Investigating the effect of machining parameters on residual stresses of the nickel-based alloy using the genetic algorithm and neural network

Induced residual stress at the machining processes affects significantly fatigue life of manufactured components. Inconel718 superalloy has been used widely in the different parts of aerospace industries and in the rotary parts of turbines engines because of its unique properties. Therefore, evaluation of undesirable state of the residual stress in finish turning of Inconel718 is so needed. Unfortunately, the effect of machining parameters has not been investigated extensively on residual stress due to the high expenses and several difficulties for measuring residual stress. Thus, the present study has been conducted in order to obviate the mentioned requirement. At first, the residual stress was measured by X-Ray Diffraction (XRD) method. Then, the effect of machining parameters including cutting speed, feed rate, and depth of cut was investigated widely on residual stress using Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Obtained results showed that, the residual stresses were estimated accurately and finally the most suitable machining parameters were accessed so that the undesirable state of tensile residual stress was reduced in finish machining of Inconel718.