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Ability to meet customer expectations and understand customer usage behavior is fundamental to the delivery of the successful products in the Automobile industry. A combination of data collection tools such as customer survey and customer chasing technique with instrumented vehicle are generally used to get insight into the customer usage pattern and driving style. The available information can be priceless if it is used to tailor laboratory test standards representative of customer usage behavior. Test cycles, tailored based on specific customer usage behavior, have the potential to work as very effective design filter during the validation stage of new product design and development process. In this paper, a method to tailor vehicle level durability driving cycle on a laboratory scale roller Chassis dynamometer has been demonstrated. This cycle is developed based on the data collected with customer for their daily usage routine.

Accurate durability prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of time histories of strains, accelerations etc. at various locations on the vehicle structure. This is usually difficult to obtain as a typical data acquisition exercise takes lots of time, cost and effort. This paper aims to address this problem by predicting the strain time histories accurately at various locations on the vehicle chassis from a few channels of measured data using Artificial Neural Networks (ANN). The predicted strain histories were found to be quite accurate as the error in fatigue lives between the measured and the thus predicted time histories at various strain locations were found to be less than 15%. This approach was found to be very useful in collecting huge amounts of customer usage data with minimum instrumentation and small sized data loggers.