Modeling of Turning Parameters for Inconel 718 Alloy using ANN

For machining a component, it is important to understand the characteristics of work material in order to choose the appropriate cutting tool and to fix a set of machining parameters to achieve optimum output. Analytical models of machining processes require complete understanding of process mechanism and hence are difficult to be developed. Once developed, these models are useful in parametric optimization, process simulation, operation and process planning, process parameter selection, parametric analysis, process performance prediction, verification of the experimental results, and improving the process performance by implementing/incorporating the theoretical findings. Neural network models associated with artificial intelligence are known as artificial neural networks (ANNs) which are simple mathematical models in the form of defining a function. This work presents the details of the experiments carried out for data acquisition, method of building the ANN models and their validation. These models can be used for predicting the output for a chosen set of input variables or for a specific desired output, finding the set of input variables to be chosen. This work resulted in developing models for the turning process for Inconel 718 alloy in a scientific manner. It also enables further scope of identifying the optimized set of turning parameters for Inconel 718 material using the newly developed coated carbide tools, achieving quality surface and productivity.