Drilling of Polymeric Nano Composite Laminates Analyzing using Optimal Neural Network

Abstract

In the mechanical field drilling is a very important machining practice for fitting or cutting the materials keep away from of any disturbance. The employ of composite materials has enlarged in different areas of science and technology due to their special physical and mechanical properties. Carbon nano fibre (CNF) is a higher thermal conductivity and low material density formulates the sensitive application. The significant intention of the proposed technique is to frame an Artificial Neural Network (ANN) with the aid of optimization techniques. The ANN utilized to predict the outputs such as the delamination factor (DF) thrust force (TF) and acoustic emission parameters such as AE root mean square (RMS), AE energy, AE rise time, AE counts to peak, AE average frequency, AE counts, AE peak amplitude, AE hits and AE average signal level (ASL) of the known input values cutting speed, feed rate and percentage of CNF. The different optimization algorithms such as cuckoo search (CS), harmony search (HS) and Artificial Fish Swarm Optimization (AFSO) are utilized to find the optimal weights α and β of the ANN structure. All optimum results demonstrate that the attained error values between the output of the experimental values and the predicted values are closely equal to zero in the designed network. From the results, the minimum error of 85.53% is determined by ANN to attain by the AFSO algorithm.