- Email: [email protected]
Adaptive systems for machining process monitoring and control
ointof view
of
survey of the relevant l~terat~re.
Systematic
rep~ace~le~t of ality-oriented ma As far as machining is concerned, workp iece , tool, coo
ection with process control
is a founding
the process res onse is subject to change with at is to the point here - with the cutting he idea of adapting these co~~~tio~~sin order ven performances against process changes was illtrod~c~ on thl: rationale of feedback control: to implement closed loops such that each command has a sensor to verity if the corrective actions do provide effective results. In the last few years, a tremendous development in the evolution of rear-tin-z control technologies was achieved in the area of the nove! discip!ine of Adapti giving rise to two fundamental design frameworks, best known as Adaptive Systems (MRAS) and Self-Tuning Regulators (STR). Unfortunately, when speaking about AC, the communities of control and of manufacturing engineers do not share always a common language. As a matter of fact, a standard terminology is not agreed with. The intended goal of the present paper is to contribute to a unifying approach bzsedon a review of the recent developments and current trends in adaptive monitoring and conaol with application to machining processes. In the next Section 2, the main architectures of adaptive systems are dealt with in terms
of modern control theory: methods for designing computer-based systems and digital models for implementing real-time control algorithms are presented here. Section 3 is addressed to some problems arising in the application oi’ theoretical results to machining processes at the production level: also in-process sensors for closed-loop considered in this section. Finally, Section 4 contains the concluding remarks. 0924-0136i97~%17.00 Q 1997 Elswkr Science S.A. All rights resemv~ PH 09244136 (96) 02555-l
control
are
G.E.
76
D’Ewico ~3o1cmalof MaterialsProcessing Technology64 (1997) ~5-84
2. MODERN ADAPTIVE CON
S
Modern monitoring and control systems are built up on computer-based architectures, as shown in the block diagram in Figure 1. For process control, the process is modelle describing causes and effects that are non-instantaneously related, according to re~at~o~~sh~~s based on the physical nature of the process: a process descriptor is either a variable or a parameter depending on its rate of variation with time: fast descriptors are considered variables, while slow descriptors are considered parameters.
PROCESS
Figure
1:
Block diagram of a computer-controlled system.
In Figure 1, the blocks A/D and D/A represent the Analog to Digital and the Digital to Analog converters, where the conversion is synchronised with the sampling instants (n) (that is a sequence of natural numbers) by the real-time clock in the system. Further, it is supposed that the operation of signal sampling is periodic and its inverse, the signal reconstruction is piece-wise constant, which is implemented using a mechanism known as Zero-Order Sample and Hold circuit (ZOSH). The inputs to the process u(t) (the control) and d(t) (the disturbance), and the measured output y(t) are continuous time signal. From the control software point of view the signals u(t) and y(t) are considered at the sampling instants only and are represented as sequences of numbers (~0)) and (v(11]which describe a model of the computer-controlled system known as the stroboscopic model. The problem here is to implement a function u(t) with the objective of obtaining a desired y(t), namely the continuous-time representation of uC. The control strategy can be described in terms of a pseudo-code by the following timed loop: (i): (ii): (iii):
wait for the clock; sense y; accomplish the A/D conversion;
77
with the meaning that, for a given xu):
where A(q-‘)=l+alq’;...+,llq-~
and
polynomials, so that eqn. (2) can be given a (SISO) model CiP of the computer-controls
(q-‘)=b,+b,q-‘+..a
are co-prime Single Output d inchkve of
interfaces, actuator, selasor and process un Gp(q) =q%(q-
‘)/A(q-*) ==ye’)/u(j)
(3)
roblem is to implement a desired closed loop transfer function:
(4) Use of a second order transfer function is frequent in control practice for modelling continuous-time feedback systems because significant specifications can be given looking at two modes whose contribution is relatively dominant in the transient behaviour. In terms of a digital model, this translates in using the following equation for the polynomial in the denominator of the model (4):
G.E. D Brico/Journal
78
oj' Materials Processmg
Technology 64 (1997) TL5’4
where SC 1 is the damping ratio and (clthe natural frequency of a continuous-time secon order system, sampled whit the sampling period r. Sand UIcan be used to specify the ste response characteristics of the system in terms of maximum percentage overshoot MO=lo&_ emXdX1 4)1/z and peak overshoot time tp=zIw( 1-a)“‘. A compendious and authoritative treatment of general theory and design of AC systems is the book by Astrom and Wittenmark [I]. Control theorists distinguish Adaptive Control from Gain Scheduling (GS), which is a special kind of open-loop adaptation (Figure 2).
