The typical machining process monitoring system operates according to the following
rationale. In the cutting region there are several process variables, such as
cutting forces, vibrations, acoustic emission, noise, temperature, surface finish,
etc. that are influenced by the cutting tool state and the material removal
process conditions. The variables those are prospectively effective for machining
process monitoring can be measured by the application of appropriate physical
sensors. Signals detected by these sensors are subjected to analogue and digital
signal conditioning and processing with the aim to generate functional signal
features correlated (at least potentially) with tool state and/ or process conditions.
Sensor signal features are then fed to and evaluated by cognitive decision making
support systems for the final diagnosis. This can be communicated to the human
operator or fed to the machine tool numerical controller in order to suggest
or execute appropriate adaptive/corrective actions. The sequence of activities
in sensor monitoring of machining process conditions can be surmised.
SENSORS AND SENSOR SYSTEMS FOR MACHINING
The measuring techniques for the monitoring of machining operations have traditionally
been categorized into two approaches: direct and indirect. In the direct approach
the actual quantity of the variable, e.g., tool wear, is measured. Examples
of direct measurement in this case are the use of cameras for visual inspection,
radioactive isotopes, laser beams and electrical resistance. Many direct methods
can only be used as laboratory techniques. This is largely due to the practical
limitations caused by access problems during machining, illumination and the
use of cutting fluid. However, direct measurement has a high degree of accuracy
and has been employed extensively in research laboratories to support the investigations
of fundamental measurable phenomena during machining processes.
Motor power and current: Electric drives and spindles provide the mechanical
force necessary to remove material from the part. By the measurement of motor
related parameters such as motor power or current, both process power and, more
recently, measures of the machine tool and drive condition can be realized.
The major advantage of motor related parameters to detect malfunctions in the
cutting process is that the measurement apparatus does not disturb the machining.
The capacity to measure power already exists in the drive controller as part
of the drive control loop or can be readily retrofitted and is suitable for
use in production environments.
Power and current measurement technology: Retrofit power measurement
solutions are an economical monitoring solution for many machining operations.
However, the latest modern open control systems allow access to internal signals
in the numerical controller such as motor power and motor current. Software
can be seamlessly integrated into the CNC control and provides the user with
a dedicated monitoring interface via the Human Machine Interface (HMI). Over
the last decade, this technology has become commonplace in industry. A logical
extension of this approach is the adoption of control parameters based on internal
control signals. Adaptive Control Optimize (ACO) and Adaptive Control Constraint
(ACC) based algorithms have been developed and implemented using both internal
control signals and additional sensors (Klocke et al.,
Power monitoring signal features: Motor current and power sensing use
the motor itself as an indirect sensor of cutting force. Thus, when using sensor
systems based on motor current or power, it is crucial that the relationship
between input current/power and output force/torque is linear and understood.
The signal features and uses of current/power monitoring face a number of issues,
including (Kettetler, 1999): (1) the amount of spindle
power required for material removal may be a very small part of total power,
e.g., for small diameter drilling and finish machining; (2) the spindle motor
power is proportional to the resultant cutting force, the least wear sensitive
parameter; (3) temperature rises inherent in electrical motors influence power
consumption and (4) drive motors are highly dependant on the axis lubrication
state, transverse rate and axis condition.
Force and torque: A certain force is required for cutting operation
to separate and remove the material. The monitoring of cutting forces in machining
for the validation of analytical process models, the detection of tool failure,
etc., has been used extensively by researchers (Byrne et
al., 2004). This is due to the high sensitivity and rapid response of
force signals to changes in cutting states. Torque sensors, like force sensors,
also consist of a mechanical structure that responds to a deformation but in
this case the applied load are torsional. The underlying force measurement technology
is often identical but the application of torque sensors and the method of signal
transmission from rotating tool holders are different.
Force and torque measurement technology: Force and torque sensors generally
employ sensing elements that convert the applied force or torsional load into
deformation of an elastic element. The two main sensor types used are piezoelectric
based and strain based sensors.
Piezoelectric sensors: Direct force measurement using piezoelectric
sensors is possible when the force transducer is mounted in line with the force
path. In cases where more measurement flexibility is required, multi-component
force transducers have been developed and are used extensively in lab based
applications. Rotating cutting force dynamometers are also available that contain
the force sensing elements capable to measure 3 components of force and torque.
The data is transmitted from the rotating part of the sensor to a stator via
telemetry. Rotating cutting force dynamometers can operate at speeds of up to
20,000 rpm and have been used for high speed milling of aerospace materials.
Developments like the integration of force sensors into the machine structure
have taken place over the last 10 years with concepts developed for milling
(Byrne et al., 1995). Figure 2
shows Arrangement of sensors on the spindle.
