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Customer loads correlation in truck engineering

Korrelation der Kundenbedürfnisse in der Lastkraftwagentechnik
 
: Dressler, K.; Speckert, M.; Müller, R.; Weber, C.

International Federation of Automotive Engineering Societies -FISITA-:
FISITA World Automotive Congress. Congress proceedings. Vol. 7: Testing & simulation : 14 - 19 September 2008, Munich, Germany
Wiesbaden: Springer Automotive Media, 2008
pp.536-546
World Automotive Congress <32, 2008, München>
English
Conference Paper
Fraunhofer ITWM ()

Abstract
Safety and reliability requirements on the one side and short development cycles, low costs and lightweight design on the other side are two competing aspects of truck engineering. For safety critical components essentially no failures can be tolerated within the target mileage of a truck. For other components the goals are to stay below certain predefined failure rates. Reducing weight or cost of structures often also reduces strength and reliability. The requirements on the strength, however, strongly depend on the loads in actual customer usage. Without sufficient knowledge of these loads one needs large safety factors, limiting possible weight or cost reduction potentials. There are a lot of different quantities influencing the loads acting on the vehicle in actual usage. These 'influencing quantities' are, for example, the road quality, the driver, traffic conditions, the mission (long haulage, distribution or construction site), and the geographic region. Thus there is a need for statistical methods to model the load distribution with all its variability, which in turn can be used for the derivation of testing specifications. This paper describes new methods for the derivation of customer-correlated loads from field measurements. Simply taking the worst case ever measured as target load for testing is not sufficient. Instead assumptions on the distribution of the most important influence quantities are applied to derive the distribution of the loads acting on various spots on the truck (wheel forces, cabin accelerations etc.). Depending on the data, this process might involve parametric distribution estimation as well as Monte-Carlo methods for the simulation of customer loading. The result of this step is a certain percentile (e.g. a 95% customer) for the load acting on each component. In a second step this percentile is mapped to proving ground test scenarios corresponding to the derived customer loading profile. Based on this schedule testing is both customer-correlated (because of the derivation process) and accelerated (because of the special design of the test tracks). Component loads for further testing on servo-hydraulic rigs can also be derived from these test track schedules.

: http://publica.fraunhofer.de/documents/N-117809.html