operating conditions
regulator .__ parameters ___r
SCHEDULING i
REGULATOR
L--_
1
Figure 2: Block diagram of a Gain Scheduling system. Closed-loop AC systems are regulators with adjustable parameters founded on the idea of integrating on-line process identification and real-time regulator design. This is shown by the two loops in the block diagram of Figure 3: the inner (fast) loop is aimed at output variable feedback implementation on the assumption that process parameters have known values, the outer (slow) loop is aimed at feedback design based on parameter estimation.
--.--
process identification
r-)i
PROCESS
I-+-------,
COMPUTER
Figure 3: A simplified illlustrationof an Adaptive Control system.
i
j;
EGLJLAT
recess parameters
4
regulator parameters
1 I
I i
U
REGULATOR
Figure 4: Mock diagrams of MRAS (a) and STR (b).
80
G.E. D ‘Em.co /Jourt~nl of MaterialsProcessing TechnoloQ 64 (I 997) 75-84
To improve the performance of an AC system, it is a good practice to use also a thir level loop aimed at co-ordination of human interaction and feedback and Figure S-shows an example of a three loop architecture of an indirect STR.
Figure 5: An indirect STR with a co-ordination loop. Although various design methods can be used, adaptive control algorithms can be basically divided into indirect and direct depending on which parameters are estimated (if process or controller parameters). For example, in indirect self-tuning algorithms it is assumed that the process has a given structure with unknown parameters which are estimated on-line; the regulator parameters are then indirectly calculated according to the estimates of the process parameters. in direct self-tuning algorithms the closed-loop model is re-parameterized making it possible to estimate the regu!ator parameters directly, so that calculations are significantly reduced. Many general purpose algorithms for adaptive control implementation are collected and deeply discussed by Astrom and Wittenmark [ 1J. For online machining process modelling and adaptive control, a fast algorithm is proposed using results from numerical simulation by Fassois, Eman and Wu 121.
3. APPLICATIONS OF ADAPTIVE CONTROL TO NIAC
ING SYSTEMS
A historical perspective of techniques and concepts relevant to applications of AC to machining processes is given by Ulsoy and Koren [3]. Developments and trends in monitoring and control of machining processes are surveyed by Tonshoff et al. [4]. Manufacturing engineers usually define an AC system as a closed-loop control that, being applied to machine tools equipped with sensors for monitoring the conditions of tools
IS
uring the process by
erations the interest is still at a rding as laboratory or s~~~~a~~~~ research stage and the current level of ~~dustr~a~usage reflects ~nder-ex~~o~~t~o~~ of AC potential. They uggest that this situation is due to the lack o reliable sensors for shop floor applications an to some malfunctioning related to the ossible raising of Insta problems. This state of affair can ubl ished results ]S-35 ]. As far as instability is concerne nd in the literature. For asory and oren example, ose a sampled-data system based on feedback of cutting force measurement in tarring with a constant cutting force constraint. T faced to stability considerations and use a policy for adjusting the controller gain. 3h and Kim ]6] propose a model of the turning process which takes into account the compliance of the mat ine tool for the feedback control of feed force by adjusting the feed rate. They demonstrate that a conventional proportional Integrative (PI) cont.rol!sr cannot guaranty stability, while experimental evidence is given to the performance improvement provided by an AC algorithm. nr”ctical issue which attracted It is known that sensing for machine tool applications is a r14 commercial interests and pushed the development of unmanned manufacturing engineering. An early and excellent contribution to the assessment of relevant methods and technologies can be found in Micheletti, Konig and Victor 171. Further reviews are provided by Tlusty and Andrews [is] and by Lister and Barrow [9]. For a comprehensive survey of in process techniques both for tools and workpiece, the interested reader is referred to the twofold paper by Shirashi [lo]. It should be stressed here that while the state of the art regarding sensing technologies is evolutionary, the classification of methods for wear measurement is based on the criterion of separating direct measurements (but inherently intermittent) from continuous (but indirect) estimates. Direct methods are essentially referred to tool geometry and wear morphology, while indirect methods are based on relationships which correlate
82
G.E. D ‘.&rk~o/ Jmrnai qf Afatertuls Processrng Technology 61(1997
3-W
