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The automobile industry strives to develop high-quality vehicles quickly that fulfill the buyer’s needs and stand out within the competition. Full utilization of simulation and Computer-Aided Engineering (CAE) tools can empower quick assessment of different vehicle concepts and setups without building physical models. Vehicle execution assessment is critical in the vehicle development process, requiring exact vehicle steering system models. The effect of steering system stiffness is vital for vehicle handling, stability, and steering performance studies. The overall steering stiffness is usually not modeled accurately. Usually, torsion bar stiffness alone is considered in the modeling. The modeling of overall steering stiffness along with torsion bar stiffness is studied in this paper. Another major contributing factor to steering performance is steering friction. The steering friction is also often not considered properly.

Designing a vehicle chassis involves meeting numerous performance requirements related to various domains such as Durability, Crashworthiness and Noise-Vibration-Harshness (NVH) as well as reducing the overall weight of chassis. In conventional Computer Aided Engineering (CAE) process, experts from each domain work independently to improve the design based on their own domain knowledge which may result in sub-optimal or even non-acceptable designs for other domains. In addition, this may lead to increase in weight of chassis and also result in stretching the overall product development time and cost. Use of Multi-Disciplinary Optimization (MDO) approach to tackle these kind of problems is well documented in industry. However, how to effectively formulate an MDO study and how different MDO formulations affect results has not been touched upon in depth.

The excitation to a vehicle is from two sources, road excitation and powertrain excitation. Vehicle Suspension is designed to isolate the road excitation coming to passenger cabin. Powertrain mounts play a vital role in isolating the engine excitation. The current study focuses on developing an analytical approach using Low-Fidelity computer programs to design the Powertrain Mount layout and stiffness during the initial stage of product development. Three programs have been developed as a part of this study that satisfy the packaging needs, NVH requirements and static load bearing requirements. The applications are capable of providing the Kinetic Energy Distribution and Static Analysis (Powertrain Enveloping and Mount Durability) for 3-point and 4-point mounting systems and the ideal mount positions and stiffness for 3-point mounting systems.

In automotive Body-In-White (BIW) structures, stiffness and the fatigue behavior is greatly influenced by the properties of its joints. Spot welding is one of the most widely used process for joining of sheet metals in BIW. Spot weld fatigue life under Accelerated Durability Test (ADT) is crucial for durability performance of BIW structures. Experience of BIW validations highlighted more number of spot weld failures in CAE when compared to actual tests. Hence, lot of iterations in the form of design modifications are required to be carried out to make these spot welds meet the targets which increases design & development time as well as cost. Current practice uses force-based approach for predicting spot weld fatigue life in CAE. To improve the spot weld fatigue life correlation, extensive study has been carried out on the approaches used for calculating spot weld fatigue life, namely force & stress-based approaches.

Reducing the vibrations in the powertrain is one of the prime necessities in today's automobiles from NVH and strength perspectives. The necessity of 4×4 powertrain is increasing for better control on normal road and off-road vehicles. This leads to bulky powertrains. The vehicle speeds are increasing, that requires engines to run at higher speeds. Also to save on material costs and improve on fuel economy there is a need for optimizing the mass of the engine/vehicle. The reduced stiffness and higher speeds lead to increased noise and vibrations. One more challenge a powertrain design engineer has to face during design of its transmission housings is the bending / torsional mode vibrations of powertrain assembly. This aggravates other concerns such as shift lever vibrations, shift lever rattle, rise in in-cab noise, generation of boom noise at certain speeds, etc. Hence, reducing vibrations becomes an important and difficult aspect in design of an automobile.

In today's competitive automobile marketplace with reduced vehicle development time and fewer prototypes/tests, CAE is playing very crucial role in vehicle development. Automobile environment demands ever improving levels of vehicle refinement. Performance and refinement are the key factors which can influence the market acceptance of vehicle. Driveline is one of the key systems whose refinement plays critical role in improved customer satisfaction. Because of the virtue of the driveline functionality, driveline induced noise and vibration are the most common issues in the AWD vehicle development programs. Refinement of the drive line needs complicated nonlinear full vehicle CAE MBD models for the evaluation of driveline induced noise and vibration responses at different operating conditions [1]. In this paper a simplified approach is adapted for solving the Noise & Vibration issue which has been identified at the prototype testing level of an AWD vehicle development.

