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Vehicle manufacturers face increasing challenges in auditing the build quality of their vehicles while considering increasing consumer demands regarding NVH performance. This effect is compounded with the rise in electric and hybrid vehicles. The ability to audit vehicles for a variety of noise types is becoming increasingly important; these include powertrain noise, road noise, and wind noise. An automated measurement system was developed with specific algorithms and sound quality metrics to not only audit vehicle production quality but to add objective data, pass-fail criteria, and trend analysis.

Source-path-contribution (SPC) analysis, or transfer-path-analysis, is a test based method to characterize noise and vibration contributions of a complex system. The methodology allows for the user to gain insight into the structural forces and acoustic source strengths that are exciting a system, along with the effects of the structural and acoustic paths between each source and a receiver position. This information can be utilized to understand which sources and/or paths are dominating the noise and vibration performance of a system, allowing for focused target cascading and streamlined troubleshooting efforts. The SPC process is widely used for automotive applications, but is also applicable for a wide range of product types. For each unique application the basic SPC principles remain constant, however best practices can vary for both measurement and analysis depending on the type of system being evaluated.

Binaural recordings are often used for added realism in subjective listening studies, but are commonly played back in environments that are different than those in which the recordings were taken. An important component of the added realism is the ability of the listener to locate the acoustic sources in a three dimensional space. While humans can generally do a good job of locating acoustic sources through inter-aural time differences (ITD) and inter-aural intensity differences (IID), some well documented ambiguities exist when using these acoustic cues by themselves (i.e. ITD and ILD for a source in front of or behind a listener are identical). To resolve these ambiguities, humans often rely on supplemental information from either direct visual feedback or from their knowledge of and comfort with the listening environment.

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.

Design parameters for automotive components can be highly affected by the requirements imposed for vehicle pass-by compliance. The key systems affecting pass-by performance generally include the engine, tires, intake system, and exhaust system. The development of these systems is often reliant on the availability of prototype hardware for physical testing on a pass-by course, which can lead to long and potentially costly development cycles. These development cycles can benefit significantly from the ability to utilize analytical data to guide development of component-level design parameters related to pass-by noise. To achieve this goal, test and analysis methods were developed to estimate the vehicle-level pass-by performance from component level data, both from physical and/or analytical sources. The result allows for the estimation of the overall vehicle-level pass-by noise along with the contributions to the total and dominant frequency content from each of the key noise sources.

As an agricultural tractor OEM was moving a new tractor model from development into production, an objectionable cab “boom” was identified that was not present in the preproduction pilot -level tractors. The cab boom was identified as a low frequency tone causing an increase of 7 (dBA) over the level in the pilot tractors, which was deemed unacceptable. The process used by the tractor OEM engineering team to address this problem has been widely used and refined in the automotive industry, but it is relatively new in the agricultural/off-road vehicle industry. This paper describes the source-path-receiver approach that led to identifying the exhaust tip as the source and the vibro-acoustic coupling of a windshield structural mode with an acoustic cab cavity mode as the path of the boom event.

For the development of a self-propelled crop spraying machine, a hybrid experimental and analytical Source-Path-Contribution (SPC) approach is utilized by a leading agricultural equipment manufacturer. The objective is to predict noise and sound quality in the cab before prototypes are assembled, so that dB(A) and SQ targets can be assessed early on and better specifications sent to suppliers to achieve these vehicle-level targets. The experimental SPC task is conducted on the current crop sprayer model, which has the same cab but different engine, transmission and hydraulics than the new model. A hybrid FE-SEA model of the current cab is developed and run at load cases derived from test data. The SEA approach is needed to evaluate the effect of cab acoustic treatments, which are not accounted for in the SPC experimental model. Contributions to in-cab noise for the current sprayer are estimated from both experimental and analytical SPC.

Identical fiber wheel liners were installed on two different mid size vehicles in order to compare the noise reduction for each vehicle. The fiber liners represented material in current production. A baseline noise level was established with the existing plastic wheel liners and then comparisons were made with fiber wheel liners. Noise levels were compared in the wheel well and in the interior for similar vehicle operating conditions. For both vehicles, significant tire noise reduction at the source was measured with fiber liners compared to plastic liners. One of the vehicles also demonstrated noise reduction in the passenger cabin with fiber liners. Insight into potential explanations for these differences was provided by comparing the noise levels at different locations within the vehicles. The results show how fiber liners are an additional tool to reduce the noise in a vehicle and how the NVH design for the balance of the vehicle can leverage the NVH impact of these parts.

With the extensive improvements achieved in vehicle driveline and road noise quality manufacturers are turning their attention to component and ancillary noise sources and expecting their suppliers to include sound quality in the assessment of their designs. This paper describes an investigative project into the principal components contributing to the perceived sound quality of powered seat adjusters in passenger vehicles and the statistical methods of analyzing jury preference data.