weaf or failure of tool to appropriate variables relevant to the cutting acoustic emission, force, torque, power and temperature. The major part of relevant casestudies focuses on the turning process. A review of tool wear and failure monitoring techniques for turning is given by Dan and Mathew [ 111: according to their conclusions, few of such techniques have been used successfully in industries; further, a global approach able to manage more than particular classes of tooi failure modes is not still fully developed. Perhaps it should be also pointed out that persistency of such a criterion to classify wear measurements methods reflects that up to now there is no hybrid method which provides both direct and continuous monitoring of tool conditions during a cutting process and a compromise is needed. An approach to sensor fusion is proposed by Dornfeld [ 121based on neural networks. As far as tool monitoring is concerned, AC approaches are focused on indirect methods, which are clearly more appropriate to the aim of continuous process control ( [13]). Among the process variables relevant to the wear and failure mechanisms, cutting force, power and temperature are being considered most appealing for industrial use: applicability of each of them is discussed in an adaptive comrol simulation by D’Errico, Rutelli and Settineri [ 141. Cutting power is used and deeply discussed also in a work by Cuppini, D’Errico and Rutelli [ 151, and by Mannan and Broms [ 161. One of the first AC approach using cutting forces is developed by Koren and Masory [17] in application to a CNC lathe. Also Mackinnon, Wilson and Wilkinson IIS] are involved with the objective of maxim%ng the feed r,& against a force or torque constraint by means of an adaptive strategy aimed at: preventing premature tool failures, assuring chip-breaking and minimising air-cutting time with application to a NC turret lathe. A discrete-time model is developed by Tomizuka and Zhang 1191for predicting the dynamics between feedrate and cutting farce. This model is used for force regulation (an innovative piezo-electric crystal device, mounted on the tool carriage of a lathe is used as cutting force sensor). An approach to make this controller adaptive to changes in cutting conditions and material properties is suggested too. Further in turning process, Toutant et uf. [20] give a contribution toward feedrate compensation for constant cutting, Masory and Karen [5] and Hwang, Oh and Kim [6] have already been mentioned for their contributions also to the stability problem. In peripheral milling, Elbestawi and Sagherian [21] are concerned with the problem of regulating the cutting force under varying cutting conditions. Several parameter adaptive control strategies and structures are applied on the base of a mpdel which describes the relationship between feed rate and cutting force, and includes the effect of spindle stiffness on chip formation. A theoretical AC approach to machining processes based on physical constraints of tool wear mechanisms was developed by Yen and Wright [22]. In 1221 tool wear mechanisms arc mod&d for adaptive control aimed at maximum production rate compatible with constraints on plastic deformation, crater wear, and fracture; such constraints are suggested to be dealt with by using cutting force or temperature sensors. It is known that the devices currently used for measuring the cutting temperature are: infrared sensors, thermocouples, and the tool-workpiece thermoelectric interface [23-273. Relationship between cutting temperature and tool wear is also being deeply investigated
machj~i~g faults, that rna~ufa~tur~~g costs. ulsory task, as
it
fo~~dat~o~s of at can be followe at the successfd ement closed loops so that each corn
has a sensor for verifying
that the
systems. The feasibility of such systems is given evidence and a lot of research work has been devoted to he ridging the gap between academic achievements and industrial usage. lications to machining processes: approach is still not mat Nevertheless a uni asis is gives; to the zkqxivity of so far, from the point of view of control engine the control system, while from the point of view of manufacturing engineers a major importance is attributed to the adaptivity of the controlled process.
1. 2. 3. 4.
AstrOm, K.J., B. Wittenmark, Adaptive Control, Addison-Wesley, Reading, 1989. J. ofEng. Indus.. 111 (1989) 133. Fassois, S. D., K. F. Eman, S. . Wu, Trajzs. A Ulsoy,A.G., Y.Koren, IEEE Control System,.9/3 (1989) 33. Tanshoff, H.K., J.P. Wulsberg, H.J.J. Kals, W. Kijnig, CA. van Luttervelt, Anaz. CIKP, 37/2 (1988) 6 I 1. 5. Masory, O., Y. Karen, Ann. CIRP, 29/l, ( 1980) 28 1. 6. Hwang, H.Y., J.H. Oh, K.J. Kim, Int. J. Mach.Tools Manufact., 29t2 t 198% 375. 7. Micheletti, G.F., W. K6nig, H.R.Victor, Ann. CARP, 2512 (1976) 483. 8. Tlusty, J., G.C. Andrews, Ann. C...RP, 3212 (1983) 563.
