MODELLING AND ANALYSIS OF INDIAN RAILWAY WAGON WHEEL USING ANSYS AND ARTIFICIAL NEURAL NETWORK

Abstract: In the present study, the wear analysis of an Indian railway wagon wheel (IRWW) was modelled using modelling software CATIA and applied various material combinations and tested for its performance and wear slippage at distinct load applications. In the real-world applications, wear produces wheel-surface slippage, resulting in deformation and movement of the wheel beneath the track surface. To address this issue, a thorough investigation of rolling contact on train wheels was undertaken to lessen the likelihood of failure. In the present investigation, the IRWW was initially designed and modelled in CATIA and uploaded to ANSYS to make the analysis. The stress generated by increasing contact load at rail-wheel assembly in terms of stress, strain, total deformation, and safety factor were determined for various load applications. Later, the acquired results were validated using the Artificial Neural Network (ANN) of Machine Learning (ML) Approach. The results showed that the overall deformation applied under various loads was within the limit.

Keywords: CATIA, ANSYS, stress, strain, total deformation, IRWW, ANN