The powertrain mount is an important component, which reduces the vibrations
generated from the powertrain. Vibration isolation is achieved with help of
modal separation by predicting the kinetic energy fraction (KEF) and natural
frequency (NF) at each mode. The soft mounts reduce vibrations transferred from
the engine to the chassis, but if stiffness is very low, the displacement of the
mount will be high, and hence, the lifetime of the mount will be less. Vibration
isolation using a powertrain mount is a compromise between the displacement of
the mount, displacement of the center of gravity of the powertrain, KEF, and NF.
In this paper knowledge-based engineering (KBE) application methodology is
explained to initially find out the optimum values of mount parameters using
permutation and the combination of mount stiffness, mount angle, and mount
locations. Using these permutations and combinations, KEFs, NF, and the
displacement of the center of gravity of the powertrain are found. At different
loading conditions of the powertrain, results are sorted out based on the
highest KEFs and lowest displacement of the center of gravity of the powertrain.
Using this approach, the best mount stiffness, mount angle, and mount locations
are decided, which ultimately gives a good vibration isolation. Integration of
the calculated results with computer-aided design (CAD) software provides the
engine envelope with calculated displacement which is used for packaging the
engine compartment. All of the above features are verified with a test result
and modifications with case studies are proposed to improve the noise,
vibration, and harshness (NVH). The objective of this paper is to apply a KBE
approach in powertrain mounting system (PMS) design to improve the productivity
of the PMS design engineer and accelerate the design and development process. A
KBE approach helps to increase the productivity and accuracy of mount designing
parameters during the early design stage without the need for computer-aided
engineering (CAE) software.