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Lean approaches are being implemented in various manufacturing facilities across the globe. The application of lean approaches are extended to Body proto build shop to maximize the efficiency of the shop with lesser floor space and optimized equipment. Weld fixture, Weld equipment and assembly tools are the major tools required essentially for proto BIW assembly. This paper explains how the Weld equipment planning was carried out with lean approaches and implemented effectively in proto body assembly shop. The implemented lean concepts are compared with Italy and Japanese proto body build makers to validate the frugal planning of the facility for the said intent. The implemented facility is capable of producing more than a model at a time. Weld parameter selection for weld gun, gun movement to the fixture with minimized change over time and movable weld gun gantry are the lean approaches implemented.

The cabin cool down performance is influenced by heat load, AC system components and Air handling components. The air handling components are AC duct, vane and vent. Design of AC duct vane plays a crucial role in the airflow directivity in cabin which enhances the cabin cool down performance. Simulations are carried out by rotating the vanes manually and requires post process for every iteration. It leads to more time consuming and more number of simulations to achieve the target value. Research articles focusing on automation and optimization of vane articulation studies are scanty. Thus, the objective of this work is to execute the vane articulation study with less manual intervention. A parametric approach is developed by integrating ANSA and ANSYS FLUENT tools. With Direct Fit Morphing and DoE study approach from ANSA delivers the surface mesh model for the different vane angle configurations.

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.

Connected vehicles technology is experiencing a boom across the globe. Vehicle manufacturers have started using telematics devices which leverage mobile connectivity to pool the data. Though the primary purpose of the telematics devices is location tracking, the additional vehicle information gathered through the devices can bring in much more insights about the vehicles and its working condition. Cloud computing is one of the major enabled for connected vehicles and its data-driven solutions. On the other hand, machine learning and data analytics enable a rich customer experience understanding different inferences from the available data. From a fleet owner perspective, the revenue and the maintenance costs are directly related to the usage conditions of the vehicle. Usage information like load condition could help in efficient vehicle planning, drive mode selection and proactive maintenance [1].

Access to Knowledge resources around the world demands special skills and calls for judicious investments. Networking is an effective tool and building networks through consortia approach is dire need of the hour. Although this approach has been used in academia, its application in industry, especially among corporate entities, is rare. This paper describes in brief an optimised way to access databases, subscriptions & memberships of different technical societies from global platforms for research & progress of Indian automotive industry. It is imperative for Indian automotive industry to enhance and strengthen its knowledge resource base, particularly in product development and manufacturing domains. This will enable the industry to achieve its mission -“AMP-2016” of becoming a hub for global auto industry. Instead of “compete and grow,” an approach of “collaborate and grow” is thought of.

The complexity of Urban driving conditions and the human behavior introduces undesired variabilities while establishing Fuel economy for a vehicle. These variabilities pose a great challenge while trying to determine that single figure for assessment of vehicle’s fuel efficiency on an urban driving cycle. This becomes even more challenging when two or more vehicles are simultaneously evaluated with respect to a reference vehicle. The attempt to fit a generalized linear model, between Fuel Economy as predicted variable and components of a driving cycle as predictor variables produced oxymoronic and counter-institutive results. This is primarily due to existence of multi-collinearity among the predictor variables. The context of the study is to consider the event of driving on a cycle as a random sampling experiment. The outcome of a driving cycle is summarized into a list of predictor variables or components.

Automotive Original Equipment Manufacturers (OEMs) are always striving to deliver fast Air-Conditioning (AC) pulldown performance with consistent distribution of cabin temperature to meet customer expectations. The ultimate test is the OEM standard, called “AC Pull Down,” conducted at high ambient temperature and solar load conditions with a prescribed vehicle drive cycle. To determine whether the AC system in the vehicle has the capacity to cool the cabin, throughout the drive cycle test, cabin temperature measurements are evaluated against the vehicle target. If the measured cabin temperatures are equal or lower than the required temperatures, the AC system is deemed conventional for customer usage. In this paper, numerical predictions of the cabin temperatures to replicate the AC pulldown test are presented. The AC pulldown scenario is carried out in a digital Climatic Wind Tunnel simulation. The solution used in this study is based on a coupled approach.

Condition Monitoring is the process of identifying any significant change in operating parameters of a machine, which can be indicative of a failure in future. This paper discuss a non-invasive condition monitoring methodology for sensing and investigating the problems which could be identified by noise and vibrations. This could be an easy solution for predicting failures in tractors which are operational in the field. An example of engine tappet is used to demonstrate the methodology. A disturbed setting causes a distinguishable noise, referred to as “tappet rattle”. Android smartphones (with inbuilt sensors - accelerometer, gyroscope and microphone) are used to record noise and vibration from tractors in good condition as well as in disturbed condition. Time series data analysis is done to extract relevant features and then Fourier Transform is applied to the signals for extracting frequency domain signatures.

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).

Hardware-in-the-loop (HIL) test benches are indispensable for the development of modern vehicle dynamics controllers (VDCs). They can be regarded as a standard methodology today, because of the extremely safety critical nature of the multi-sensor and multi-actuator systems used in vehicle dynamics control. The required high quality standards can only be ensured by systematic testing within a virtual HIL environment before going into a real car. The steering system is an important aspect of the automobile from operational safety and driver enjoyment perspectives. Current Problem/Opportunity is realistic subjective steering feel prediction before vehicle build. And upfront predict the handling characteristics more accurately with subjective feel before proto build. Current Issue is difficult to convert the objective data into subjective feel and difficult to incorporate the nonlinear steering characteristics with hysterics, friction and power assist curves using virtual simulation.