ON-LINE MONITORING OF TOOL WEAR AND SURFACE ROUGHNESS BY ACOUSTIC EMISSIONS IN CNC TURNING

Tippa S. Reddy and Chevi E. Reddy

Keywords

Acoustic emission, tool wear, surface roughness, monitoring

Abstract

Every tool is subjected to wear in machining. The wear of the tool is gradual and reach its limit of life which is identified when the tool no longer produce the parts to required quality. There are various types of wear a single point cutting tool may be subjected in turning. Of these, flank wear on the tool significantly affects surface roughness. The other types of tool wears are generally avoided by proper selection of tool material and cutting conditions. On-line tool wear compensations and surface roughness measurements 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 tool wear and surface roughness monitoring. The AE signals are very sensitive to changes in cutting process conditions. The gradual flank wear of the tool in turning causes changes in AE signal 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 a high gain pre-amplifier. The amplified signals then recorded on to a computer and then analysed using MAT LAB. A program is written to measure AE signal parameters like Ring down count (RDC), Signal Rise Time, and RMS voltage. The surface roughness is measured by roller ended linear variable probe, fitted and moved along with tool turret on a CNC lathe machine. The linear movements of probe are converted in the form of continuous signals and are displayed on-line in the computer. Flank wear is measured by Toolmaker’s Microscope. The results thus plotted show a significant relation between Flank Wear and Surface Roughness with AE signal parameters. The conclusions are made for predicting tool wear and surface roughness by suggesting consistent values and ranges for on-line monitoring AE signal parameters.

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