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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