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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).

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.