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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.

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.

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.

Fatigue life prediction is the most promising technique for drastic reduction of durability evaluation time, which is a critical element in the product development cycle. By using this technique, it is possible to reduce development time and cost, identify failure modes early in the development cycle, and design the component for optimum life. This paper discusses the optimisation of an important two-wheeler component namely, the center stand, using fatigue life prediction techniques. Also, it aims at establishing correlation among various customer usage patterns, accelerated endurance tests, and fatigue life prediction results using both experimental data and finite element (FE) models.

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.

Automotive manufacturers today are faced with the challenge of producing high quality products in a short time. In such a competitive environment, higher reliability gives competitive edge to the manufacturers. However, higher reliability involves increased durability testing on their part, which leads to an increase in development time and cost. Therefore, it becomes imperative to re-look the existing test standards as against the actual customer usage conditions thereby optimizing the testing time and the development costs. With that goal in mind, this paper elaborates the procedure used for establishing a correlation between three structural durability test rigs and customer usage on the road. Two different approaches were adopted to arrive at a final correlation between the structural test rigs and the customer usage on the road. The first approach arrives at a one to one correlation based on the failure modes of different components observed on each of the test rigs and in field.

This paper aims at the estimation of dynamic wheel loads of a two-wheeler through mathematical modeling that will aid during the initial stages of product development. A half car model that represents a two-wheeler was used for this purpose. Road displacements were given as input to the model and the wheel loads estimated. Actual road data obtained from two-poster rig was used as input to the model thereby making it possible to calculate the wheel loads for different customer usage conditions on different roads. In this paper, a severe rough road was chosen for verification of the model with that of the rig as the rider dynamics on such roads are the most difficult to simulate even on the rigs. The estimated values from model were verified with those measured using a two-poster rig for the same road displacement. Attempt has been further made to establish a correlation between the ride comfort predictions from the model and the two-poster rig.