Acoustic emission measuring technology and sensors: Piezoelectric sensor
technology is particularly suitable for measuring Acoustic Emission (AE) in
machining process monitoring (Rogers, 1979). With very
wide sensor dynamic bandwidth from 100-900 kHz, AE can detect most of the phenomena
in machining, though significant data acquisition and signal processing is required
(Rogers, 1979) (Fig. 1).
|| Sources of AE in machining
|| Arrangement of sensors on the spindle
This presents problems for signal processing and band pass filters usually
provide great flexibility for AE detection by selecting appropriate frequency
ranges. The output signal from the AE sensor is fed through a pre amplifier
that has a high input impedance and low output impedance. A Root Mean Square
(RMS) converter, gain selection unit and filters are also typically contained
within the pre amplifier housing. Performed test cuts to detect fracture and/or
monitor the condition of small drills using AE features. AE applications by
employing simple process-adapted band pass filters, a rectifier and a low pass
filter to convert the normally high frequency AE signal to low frequency signals.
AE signal transmission and sensor location: The high frequency and low
amplitude nature of AE means that signal transmission via a coupling fluid is
possible. By the location of the AE sensor on the coolant supply nozzle, the
coolant can be used as transmission path. The signal transmission methods had
a distinct advantage for rotating tools such as in milling and drilling. Various
other methods of signal transmission from AE sensor to AE coupler/signal processor
are common to other sensing applications, including slip rings, inductive coupling
and radio frequency transmission. Jemielniak investigated aspects of AE signal
processing in machining and proposed that in the machine tool environment the
AE signal is repeatedly reflected from the inner surfaces of the structure where
the sensor is mounted.
Here, a survey of applications related to the main goals of advanced monitoring
of machining operations is presented and a summary of viable solutions as a
function of the monitoring scopes is reported.
Tool conditions: Kuljanic focus on the application of AE for tool wear
estimation in milling using WPD to build an automatic tool wear classification
system (Kuljanic et al., 2008). Axinte and Gindy
try to correlate broaching tool conditions to output signals of multiple sensors:
AE, vibration, cutting force and broaching machine hydraulic pressure. In they
assess the use of spindle power signal for TCM in milling, drilling and turning:
this method is successful for continuous turning and drilling while it shows
low sensitivity for discontinuous milling (Axinte and Gindy,
2003). Teti and Baciu (2004) apply an intelligent
monitoring system based on audible sound energy for in-process tool state recognition
in band sawing of Al alloy and low C steel. A real-time tool breakage monitoring
system for milling is presented based on cutting force indirect measurement
through feed drive AC motor current, whose sensitivity is sufficient to identify
tool breakage (Teti and Baciu, 2004). Ryabov develop
an online tool geometry measurement system based on a laser displacement meter.
It build up a vision system to detect small diameter tap breaks hardly perceived
by indirect in-process monitoring methods as AE, torque and motor current; they
propose an online drill wear estimation method based on spindle motor power
signal during drilling. Arrazola uses micro-scale thermal imaging to identify
effects of steel machinability change on cutting zone temperature and related
tool wear mechanisms (Arrazola et al., 2008).
End milling cutters are generally used for milling either soft or tough materials.
In order to develop monitoring functions, displacement sensors are installed
on the spindle unit of a high precision machining center. Figure
3 shows a vertical type-machining center The PCD end-milling cutter having
four straight flutes was used. High-pressure coolant jet was employed for cooling
and lubrication of the high-speed machining operations. The spindle has constant
position preloaded bearings with oil-air lubrication and the maximum rotational
speed is 20 000 rpm. Figure 3 shows four eddy-current displacement
sensors are installed on the housing in front of the bearings to detect the
radial motion of the spindle. The specifications of the sensor are as follows:
the diameter is 5.4 mm and the length is 18 mm; measurement range is 1 mm; nominal
sensitivity is 0.2 mm V-1; dynamic range is 1.3 kHz; linear sensitivity
is ±1% of full scale. Figure 3 shows the sensor locations.
The two sensors S1 and S3 are aligned opposite in the
x-direction and the other two S2 and S4 are aligned in
Chip conditions: Govekar use filtered AE spectrum components for chip
form classification. Kim and Ahn propose a method of chip disposal state monitoring
in drilling based on spindle motor power features. Apply WPT and spectral estimation
of cutting force signals for chip form recognition.