GE. D ‘Errico/ Journal of Materials Processing Technology 64 (I 997) W34
84
G.Bmow, Proc.2M 6~2. Con? MTDR, Manchester-UK, (1986) 271. 10 Shirajshi, M., Precision Engng, Part 1: 1014 (198S) 179; Part 2: 1 l/l (1989) 27. 11. Dan, L., J.Mathew, Int. J. Mach. Tools ~anufact , 3014 (1990) 579. 12. Dornfeld, D.A., Ann CIRP, 39/l (1990) 101.12. 13. D’E&,o, G.E., Proc. IM Cc@, IEEE IEColG”89, Philadelphia-USA, Nov. 6-10, 3 g.
L&r,
P.M.,
(1989) 652. 14. D’Errico, G.E., G.Rutelli, L.Settineri, Proc. 10th Int. Co@. CAPE, Palermo-I, June 7-9 (1994) 64. 15. Cuppini, D., G.E.D’Errico, G.Rutelli, Wear, 139 (1990) 303. 16. Mannan, M.A., S.Broms, Ann. CIRP, ‘38/l (1989) 347. 17. Koren, Y., O.Masory, Ann. CIRP, 3011 (198 1) 373. 18. Mackinnon, R., G.E.Wilson, A.J.Wilkinson, Proc.2.5th Int. Conf MTDR, Birmingham-UK, April 22-24 (1985) 177. 19. Tomizuka, M., S.Zhang, J. Dynamic Syst., Meas. Control, 108 (1986) 215. 20. Toutant, R., S.Balakrishnam, S.Onyshko, N.Popplewell, IEEE Control Systems, 1316 (1993) 44. 21. Elbestawi, M.A., RSagherian, ht. J. Mach.Too1.sManufact., 2’713 (1987) 399. 22. Yen, D.W., PK. Wright, Trans. ASME J. of Eng. Indus., 105 (1983) 3 1. 23. Byrne,G., Int. J. Mach Tools Manufact., 2712 (1987) 215. 24. Chow,J.G., P.K.Wright, Trans. ASME J. Engng. for Industry, 1 lO/Feb (1988) 56. 25. Colding, B.N., Ann. CII?P, 40/l (1991) 35. 26. Uehara,K., M.Sakurai, T.Ikeda, Ann. CIRP, 41/l (1992) 75. 27. Young, H.T., T.L.Chou, Int. J.Mech.Sci, 36/10 (1994) 931.26. 28. Mathew,P., Int. J. Mach Tools Manufact., 2914 (1989) 481. 29. Colwell,L.V., Ann. CIRP, 24/l (1975) 73. 30. Redford,A.H., B.Mills, S.Akhtar, Ann. CII?P, 25/l (1976) 89. 31. Venuvinod,P.K., W.S.Lau, C.Rubenstein, Ann. CIRP,33/1 (1984) 55. 32. Donovan, A., W. Scott, Int. J. Mach. Tools Manufact., 35/l 1 (1995) 1523. 33. D’Errico, G.E., J. Mechanical Working Tech., 20 (1989) 3. 34. D’Errlco, G.E., A System for Adaptive Control of Metal Cutting Processes, Italian Patent No. 67432-A/88 (1988). 35. D’Errico, GE., L. Settineri, Proc. 3rd IEEE Conf Control Applications. GlasgowUK Aug. 24-27, 2 (1994) 1165.
of
survey of the relevant l~terat~re.
Systematic
rep~ace~le~t of ality-oriented ma As far as machining is concerned, workp iece , tool, coo
ection with process control
is a founding
the process res onse is subject to change with at is to the point here - with the cutting he idea of adapting these co~~~tio~~sin order ven performances against process changes was illtrod~c~ on thl: rationale of feedback control: to implement closed loops such that each command has a sensor to verity if the corrective actions do provide effective results. In the last few years, a tremendous development in the evolution of rear-tin-z control technologies was achieved in the area of the nove! discip!ine of Adapti giving rise to two fundamental design frameworks, best known as Adaptive Systems (MRAS) and Self-Tuning Regulators (STR). Unfortunately, when speaking about AC, the communities of control and of manufacturing engineers do not share always a common language. As a matter of fact, a standard terminology is not agreed with. The intended goal of the present paper is to contribute to a unifying approach bzsedon a review of the recent developments and current trends in adaptive monitoring and conaol with application to machining processes. In the next Section 2, the main architectures of adaptive systems are dealt with in terms
of modern control theory: methods for designing computer-based systems and digital models for implementing real-time control algorithms are presented here. Section 3 is addressed to some problems arising in the application oi’ theoretical results to machining processes at the production level: also in-process sensors for closed-loop considered in this section. Finally, Section 4 contains the concluding remarks. 0924-0136i97~%17.00 Q 1997 Elswkr Science S.A. All rights resemv~ PH 09244136 (96) 02555-l
control
are
G.E.
