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Noise and vibration evaluation of driveline clunk can be challenging as it is the result of driver input conditions and is transient in nature. As with many noise and vibration challenges, the use of computer-aided engineering (CAE) simulation is useful as it allows for detailed study of the phenomenon and prediction of potential improvements. A hybrid approach of physical test-based measurements and CAE analysis can be used to leverage the advantages of CAE in a comprehensive evaluation including the total vehicle noise, vibration, and harshness (NVH) performance. In this paper we present work performed to facilitate engineering evaluations of driveline clunk using both measured test and CAE simulation data. We explain how we used measured test data to inform the CAE analysis, how the simulation approached modelling of the transient clunk event, and how the measured data was used to provide contextual sound for realistic evaluation of the CAE output as heard by the passengers.

A manufacturer of automotive equipment set out to implement a process to include sound and vibration quality targets for powertrain and road noise. CAE models have been successfully used in the early phase of the vehicle development process, but the use of these models to assess the customer’s subjective sound and vibration experience is often missing. The goal here was to use a CAE model driven by sound and vibration quality targets for early identification of problem areas based on jurors’ preference. These quality targets were cascaded via Source-Path-Contribution (SPC), and optimizations were performed to meet the targets using the CAE model.

Typical approaches to regulating sound performance of vehicles and products rely upon A-weighted sound pressure level or sound power level. It is well known that these parameters do not provide a complete picture of the customer’s perception of the product and may mislead engineering efforts for product improvement. A leading manufacturer of agricultural equipment set out to implement a process to include sound quality targets in its product engineering cycle. First, meaningful vehicle level targets were set for a tractor by conducting extensive jury evaluation testing and by using objective metrics that represent the customer’s subjective preference for sound. Sensitivity studies (“what-if” games) were then conducted, using the predicted sound quality (SQ) index as validation metric, to define the impact on the SQ performance of different noise components (frequency ranges, tones, transients).

A unique Matlab-based coded engineering software tool (Time-Frequency Analyzer Core®) was developed that allows users to process acquired time data to help in identifying sources and paths of noise and vibration (in the experience of the authors). The Time-Frequency Analyzer Core (TFAC) software does not replace commercial off the shelf software/hardware NV specific tools such as modal analysis, ODS, acoustic mapping, order tracking, etc., rather it aims at providing basic, yet powerful data inspection and comparison techniques in a single software tool that facilitates drawing conclusions and identifying most effective next steps. The features and advantages of using this software tool will be explained, along with a description of its application to a few different cases (automotive and off highway/agricultural).

The role of NVH test development has changed from addressing a system-level NV concern late in the design cycle (firefighting) to having well established NV optimized test procedures in place. One way this is achieved is by leveraging the information gained during troubleshooting of current product to improve the future product development process for noise and vibration. Today, most NV groups/laboratories use optimized test procedures for creating accurate, consistent, and efficient test results. This still requires expertise to post-process data, compute targets and interpret results to guide product development. This step is often overlooked and, in recent years, due to the lack of NV expertise of “younger” labs (typically in non-automotive industries) or of more established labs affected by the economic downturn (early retirements, lay-offs, especially in the automotive industry) there has been a growing need for automated post-processing “intelligent” procedures.

Vehicle NVH (Noise Vibration & Harshness) is of continued concern to customers in this increasingly competitive market and driveline NVH performance is a key factor in overall vehicle quality. A typical way to increase this quality is the use of axle end of line test stands that utilize NVH signal analysis methods to offer pass/fail criteria. In the manufacturing environment there are high costs associated with axle assemblies that are rejected due to the amount of time for NVH analysis to determine root cause for the failure. Of more interest to both product development and manufacturing activities is the ability to understand the root cause of the failures from the axle end of line test stand. This information can improve the manufacturing process by eliminating errors, streamlining re-build activities, aiding in product design improvements, and in turn reducing cost.