005-84765

[8] N. Suh and A. Farid, Axiomatic Design in Large Systems: Complex Products, Buildings, and Manufacturing Systems, Switzerland: Springer, 2016.

[9] N. Suh, “Axiomatic Design of Mechanical Systems,” Journal of Mechanical Design, vol. 117, no. B, pp. 2-10, 1995.

[10] Gibson J.,1979, The Ecological Approach to Visual Perception: The Theory of Affordances, Houghton Mifflin, pp 127–143