76
D’Ewico ~3o1cmalof MaterialsProcessing Technology64 (1997) ~5-84
2. MODERN ADAPTIVE CON
S
Modern monitoring and control systems are built up on computer-based architectures, as shown in the block diagram in Figure 1. For process control, the process is modelle describing causes and effects that are non-instantaneously related, according to re~at~o~~sh~~s based on the physical nature of the process: a process descriptor is either a variable or a parameter depending on its rate of variation with time: fast descriptors are considered variables, while slow descriptors are considered parameters.
PROCESS
Figure
1:
Block diagram of a computer-controlled system.
In Figure 1, the blocks A/D and D/A represent the Analog to Digital and the Digital to Analog converters, where the conversion is synchronised with the sampling instants (n) (that is a sequence of natural numbers) by the real-time clock in the system. Further, it is supposed that the operation of signal sampling is periodic and its inverse, the signal reconstruction is piece-wise constant, which is implemented using a mechanism known as Zero-Order Sample and Hold circuit (ZOSH). The inputs to the process u(t) (the control) and d(t) (the disturbance), and the measured output y(t) are continuous time signal. From the control software point of view the signals u(t) and y(t) are considered at the sampling instants only and are represented as sequences of numbers (~0)) and (v(11]which describe a model of the computer-controlled system known as the stroboscopic model. The problem here is to implement a function u(t) with the objective of obtaining a desired y(t), namely the continuous-time representation of uC. The control strategy can be described in terms of a pseudo-code by the following timed loop: (i): (ii): (iii):
wait for the clock; sense y; accomplish the A/D conversion;
77
with the meaning that, for a given xu):
where A(q-‘)=l+alq’;...+,llq-~
and
polynomials, so that eqn. (2) can be given a (SISO) model CiP of the computer-controls
(q-‘)=b,+b,q-‘+..a
are co-prime Single Output d inchkve of
interfaces, actuator, selasor and process un Gp(q) =q%(q-
‘)/A(q-*) ==ye’)/u(j)
(3)
roblem is to implement a desired closed loop transfer function:
(4) Use of a second order transfer function is frequent in control practice for modelling continuous-time feedback systems because significant specifications can be given looking at two modes whose contribution is relatively dominant in the transient behaviour. In terms of a digital model, this translates in using the following equation for the polynomial in the denominator of the model (4):
G.E. D Brico/Journal
78
oj' Materials Processmg
Technology 64 (1997) TL5’4
where SC 1 is the damping ratio and (clthe natural frequency of a continuous-time secon order system, sampled whit the sampling period r. Sand UIcan be used to specify the ste response characteristics of the system in terms of maximum percentage overshoot MO=lo&_ emXdX1 4)1/z and peak overshoot time tp=zIw( 1-a)“‘. A compendious and authoritative treatment of general theory and design of AC systems is the book by Astrom and Wittenmark [I]. Control theorists distinguish Adaptive Control from Gain Scheduling (GS), which is a special kind of open-loop adaptation (Figure 2).
operating conditions
regulator .__ parameters ___r
SCHEDULING i
REGULATOR
L--_
1
Figure 2: Block diagram of a Gain Scheduling system. Closed-loop AC systems are regulators with adjustable parameters founded on the idea of integrating on-line process identification and real-time regulator design. This is shown by the two loops in the block diagram of Figure 3: the inner (fast) loop is aimed at output variable feedback implementation on the assumption that process parameters have known values, the outer (slow) loop is aimed at feedback design based on parameter estimation.
--.--
process identification
r-)i
PROCESS
I-+-------,
COMPUTER
Figure 3: A simplified illlustrationof an Adaptive Control system.
i
j;
EGLJLAT
recess parameters
4
regulator parameters
1 I
I i
U
REGULATOR
Figure 4: Mock diagrams of MRAS (a) and STR (b).
