06-03-2013, 02:08 PM
Condition Monitoring of Machine Tools and Machining Processes using Internal Sensor Signals
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Abstract
Condition monitoring of critical machine tool components and machining processes
is a key factor to increase the availability of the machine tool and achieving
a more robust machining process. Failures in the machining process and
machine tool components may also have negative effects on the final produced
part. Instabilities in machining processes also shortens the life time of the
cutting edges and machine tool.
The condition monitoring system may utilise information from several sources
to facilitate the detection of instabilities in the machining process. To avoid
additional complexity to the machining system the use of internal sensors is
considered. The focus in this thesis has been to investigate if information
related to the machining process can be extracted directly from the internal
sensors of the machine tool.
The main contibutions of this work is a further understanding of the direct
response from both linear and angular position encoders due the variations in
the machining process. The analysis of the response from unbalance testing
of turn tables and two types of milling processes, i.e. disc-milling and slotmilling,
is presented. It is shown that operational frequencies, such as cutter
frequency and tooth-passing frequency, can be extracted from both active and
inactive machine axes, but the response from an active machine axis involves
a more complex analysis. Various methods for the analysis of the responses
in time domain, frequency domain and phase space are presented.
Background
Machine tools are composed of several subsystems, such as structures, electrical
drive systems, controllers and actuators, which are all involved when
performing the desired machining operations. The mechanical structure of
the machine tool is often designed to be extremely rigid to withstand the
forces created during the machining operation. Multitask machine tools are
designed to perform several different machining operations such as turning,
milling, drilling etc. in the same setup, which requires more degrees of freedom
than dedicated machine tools. The additional number of degrees of freedom
however, comes with a price - some of the rigidity is sacrificed. Multitask
machine tools are not used for their rigidity, but for their capacity of handling
large and geometrically advanced components and for their flexibility to allow
manufacturing in a single setup, i.e. without the need of refixturing the
component.
Aim and scope
The aim of this work is to investigate the possibilities to use internal machine
tool signals for condition monitoring of machine tools and machining processes.
This is important in order to achieve more robust machining processes without
adding complexity to the overall machining system. In this work, a 5-axis
multitask machine and various material removal processes, such as milling
and drilling, are considered.
Condition monitoring involves measuring, processing and analysis of signals,
the characterics of the measured signals must be known in order to select
appropriate methods for the processing and analysis of them. A major part in
this work is therefore to study the responses during various type of excitations
of the machine tool and present suitable strategies to extract the useful part
from the signals.
Research approach
The thesis has taken an experimental approach and is based on observations
obtained during machining in a modern 5-axis multitask machine tool. Various
experiments have been performed which allow the systemtic study of certain
phenomena. The main focus has been to study the time behaviour of the
output signals due to vibration generated for various periodic excitation and
vibration generated from rotating unbalance and vibrations generated from
impacts.
The characteristic behaviour of the encoder output signals was initially unknown
and needed a thorough investigation before any further analysis of them
could be undertaken. To get a fundamental understanding of the behaviour
of the signals, initial experiments with minimal complexity have been carried
out, including both non-machining and various machining tests.
Principles of condition monitoring
Maintaining the health of macine tools and establishing stable machining processes
is of major importance to reduce the risk of malfunctioning equipment
and ensure that high quality parts are produced. This can be achieved by testing
of critical machine tool components and online measuring and analysis of one
or more quantities from the machining process in order to adjust the process
towards more stable machining regions. From the initial acceptance tests of
machine tools this chapter reviews some principles of condition monitoring of
the machining process using various methods found in the literature.
Acceptance testing of machine tool
components
Testing of machine tool components is important through its life cycle to avoid
severe breakdowns during operation. The testing procedure itself is carried
out both at the suppliers shop and after the installation. Generally, a factory
acceptance test, FAT, is carried out first at the suppliers shop before
the delivery of the machine tool. After installation at the customers shop,
an installation acceptance test, IAT, is performed as a final validation. Rearrangement
of machine parks at the customers shop, which may affect the
alignment of structural components, is another reason when acceptance tests
should be performed.
Role of condition monitoring systems
The machining process is either continuous, such as turning or drilling, or
intermittent, such as the milling operation. Continuous operations are performed
with single cutting edge, removing material from one spindle revolution
to the next. Intermittent operations involve one or more cutting edges, removing
material from one tooth to the other. In both cases, material is removed
from the workpiece under the generation of chips. The machining operations
are controlled by various parameters, such as the spindle speed, depth of cut
and feed rate, etc.
If the correct machining parameters are set, the machining operation is expected
to perform well and the final produced part will meet the final requirements
given in the part specification. Depending on the machining process
characteristics, the cutting tool may have a short or long life time. For cost
effective production, the number of tool changes should be kept at minimum
and the cutting tool inserts must be used close to the limiting tool life without
violating the overall machining system. This requires in-depth knowledge
about the tool wear rate and maximim tool life for the actual machining setup
and machining conditions.
Sensorless condition monitoring
Sensorless condition monitoring is about monitoring of the machining process
by utilising the existing sensors and signals in the machine tool without using
external sensors, such as force sensors and accelerometers. The main advantage
is that the additional complexity is kept to a minimum while reducing
the cost of the condition monitoing system. Two types of internal signals are
considered, i.e. internal drive signals and encoder signals.
Internal drive signals
Internal drive signals, such as the spindle motor current and feed drive current,
can be measured with a non-intrusive Hall effect sensor [4]. Measuring of
the current signal from the servo drive motors and spindle motors has been
widely used as a means of indirect measuring of the cutting force to avoid
the impracticability of force dynamometers. However, the observed current
signal from the servo drive motor contains additional components related to
acceleration and deceleration of the work table, friction force in the guide
way, feed direction change, etc. Thus, to obtain a reliable estimation of the
cutting force, the undesired components in the current signals must first be
removed, which can be accomplished using special pre-processing methods.
The cutting force estimation method presented by Kim et al. [5] also makes
use of the internal feed rate signals to generalise the cutting force estimation
to multi-axis machining. [6] utilised the spindle power consumption signal and
feed drive current signals to estimate tool wear in high-speed milling.