Abstract
Abstract
White layer is often formed in hard machined surfaces when a high cutting speed, a worn tool, or a tool with low thermal conductivity is used. A thicker white layer indicates severer thermal damage. To reveal the relationship between the thickness of white layer and a variety of cutting parameters, machining experiments are performed using hardened AISI 52100 steel as work material. The results show that the use of coolant, tool material with high thermal conductivity, and the reductions of feed rate, cutting speed, tool nose radius, and tool flank wear all tend to reduce the chance of white layer formation and decrease its thickness. Based on this, three statistical models are proposed for the prediction of the thickness of white layer in hard machining. A non-linear regression model is selected for its best fitting results among the three.