Abstract: An artificial neural network (ANN) based models has been formulated for investigation  and prediction of the relationship between various machining process parameters and the power consumption during turning of material such as En8, En1A, S.S.304, Brass and Aluminium. The input parameters of the ANN model are the cutting tool parameters, machine specification, work piece parameters, environmental parameters and the cutting process parameters. The output parameter of the model is power consumed during the turning process. The model consists of a three layered feed forward back propagation neural network. The network is trained with pairs of inputs/outputs database generated when machining of ferrous and nonferrous material. A very superior performance of the neural network, in terms of conformity with experimental data, was achieved. The model can be used for the analysis and prediction of the multifaceted relationship between input and output parameter. This paper presents the ANN model for predicting the power consumption performance measure in the machining process by considering the Artificial Neural Network (ANN) as the essential technique for measuring power consumption. Utilization of ANN-based modeling is also presented to show the required fundamental elements for predicting power consumption in the turning process. In order to investigate how competent the ANN technique is at estimating the prediction value for power consumption, a real machining experiment is performed in this study. In the experiment, more than 200 samples of data concerned with turning process using field data based approach of experimentation. It was found that the 13–10–1 network structure gave the best ANN model in predicting the power consumption value
 
Key words: Artificial Neural Network, comprehensive model, Dry Turning, Machining process.

Cite: Mangesh R. Phate, Shraddha B. Toney, "Formulation of Artificial Neural Network (ANN) Based Model for the Dry Machining of Ferrous & Non-ferrous Material used in Indian Small Scale Industri," International Journal of Materials Science and Engineering, Vol. 4, No. 3, pp. 145-160, September 2016. doi: 10.17706/ijmse.2016.4.3.145-160