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Optimization of Proving Ground Durability Test Sequence Based on Relative Damage Spectrum
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In competitive vehicle market, the product must be designed and validated in shorter time span without compromising the quality. The durability of the vehicle is tested either by on road trials undertaken at the actual customer supplication sites for large time period or in the accelerated rough surfaces called “Proving ground” to validate in shorter time span. Accelerated proving ground durability testing plays a vital role in enabling shorter product development cycles by simulating the road load influences alone from the actual field conditions.
It is imperative to simulate the test vehicle at proving ground (PG) testing such that it replicates the same damage that occurs in the field due to road loads. PG validation requires a specific durability test sequence for every segment of commercial vehicles due to different customer usage applications and terrain conditions. This diversity in applications and terrains induce structural damage at different range of frequencies. Hence the PG test sequence must be optimal enough such that it includes all the range of frequencies representing the actual customer load spectra. For building the optimized test schedule, frequency based pseudo damage methodology called Relative Damage Spectrum (RDS) is incorporated.
This method enhances the proving ground durability validation process by providing optimized test schedules to prevent redundant vehicle testing.
|Technical Paper||Implementation of CAE in Full Vehicle Development|
|Technical Paper||A Durability Analysis Case Study of SUV and MUV Using Measured Proving Ground Road Profiles|
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CitationP, P., J, P., and Palanisamy, K., "Optimization of Proving Ground Durability Test Sequence Based on Relative Damage Spectrum," SAE Technical Paper 2018-01-0101, 2018, https://doi.org/10.4271/2018-01-0101.
Data Sets - Support Documents
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