A tractor is vehicle specifically designed to deliver a high tractive effort at slow speeds for carrying out various agriculture operations like ploughing, rotavation etc. using implement. Hydraulic system is a key feature which connects these implements with the tractor. It controls the position and draft of the implement depending upon the type of crop, farming stage, implement type and soil conditions. These variations induces extreme range of load on the hydraulic system, thus making it challenging to design these components. Bell crank assembly is one of the main components of hydraulic system which controls the draft (thus, the loads experienced by tractor) through load sensing mechanism. Often bell crank assembly failures are reported from field due to uneven soil hardness and presence of rocks. This paper studies one of such bell crank assembly failures in the field. The failure was reported after half life cycle of usage during agriculture Operation.

The present scenario in automobile industry is formed on developing smart vehicles by introducing various feature towards fuel efficient, low emission, weight reduction, and advance safety feature with hybrid and micro-hybrid vehicles. One such feature gaining more popularity is the Belt Driven Starter Generator [1] for its contribution towards fuel efficiency, emission reduction [2], weight reduction and convenient packaging with engine/electrical interface. However this invention puts challenge of integration and increase in loading to various system like power steering pump and crank shaft pulley, as all these systems are interlinked with a common belt. In this interface links we observed the steering pump hub under risk of structural failure due to additional load to support Belt Driven Starter Generator. Failure to identify safe limits of hub load can affect safe vehicle operation [3].

There is a growing need for the accurate Computer Aided Engineering (CAE) models for vehicle performance evaluation. The reduced product development time and complexity of the vehicle evaluation demands accurate prediction with CAE models. Vehicle dynamics performance evaluation is very critical in vehicle development process, which require very accurate vehicle and tire models. The tire characteristics are represented as mathematical, physics based and empirical models. There are different types of tire models exist like Fiala, PAC, SWIFT and FTire etc, which can be used for vehicle handling, ride and steering performance evaluation. There is a need to study and understand these tire models before applying to specific vehicle dynamic performance. There is a challenge to get the tire models as tire modeling require lot of tests and time consuming.

Crashworthiness enhancement of vehicle structures is a very challenging task during the early design development process. Major factors influencing occupant injury in frontal impact are vehicle front crush space, crash pulse severity, restraint properties and occupant packaging space. This paper establishes a methodology to define suitable criterion that will guide the designers to select the optimal values of the above mentioned parameters during the early phase of the vehicle development. The usage of lumped mass models, pulse characterization techniques were explored to validate the results. Efficient crash energy management, the concepts of ride down and restraint efficiency parameters were also discussed in the paper.

The SAE J1100 based standard cargo volume index methods and predefined luggage objects are very specific to United States population. The European luggage volume calculation and standard luggage calculations are primarily based on DIN and ISO standards. Luggage volume declaration by manufacturers are based on any of these methods. The calculations are complicated and there is a possibility of declaring different values for similar luggage compartments. The major purchase decision of vehicle is based on its luggage capacity and current methods are very limited to make an intelligent decision by a customer. Market specific customer usage patterns for luggage requirements and protecting them in vehicle architecture upfront in concept stage is important to retain the market position and buying preference of customers. The usage patterns is collected from customer clinics and marketing inputs.

Light weighting in modern automotive powertrains call for use of plastics (PP, PA66GF35) for cam covers, intake manifolds and style covers, and noise encapsulation covers. Conventionally, in early stage of design these components are evaluated for static assembly loads & gasket compression loads at component level. However, engine dynamic excitations which are random in nature make it challenging to evaluate these components for required fatigue life. In this paper, robust methodology to evaluate the fatigue life of engine style cover assembly for random vibration excitations is presented. The investigation is carried out in a high power-density 4-cylinder in-line diesel engine. The engine style cover (with Polyurethane foam) is mounted on cam cover and the intake manifold using steel studs and rubber isolators to suppress the radiated noise.

