| Summary: | Machining is the most important part of the manufacturing processes. Machining deals with the process of removing materialfrom a work piece in the form of chips. Machining is necessary where tight tolerances on dimensions and finishes arerequired. The common feature is the use of a cutting tool to form a chip that is removed from the work part, called Swarf.Every tool is subjected to wear in machining. The wear of the tool is gradual and reaches certain limit of life which is identifiedwhen the tool no longer produce the parts to required quality. There are various types of wear a single point cuttingtool may be subjected to in turning. Of these, flank wear on the tool significantly affects surface roughness. The other typesof tool wears are generally avoided by proper selection of tool material and cutting conditions. On-line surface roughnessmeasurements gained significant importance in manufacturing systems to provide accurate machining. The Acoustic Emission(AE) analysis is one of the most promising techniques for on-line surface roughness monitoring. The AE signals arevery sensitive to changes in cutting process conditions. The gradual flank wear of the tool in turning causes changes in AEsignal parameters. In the present work investigations are carried for turning operation on mild steel material using HSS tool.The AE signals are measured by highly sensitive piezoelectric element; the on-line signals are suitably amplified using ahigh gain pre-amplifier. The amplified signals then recorded on to a computer and then analyzed using MAT LAB. A programis developed to measure AE signal parameters like Ring down count (RDC), Signal Rise Time and RMS voltage. Thesurface roughness is measured by roller ended linear variable probe, fitted and moved along with tool post on a CNC lathemachine. The linear movements of probe are converted in the form of continuous signals and are displayed on-line in thecomputer. The results thus plotted show a significant relation between Surface Roughness and AE signal parameters. Theconclusions are made for predicting surface roughness by suggesting consistent values and ranges for on-line monitoringAE signal parameters
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