Modeling and Prediction Using Regression, ANN and Fuzzy Logic of Real Time Vibration Monitoring on Lathe Machine in Context of Machining Parameters

Machine tool vibration plays a dominant role in the surface finish, dimensional and geometrical tolerances of the machined work piece. Condition of the machines includes collected data, such as vibration analysis, oil and wears debris analysis, ultrasound, temperature and performance evaluation. Out of these the vibrations have been measured and its effect has been studied. The present paper deals with the measurement of acceleration during machining of Cast Iron on lathe machine. 33full factorial design of experiments were selected, experiments are performed by varying machining parameters such as spindle speed, feed rate and depth of cut. ANOVA and Regression analysis has been carried out to know the significance of these parameters. Even Artificial Neural Network (ANN) and Fuzzy Logic based models have been developed to predict Acceleration in the context of these input parameters. The predicted results obtained from the developed models are compared with the experimental one. Results shows that the developed models having more than 95% accuracy, which leads the use of it in predicting the acceleration within the range of the specified input parameters for a given machine too.