Venuvinod used a variety of sensors to obtain stable clusters of chip form
under varying dry cutting conditions through geometric transformations of the
control variables: they aimed at recognising chip entanglements, chip size (including
continuity) and chip shape. Andreasen and De Chiffre develop and test a laboratory
system for automatic chip breaking detection via frequency analysis of cutting
Process conditions: Brophy classify drilling operations as normal
or abnormal (tool breakage or missing tool) using spindle power
signals (Brophy et al., 2002). Mezentsev
et al. (2002) develop a method for fault detection in tapping based
on torque and radial force; the method allows to identify typical faults of
tapping operations: axial misalignment, tap run out, tooth breakage both singly
and in a combined way. SFs to identify variable process conditions in Al alloy
milling. Pujana report on a new method to assess cutting variables (shear angle,
chip thickness, tool vibration amplitude, strain, strain rate) and chip topology
by means of high speed photography combined with laser printed square grid patterns
on the workpiece at industrial cutting speeds and feeds (Mezentsev
et al., 2002).
Surface integrity: Azouzi and Guillot apply cutting parameters and two
cutting force components for online estimation of surface finish and dimensional
deviations. Huang and Chen employ a statistical approach to correlate surface
roughness and cutting force in end milling operations. Abouelatta and Madl develop
a method of surface roughness prediction in turning based on cutting parameters
and FFT analysis of tool vibrations.
|| High speed vertical milling center
Salgado use singular spectrum analysis to decompose the vibration signals for
in-process prediction of surface roughness in turning. Song investigate time
series analysis of vibration acceleration signals measured during cutting operations
for real-time prediction of surface roughness. Axinte using AE signals backed
up by cutting force data, report on process monitoring to detect surface anomalies
when abusively broaching and milling difficult-to-machine aerospace materials.
Machine tool state: Verl proposed a system for feed drives wear monitoring
based only on signals available in controlled drives: position, speed and motor
current. The algorithm compares current characteristic parameters with those
detected when the machine is new. Zhou introduced a systematic method to design
and implement an integrated intelligent monitoring system, with modular and
reconfigurable structure, to monitor power, vibration, temperature and drive
and spindle pressure for condition monitoring, fault diagnosis and maintenance
planning in flexible manufacturing cells. Saravanan present an analysis of failure
frequency and downtime of critical subsystems in a lathe. The highest number
of failures took place in electrical and headstock subsystems.
Chatter detection: Chatter can be detected by monitoring the power spectrum
of the displacement of a work piece during the machining operation. Kuljanic
analyse chatter identification methods used in research and investigate an industrial
chatter detection system by comparing several sensors: the best results were
given by a multi-sensor system using an axial force sensor and two accelerometers.
Berger apply wavelet decomposition of cutting force signals to discriminate
between chatter and non chatter states (Berger et al.,
1998). Govekar use entropy rate of resultant cutting force signals to detect
broken chip formation and chatter onset in turning. Kwak and Song develop a
method based on AE signals to recognise chatter vibration in grinding (Mezentsev
et al., 2002). Yoon and Chin apply wavelet transform of cutting force
signals for real-time detection of chatter in end milling operations.
DECISION MAKING SUPPORT SYSTEMS AND PARADIGMS
In monitoring and control activities for modern untended manufacturing systems,
the role of cognitive computing methods employed in the implementation of intelligent
sensors and sensorial systems is a fundamental one. A conspicuous number of
schemes, techniques and paradigms have been used to develop decision making
support systems functional to come to a conclusion on machining process conditions
based on sensor signals data features. The cognitive paradigms most frequently
employed for the purpose of sensor monitoring in machining, including neural
networks, fuzzy logic, genetic algorithms able to synergically combine the capabilities
of the various cognitive methods, are briefly reviewed.
Neural networks: Neural Network (NN) is a simplified model of the human
brain that assumes that computation is distributed over several highly interconnected
processing elements, called neurons or nodes which operate in parallel. NN exhibit
characteristics such as mapping capabilities or pattern association, generalization,
robustness, fault tolerances and parallel and high speed information processing.
It adopts various learning mechanisms of which supervised learning and unsupervised
learning methods have to be very popular. NN have been successfully applied
to problems in the field of pattern recognition, image processing, data compression,
forecasting and optimization (Rajasekaran and Vijayalakshmi,
Neural network models: NN is a data base processing system consisting
of a large number of artificial neurons in an architecture inspired by the structure
of the cerebral cortex of the brain. A NN provides a mapping through which points
in the input space are associated with corresponding points in an output space
on the basis of designated attribute values, of which class membership can be
one. NN can be employed as mapping devices, pattern classifiers or patterns
completers. For more information on NN, Knowledge is built into a NN by training.
Some NN can be trained by feeding them with typical input patterns and expected
output patterns. The error between actual and expected outputs is used to modify
the weight of the connections between neurons.
