This work documents an investigation of the degradation of a variety of different
tools whilst conducting milling operations on a computer numerical controlled
(CNC) milling machine. The potential of a range of sensors to detect tool degradation
has been investigated and the outputs have been incorporated into a monitoring
system.
Progressive degradation under nominal rough and finish face milling and rough
groove milling has been investigated using a two point grooving tool and four
and eight point face milling tools on En8, En24 and En24T workpiece materials.
Rapid degradation of the cutting tool has also been observed under rough milling
conditions using four and eight point face milling tools, whilst machining n8 and
En24T materials in a variety of simulated and actual tool breakage situations.
A limited investigation of the effect of the individual wear geometries associated
with both progressive and instantaneous tool degradation has been conducted by
simulating these geometries and carrying out rough miffing tests using a four point
face milling tool on a workpiece of En8 material. Similarly, a limited investigation
of the effect of machining on different machines has also been undertaken.
A number of different sensing technologies have been used, including conventional
sensors such as spindle current and cutting force but also novel sensing techniques
such as Acoustic Emission. These have been combined using artificial intelligence
techniques to provide automatic recognition of the tool wear state. Similarly, the
feasibility of breakage detection/prediction has also been demonstrated.CEC - BRITE EURAM Programm