Abstract
Factors like cutting force, cutting
temperature, acoustic emission signals and vibration signals can be
effectively used to predict tool wear. Even though each of these factors are
used individually to predict tool wear, a more accurate prediction will be
possible if all these factors are considered collectively since each of these
factors predict tool wear in their own characteristic fashion. For example,
temperature responds to flank wear and crater wear in a better way than to
fracture type of tool failure, whereas cutting force has a better response to
fracture type of tool failure. Hence a better prediction of tool wear
consisting of different modes is possible by considering the response to these
representative factors collectively.
In the present work, an attempt was made to use a
combination of cutting force and cutting temperature along with cutting
velocity and feed rate to predict the tool wear during hard turning of AISI
4340 Steel having a hardness of 46HRc using a multi coated hard metal insert
with a sculptured rake face. A regression model was developed to fuse the
cutting force and cutting temperature signals and to predict flank wear.
Confirmatory experiments were conducted to validate the predictions of the
regression model. The predictions matched well with the experimental results.
Keywords—. Tool wear, Sensor Fusion, Hard
Turning
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