Prediction and Experimental Study on Cutting Force of Austempered Vermicular Graphite Cast Iron Using Artificial Neural Network
Keywords:artificial neural network, cutting force, machining, vermicular graphite cast iron, heat treatment
In this study, a technique is proposed to predict cutting force of austempered vermicular graphite cast irons (VGCI) which are widely used in the industry by using neural network. The effect of austempering heat treatment on the cutting force was experimentally achieved. The samples were austenitized at 900 °C for 90 minutes and then austempered at different temperatures (320 °C and 370 °C) for 60, 90 and 120 minutes. Machinability tests were carried on under dry conditions at the CNC machining center with the cutting parameters selected in accordance with ISO 3685. In the experiment, cutting force depending on hardness, cutting speed and feed rate were measured. These results were used for input parameters (training, testing and validation) of Artificial Neural Network (ANN) and prediction model was developed. Output value of ANN and experimental results were compared and accuracy of ANN is 99.99% and 99.62% for training and test values, respectively.