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The optimal selection of vehicle body and powertrain mounts from “mount libraries” is one of the major undertakings to achieve optimal vehicle dynamics and N&V performance through the reuse of existing mount designs. The great challenges of the process are due to the facts that conventional optimization procedures, either through simulation or DOE, can not be used directly because the stiffness rates of the mounts are scattered and bundled. Sorting out the best through hardware tests is generally unrealistic simply due to the huge number of mount combinations. This paper presents a new approach to the optimal mount selection, and demonstrates through applications that it is efficient and reliable. This approach characterizes a mount by its effective stiffness rate and evaluates its deviation from an associated target. Continuous dummy variables are used to determine the selection targets through conventional processes for performance optimization.

This paper presents the fundamental principles of variation analysis and robust design for dimensional datum schemes. The kinematics equations for rigid body motions are simplified through linearization. The simplified formulations explicitly relate the dimensional deviations of a rigid part with its datum scheme configuration and dimensional variations at datum target points. This simplified approach can be used with either the first order Taylor series approximation or Monte Carlo simulation to study the statistical characteristics of datum scheme variations. A headlamp case study is presented that shows the application procedures and demonstrates that both Taylor series and Monte Carlo methods generate comparable results, but the former offers more efficiency and convenience due to its close form formulation. This approach has found many applications especially in on-site problem solving and fast what-if studies.