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In many cases, the results from a sound transmission loss (STL) test can differ from facility to facility. Despite the presence of standardized test specifications such as SAE J1400 [1], many issues can create variations in the data unique to that particular setup. These situations have presented themselves in recent tasks in which a reverberation room was relocated into a smaller area than was previously available. Current test projects in the relocated test facility have shown a need to better understand the details influencing the quality of the test data. Issues such as the sample orientation, the volume of the reverberation room, the quality of the sealing between the reverberation and anechoic rooms and even the material itself or the operator's style of positioning the sample all had to be reconsidered. It was determined that an investigation into causes of these differences needed to be launched to improve the setup of the STL test and provide more reliable data.

Lear Corporation has developed a new OneStep™ Liftgate trim module. The panel consists of all mechanical components and a trim cover assembled into one module. This structural liftgate uses the trim substrate and a “beam” as the common attachment point for all liftgate hardware. The assembly includes all of the liftgate components mounted to the back of the interior trim panel.

In recent years, the perceived quality of powered seat adjusters based on their sound during operation has become a primary concern for vehicle and seat manufacturers. Historical noise targets based on overall dB(A) at the occupant's ear have consistently proved inadequate as a measure of the sound quality of a seat adjuster. Significant effort has been devoted to develop alternative sound quality metrics that can truly discriminate between “good” and “bad” seat adjusters. These new metrics have been successfully applied for some years by product development engineers in test labs. However, in the assembly plant the sound quality of the seat adjuster is still assessed subjectively by an operator at the end of the assembly line. The main problem with this approach is not only the lack of consistency and repeatability across large samples of seat tracks, but also the fact that the only feedback provided from the end-of-line to the product development team is of subjective nature.

At certain key stages in the vehicle development process, prototype vehicles are available for NVH testing. This testing fulfills two functions: primarily it is used to assess the status of the vehicle to the program NVH performance targets, but it also provides an opportunity to validate the vehicle SEA model. These single vehicle test events provide a snapshot of the NVH performance but do not provide any understanding of the variability of the NVH performance, which is due to many factors: components, build or assembly and test setup variability. SEA models can be used to estimate the vehicle level variability, if the variability of the interior components is understood, but there is limited data available to confirm the accuracy of these predictions. In this paper we examine the repeatability and reproducibility through a standard gage R&R study of Engine Noise Reduction (engine NR) and Tire NR testing.

Wireless Power Transfer (WPT) promises automated and highly efficient charging of electric and plug-in-hybrid vehicles. As commercial development proceeds forward, the technical challenges of efficiency, interoperability, interference and safety are a primary focus for this industry. The SAE Vehicle Wireless Power and Alignment Taskforce published the Recommended Practice J2954 to help harmonize the first phase of high-power WPT technology development. SAE J2954 uses a performance-based approach to standardizing WPT by specifying ground and vehicle assembly coils to be used in a test stand (per Z-class) to validate performance, interoperability and safety. The main goal of this SAE J2954 bench testing campaign was to prove interoperability between WPT systems utilizing different coil magnetic topologies. This type of testing had not been done before on such a scale with real automaker and supplier systems.