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The air pollution is increasing at an alarming rate now a day mainly due to emissions coming out of automotive vehicles. The exhaust emissions gases are hazardous to human health. The increased number of vehicles on road will make the scenario even worse. In order to control the pollution level, the regulatory bodies are now implementing stringent emission norms. In India, the regulatory authorities has framed the transition of BS IV to BS VI emission norms in 2020 by skipping the BS V emission norms which makes the automotive industries to work on more advanced fuel management technologies. It is more tedious to control the tail pipe emissions beyond BS IV emission norms with the conventional carburetor system since it is operating on open loop system. It is evident that in order to meet the stringent emission norms we need to have a closed loop system which controls the Air Fuel Ratio (AFR) close to stoichiometric to increase the conversion efficiency of the catalytic converter.

In this ever-changing field of automotive industry where there is high competition to seek a place in market, it is necessary to design and develop products with a minimum lead time meeting the target specifications. This is normally achieved through product variants specific to customer requirements from the repository of design base created. Design bases are created based on the previous products and by bench marking. Vacuum Pump is an engine driven part identified for this study. The main aim of this study is to create a mathematical model, and use Artificial Neural Networking (ANN) to arrive at a design base. In the process of building the mathematical model, two key design parameters namely profile and performance were identified and model was constructed. Using this mathematical model, proto samples were prepared for a range of vacuum pump capacities and were tested. These test results were used for training the ANN to create the design base for any future design.