In design, trade-offs are made between quality, cost and schedule. As the versatility of design tools has increased, so has the difficulty of making these trade-offs. This is especially true when choosing appropriate complexities for analytical and computational models. A fact known to designers and analysts is that more complex models yield more accurate results, but take longer to create and analyze. This paper shows that in an iterative design process there exists an optimal model complexity for each iteration, and alternatively, one optimal for the process as a whole. Quantitative methods for determining these complexities are developed, and three strategies for controlling the design modelling process are contrasted. Dependent on the stopping criteria, the best strategy will either minimize resource expenditure subject to quality requirements, or conversely, will maximize quality subject to resource constraints. These concepts are formalized with a view towards the design theory and methodology research community providing a first step in quantifying model trade-offs. An illustrative shape-optimal design example is shown wherein cost savings up to 84% were achieved. These savings were seen using several standard optimization algorithms. A customized optimization algorithm that incorporates this work should produce even better results.

This work provides an overall framework for discovering fundamentals of engineering design processes, which can eventually be used to improve process performance. This framework is based on the hypothesis that design processes are dynamic systems, and that the large body of system analysis techniques can be applied. To use these techniques, design processes are described on structural, behavioral, and functional levels. It appears that the process structure involves paired methods and modeling schemata, and that the application of a set of these pairs implements an evolution of metamodels with iterative refinement. That is, products may evolve through a sequence of mapping and iterative redesign steps. Process behavior has been observed, and the requirements and beginnings of a mathematical model are described. Process function is briefly discussed as necessary for development of process performance measures. Such a deep understanding of processes is required to implement effective selection of method and modeling schema pairs, and to facilitate the development of new pairs.

This paper analyzes the information environment of a product development program in a manufacturing enterprise as a domain in which intelligent agents can facilitate coordination. The information environment refers to information accessed, shared and generated in a product development program. The analysis suggests a structure for defining agents and agent interactions, and clarifies coordination needs in the context of the product development process. An architecture of intelligent agents embedded in a coordination environment is proposed as a means of providing infrastructure and process support to product development. The process of extension of requirements, and the selection and parametric refinement of handbook design solutions of a relief valve is used to illustrate the analysis and architecture for coordination in product development. The functional requirements identified for product development coordination are: (a) explicit representation of development team perspective and product model aspect definitions and mappings among them, (b) encapsulation of views and changes of the product models by intelligent agents, and (c) management of scenarios of interaction between perspectives through their agents. Representation of the dynamics and hierarchy in product development environments is outlined.

Naive interpretations of concurrent engineering may expect extreme parallelization of tasks and simultaneous accommodation of multiple perspectives. In fact, from our efforts at modeling tasks in a MEMS (Micro-Electro-Mechanical Systems) pressure sensor design project, it appears that data dependencies due to the structure of tasks and the product itself result in scenarios of decision and action that must be carefully coordinated. This paper refines a previously described information model for defining evolving contexts of product model aspects and team member perspectives, with software agents acting on behalf of team members to execute tasks. The pressure sensor design project is analyzed in the framework of the information model. A scenario of decision and action for design of the pressure sensor is modeled as a design process plan. Conflict on a shared parameter occurs as a consequence of introducing some parallelism between the capacitance and deflection agents in the process. We present a technique for negotiating such conflicts by definition and propagation of utility functions on decision parameters and axiomatic negotiation.

Comb actuators are electrostatic drives commonly used in micro-electromechanical systems (MEMS). These devices are typically fabricated using standard Integrated Circuit (IC) fabrication techniques such as lithography, oxidation, chemical vapor deposition and etching. The design of MEMS devices such as comb actuators is a complex process due to the dimensionality of the parametric design space and the parametric interdependencies imposed by performance, manufacturability and reliability constraints. We present a study aimed at the decomposition and structured organization of the various design and manufacturability considerations for comb actuators. The value of this knowledge organization is illustrated by the formulation and solution of a design problem that ensures optimal parametric design and layout of a comb actuator on a silicon wafer. We combine analytical models and specialized fabrication “design rules” to obtain optimal solutions for two interesting problems in MEMS comb actuator design.