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threshold a ‘decision’ is made that the tool is worn. The success of this strategy
depends upon the degree to which the mean value of the sensor output actually
represents the state (and progress) of tool wear.
1.2.4.2 Sensor Fusion
With a specific focus for the monitoring in mind, researchers have developed over
the years a wide variety of sensors and sensing strategies, each attempting to pre-
dict or detect a specific phenomenon during the operation of the process and in
the presence of noise and other environmental contaminants. A good number of
these sensing techniques applicable to manufacturing have been reviewed in the
early part of this chapter. Although able to accomplish the task for a narrow set of
conditions, these specific techniques have almost uniformly failed to be reliable
enough to work over the range of operating conditions and environments com-
monly available in manufacturing facilities. Therefore, researchers have begun to
look at ways to collect the maximum amount of information about the state of a
process from a number of different sensors (each of which is able to provide an
output related to the phenomenon of interest although at varying reliability). The
strategy of integrating the information from a variety of sensors with the expecta-
tion that this will ‘increase the accuracy and . resolve ambiguities in the knowl-
edge about the environment’ (Chiu et al. [14]) is called sensor fusion.
Sensor fusion is able to provide data for the decision-making process that has a
low uncertainty owing to the inherent randomness or noise in the sensor signals,
includes significant features covering a broader range of operating conditions, and
accommodates changes in the operating characteristics of the individual sensors
(due to calibration, drift, etc.) because of redundancy. In fact, perhaps the most
advantageous aspect of sensor fusion is the richness of information available to
the signal processing/feature extraction and decision-making methodology em-
ployed as part of the sensor system. Sensor fusion is best defined in terms of the
‘intelligent’ sensor as introduced in [15] since that sensor system is structured to
utilize many of the same elements needed for sensor fusion.
The objective of sensor fusion is to increase the reliability of the information so
that a decision on the state of the process is reached. This tends to make fusion
techniques closely coupled with feature extraction methodologies and pattern rec-
ognition techniques. The problem here is to establish the relationship between
the measured parameter and the process parameter. There are two principal ways
to encode this relationship (Rangwala [13]):
· theoretical – the relationship between a phenomenon and the measured param-
eters of the process (say tool wear and the process); and
· empirical – experimental data is used to tune parameters of a proposed model.
As mentioned earlier, reliable theoretical models relating sensor output and pro-
cess characteristics are often difficult to develop because of the complexity and
variability of the process and the problems associated with incorporating large
numbers of variables in the model. As a result, empirical methods which can use
1.2 Principles of Sensors in Manufacturing 21
sensor data to tune unknown parameters of a proposed relation are very attrac-
tive. These types of approaches can be implemented by either (a) proposing a rela-
tionship between a particular process characteristic and sensor outputs and then
using experimental data to tune unknown parameters of a model, or (b) associat-
ing patterns of sensor data with an appropriate decision on the process state with-
out consideration of any model relating sensor data to the state. The second
approach is generally referred to as pattern recognition and involves three critical
stages (Ahmed and Rao [16]):
· sampling of input signal to acquire the measurement vector;
· feature selection and extraction;
· classification in the feature space to permit a decision on the process state.
The pattern recognition approach provides a framework for machine learning and
knowledge synthesis in a manufacturing environment by observation of sensor
data and with minimal human intervention. More important, such an approach
allows for integration of information from multiple sources (such as different sen-
sors) which is our principal interest here.
Sata et al. [17, 18] were among the first researchers to propose the application
of pattern recognition techniques to machine process monitoring. They attempted
to recognize chip breakage, formation of built-up edge and the presence of chatter
in a turning operation using the features of the spectrum of the cutting force in
the 0–150 Hz range. Dornfeld and Pan [19] used the event rate of the rms energy
of an acoustic emission signal along with feed rate and cutting velocity in order to
provide a decision on the chip formation produced during a turning operation.
Emel and Kannatey-Asibu [20] used spectral features of the acoustic emission sig-
nal in order to classify fresh and worn cutting tools. Balakrishnan et al. [21] use a
linear discriminant function technique to combine cutting force and acoustic
emission information for cutting tool monitoring.
