Investigations into Effect of Cutting Conditions On Surface Roughness Under MQL Turning of AISI 4340 by ANN Models

Abstract

Enormous usage of cutting fluid in industries is reporting the issues related to health of employee, environmental pollution which promotes the Minimum Quantity Lubrication (MQL). The study reports surface roughness modelling in CNC turning under minimum quantity lubrication of EN 24, taking into account effect of cutting environment, cutting conditions using artificial neural networks (ANN). The optimisation of models are processed using the input-output data sets, ANN model, the repetitions in training the neural network, rate, hidden nodes and the training function. ANN analysis with multilayer feed forward structure under MATLAB is adopted in the analysis. Finally, After the training, the ANN is tested in order to evaluate its predictive and generalization performances. Testing the ANN is carried out by applying a new input data set, which was not included in the training process. The well known statistical tools coefficient of determination is used for this benchmarking. The adequacy of the ANN is evaluated by considering the coefficient of correlation (R).