Powertrain Mounting Robust Evaluation Methodology Utilizing Minimal Hardware Resources

2017-01-1823

06/05/2017

Features
Event
Noise and Vibration Conference and Exhibition
Authors Abstract
Content
Powertrain mounting systems design and development involves creating and optimizing a solution using specific mount rates and evaluation over multiple operating conditions. These mount rates become the recommended “nominal” rates in the specifications. The powertrain mounts typically contain natural materials. These properties have variation, resulting in a tolerance around the nominal specification and lead to differences in noise and vibration performance. A powertrain mounting system that is robust to this variation is desired. The design and development process requires evaluation of these mounts, within tolerance, to ensure that the noise and vibration performance is consistently met. During the hardware development of the powertrain mounting system, a library of mounts that include the range of production variation is studied. However, this is time consuming. In this paper, a methodology is described to reduce the hardware evaluation time and provide a recommended optimal solution that is robust in the presence of production mount property variation. The method is based on making strategic, but limited, hardware measurements which are used to create a response surface model of critical noise and vibration performance attributes. The response surface model is employed to study the effects of mount property variation on the system. The results are validated with an analytical mass/stiffness model and confirmed with hardware testing. It is shown that change in mount stiffness from mount property variation is a predictable and repeatable result. The method can reduce evaluation time and provide a robust solution with acceptable accuracy.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1823
Pages
6
Citation
Kinchen, D., "Powertrain Mounting Robust Evaluation Methodology Utilizing Minimal Hardware Resources," SAE Technical Paper 2017-01-1823, 2017, https://doi.org/10.4271/2017-01-1823.
Additional Details
Publisher
Published
Jun 5, 2017
Product Code
2017-01-1823
Content Type
Technical Paper
Language
English