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Twist beam is a type of suspension system that is based on an H or C shaped member typically used as a rear suspension system in small and medium sized cars. The front of the H member is connected to the body through rubber bushings and the rear portion carries the stub axle assembly. Suspension systems are usually subjected to multi-axial loads in service viz. vertical, longitudinal and lateral in the descending order of magnitude. Lab tests primarily include the roll durability of the twist beam wherein both the trailing arms are in out of phase and a lateral load test. Other tests involve testing the twist beam at the vehicle level either in multi-channel road simulators or driving the vehicle on the test tracks. This is highly time consuming and requires a full vehicle and longer product development time. Limited information is available in the fatigue life comparison of multi-axial loading vs pure roll or lateral load tests.

Modern day automotive market demands shorter time to market. Traditional product development involves design, virtual simulation, testing and launch. Considerable amount of time being spent on virtual validation phase of product development cycle can be saved by implementing machine learning based predictive models for key performance predictions instead of traditional CAE. Durability oil canning loadcase for vehicle hood which impacts outer styling and involves time consuming CAE workflow takes around 11 days to complete analysis at all locations. Historical oil canning CAE results can be used to build ML model and predict key oil canning performances. This enables faster decision making and first-time right design. In this paper, prediction of buckling behaviour and maximum displacement of vehicle hood using ML based predictive model are presented. Key results from past CAE analysis are used for training and validating the predictive model.

Computer Aided Engineering (CAE) simulations are an integral part of the product development process in an automotive industry. The conventional approach involving pre-processing, solving and post-processing is highly time-consuming. Emerging digital technologies such as Machine Learning (ML) can be implemented in early stage of product development cycle to predict key performances without need of traditional CAE. Oil Canning loadcase simulates the displacement and buckling behavior of vehicle outer styling panels. A ML model trained using historical oil canning simulation results can be used to predict the maximum displacement and classify buckling locations. This enables product development team in faster decision making and reduces overall turnaround time. Oil canning FE model features such as stiffness, distance from constraints, etc., are extracted for training database of the ML model. Initially, 32 model features were extracted from the FE model.

New trend in steering system such as EPS is coming up, but still hydraulic power steering system is more prevalent in today’s vehicles. Power steering pump is a vital component of hydraulic power steering system. Failure of steering pump can lead to loss of power assistance. Prediction of hub load on pump shaft is an important design input for pump manufacturer. Higher hub loads than the actual designed load of pump bearing may lead to seizure of pump. Pump manufacturer has safe limits for hub load. Simulations can assist for optimization of belt layout and placement of accessories to reduce the hub load. Lower hub load can have direct effect on improvement of pump durability. This paper deals with dynamic simulation of belt drive system in MSC.ADAMS as well as vehicle level measurement of hub load on power steering pump.

In recent years, the push for reduced product development timelines has been more than ever with significant changes in the automotive market. High electrification, intelligent vehicle systems and increased number for car manufacturers are a few key drivers to the same. The front loading of development activities is now a key focus area for achieving faster product development. From vehicle dynamics point of view availability of subjective evaluation feedback plays a key role in optimization various system specifications. This paper discusses an approach for front loading through parallel development of the tire and vehicle chassis system, using advanced simulation and driving simulator technology. The proposed methodology uses virtual tire models which in combination with real-time vehicle model enables subjective evaluation of vehicle performance in driver-in-loop simulators.