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Body in White (BIW) of an automobile serves as the shell, on which all the components that make up a vehicle, are mounted. The BIW is an assembly of press formed sheet metal components. The sheet metal composition of each component varies based on the form and functionality requirement of that component. The resulting assembly has multiple weld joineries with dissimilar compositions. The weld integrity of the joineries is crucial in maintaining the geometrical and structural integrity of the BIW. The primary welding method used in BIW assembly is Resistance Spot Welding (RSW). The quality of the weld is an outcome of a combination of multiple weld parameters. These parameters are majorly estimated based on the joinery thicknesses and material combinations. Multiple welding and testing iterations are done to fine tune the parameters for an optimum weld joinery. This is a very tedious process which increases the process time of a BIW assembly.

Tensile Testing is one of the most used and highly reliable method of mechanical testing to evaluate the tensile properties of the material. However, there is a large scope for discussing the behavior of the metals based on the direction of rolling and the tensile specimen size used for testing. This paper discusses the variation observed in the tensile values along the direction of rolling and traverse to the direction of rolling for S460MC. It also evaluates the variation observed in the values based on the various gauge lengths (GL) commonly used in testing as per international standards (80mm, 50mm and 25mm GL). It is observed that perpendicular to the direction of rolling, the Yield and Tensile strength of the material increase marginally while the Elongation percentage (%E) decreases by a small margin irrespective of the gauge length taken into consideration.

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.

Steering returnability while driving is one of the most important parameter which affects the drive pleasure and handling of a vehicle. Steering returnability refers to the automatic returning response of the steering wheel after taking a full turn while vehicle is being steered during driving. Evaluating steering response characteristics of any vehicle in a virtual environment at early stage of a product development saves significant development time and cost. Through this paper an attempt has been made to develop a methodology for selection of suspension hard points which influences steering returnability characteristics of a vehicle at an early product design stage. Conventionally, suspension kinematic parameters such as Caster angle, Steering axis inclination (SAI), etc. are iterated during vehicle design stage to achieve desired Steering returnability.

The automotive industries around the world is undergoing massive transformation towards identifying technological capabilities to improve vehicle performance. In this regard, the engine dynamic torque plays a crucial role in defining the transient performance and drivability of a vehicle. Moreover, the dynamic torque is used as a visualization parameter in performance prediction of a vehicle to set the right engineering targets and to assess the engine potential. Hence, an accurate measurement and prediction of the engine dynamic torque is required. However, there are very few methodologies available to measure the engine dynamic torque with reasonable accuracy and minimum efforts. The measurement of engine brake torque using a torque transducer is one of the potential methods. However, it requires a lot of effort and time to instrument the vehicle. It is also possible to back-calculate the engine torque based on fuel injection quantity and other known engine parameters.

Phosphating is the most preferred surface treatment process used for auto body sheet panel before painting due to its low-cost, easy production process, good corrosion resistance, and excellent adhesion with subsequent paint layer. There are different phosphating processes used for ferrous metal like zinc phosphating, iron phosphating, di-cationic & tri-cationic phosphating, etc. Among these phosphate coatings, the best corrosion resistance and surface adhesion are achieved by tri-cationic phosphate coatings (zinc-nickel-manganese phosphate). Many new technologies of phosphating are evolving. Key drivers for this evolution are increasing demand for higher corrosion resistance, multi-metal car body processing in same phosphating bath and sustainability initiatives to reduce the carbon footprints. We have evaluated two of these recent technologies.

Fuel economy, performance and drivability are the three important parameters for evaluating the vehicle performance. Powertrain matching plays a major role in meeting the above targets. Fuel economy is measured based on city, highway and some user defined driving cycles which can be considered as real world usage profiles. Performance and Drivability is evaluated based on the in-gear, thru-gear (acceleration performance) and grade-ability performance. The load collective points of the engine greatly influence the engines performance, fuel economy and emissions, which in-turn depends on the N/V ratio of the vehicle. The optimal selection of gear and final drive ratios plays a key role in the optimization of the Powertrain for a particular vehicle. The current study involves dynamic simulation of the vehicle performance and fuel economy at transient engine test-bed for different gear and final drive ratio combinations using AVL DynoExcat-dynamometer.

This paper presents a comprehensive model of a hydraulic power steering system for predicting the transient responses under various steering inputs. The hydraulic system model, which integrates together all fluid line elements and hydraulic components, is formulated using the MSC Easy5 software. A full vehicle model is developed in ADAMS/Car. Functional Mock up Interface (FMI), a tool independent standard is used for co-simulation of ADAMS and Easy5 Dynamic models. This paper describes a co-simulation methodology developed using FMI interface for full vehicle Simulations using hydraulic power steering. A virtual simulation scheme is developed to obtain the system transient responses and the results are compared with those measured from the tests. In general, the simulation results agree with those obtained from the tests under the same steering inputs and operating conditions.

Ride is an important attribute which must be accounted in the passenger segment vehicles. Excessive H point acceleration, Steering wheel acceleration, Pitch acceleration can reduce the comfort of the driver and the passengers during high frequency and low frequency rough road events. Excessive Understeer gradient, roll gradient, roll acceleration and Sprung mass lift could affect the Vehicle driver interaction during Steady state cornering, Braking and Step steer events. The concept architecture of the vehicle plays an important role in how comfort the vehicle will be. This paper discusses how to improve SUV ride performances by keeping handling performance attributes same or better than base vehicle. Multi Objective Optimization was carried out by keeping spring, bushing and damper characteristic as the design variables to avoid new system or component development time and cost.

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).