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Typically, the preliminary design of mechanical components such as gears is carried out using standardized design processes such as those developed by the American Gear Manufacturers Association (AGMA). These design standards include a large number of ‘correction factors’ to account for various uncertainties. As the knowledge about these uncertainties increases, it becomes possible to include them systematically in the design procedure, thereby reducing the number of empirical correction factors. Robust design provides a way to design in the presence of various uncertainties. In this article, a design method is proposed to eliminate empirical correction factors and is demonstrated by eliminating two correction factors from the AGMA design standards for a spur gear, namely, the factor of safety in contact and the reliability factor by the formal introduction of uncertainty in the magnitude of load and material properties. The proposed method is illustrated with the design of an automotive gear with...

Gear design is a complex process. When new materials and manufacturing processes are introduced, traditional empirical knowledge is unavailable and considerable effort is required to find starting design concepts. This forces gear designers to go beyond the traditional standards-based design methods. The method presented here, the concept exploration method, is based on the compromise decision support problem and is demonstrated for gear design which requires the simultaneous exploration of geometry, material and manufacturing spaces to exploit synergies. The results obtained are in agreement with existing knowledge. To further develop design guidelines, ternary contour plots of goal achievement are created to show the interdependence among various design goals. These ternary charts help gear designers to visualize and understand the complexity and compromises involved and help them to make trade-offs among individual goals.

A bilevel optimization formulation of policy design problems considering multiple objectives and incomplete preferences of the stakeholders is presented. The formulation is presented for Feed-in-Tariff (FIT) policy design for decentralized energy infrastructure. The upper-level problem is the policy designer's problem and the lower-level problem is a Nash equilibrium problem resulting from market interactions. The policy designer has two objectives: maximizing the quantity of energy generated and minimizing policy cost. The stakeholders decide on quantities while maximizing net present value and minimizing capital investment. The Nash equilibrium problem in the presence of incomplete preferences is formulated as a stochastic linear complementarity problem and solved using expected value formulation, expected residual minimization formulation, and the Monte Carlo technique. The primary contributions in this article are the mathematical formulation of the FIT policy, the extension of computational policy de...

Model refinement is crucial to managing complexity in the design of complex engineered systems, particularly in the context of integrated product and materials design. In order to manage complexity, a systematic approach for model refinement is presented in this article. The approach is based on a value-of-information-based process performance indicator (PPI). The systematic approach and indicator can also be used to make refinement decision using multiple models when, for example, models are refined through model replacement. Both are particularly well-suited for integrated product and materials design, and other scenarios where perfect knowledge of the true system behavior and error-bounds is not available throughout the entire design space. In this article, the proposed approach is applied to model refinement for designing multifunctional blast resistant panels (BRPs). It is shown that the proposed approach based on the PPI is useful for model refinement particularly when accuracy of the prediction or ...

The appropriateness of a simulation model for engineering design is dependent on the trade-off between model accuracy and the computational expense for its development and execution. Since no simulation model is perfect, any simulation model for a system's physical behaviour can be refined further, although likely at an increased computational cost. Hence, the question faced by a designer is ‘How much refinement of a simulation model is appropriate for a particular design problem?’ The simplified nature of simulation models results in two types of uncertainty—variability, which can be modelled using probability distribution functions and imprecision, best modelled using intervals. Value-of-information has been used in the engineering design literature to decide whether to make a decision using the available information or to gather more information before making a decision. However, the main drawback of applying existing value-of-information based metrics for model refinement problems is that exi...