A New Approach to Predict Machining Force and Temperature With Minimum Quantity Lubrication

Abstract

A new model to predict cutting force and temperature is developed by incorporating the lubrication and cooling effects generated from minimum quantity lubrication (MQL) machining. The boundary lubrication theory is utilized to estimate the friction behavior in prediction model. The model is capable of predicting cutting force and temperature in MQL machining directly from given cutting conditions, as well as material properties. Subsequently, the response of temperature distributions to chip formation and MQL is quantified on the basis of a moving heat source/loss model which iterates with the initial cutting force to achieve the final predictions. The predicted cutting temperature and cutting force are validated by the experimental data for AISI 9310 steel and AISI 1045 steel, respectively. Results show that under cutting speeds of 223–483 m/min, feed rates 0.10–0.18 mm/rev, depth of cut 1.0mm, the predicted cutting temperature at the tool-chip interface are generally lower than experimental measurements by 2% to 19%. And the model provides an average error of 11% for temperature prediction. With respect to cutting force prediction, the model provides a prediction error of 13% on the average in the cutting direction and 12% in the thrust direction within the experimental test condition range (cutting speeds of 45.75–137.25m/min, feeds 0.0508–0.1016 mm/rev, and depth of cut 0.508–1.016mm). In actual machining, the effects of possible tool wear causing higher temperature and force can contribute to deviations from model predictions involving only sharp tools.