Analysis of Natural Frequency of a Rectangular Beam Using Finite Element Analysis and Artificial Intelligence
Keywords:
Natural frequency, Copper material, Artificial neural network, rectangular beam, Finite Element Analysis and Linear Regression.Abstract
Natural vibrations are the unforced oscillations of an elastic body that occur at the natural frequency. A substantial increase in vibration amplitude occurs when an object vibrates at a frequency that is equal to its natural frequency, which could cause irreparable harm. Therefore, it is essential to comprehend the natural frequency. In order to predict the natural frequency or free vibration characteristics of a rectangular copper beam that is simply supported and cantilevered, machine learning techniques are used to examine the natural frequency of the beam. Here copper material properties is used to predict, where copper has minimal chemical reactivity, is malleable and ductile, and is an excellent conductor of heat and electricity. An artificial neural network and linear regression algorithm model has been developed to estimate relationship between material properties, angular frequency and natural frequencies obtained by Euler Bernoulli method and Ansys 14.5 software as an output layer. Without the need to solve any differential equations or undergo time-consuming experimental procedures, the proposed machine learning algorithms can predict the natural frequencies. The results show that artificial intelligence (AI) can be efficiently adapted to modal analysis problems of beams. The graph behaviour on the natural frequency from AI is also demonstrated.
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