Meritor (United States)

<div class="htmlview paragraph">In this paper, we describe the development of multi-axis, accelerated durability tests for commercial vehicle suspension systems. The objective of the exercise is to design accelerated durability tests that have well-defined correlation with customer usage. The procedure starts with a definition of the vehicle's duty cycle based on the expected operational parameters, namely: road profile, vehicle speed, and warranty life. The second step is determining the durability proving ground test schedule such that the accumulated pseudo-damage (based on spindle loads) is representative of the vehicle's duty cycle. The third step in the process is developing a multi-axis laboratory rig test for the suspension system, such that the accumulated damage in the proving ground is replicated in a compressed time frame. The final step in the process is to design a single-axis, accelerated durability test for the suspension subsystem, such that the damage content in selected areas of interest is equivalent to that of the multi-axis rig test. The last step can be accomplished only if the loading is nearly proportional, such that the stress state at each of the critical locations can be represented by a uni-axial state of stress. We demonstrate the procedure through an example involving a trailing arm used in the front suspension of a class-8 tractor-semi-trailer combination, the duty cycle of which corresponds to a regional-haul application in the Midwestern states.</div>

<div class="htmlview paragraph">In the development and manufacture of vehicle components and systems, it is often necessary to quickly identify optimal design modifications for mitigating noise and vibration problems to meet the production schedule. To address this need, experimental techniques for determining the sensitivity of forced vibration response to changes in mass, damping or stiffness properties are of great use. In order to distinguish physical changes in the system from nonlinear input-output distortion, experimental techniques for identifying nonlinear input-output models in mechanical systems are also needed. The use of experimental sensitivity measurements and analyses for studying linear and nonlinear forced vibration data is examined in this work. Embedded sensitivity functions are first used to identify design modifications for reducing a vibration resonant problem. These sensitivity functions are then applied to characterize and identify a nonlinear mechanical system and the accuracy of estimated nonlinear parameters with respect to several factors is examined. A vehicle exhaust system is used to experimentally demonstrate the results achieved using the diagnostic and system identification methods.</div>

The use of embedded sensitivities in vibration-based damage identification for structuralhealth monitoring is discussed. Embedded sensitivity functions are algebraic combinationsof measured frequency response function data that determine how the forced responses ofstructural systems change with perturbations in mass, damping, or stiffness. After thetheory of embedded sensitivity functions is reviewed, they are applied to characterizedamage in an analytical three-degree-of-freedom system, a bench top two-degree-of-freedomsystem, and a full-scale exhaust system. By comparing embedded sensitivity functions withfinite difference functions using undamaged and damaged frequency responsefunctions, damage is shown to be properly detected, located, and quantified intheory and practice assuming that structures are only damaged in one location.Demonstrations on the bench top system and exhaust system indicate that thetechnique is most effective when certain frequency ranges in the data are analyzed toavoid ranges of low signal-to-noise ratios and when changes to frequency responsefunctions are not excessive to avoid distortions in the estimated perturbations.

It was previously reported that the time to stress corrosion cracking (tf) for generator retaining ring steel (50Mn18Cr4) in a nitrate solution could be prolonged by >6 times using Er+Cr ion implantation in very heavy doses. Arsenic ion implantation had a greater effect per unit dose on tf than Er+Cr ion implantation by two orders of magnitude. However, recent research has shown that excessive doses of As ions can decrease tf and that the improvement in tf attributable to As ion implantation is limited to only 3 times that of unimplanted samples. In this study, Er+As ion implantation was examined. It was found to increase tf by up to one order of magnitude with a relatively high effectiveness per unit dose. The mechanisms causing the protective effect of Er+As ion implantation were investigated using electrochemical testing, Auger electron spectroscopy, Rutherford backscattering spectrometry, and x-ray photoelectron spectroscopy.

The complex nature of noise/vibration generation and transmission in rotating machinery has made it indispensable to employ various techniques to identify the corresponding sources and paths. A theoretical approach via finite-element analysis (FEA), an experimental approach via direct measurement, and a signal processing approach via feature extraction of the measured data have all made significant contributions. Yet, there has not been much interdisciplinary effort among the three areas although their mutually complementary roles have long been recognized. In this paper, a synergetic approach is proposed to noise/vibration source and transmission path identification in order to take advantage of all the merits of the three different approaches. The proposed approach is discussed with a rotary compressor as a testbed. In the new approach, the experimentally measured sound intensity map is initially used to suggest the active radiation area, which guides us to instrument the rotary compressor at the suspected noise/vibration source positions. The collected data from the compressor is then processed to unravel the candidate source(s). Various signal processing techniques such as filtering, Hilbert transform, and fast Fourier transform are used to enhance the desired features buried in the experimental data. Finally, modal analysis via FEA and direct measurement further help to pinpoint the true source(s) and simultaneously verifies the conjecture following the experimental data processing.