Identifying failure paths and potentially hazardous scenarios resulting from component faults and interactions is a challenge in the early design process. The inherent complexity present in large engineered systems leads to nonobvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques focus on single hazards (fault trees), single faults (event trees), or lists of known hazards in the domain (hazard identification). Early in the design of a complex system, engineers may represent their system as a functional model. A function failure reasoning tool can then exhaustively simulate qualitative failure scenarios. Some scenarios can be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. The incidences of specific subgraphs in graph representations of known hazardous scenarios are used to train a classifier to distinguish hazard from nonhazard. The algorithm identifies the scenario most likely to be hazardous, and presents it to the engineer. After viewing the scenario and judging its safety, the engineer may have insight to produce additional hazard rules. The collaborative process of strategic presentation of scenarios by the computer and human judgment will identify previously unknown hazards. The feasibility of this methodology has been tested on a relatively simple functional model of an electrical power system with positive results. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.

When developing a product, designers must decide what consumer variation will be addressed and how they will address it, because each consumer has a unique set of human factors, preferences, personal knowledge, and solution constraints. Numerous design methodologies exist to support the design of a product or set of products that address this consumer variation. However, currently there is little work supporting the selection of a design methodology, resulting in an ad hoc or a priori decision before conceptual design begins. This paper presents an affordance-based design method for use prior to conceptual design to help designers understand the consumer variation that is present. This facilitates the creation of a product or set of products that meets the demands of both the consumer(s) and the organization that is developing the product. Once consumer variation is understood, conceptual design can be performed with a more complete understanding of the overall problem.

New styles can be created by modifying existing ones. In order to formalize style change using grammars, style has to be formally defined in the design language of a grammar. Previous studies in the use of grammars for style change do not give explicit rationale for transformation. How would designers decide which rules to modify in a grammar to generate necessary changes in style(s) of designs? This paper addresses the aforementioned issues by presenting a framework for strategic style change using goal-driven grammar transformations. The framework employs a style description scheme constructed by describing the aesthetic qualities of grammar elements using adjectival descriptors. We present techniques for the formal definition of style in the designs generated by grammars. The utility of the grammar transformation framework and the style description scheme is tested with an example of mobile phone design. Analyses reveal that constraining rules in grammars is a valid technique for generating designs with a dominance of desired adjectival descriptors, thus aiding in strategic style change.