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An application of variation simulation for predicting vehicle interior driveline vibration is presented. The model, based on a “Monte Carlo”-style approach, predicts the noise, vibration and harshness (NVH) response of the vehicle driveline based on distributions of imbalance and runout derived from manufacturing production variation (the forcing function) and the vehicle's sensitivity to the forcing function. The model is used to illustrate the change in vehicle interior vibration that results when changes are made to production variation for runout and imbalance of driveline components, and how those same changes result in different responses based on vehicle sensitivity.

The installation of various liners into the propeller shaft tube is a traditional driveline NVH treatment to attenuate driveline vibration. The most commonly used liners include rolled paper, C-cut cardboard, corrugated cardboard, etc. These traditional liner treatments are expected to provide damping to the driveline system to reduce the vibration levels. However their added level of damping and effectiveness to the driveline system are limited, particularly when dealing with driveline gear mesh vibration and noise. This paper presents a novel type of liner treatment - tunable liners. The liner is designed such that it functions as a tuned dynamic vibration absorber. Through proper design of the liner, it can be tuned for bending and torsion modes at the same time. The liner design parameters and their impact on the frequency tuning are analyzed and studied through both physical testing and FEA analysis.

The first part of a detailed examination of multivariate correlation of several axle assembly and component parameters to the assembly NVH performance (vibration) measured at the end of the assembly process is presented focusing on preparing the data for multivariate regression analysis. The study is based on test results and measurements acquired from multiple axle assemblies built with the same hypoid gearset, thus effectively eliminating the affect of gearset variation on the test result. Several major components within the axle are considered including the differential housing (that controls wheel differentiation during turns), the axle housing, and several assembly parameters.

The second part of a detailed examination of multivariate correlation of several axle assembly and component parameters to the assembly NVH performance (vibration) measured at the end of the assembly process is presented focusing on the multivariate linear regression analysis. The study is based on test results and measurements acquired from multiple axle assemblies built with the same hypoid gearset, thus effectively eliminating the affect of gearset variation on the test result. Several major components within the axle are considered including the differential housing (that controls wheel differentiation during turns), the axle housing, and several assembly parameters. Details of the multivariate regression include formulation of the linear regression model, model refinements through analysis of subsets of the variables, tests of significance and residual analysis.

Alternative powertrains, in particular electric and plug-in hybrids, create a wide range of unique and challenging NVH (noise, vibration & harshness) issues in today's automotive industry. Among the emerging engineering challenges from these powertrains, their acoustic performances become more complicated, partially due to reduced ambient masking noise level and light weight structure. In addition, the move away from conventional displacement engines to electrical drive units (EDU) has created a new array of NVH concerns and dynamics, which are relatively unknown as compared to the aforementioned traditional setups. In this paper, an NVH optimization study will be presented, focusing on four distinct factors in electric drive unit gear mesh source generation and radiation: EDU housing and bearing dynamics, gear geometry, EDU shafting torsional dynamics, and EDU housing structure. The study involves intensive FEA modeling/analyses jointly with physical validation tests.