80
G.E. D ‘Em.co /Jourt~nl of MaterialsProcessing TechnoloQ 64 (I 997) 75-84
To improve the performance of an AC system, it is a good practice to use also a thir level loop aimed at co-ordination of human interaction and feedback and Figure S-shows an example of a three loop architecture of an indirect STR.
Figure 5: An indirect STR with a co-ordination loop. Although various design methods can be used, adaptive control algorithms can be basically divided into indirect and direct depending on which parameters are estimated (if process or controller parameters). For example, in indirect self-tuning algorithms it is assumed that the process has a given structure with unknown parameters which are estimated on-line; the regulator parameters are then indirectly calculated according to the estimates of the process parameters. in direct self-tuning algorithms the closed-loop model is re-parameterized making it possible to estimate the regu!ator parameters directly, so that calculations are significantly reduced. Many general purpose algorithms for adaptive control implementation are collected and deeply discussed by Astrom and Wittenmark [ 1J. For online machining process modelling and adaptive control, a fast algorithm is proposed using results from numerical simulation by Fassois, Eman and Wu 121.
3. APPLICATIONS OF ADAPTIVE CONTROL TO NIAC
ING SYSTEMS
A historical perspective of techniques and concepts relevant to applications of AC to machining processes is given by Ulsoy and Koren [3]. Developments and trends in monitoring and control of machining processes are surveyed by Tonshoff et al. [4]. Manufacturing engineers usually define an AC system as a closed-loop control that, being applied to machine tools equipped with sensors for monitoring the conditions of tools
IS
uring the process by
erations the interest is still at a rding as laboratory or s~~~~a~~~~ research stage and the current level of ~~dustr~a~usage reflects ~nder-ex~~o~~t~o~~ of AC potential. They uggest that this situation is due to the lack o reliable sensors for shop floor applications an to some malfunctioning related to the ossible raising of Insta problems. This state of affair can ubl ished results ]S-35 ]. As far as instability is concerne nd in the literature. For asory and oren example, ose a sampled-data system based on feedback of cutting force measurement in tarring with a constant cutting force constraint. T faced to stability considerations and use a policy for adjusting the controller gain. 3h and Kim ]6] propose a model of the turning process which takes into account the compliance of the mat ine tool for the feedback control of feed force by adjusting the feed rate. They demonstrate that a conventional proportional Integrative (PI) cont.rol!sr cannot guaranty stability, while experimental evidence is given to the performance improvement provided by an AC algorithm. nr”ctical issue which attracted It is known that sensing for machine tool applications is a r14 commercial interests and pushed the development of unmanned manufacturing engineering. An early and excellent contribution to the assessment of relevant methods and technologies can be found in Micheletti, Konig and Victor 171. Further reviews are provided by Tlusty and Andrews [is] and by Lister and Barrow [9]. For a comprehensive survey of in process techniques both for tools and workpiece, the interested reader is referred to the twofold paper by Shirashi [lo]. It should be stressed here that while the state of the art regarding sensing technologies is evolutionary, the classification of methods for wear measurement is based on the criterion of separating direct measurements (but inherently intermittent) from continuous (but indirect) estimates. Direct methods are essentially referred to tool geometry and wear morphology, while indirect methods are based on relationships which correlate
82
G.E. D ‘.&rk~o/ Jmrnai qf Afatertuls Processrng Technology 61(1997
3-W
weaf or failure of tool to appropriate variables relevant to the cutting acoustic emission, force, torque, power and temperature. The major part of relevant casestudies focuses on the turning process. A review of tool wear and failure monitoring techniques for turning is given by Dan and Mathew [ 111: according to their conclusions, few of such techniques have been used successfully in industries; further, a global approach able to manage more than particular classes of tooi failure modes is not still fully developed. Perhaps it should be also pointed out that persistency of such a criterion to classify wear measurements methods reflects that up to now there is no hybrid method which provides both