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Structural elastomer components like bushes, engine mounts are required to meet stringent and contrasting requirements of being soft for better NVH and also be durable at different loading conditions and different road conditions. Silent block bushes are such components where the loading in radial direction of bushes are high to ensure the durability of bushes at high loads, but has to be soft on torsion to ensure good NVH. These requirements present with unique challenge to optimize the leaf spring bush design, stiffness and material characteristics of the rubber. Traditionally, bushes with varying degree of stiffness are selected, manufactured and tested on vehicle and the best one is chosen depending on the requirements. However, this approach is costly, time consuming and iterative. In this study, the stiffness targets required for the bush were analysed using static and dynamic load cases using virtual simulation (MSC.ADAMS).

A critical requirement for product design and development is meeting vehicle dynamic performance. Customers changing needs puts tremendous pressure on automotive businesses to launch new vehicles within short durations of time. This makes it mandatory to have a wide-ranging virtual simulation and vigorous validation process to provide best in class ride and handling performance of vehicles. Physical testing of prototypes is the most time-consuming activity, so there is a need of front loading to substitute these requirements at the initial stage of the development cycle. This paper summarizes the overall process for front loading vehicle dynamics requirements during basic architecture definition using virtual simulation. Basic dimensions, CG, weight distribution and steer angle of the new vehicle are derived using concept calculations based on benchmark vehicles. Vehicle dynamics trials are then done for the benchmark vehicles.

In driving, Steering is the input motion to the vehicle. The driver uses steering input to change the direction of the vehicle. During Parking or U turn bends the Steering is locked and later released to follow the desired path. Steering return ability is defined as the ratio of difference between steering wheel position at lock condition and steering wheel angle after 3 seconds of release to the steering wheel angle at lock condition. Having proper steering return ability characteristics has an important effect on vehicle steering characteristics. In this study, a full vehicle ADAMS model is prepared, and virtual steering return ability have been simulated in ADAMS/CAR for a Pickup truck vehicle. Simulated responses in the steering wheel angle have been validated by comparison with measurements. A Design of Experiment study is setup and Iterations are carried out to find the effect of Hard points and friction parameters.

Fatigue life estimation of automotive components is a critical requirement for product design and development. Automotive companies are under tremendous pressure to launch new vehicles within short duration because of customer’s changing preferences. There is a necessity to have a comprehensive virtual simulation and robust validation process to evaluate durability of vehicle as per customer usage. Test track and field test are two of the most time-consuming activities, so there is a need of simulation process to substitute these requirements. This paper summarizes the overall process of Accelerated Durability Test with measured road loads. Based on category of vehicle, type road profiles and the customer usage pattern, the wheel forces, strains and acceleration are measured which is used to derive the equivalent duty cycles on proving ground. The wheel force transducers (WFT) are used to derive loads for fatigue life estimation.

Accurate ride and handling prediction is an important requirement in today's automobile industry. To achieve the same, it is imperative to have a good estimation of damper model. Conventional methods used for modelling complex vehicle components (like bushings and dampers) are often inadequate to represent behaviour over wide frequency ranges and/or different amplitudes. This is difficult in the part of OEMs to model the physics-based model as the damper’s geometry, material and characteristics property is proprietary to part manufacturer. This is also 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 damper force accurately at different velocity/ frequency and amplitude of measured data using Artificial Neural Networks (ANN).