Search Results

This paper presents a study of axle system NVH (noise, vibration and harshness) performance using DFSS (Design for Six Sigma) methods with the focus on the system robustness to typical product variations (tolerances / manufacturing based). Instead of using finite element as the simulation tool, a lumped parameter system dynamics model developed in Matlab/Simulink is used in the study, which provides an efficient way in conducting large size analytical DOE (Design of Experiment) and stochastic studies. The model's capability to predict both nominal and variance performance is validated with vehicle test data using statistical hypothesis test methods. Major driveline system variables that contribute to axle gear noise are identified and their variation distributions in production are obtained through sampling techniques.

The role of NVH testing has evolved from a firefighting role and a period of exploration to a well defined standard test role in the product development and validation process. Integral to this process is robust engineering, which drives the need to execute many tests quickly, efficiently and accurately. This allows the NVH specialist to concentrate on interpretation of results and spend less time on the acquisition of data. As the volume of data grows, this creates the opportunity to data mine an NVH database to compare results from large sample sizes and focus on product variation. Today's NVH laboratory is accountable for producing high quality, consistent, timely, and cost effective test reports. The basic core of the test has to be easy to set up and execute for a novice, yet still allow for exploratory tests by specialists as necessary. The NVH laboratory is now subject to the same budgetary pressures and quality audits as other testing operations.