International Journal of Analytical, Experimental and Finite Element Analysis
Volume 12 · Issue 3 · September 2025 · pp. 11–18
Research Article · Peer Reviewed
Received: August 05, 2025 · Accepted: September 20, 2025 · Published: September 30, 2025
DOI: 10.26706/ijaefea.3.12.20250901
Open Access · CC BY 4.0

Intelligent Vibration Prediction of Thermally Stressed Rotating Functionally Graded Beams Using Neural Networks Approach

Uttam Kumar Kar*, Milan Banerjee, Pushpita Dasgupta, Vinay Kumar, P. S. Rao

Department of Mechanical Engineering, Christian College of Engineering and Technology, Bhilai, India

*Corresponding author:v.kumar@ccetbhilai.ac.in

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Abstract

The rotational components are widely used in many engineering applications such as turbine blades, high compressor blades, gyroscopes etc. The vibration analysis of such components is very essential to understand their dynamic behaviour at complex environment. Present work mainly focuses on the development of a novel AI-based approach for the vibration analysis of a functionally graded (FG) rotating beam exposed to high temperature environment. To account the non-linearity and complex interaction between material compositions, centrifugal force and high thermal load feed forward multilayer perceptron based artificial neural network model is proposed. The vibration responses of FG beam for different grading indices, temperature changes and rotational speeds are first calculated using finite element based simulation and furthermore FE data is imported in ANN model for training and testing. After input-output interactions the vibration behaviour is predicted and compared with FE simulation with unknown inputs. The results show that the proposed intelligent approach is powerful tool to predict the dynamic response of thermally loaded rotating FG components especially at condition monitoring.

Keywords

FG rotating beam Artificial Neural Network High temperature Vibration analysis

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