Most drill wear monitoring research found in the literature is based on
conventional vibration technologies. However, these conventional approaches still have
not attracted real interest from manufacturers for multiples of reasons: some of these
techniques are not practical and use complicated Tool Condition Monitoring (TCM)
systems with less value in industry. In addition, they are also prone to give spurious drill
deterioration warnings in industrial environments. Therefore, drills are normally replaced
at estimated preset intervals, sometimes long before they are worn or by expertise
Two of the great problems in the implementation of these systems in drilling are:
the poor signal-to-noise ratio and the lack of system-made sensors for drilling, as is
prevalent in machining operations with straight edge cutters. In order to overcome the
noise problems, many researchers recommend advanced and sophisticated signal
processing while the work of Rehorn et al. (2005) advises the following possibilities to
deal with the lack of commercial system-made sensors:
Some research should be directed towards developing some form of
instrumented tool for drill operations.
Since the use of custom-made sensors is being ignored in drilling operations,
effort should be focused on intelligent or innovative use of available sensor
It is expected that the latter could minimize implementation problems and allows an
optimal drill utilization rate by means of modern and smart sensors.
In addition to the accelerometer sensor commonly used in conventional methods,
this work has considered two other sensor-based methods to monitor the drill wear
indirectly. These methods entail the use of an instrumented drill with strain gauges to
measure the torque and the use of an encoder to measure the Instantaneous Angular
Speed (IAS). The signals from these sensors were analyzed using signal processing
techniques such as, statistical parameters, Fast Fourier Transform (FFT), and a
preliminary Time-Frequency (TF) analysis. A preliminary investigation has revealed that
the use of a Regression Analysis (RA) based on a higher order polynomial function can
very well follow and give prognosis of the development of the monitored parameters.
The experimental investigation has revealed that all the above monitoring systems
are sensitive to the deterioration of the drill condition. This work is however particularly
concerned with the use of IAS on the spindle of the drill, compared to conventional
monitoring systems for drill condition monitoring. This comparison reveals that the IAS
approach can generate diagnostic information similar to vibration and torque
measurements, without some of the instrumentation complications. This similitude seems
to be logical, as it is well known that the increase of friction between the drill and workpiece
due to wear increase the torque and consequently it should reduce or at least affect
the spindle rotational speed.
However, the use of a drill instrumented with a strain gauge is not practical,
because of the inconvenience it causes on production machines. By contrast, the IAS
could be measured quite easily by means of an encoder, a tachometer or some other smart
rotational speed sensors. Thus, one could take advantage of advanced techniques in
digital time interval analysis applied to a carrier signal from a multiple pulse per
revolution encoder on the rotating shaft, to improve the analysis of chain pulses. As it
will be shown in this dissertation, the encoder resolution does not sensibly affect the
analysis. Therefore, one can easily replace encoders by any smart transducers that have
become more popular in rotating machinery. Consequently, a non-contact transducer for
example could effectively be used in on-line drill condition monitoring such as the use of
lasers or time passage encoder-based systems.
This work has gained from previous research performed in Tool Condition
Monitoring TCM, and presents a sensor that is already available in the arsenal of sensors
and could be an open door for a practical and reliable sensor in automated drilling.
In conclusion, this dissertation strives to answer the following question: Which one of
these methods could challenge the need from manufacturers by monitoring and
diagnosing drill condition in a practical and reliable manner? Past research has
sufficiently proved the weakness of conventional technologies in industry despite good
results in the laboratory. In addition, delayed diagnosis due to time-consuming data
processing is not beneficial for automated drilling, especially when the drill wears rapidly
at the end of its life. No advanced signal processing is required for the proposed
technique, as satisfactory results are obtained using common time domain signal
processing methods. The recommended monitoring choice will definitely depend on the
sensor that is practical and reliable in industry.
Dissertation (MEng)--University of Pretoria, 2013.