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The major area in which the automotive manufacturers are working is to produce high-performance vehicles with lighter weight, higher fuel economy and lower emissions. In this regard, hollow camshafts are widely used in modern diesel and gasoline engines due to their inherent advantages of less rotational inertia, less friction, less weight and better design flexibility. However, the dynamic loads of chain system, valve train and fuel injection pump (if applicable) makes it challenging to design over-head hollow camshafts with the required factor of safety (FOS). In the present work, high-fidelity FE model of a hollow camshaft assembly is simulated to evaluate the structural performance for assembly loads, valve train operating loads, fuel injection pump loads and chain system loads. The investigation is carried out in a high power-density (70 kW/lit) 4-cylinder in-line diesel engine.

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.