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Loss of lubrication within an axle assembly due to the formation of through-thickness cracks in structural components can result in severe durability issues for the internal parts (gears, splines, bearings, etc.). One such example of a structural crack resulting in lubrication leakage can be observed in the cover pan of a Salisbury axle that has been subjected to cyclic fore-aft loading conditions (which are intended to replicate the loads acting on the axle during vehicle acceleration or deceleration). Investigation of the cover pan crack locations was performed using Magnetic Particle Inspection (MPI) and indicated the formation and propagation of multiple cracks adjacent to the cover pan bolt holes.

It has been well-documented in academic literature that, when subjected to compressive cyclic loading (R = -∞), weldments can experience fatigue failure. However, unlike non-welded components, it has been shown that mean stress has a negligible impact on the fatigue life of welds (Gurney, 1979). Currently, most analytical weld prediction methods neglect the influence of mean stress and instead focus only on the relationship between the stress (or strain) amplitude and the respective number of cycles to failure.

A hypoid gear drive transforms torque between crossed axes and is widely used in front and rear axles. Current requirements for greater engine power and higher fuel efficiency mean hypoid gear axles must have a higher power density. Therefore, engineers need a tool, which efficiently and accurately optimizes hypoid gear stress, to check design feasibility before prototyping and testing is done. This paper proposes a new, practical methodology that more accurately and efficiently calculates the stress of hypoid gears under loading.

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.

Ideally, the calculation of driveline component imbalance sensitivity is a straightforward operation of normalizing the changes in dynamic responses that occur when a known imbalance is added to a rotating component. In practice, however, overlapping driveline component orders (and wheel order harmonics) often prohibit the measurement repeatability required to distinguish these changes. A solution to the measurement repeatability issue is presented for chassis dynamometer testing, based on prescribing minor adjustments to the roll speeds for different wheels in order to separate the orders of various rotating components.

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.