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The perceived interior noise has been one of the major driving factors in the design of automotive interior assemblies. Buzz, Squeak and Rattle (BSR) issues are one of the major contributors toward the perceived quality in a vehicle. Traditionally BSR issues have been identified and rectified through extensive hardware testing. In order to reduce the product development cycle and minimize the number of costly hardware builds, however, one must rely on engineering analysis and simulation upfront in the design cycle. In this paper, an analytical and experimental study to identify potential BSR locations in a cockpit assembly is presented. The analytical investigation utilizes a novel and practical methodology, implemented in the software tool Nhance.BSR, for identification and ranking of potential BSR issues. The emphasis here is to evaluate the software for the BSR predictions and the identification of modeling issues, rather than to evaluate the cockpit design itself for BSR issues.

Today's highly competitive automotive industry is constantly looking for ways to improve the perceived quality of its vehicles. Perceived quality defined as the sense of touch, feel and sound that the customer perceives in a vehicle is seen as one of the areas with maximum potential for increasing customer satisfaction. Buzz, Squeak and Rattle (BSR) is one of the major contributors towards the perceived quality in a vehicle. Almost all of the annoying noises that the customer hears can be classified into a buzz, squeak or rattle. Traditionally BSR in subsystems and components of a vehicle have been identified and rectified through extensive hardware testing. With the auto companies and suppliers being challenged to cut structural costs, eliminate costly hardware builds, and bring products to market faster by reducing development cycles, increasing math analysis of subsystems and components for such perceived quality issues is desirable.

Better dynamic characteristics and longer fatigue life implies a robust product which has a direct bearing on customer perception of quality in passenger vehicles, and is one of the major factors affecting customer loyalty. Traditional optimization of dynamic characteristics is first performed to improve fundamental bending and torsion frequencies of automotive body structures. Such analyses typically involve mass minimization for which gage sensitivities are used to identify critical parts. It is recognized that most fatigue failures in an automobile body structure occur at spot welds, and almost none of the traditional optimization methods include the influence of spot weld layout on dynamic characteristics and durability. Also, load path identification tools currently available are not very effective for large & complicated parts such as body panels, floor panels, etc. since typically only small regions of such parts absorb energy.