Supervised learning: In supervised learning, a teacher is
assumed to be present during the learning process, i.e., the network aims to
minimize the error between the target (desired) output presented by the teacher
and the computed output, to achieve better performance (Zurada,
2003). Among supervised learning models, backpropagation (BP) NN which are
multiple-layered feedforward (FF) NN, have been very popular for their performance.
Training of these NN depends very much on the initial weight values.. This brings
the NN to a balance between training and testing errors and enables a notable
reduction in the number of hidden nodes. Further supervised NN approaches are
also considered here due to their use in decision making during monitoring of
machining: probabilistic NN (PNN), recurrent NN (RNN), artificial cellular NN
(ACNN), fuzzy logic NN (FLNN) or neurofuzzy systems (NFS) combining NN and FL
methods to integrate the benefits of both paradigms.
Unsupervised learning: In unsupervised learning, there is no teacher
present to hand over the desired output and the network so that each hidden
processing element responds strongly to a different set or closely related group
of stimuli. These sets of stimuli represent clusters in the input space which
typically stand for distinct real concepts. It is used to perform clustering
as the unsupervised classification of objects without providing information
about actual classes. Among unsupervised learning paradigms, the Self-Organising
Map (SOM) NN has been largely used for their performance.
NN applications to sensor monitoring of machining: The use of Probabilistic
NN for automated classification of broaching tool conditions utilizing cutting
force data is described in. Trials with short broaching tools that simulate
the roughing stage of industrial broaching were carried out to produce square
profile slots while detecting cutting force signals. To reproduce real industrial
tool failures, where both tool wear and single tooth chipping or breakage may
randomly occur, the broaching tools had cutting teeth in different conditions:
fresh, worn, chipped tooth, broken tooth. The push-off force Fy was selected
as the most sensitive to tool conditions. Tool failure recognition was based
on the extraction of a set of N characteristic points from the Fy plot by repetitive
selection of local maxima to construct N-elements feature vectors (pattern vectors).
Pattern vectors for different tool conditions were used as inputs to a PNN with
4 tool state classes: fresh, worn, chipped, broken. The success rate achieved
was as high as 92%. A scheme of the tool failure recognition paradigm is shown
in Fig. 4.
Using RNN data processing, accurate flank wear estimations were obtained for
the operating conditions adopted in the experimentation. Fractal dimensions
were used as input features to a RNN for flank wear land estimation. The development
of this estimator comprised four stages: (1) signal representation, (2) signal
separation, (3) feature extraction and (4) state estimation (flank wear land).
An intelligent multi-sensor chatter detection system for milling using two accelerometers
and one axial force sensor embedded in the milling machine was investigated
Particular attention was paid to industrial needs: (a) no reduction in machine
stiffness; (b) compatibility with pallet and tool changers; (c) no restriction
on tools, parts and cutting parameters; (d) robustness against sensing units
failures; and (d) independence from cutting conditions and system dynamics.
To evaluate the system capability for a broad application range, different test
setups with diverse milling machines, toolings, sensor systems and work materials
were used. A NN approach was used for decision making, comprising an ACNN applied
to acceleration signals and a fuzzy NN for axial force signals. Good levels
of NN accuracy were obtained with all single sensor signals (Halgamuge
and Glesner, 1984).
To realise the concept of multi-sensor chatter detection (Fig.
5), the NN outputs for each single sensor signal were combined through:
(1) linear combination of single sensor chatter indicators; (2) a separate NN
for multi-sensor classification; (3) fuzzy logic classification (Sugeno fuzzy
model); and (4) statistical inference classification based on conditional probability,
i.e., the probability that the system is unstable for a specific combination
of single chatter indicators. The accuracy of the first three approaches was
very high: 95-96%.
|| Schematic of the tool condition recognition system
|| Outline of the multi-sensor chatter detection system
|| Fuzzy logic data processing
But residual accuracy in case of sensing unit malfunctions dropped notably:
50-75%. The behaviour of the forth approach was quite different: accuracy was
slightly lower, 94% but insensitivity to malfunctions was extremely robust:
90-92%. Thus, the statistical inference multi-sensor chatter indicator, combining
NN data processing and statistical methods to achieve both high accuracy and
high robustness, was assessed as the most suitable for industrial milling applications.
Sensor monitoring method, based on spindle motor power sensing and NN processing,
was evaluated for chip disposal state detection in drilling. Spindle motor power
measurements have the advantage of being easily realised during machining. From
them, selected features such as variance/mean, mean absolute deviation, gradient
and event count were calculated to form input vectors to a FF BP NN for decision
making on chip disposal state.
