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The main challenge in today's modern diesel engines is to design the parts, which should withstand higher temperatures. To achieve this, selection of materials and tolerances are very important. The product identified for this study is an oil pump, which is an engine auxiliary component. The function of oil pump is to supply oil to different parts of the engine to lubricate and reduce the overall engine friction. The different speed and load condition for which the engine is subjected pose a challenge to the oil pump, to supply necessary quantity of oil at required pressures. Normally, the oil pump is subjected to a temperature of 120°C at higher speeds. However, the peak oil temperature in modern diesel engines can be as high as 140°C. When the existing pump was tested at full speed and suddenly decelerated to idle speed, it was observed that the minimum oil pressure was not maintained for engine lubrication.

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.