ABSTRACT
Wear of a cutting tool in a machining operation is highly undesirable because it severely degrades the quality of
machined surfaces and causes undesirable and unpredictable changes in the work geometry. From a process automation
point of view, it is therefore necessary that an intelligent sensing system be devised to detect the progress of tool wear
during cutting operations so that worn tools can be identified and replaced in time. As a ‘non – destructive’ sensing
methodology, Acoustic Emission (AE) based techniques offer some advantages over force or power based tool monitoring
techniques because of the close relationship between the generation of the emission signal and the fracture or wear
phenomenon in machining. The generation of the AE signals directly in the cutting zone makes them very sensitive to
changes in the cutting process. Acoustic Emission Techniques (AET) is a relatively recent entry into the field of Non –
Destructive Evaluation (NDE) which has particularly shown very high potential for material characterization and damage
assessment in conventional as well as non-conventional processes. This method has also been widely used in the field of
metal cutting to detect process changes like tool wear etc. In this research work the results obtained from the analysis of
Acoustic Emission sensor employs to predict flank wear in turning of C45 steel of 250 BHN hardness using Polycrystalline
diamond (PCD) insert. The correlation between the tool wear and AE parameters is analyzed using the experimental study