The manufacturing process may be monitored by a variety of sensors and, typi-
cally, the sensor output is a digitized time-domain waveform. The signal can then
be either processed in the time domain (eg, extract the time series parameters of
the signal) or in the frequency domain (power spectrum representation). The ef-
fect of this is to convert the original time-domain record into a measurement vec-
tor. In most cases, this mapping does not preserve information in the original sig-
nal. Usually, the dimension of the measurement vector is very high and it be-
comes necessary to reduce this dimension due to computational considerations.
There are two prevalent approaches at this stage: select only those components of
the measurement vector which maximize the signal-to-noise ratio or map the
measurement vector into a lower dimensional space through a suitable transfor-
mation (feature extraction). The outcome of the feature selection/extraction stage
is a lower dimensional feature vector. These features are used in pattern recogni-
tion techniques and as inputs to sensor fusion methodologies. This was illus-
trated in Figure 1.2-6.
1 Fundamentals22
1.2.5
Summary
The subject of sensors for manufacturing processes is well covered in other chapters
of this book. The material in this chapter serves to acquaint the reader with the clas-
sification of sensor systems and some of the measurands that are associated with
these sensors. How these sensor types and measurands map on to the various man-
ufacturing processes will be the subject of the rest of the text. One important factor
in the implementation of sensors in manufacturing is clearly the rapid growth of
silicon micro-sensors based on MEMS technology. This technology already allows
the integration of traditional and novel new sensing methodologies on to miniatur-
ized platforms, providing in hardware the reality of multi-sensor systems. Further,
since these sensors are easily integrated with the electronics for signal processing
and data handling, on the same chip, sophisticated signal analysis including feature
extraction and intelligent processing will be straightforward (and inexpensive). This
bodes well for the vision of the intelligent factory with rapid feedback of vital infor-
mation to all levels of the operation from machine control to process planning.
1.2 Principles of Sensors in Manufacturing 23
1.2.6
References
1 Sze, S.M. (ed.) Semiconductor Sensors;
New York: Wiley, 1994.
2 Allocca, J. A., Stuart, A., Transducers:
Theory and Applications; Reston, VA: Re-
ston Publishing, 1984.
3 Bray, D. E., McBride, D. (eds.) Nondes-
tructive Testing Techniques; New York: Wi-
ley, 1992.
4 Webster’s Third New International Diction-
ary; Springfield, MA: G. C. Merriam, 1971.
5 Usher, M. J., Sensors and Transducers; New
Hampshire: Macmillan, 1985.
6 Middlehoek, S., Audet, S. A., Silicon
Sensors; New York: Academic Press, 1989.
7 White, R. M., IEEE Trans. Ultrason. Fero-
elect. Freq. Contr. UFFC-34 (1987) 124.
8 Shiraishi, M., Precision Eng. 10(4) (1988)
179–189.
9 Shiraishi, M., Precision Eng. 11(1) (1989)
27–37.
10 Shiraishi, M., Precision Eng. 11(1) (1989)
39–47.
11 Byrne, G., Dornfeld, D., Inasaki, I.,
Kettler, G., König, W., Teti, R., Ann.
CIRP 44(2) (1995) 541–567.
12 Goch, G., Schmitz, B., Karpuschewski,
B., Geerkins, J., Reigel, M., Sprongl,
P., Ritter, R., Precision Eng. 23 (1999) 9–33.
13 Rangwala, S., PhD Thesis; Department of
Mechanical Engineering, University of Ca-
lifornia, Berkeley, CA, 1988.
14 Chiu, S. L., Morley, D. J., Martin, J. F.,
in: Proceedings of 1987 IEEE International
Conference on Robotics and Automation; Ra-
leigh, NC: IEEE, 1987, pp. 1629–1633.
15 Dornfeld, D. A., Ann. CIRP 39 (1990)
16 Ahmed, N., Rao, K. K., Orthogonal Trans-
forms for Digital Signal Processing; New
York: Springer, 1975.
17 Sata, T., Matsushima, K., Nagakura, T.,
Kono, E., Ann. CIRP 22(1) (1973) 41–42.