Fuzzy logic paradigms: Fuzzy Logic (FL) has two different meanings. In
a narrow sense, FL is a logical system which is an extension of multivalued
logic. But in a wider sense which is in predominant use today, FL is almost
synonymous with the theory of fuzzy set . A fuzzy set is a set without a crisp,
clearly defined boundary. It can contain elements with only a partial degree
of membership. A fuzzy set defines a mapping between elements in the input space
(sometimes referred to as the universe of discourse) and values in the interval
[0,1]. A membership function is a curve that defines how each point in the input
space is mapped to a membership value (degree of membership or truth degree)
between 0 and 1. The membership function can be any arbitrary curve, the shape
of which can be defined as a function suitable from the point of view of simplicity,
convenience, speed and efficiency. A fuzzy inference system calculation comprises
the 5 steps illustrated in Fig. 6.
Fuzzy logic applications to sensor monitoring of machining: The application
of a Fuzzy Decision Support System, (FDSS Fuzzy Flou) is presented
for tool wear estimation during turning using cutting force components measurements.
The architecture of the FDSS consists of a knowledge base, an inference engine
and a user interface. The knowledge base has two components: the linguistic
term base and the fuzzy production rule base. The linguistic term base is divided
into fuzzy premises and fuzzy conclusions. Knowledge is represented by a set
of if-then rules which specify a relationship between observations (causes)
and conclusions (effects). The knowledge base can be created directly from the
monitor using the tree view(see below) or can be written in a text editor and
loaded into the FDSS. In in-process monitoring during quasi orthogonal cutting
of metal alloys was attempted through sensor fusion of frequency features extracted
from AE signals through diverse forms of signal analysis.
|| Structure of genetic algorithms and genetically based operators
|| GA learning process of the FDSS Fuzzy Flou knowledge base
These features were processed by a FL based pattern recognition method to develop
a multi-purpose intelligent sensor system for classification of tool wear level
and workpiece heat treatment state for two work materials: low C steel and 7075
Al alloy (Teti, 1995).
Genetic algorithms, hybrid systems, etc.: Genetic Algorithms (GA) belong
to a branch of computer science called natural computation where
programmers, inspired by phenomena in the biological world, create models of
these systems on a computer. This technique can solve complex problems by imitating
Darwinian theories of evolution on a computer. The first step in the use of
a GA is building a computer model to represent a given problem. Interacting
variables in the problem are first combined and encoded into a series of binary
strings (rows of ones and zeros) to form numerical chromosomes.
The computer randomly generates an entire population of these chromosomes
and ranks them based on a fitness function which determines how
well they solve the problem. Those strings which are deemed the fittest
are allowed to survive and reproduce with other chromosome
strings, through genetic operators such as crossover and mutation,
to create offspring chromosomes. This population of strings evolves
by continuously cycling the genetic operators (Achichea
et al., 2000) (Fig. 7).
In GA are utilized to automatically construct a FL knowledge base (KB) from
a set of experimental data on tool wear monitoring during turning without requiring
any human expert intervention.
The performance of this FL-GA system is compared with the performance of classical
FL and NN systems for application to tool wear estimation. The construction
of a FL KB necessitates skills and expertise. The operator has to analyze the
dependence of Fc on VB so that the experimental results have to be presented
in a conveniently understandable form. This makes FL systems rather difficult
for practical implementation in their human manual form. This problem can be
solved using a GA to automatically construct the FL KB (Fig. 8).
The learning time of the GA method was the shortest among the considered methods,
making it very convenient for shop floor use. Moreover, one can specify the
maximum complexity level together with how much emphasis the GA must place on
accuracy increase versus complexity reduction. There are definite advantages
for practical applications since the GA provides more generality to the KB (Achichea
et al., 2002).
The novel systems will need to be robust, reconfigurable, reliable, intelligent
and inexpensive in order to meet the demands of advanced manufacturing technology.
The future enhancement of machining systems and their operation performance
will vitally depend upon the development and implementation of innovative sensor
monitoring systems. These demands include increasingly small, precision and
complex products for applications in biomedicine, transportation, MEMS devices,
etc., as well as ubiquitous sensor systems for machine and system monitoring
to reduce resource requirements and insure that manufacturing systems operate
efficiently with minimal energy consumption and environmental impact. The main
reason seems to be the difficult, sophisticated usage of these techniques and
methods. One of the main challenges in future machining process monitoring systems
is the development of algorithms and paradigms really autonomous from machine
tool operators, who are not required to know about methods like neural networks,
fuzzy logic etc., with signal feature extraction and decision making performed
without intervention of the operator.