Predictive Modeling for Ductile Machining of Brittle Materials
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Brittle materials such as silicon, germanium, glass and ceramics are widely used in semiconductor, optical, micro-electronics and various other fields. Traditionally, grinding, polishing and lapping have been employed to achieve high tolerance in surface texture of silicon wafers in semiconductor applications, lenses for optical instruments etc. The conventional machining processes such as single point turning and milling are not conducive to brittle materials as they produce discontinuous chips owing to brittle failure at the shear plane before any tangible plastic flow occurs. In order to improve surface finish on machined brittle materials, ductile regime machining is being extensively studied lately. The process of machining brittle materials where the material is removed by plastic flow, thus leaving a crack free surface is known as ductile-regime machining. Ductile machining of brittle materials can produce surfaces of very high quality comparable with processes such as polishing, lapping etc. The objective of this project is to develop a comprehensive predictive model for ductile machining of brittle materials. The model would predict the critical undeformed chip thickness required to achieve ductile-regime machining. The input to the model includes tool geometry, workpiece material properties and machining process parameters. The fact that the scale of ductile regime machining is very small leads to a number of factors assuming significance which would otherwise be neglected. The effects of tool edge radius, grain size, grain boundaries, crystal orientation etc. are studied so as to make better predictions of forces and hence the critical undeformed chip thickness. The model is validated using a series of experiments with varying materials and cutting conditions. This research would aid in predicting forces and undeformed chip thickness values for micro-machining brittle materials given their material properties and process conditions. The output could be used to machine brittle materials without fracture and hence preserve their surface texture quality. The need for resorting to experimental trial and error is greatly reduced as the critical parameter, namely undeformed chip thickness, is predicted using this approach. This can in turn pave way for brittle materials to be utilized in a variety of applications.