direct and continuous monitoring of tool conditions during a cutting process and a compromise is needed. An approach to sensor fusion is proposed by Dornfeld [ 121based on neural networks. As far as tool monitoring is concerned, AC approaches are focused on indirect methods, which are clearly more appropriate to the aim of continuous process control ( [13]). Among the process variables relevant to the wear and failure mechanisms, cutting force, power and temperature are being considered most appealing for industrial use: applicability of each of them is discussed in an adaptive comrol simulation by D’Errico, Rutelli and Settineri [ 141. Cutting power is used and deeply discussed also in a work by Cuppini, D’Errico and Rutelli [ 151, and by Mannan and Broms [ 161. One of the first AC approach using cutting forces is developed by Koren and Masory [17] in application to a CNC lathe. Also Mackinnon, Wilson and Wilkinson IIS] are involved with the objective of maxim%ng the feed r,& against a force or torque constraint by means of an adaptive strategy aimed at: preventing premature tool failures, assuring chip-breaking and minimising air-cutting time with application to a NC turret lathe. A discrete-time model is developed by Tomizuka and Zhang 1191for predicting the dynamics between feedrate and cutting farce. This model is used for force regulation (an innovative piezo-electric crystal device, mounted on the tool carriage of a lathe is used as cutting force sensor). An approach to make this controller adaptive to changes in cutting conditions and material properties is suggested too. Further in turning process, Toutant et uf. [20] give a contribution toward feedrate compensation for constant cutting, Masory and Karen [5] and Hwang, Oh and Kim [6] have already been mentioned for their contributions also to the stability problem. In peripheral milling, Elbestawi and Sagherian [21] are concerned with the problem of regulating the cutting force under varying cutting conditions. Several parameter adaptive control strategies and structures are applied on the base of a mpdel which describes the relationship between feed rate and cutting force, and includes the effect of spindle stiffness on chip formation. A theoretical AC approach to machining processes based on physical constraints of tool wear mechanisms was developed by Yen and Wright [22]. In 1221 tool wear mechanisms arc mod&d for adaptive control aimed at maximum production rate compatible with constraints on plastic deformation, crater wear, and fracture; such constraints are suggested to be dealt with by using cutting force or temperature sensors. It is known that the devices currently used for measuring the cutting temperature are: infrared sensors, thermocouples, and the tool-workpiece thermoelectric interface [23-273. Relationship between cutting temperature and tool wear is also being deeply investigated
machj~i~g faults, that rna~ufa~tur~~g costs. ulsory task, as
it
fo~~dat~o~s of at can be followe at the successfd ement closed loops so that each corn
has a sensor for verifying
that the
systems. The feasibility of such systems is given evidence and a lot of research work has been devoted to he ridging the gap between academic achievements and industrial usage. lications to machining processes: approach is still not mat Nevertheless a uni asis is gives; to the zkqxivity of so far, from the point of view of control engine the control system, while from the point of view of manufacturing engineers a major importance is attributed to the adaptivity of the controlled process.
1. 2. 3. 4.
AstrOm, K.J., B. Wittenmark, Adaptive Control, Addison-Wesley, Reading, 1989. J. ofEng. Indus.. 111 (1989) 133. Fassois, S. D., K. F. Eman, S. . Wu, Trajzs. A Ulsoy,A.G., Y.Koren, IEEE Control System,.9/3 (1989) 33. Tanshoff, H.K., J.P. Wulsberg, H.J.J. Kals, W. Kijnig, CA. van Luttervelt, Anaz. CIKP, 37/2 (1988) 6 I 1. 5. Masory, O., Y. Karen, Ann. CIRP, 29/l, ( 1980) 28 1. 6. Hwang, H.Y., J.H. Oh, K.J. Kim, Int. J. Mach.Tools Manufact., 29t2 t 198% 375. 7. Micheletti, G.F., W. K6nig, H.R.Victor, Ann. CARP, 2512 (1976) 483. 8. Tlusty, J., G.C. Andrews, Ann. C...RP, 3212 (1983) 563.
GE. D ‘Errico/ Journal of Materials Processing Technology 64 (I 997) W34
84
G.Bmow, Proc.2M 6~2. Con? MTDR, Manchester-UK, (1986) 271. 10 Shirajshi, M., Precision Engng, Part 1: 1014 (198S) 179; Part 2: 1 l/l (1989) 27. 11. Dan, L., J.Mathew, Int. J. Mach. Tools ~anufact , 3014 (1990) 579. 12. Dornfeld, D.A., Ann CIRP, 39/l (1990) 101.12. 13. D’E&,o, G.E., Proc. IM Cc@, IEEE IEColG”89, Philadelphia-USA, Nov. 6-10, 3 g.
L&r,
P.M.,
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