ANN Based Surrogate Model for Fillet Stress Prediction of Automotive Crankshafts

Designing of automotive crankshaft is a time-consuming process and often involves several iterations of crankshaft designs being validated by calculations till a safe design is conceptualized. In the initial phases of design process, Finite Element Analysis (FEA) methods are conventionally used for design validation of crankshaft by stress and fatigue calculations. Setting up of FEA models to find crankshaft fillet stress is laborious and often the design is required to undergo a few iterations of changes before it can be deemed safe for the next phases of development. This study aims to implement an Artificial Neural Network (ANN) prediction model to predict crankshaft fillet stress under a 3-point bending scenario and evaluates its performance. The objective of this study was to develop a Machine learning prediction model based on Artificial Neural Networks that predicts maximum von mises stress at Crankshaft fillets region. A dataset containing previous FEA stress data was used to train the model. The dataset was created by performing FEA calculations and it was split into training and validation sets. The ANN model was then trained and validated using the dataset and it takes four geometric parameters and Peak Firing Pressure (PFP) load to predict the maximum fillet stress value. Feature importance study was performed which showed two redundant features out of seven variables in the dataset. The non-linear relationship of predictors and the output variable was approximated by the ANN model with an acceptable accuracy and the model was able to achieve a R2 score of 0.984. This model can be used alternatively to CAE analyses for crankshaft fillet stress calculation, to save development time in the initial design phases.