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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.

A methodology is presented to do accelerated vibration durability test, on Electro Dynamic Shaker (EDS) by using Power Spectral Density (PSD) profile based on typical customer usage pattern. A generalized iterative procedure is developed to optimize input excitation PSD profile on EDS for simulating the exact customer usage conditions. The procedure minimizes the error between the target channels measured on road and the response channels measured on EDS. Also, response of accelerometers and strain gauges at multiple locations on the test component are arrived at based on a single input excitation using this procedure. The same is verified experimentally as well. Different parameters like strain, acceleration, etc. are simulated simultaneously. This methodology has enabled successful simulation of road conditions in lab, thereby arriving at a correlation between rig and road. The correlation obtained is based on the simulation of the same failure mode as that of the road on the rig.

In the automotive industry, multiple prototypes are used for vehicle development purposes. These prototypes are typically put through rigorous testing, both under accelerated and real world conditions, to ensure that all the problems related to design, manufacturing, process etc. are identified and solved before it reaches the hands of the customer. One of the challenges faced in testing, is the low repeatability of the real world tests. This may be predominantly due to changes in the test conditions over a period of time like road, traffic, climate etc. Estimating the repeatability of a real world test has been difficult due to the complex and multiple parameters that are usually involved in a vehicle level test and the time correlation between different runs of a real world test does not exist. In such a scenario, the popular and the well-known univariate correlation methods do not yield the best results.

Two-poster rig plays a very important role in accelerated durability evaluation in a motorcycle industry, similar to what a four-poster rig does in a car industry. The rig simulates the exact road conditions in the vertical direction through tire coupling by applying feedback control on displacement. On account of its ability to simulate to the exact customer usage conditions, it reproduces the failures realistically as it happens on the field. However, as complete vehicle is required for testing on the rig, the testing happens mostly in the advanced stages of product development. Any failures beyond the concept stage have a huge impact on the development time and cost and the same should be avoided. Therefore, in this paper, a virtual testing methodology is proposed, based on which potential failures on the vehicles can be captured at the concept design stage itself. An ADAMS model of a motorcycle was created.