In the automotive industry the requirement for low emissions has led to the demand for lightweight vehicle structures. Light weighting can be achieved through different iterative approaches but is usually time consuming. Current paper highlights deployment of the multi-loadcase optimization approach for light weighting. This work involves developing a process for multiple loadcase optimization for automotive hood. The main goal is to minimize the weight of a hood assembly by meeting strength and stiffness targets. The design variables considered in this study are thickness of the panels. Design constraints were set for stress and stiffness based on DVP (Design Verification Plan) requirement. Optimization workflow is setup in mode-frontier with design objective of minimizing weight of hood.

Highly competitive automotive market demands shorter product development cycle while maintaining higher standards of performance in terms of durability and Noise Vibration & Harness (NVH). Engine cranktrain system is one of the major vibration sources in engine and first torsional mode frequency is a key parameter which influences vibration characteristics. Current CAE (Computer Aided Engineering) workflow for evaluating cranktrain system performance is time-consuming and takes around 55 Hrs. It involves crankshaft geometry cleanup, stiffness calculation, 1D model building and post processing. Over the time, significant historical data has been created while performing this virtual simulation during the product development cycle. Having a trained Machine Learning (ML) model based on this historical data, which can predict first torsional mode frequency accelerates the virtual validation. In this paper, prediction of first torsional frequency of cranktrain system using ML is presented.

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.

For decades, customer complaints on Squeak & Rattle issues have come as a question of quality for the automotive industry. Squeak and rattle sounds are customer irritants due to their non-patterned and transient nature. Squeak is a friction induced noise that generally occurs because of rubbing of the two materials that are incompatible with each other. While rattle is a phenomenon that occurs due to the impact between the two parts having unintended gap. They are no more secondary noises and avoiding or elimination of these become significant for brand building and warranty cost reduction. Chucking is a form of squeak noise that occurs due to the interaction between uncoated seal to seal. In Swing gate, this phenomenon is seen when seal bulb inner layers are completely compressed. Swing gate have fore-aft modes that are excited due to dynamic responses from different road profiles.

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.

The tractor usage is growing in the world due to derivative of rural economy and farming process. It needed wide range of implements based on the applications of the customer. The tractor plays a major role in Agricultural and Construction applications. In a tractor, hydraulic system is act as a heart of the vehicle which controls the draft and position of the implement. Hydraulic system consists of Powertrain assembly, 3-point linkage and DC sensing assembly. The design of hydraulic powertrain assembly is challenging because the loads acting on the system varies based on the type of implement, type of crop, stage of farming and soil conditions etc., Hydraulic powertrain assembly is designed based on standards like IS 12207-2019 which regulates the test methods for the system based on the lift capacity of the tractor. In this paper, virtual simulation has been established to optimize the design and perform the test correlation.

In the past few decades, improvement on fuel efficient technologies have progressed rapidly, whereas little emphasis is being made on how the vehicle should be driven. Driving habits significantly influences fuel consumption and poor driving habits leads to increased fuel consumption. In this paper a new system called “Green Drive” is being presented wherein driving habits are closely monitored, evaluated and details are systematically presented to the user. Green Drive system monitors key driving parameters like speed, gear selection, acceleration, unwanted engine idling periods, aggressive braking and clutch override and presents an ecoscore on the infotainment system which is reflection of users driving behavior. The system also offers guidance on the scope for improving driving habits to achieve better ecoscore and hence reduced fuel consumption.

Canopy design is governed by CPCB regulations. The regulations explicitly tells about noise levels. It’s very important to have the proper ventilation of canopy to ensure the proper working at all climatic conditions. Mostly it is installed at commercial locations & hence the ownership cost matters. Reducing the footprint without affecting the power output is challenging. It implies the need of the CFD simulation to predict the cooling performance of the canopy. The baseline canopy is tested to estimate the performance parameters. It is modelled in CFD with all the minute details. All the parts including engine, alternator, fan, fuel tank are modelled. MRF(Moving Reference Frame) model used to simulate fan performance. The cooling systems like radiator & oil cooler is modelled as porous region. The total flow across canopy & the air velocity across critical regions is used to define the performance.