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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.

Today's emerging 4-wheel-drive and all-wheel-drive vehicle architectures have presented new challenges to engineers in achieving low driveline system noise. In the meantime there's also a constant pressure from increasingly stringent noise level requirements. A driveline system's NVH (noise, vibration and harshness) performance is controlled by various noise sources and mechanisms. The common noise issues include the axle gear whine, driveline imbalance/run-out, 2nd order kinematics, engine torque fluctuation, engine idle shake etc. Unfortunately various design alternatives may improve some NVH performance attributes while degrading others. It is important to balance the requirements for these noise sources to achieve an optimized driveline system NVH. However, very little literature is found on this topic. In this paper, discussions on methodologies in balancing these different driveline NVH requirements are presented.

Driveline components are designed to meet customer component-level NVH requirements as measured on a dynamometer in a laboratory environment. It is desired to evaluate the NVH characteristics of driveline components at the end of the manufacturing process and predict how this performance will compare to the component-level specification as measured on the dynamometer in the laboratory environment. A test method is presented for establishing the correlation of the NVH performance of light duty truck axles measured at the end of the manufacturing process on the plant floor to the NVH performance of the same axles measured on a dynamometer in a laboratory environment.

Gear whine can be reduced through a combination of gear parameter selection and manufacturing process design directed at reducing the effective transmission error. The process of gear selection and profile modification design is greatly facilitated through the use of simulation tools to evaluate the details of the tooth contact analysis through the roll angle, including the effect of gear tooth, gear blank and shaft deflections under load. The simulation of transmission error for a range of gear designs under consideration was shown to provide a 3-5 dB range in transmission error. Use of these tools enables the designer to achieve these lower noise limits. An equally important concern is the dynamic mesh stiffness and transmissibility of force from the mesh to the bearings. Design parameters which affect these issues will determine the sensitivity of a transmission to a given level of transmission error.

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.

This paper discusses approaches to properly design aluminum axles for optimized NVH characteristics. By effectively using well established and validated FEA and other CAE tools, key factors that are particularly associated with aluminum axles are analyzed and discussed. These key factors include carrier geometry optimization, bearing optimization, gear design and development, and driveline system dynamics design and integration. Examples are provided to illustrate the level of contribution from each main factor as well as their design space and limitations. Results show that an aluminum axle can be properly engineered to achieve robust NVH performances in terms of operating temperature and axle loads.

Powertrain noise is often dominated by the radiation from individual panels or covers. A structural side cover for a front wheel drive transmission represents a complex noise and vibration design problem. Amongst the NVH concerns are the radiation of structural borne sound and the sound transmission loss characteristics. This paper addresses the use of acoustic boundary elements and structural finite elements to predict the radiation of noise from a structural side cover. A comparison is made to experimental measurements, and discussion provided for practical application of these modeling methods to total side cover design.

The role of NVH testing has evolved from a firefighting role and a period of exploration to a well defined standard test role in the product development and validation process. Integral to this process is robust engineering, which drives the need to execute many tests quickly, efficiently and accurately. This allows the NVH specialist to concentrate on interpretation of results and spend less time on the acquisition of data. As the volume of data grows, this creates the opportunity to data mine an NVH database to compare results from large sample sizes and focus on product variation. Today's NVH laboratory is accountable for producing high quality, consistent, timely, and cost effective test reports. The basic core of the test has to be easy to set up and execute for a novice, yet still allow for exploratory tests by specialists as necessary. The NVH laboratory is now subject to the same budgetary pressures and quality audits as other testing operations.

This paper presents a study of axle system NVH (noise, vibration and harshness) performance using DFSS (Design for Six Sigma) methods with the focus on the system robustness to typical product variations (tolerances / manufacturing based). Instead of using finite element as the simulation tool, a lumped parameter system dynamics model developed in Matlab/Simulink is used in the study, which provides an efficient way in conducting large size analytical DOE (Design of Experiment) and stochastic studies. The model's capability to predict both nominal and variance performance is validated with vehicle test data using statistical hypothesis test methods. Major driveline system variables that contribute to axle gear noise are identified and their variation distributions in production are obtained through sampling techniques.