18 Matsushima, K., Sata, T., J. Fac. Eng.
Univ. Tokyo (B) 35(3) (1980) 395–405.
19 Dornfeld, D.A., Pan, C.S., in: Proceedings
of 13th North American Manufacturing Re-
search Conference, University of California,
Berkeley, CA: SME, 1985, pp. 285–303.
20 Emel, E., Kannatey-Asibu, E., Jr., in: Pro-
ceedings of 14th North American Manufactur-
ing Research Conference, University of Min-
nesota, MN: SME, 1986, pp. 266–272.
21 Balakrishnan, P., Trabelsi, H., Kanna-
tey-Asibu, Jr., E., Emel, E., in: Proceedings
of 15th NSF Conference on Production Re-
search and Technology, University of Cali-
fornia, Berkeley, CA: SME, 1989, pp. 101–
108.
1.3
Sensors in Mechanical Manufacturing –
Requirements, Demands, Boundary Conditions, Signal Processing,
Communication Techniques, and Man-Machine Interfaces
T. Moriwaki, Kobe University, Kobe, Japan
1.3.1
Introduction
The role of sensor systems for mechanical manufacturing is generally composed
of sensing, transformation/conversion, signal processing, and decision making, as
shown in Figure 1.3-1. The output of the sensor system is either given to the op-
erator via a human-machine interface or directly utilized to control the machine.
Objectives, requirements, demands, boundary conditions, signal processing, com-
munication techniques, and the human-machine interface of the sensor system
are described in this section.
1.3.2
Role of Sensors and Objectives of Sensing
An automated manufacturing system, in particular a machining system, such as a
cutting or grinding system, is basically composed of controller, machine tool and
machining process, as illustrated schematically in Figure 1.3-2. The machining
command is transformed into the control command of the actuators by the CNC
1 Fundamentals24
Fig. 1.3-1 Basic composition of sensor system for mechanical manufacturing
Fig. 1.3-2 Role of sensors in automated machining system
Sensors in Manufacturing. Edited by H.K. Tönshoff, I. Inasaki
Copyright © 2001 Wiley-VCH Verlag GmbH
ISBNs: 3-527-29558-5 (Hardcover); 3-527-60002-7 (Electronic)
controller, which controls the motion of the actuators and generates the actual
machining motion of the machine tool. The motion of the actuator, or the ma-
chining motion of the machine tool, is fed back to the controller so as to ensure
that the relative motion between the tool and the work follows exactly the prede-
termined command motion. Motion sensors, such as an encoder, tacho-generator
or linear scale, are generally employed for this purpose.
The machining process is generally carried out beyond this loop, where fin-
ished surfaces of the work are actually generated. Most conventional CNC ma-
chine tools currently available on the market are operated under the assumption
that the machining process normally takes place once the tool work-relative mo-
tion is correctly given. Some advanced machine tools equipped with an AC (adap-
tive control) function utilize the feedback information of the machining process,
such as the cutting force, to optimize the machining conditions or to stop the ma-
chine tool in case of an abnormal state such as tool breakage.
The machining process normally takes place under extreme conditions, such as
high stress, high strain rate, and high temperature. Further, the machining pro-
cess and the machine tool itself are exposed to various kinds of external distur-
bances including heat, vibration, and deformation. In order to keep the machin-
ing process normal and to guarantee the accuracy and quality of the work, it is
necessary to monitor the machining process and control the machine tool based
on the sensed information.
The objectives and the items to be sensed and monitored for general mechani-
cal manufacturing are summarized in Table 1.3-1 together with the direct pur-
poses of sensing and monitoring. Some items can be directly sensed with proper
sensors, but they can be utilized to estimate other properties at the same time.
For instance, the cutting force is sensed with a tool dynamometer to monitor the
cutting state, but its information can be utilized to estimate the wear of the cut-
ting tool simultaneously.
