Experimental and Artificial Neural Network modeling of Natural Frequency of Stepped Cantilever shaft

Ali A. Al-Saffar†*, Abbas Ali Diwan‡, Luay S. Al-Ansari†, Aseel Alkhatat†

†Mechanical Engineering Department – Faculty of Engineering – University of Kufa, Iraq

‡Nanotechnology and Advance Material Research Unit, Faculty of Engineering, University of Kufa, Iraq

*Corresponding Author Email: alia.alsaffar@uokufa.edu.iq

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The natural frequency of aluminum cantilever stepped beam (two steps) was investigated experimentally and theoretically by modeling the experimental data using artificial neural network (ANN) for different values of small and large diameters and for different lengths of larger diameter step. Two hidden layers and different number of neurons in each hidden layer were employed with the ANN. Theoretical natural frequency results using two algorithm functions (trainlm) and (trainrp) of the ANN method were compared with the experimental solution. The results showed that there was an increase in the natural frequency with the increasing of the larger diameter length of the stepped shaft and a high performance of the ANN was found to predicate the experimental results.