Almost all kinds of machining processes require sensing and monitoring to
maintain high reliability of machining and to avoid abnormal states. Table 1.3-2
gives a summary of the answers to a questionnaire to machine tool users asking
about the machining processes which require monitoring [1]. It is understood that
monitoring is imperative especially when weak tools are used, such as in tapping,
drilling, and end milling.
1.3 Sensors in Mechanical Manufacturing 25
1 Fundamentals26
Tab. 1.3-1 Objects, items, and purposes of sensing
Object of sensing and
monitoring
Items to be sensed Purpose of sensing and
monitoring
Work State of work clamping
Geometrical and dimensional
accuracy
Surface roughness
Surface quality
Maintain high quality
Avoid damage and loss of work
Machining process Force (torque, thrust)
Heat generation
Temperature
Vibration
Noise and sound
State of chip
Maintain normal machining
process
Predict and avoid abnormal state
Tool Tool edge position
Wear
Damage including chipping,
breakage, and others
Manage tool changing time,
including dressing
Avoid damage or deterioration of
work
Machine tool, and
auxiliary facility
Malfunction
Vibration
Deformation (elastic, thermal)
Maintain normal condition of ma-
chine tool and assure high accu-
racy
Environment Ambient temperature change
External vibration
Condition of cutting fluid
Minimize environmental effect
Tab. 1.3-2 Machining processes which require sensing
Kind of machining Number of answers Percentage
Tapping
Drilling
End milling
Internal turning
External turning
Face milling
Parting
Thread cutting
Others*
Total
67
66
55
51
30
25
17
13
15
338
19.8
19.2
16.8
15.1
8.9
7.4
5.0
3.9
4.4
100
* Grinding, reaming, deep hole boring, etc.
1.3.3
Requirements for Sensors and Sensing Systems
The most important and basic part of the sensor is the transducer, which trans-
forms the physical or sometimes chemical properties of the object into another
physical quantity such as electric voltage that is easily processed. The properties
of the object to be sensed are either one-dimensional, such as force and tempera-
ture, or multi-dimensional, such as image and distribution of the physical proper-
ties. The multi-dimensional properties are treated either as plural signals or a
time series of signals after scanning.
The basic requirements for the transducers and sensor systems for mechanical
manufacturing are summarized in Table 1.3-3. Figure 1.3-3 shows a schematic il-
lustration of the characteristics of a typical transducer, such as a force transducer.
1.3 Sensors in Mechanical Manufacturing 27
Tab. 1.3-3 Basic requirements for transducers and sensing systems
Performance/
accuracy
Reliability Adaptability Economy
Sensitivity
Resolution
Exactness
Precision
Linearity
Hysteresis
Repeatability
Signal-to-noise ratio
Dynamic range
Dynamic response
Frequency response
Cross talk
Low drift
Thermal stability
Stability against
environment, such as
cutting, fluid and heat
Low deterioration
Long life
Fail safe
Low emission of noise
Compact in size
Light in weight
Easy operation
Easy to be installed
Low effect of ma-
chining process
and machine tool
Safety
Good connectivity to
other equipment
Low cost
Easy to manufacture
Easy to purchase
Low power requirement
Easy to calibrate
Easy maintenance
Fig. 1.3-3 Typical input-output relation of transducer
Nonlinear range
The figure represents the relation between the change in a property of the object,
or the input and the output of the transducer. It is desirable that the transducer
output represents the property of the object as exactly and precisely as possible. It
is also essential for a transducer to output the same value at any time when the
same amount of input is given. This characteristic is called repeatability. In most
cases, the output increases or decreases in proportion to the input in the linear
range, and then gradually saturates and becomes almost constant. When the
amount of input exceeds the limit of sensing, the transducer becomes normally
malfunctioning. The measurable range of the input is called the dynamic range of
the sensor.
The ratio of output to input is called the sensitivity, and it is desirable that the
sensitivity is high and the linear range of sensing is wide. The input-output rela-
tion is sometimes nonlinear depending on the principle of the transducer, as in
the case of capacitive type proximeter (see Figure 1.3-4). Only a small range of lin-
ear input-output relation can be used in such a case when the accuracy require-
ment of sensing is high. When the nonlinear input-output relation is known ex-
actly by calibration or by other methods in advance, the nonlinearity can be com-
pensated afterwards by calculation. The nonlinear characteristics of thermocouples
are well known, and the compensation circuits are installed in most thermo-
meters for different types of thermocouples.
The input-output relation sometimes differs when the amount of input is in-
creased and decreased, as shown in Figure 1.3-5. Such a characteristic is called
hysteresis, and is sometimes encountered when a strain gage sensor is employed
to measure the strain or the force. It is almost impossible to compensate for the
hysteresis of the transducer, hence it is recommended to select transducers with
small hysteresis.
The property of the object to be sensed in mechanical manufacturing is gener-
ally time varying or dynamic. The measurable dynamic range of the transducer is
generally limited by the maximum velocity and acceleration of the output signal
1 Fundamentals28
Fig. 1.3-4 Nonlinear input-output relation
+
+–
–
and also by the maximum frequency to which the change in the input property
can be exactly transformed to the output. Figure 1.3-6 shows typical frequency
characteristics of the transducers in terms of the frequency response. The vertical
axis shows the gain or the ratio of the magnitudes of the output to the input, and
also the phase or the delay of the output signal to the input.
Some transducers show resonance characteristics, and the gain in terms of out-
put/input becomes relatively larger at the resonant frequency. It should be noted
that the phase is shifted for about k/2 at the resonant frequency. The phase shift
in the output signal cannot be avoided generally even with well-damped type or
non-resonant type transducers, as shown in the figure.
The sinusoidal wave forms of the input and the output at some typical frequen-
cies are shown in Figure 1.3-7 to illustrate the changes in the gain and the phase.
When the phase information is essential to identify the state of the object, it is
important to select a transducer with resonant frequency high enough compared
with the frequency range of the phenomenon to be sensed.
1.3 Sensors in Mechanical Manufacturing 29
Fig. 1.3-5 Hysteresis in input-output relation
Fig. 1.3-6 Frequency response
of typical transducers
+
+–
–
–p
As was mentioned before, the machining process normally takes place under
high-stress, high-strain rate and high-temperature conditions with various kinds
of external disturbances including the cutting and grinding fluids. It is therefore
understood that high reliability and stability against various kinds of disturbances
are the most important requirements for the sensors in addition to the basic per-
formance and accuracy of the transducers. According to the answers given by in-
dustry engineers to the questionnaire concerning tool condition monitoring [2],
the importance of technical criteria in selecting the sensors is in the order (1) reli-
ability against malfunctioning, (2) reliability in signal transmission, (3) ease of in-
stallation, (4) life of the sensor, and (5) wear resistance of the sensor.
The importance of items in evaluating the monitoring system is also given in
the order (1) reliability against malfunctions, (2) performance to cost ratio, (3) in-
formation obtained by the sensor, (4) speed of diagnosis, (5) adaptability to
changes of process, (6) usable period, (7) ease of maintenance and repair, (8) level
of automation, (9) ease of installation, (10) standard interface, (11) standardized
user interface, (12) completeness of manuals, and (13) possibility of additional
functions.
Table 1.3-4 summarizes items to be considered generally in selecting transdu-
cers and the sensors. It is basically desirable to implement on-line, in-process,
continuous, non-contact, and direct sensing, but it is generally difficult to satisfy
all of these requirements. The property of the object is directly sensed in the case
of direct sensing, whereas in the case of indirect sensing it is estimated indirectly
from other properties which can be easily measured and are related to the prop-
erty to be measured. It should be noted that the property of object to be estimated
indirectly must have a good correlation with the property to be measured. Indirect
sensing is useful and is widely adopted when direct sensing is difficult.
1 Fundamentals30
Fig. 1.3-7 Relation of input and output at some typical frequencies
. described in this section.
1.3.2
Role of Sensors and Objectives of Sensing
An automated manufacturing system, in particular a machining system, such as a
cutting. manufacturing
Fig. 1.3-2 Role of sensors in automated machining system
Sensors in Manufacturing. Edited by H.K. Tönshoff, I. Inasaki
Copyright © 2001 Wiley-VCH
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