- Email: [email protected]
Intelligent processing of materials
Journal of Materials Processing Technology, 36 (1993) 447-465
447
Elsevier
Intelligent processing of materials S. P i c k e r i n g
Institute for Advanced Materials, Joint Research Centre, Commission of the European Communities, P.O. Box 2, 1755 ZG Petten, The Netherlands (Received March 17, 1992; accepted July 7, 1992)
Industrial S u m m a r y This paper reviews the background to intelligent processing and presents a case study to illustrate the benefits achievable through intelligent process-control. Intelligent processing is most suitable for processes where the reasons for the failure of conventional processcontrol are well understood, where suitable sensors are available, and where a certain degree of maturity has been reached so that the process is unlikely to change drastically or to be supplanted. Materials-shaping processes meet these criteria best, and brake-forming provides an excellent example. The implementation of intelligent processing to brake-forming, based on an analysis of the process developed by Stelson, is demonstrated successfully in this paper.
Notations rp rD L t
Y. Fp aFp h(Fp)" L' 0 r
punch radius die radius half width of die sheet thickness punch travel punch force represents elastic deformation represents power-law strain-hardening e f f e c t i v e d i e h a l f - w i d t h = L - rD s i n 0 f l a n k a n g l e = s i n - 1 [(r/L) c o s fl] +/? t a n -1 [ - - ( r - - Ypm)/L]
rp+t+ro
Correspondence to: Dr. S. Pickering, Institute for Advanced Materials, Joint Research Centre, Commission of the European Communities, P.O. Box 2, 1755 ZG Petten, The Netherlands. 0924-0136/93/$06.00 © 1993 Elsevier Science Publishers B.V. All rights reserved.
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measured punch force measured punch displacement effective sheet displacement = Y~m- rD (1 -- COS0) horizontal force component including friction
F pm
Y'p.~ Fh
{ sin O- p cos O~ coefficient of friction between sheet and die sheet curvature punch moment wrap-around angle under the punch, unloaded condition wrap-around angle under the punch, loaded condition flank angle, unloaded condition = OT flank angle, loaded target flank angle free angle, unloaded free angle, loaded beam span of the free section arc length of the free section deflection of the free section Moment under the punch
K M
~u ~L Ou OL Ot LF ~L M~
1. I n t r o d u c t i o n
The term "intelligent processing" (IP) refers to an innovation in process control made possible by recent advances in computer- and sensor-technology. Process control basically comprises comparing sensor signals with set-point values and adjusting actuator signals accordingly. In terms of control circuitry, if conventional processing (CP) may be represented as a single controlloop (see Fig. 1) then intelligent control circuitry is characterized by a loop
H~te~ia)s Str~cture s~c~i~¢ation
.............i _ n
~-~
I
Process
(a)
.........
I
t
~ate~iat
~.-
~
Sensor
Material
~
.IA
i Mater,als ~
Proces~cu
(b)
Fig. 1. Schematic control circuits for conventional (a) and intelligent (b) processing.
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within a loop. Conventional process-control is concerned exclusively with process variables, e.g., temperature, pressure, etc: the sensors measure process variables and the set-points are pre-determined values of these variables. In contrast, IP is concerned with the control of material variables. The inner control-loop is the same as the conventional processing-loop and the two extra components that are indispensable for IP, namely, materials-property sensors and process models, are located in the outer loop. The purpose of the outer control-loop is to continuously adjust the process variable set-points for the inner control-loop in response to signals from the material sensors. This adjustment is effected by the process model by comparing signals from the materials-property sensors with its own set-points: these set-points are effectively the specification for the processed material. The process model thus constitutes an "intelligent" core which, by relating materials properties to processing conditions, can run the process without pre-determined processing conditions. IP is therefore distinguished from conventional processing not only in the technology used but also in its objective, which is to actively steer the process towards a goal defined in terms of the properties desired for the processed material. The active steering of the process motivates use of the adjective "intelligent". To steer the process towards its goal, relevant material properties are monitored continuously during processing in a way that amounts to in-situ quality assurance. Intelligent processing therefore achieves quality by making the product correctly in the first place rather than by the conventional practice of rejecting defective parts on the basis of inspection and testing; a method which is not only wasteful but unreliable. The purpose of this paper is to present the application of intelligent processing to brake-forming to illustrate the benefits available through intelligent process-control. To this end the principles of intelligent processing are first reviewed. The specific application including the control algorithm is then presented. Finally, conclusions are drawn on both the problem areas, and on the benefits, offered by intelligent processing.
2. The principles of intelligent processing 2.1. Strategies of process control 2.1.1. Limitations of conventional process-control Process control is the key to maximizing the quality of processed material and the reason why materials-processing techniques routinely fail to yield the quality of material of which they are inherently capable is that conventional process control is inadequate. In fact, conventional control-methods are intrinsically unsuited for obtaining optimum results in materials processing and a different approach to process control is required.
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The root of the problem with conventional process-control is that it represents an essentially deterministic view of the process, which in turn encourages a confrontational approach to problems. According to this approach, wellcharacterized input materials yield a reproducible and predictable product when subjected to well-controlled, pre-determined process conditions. Variation in the properties of the product is ascribed to random- or hiddenvariables. Therefore, to reduce variations in product properties, random variables must be identified and eliminated, and hidden variables must be discovered and controlled. Unfortunately, in many cases such a head-on approach can be technically diificult, time-consuming, and expensive. Batch-to-batch variations in the starting material is a typical case of a variable t hat is difficult to c o u n t e r a c t using conventional process-control. Intelligent process-control has emerged as an alternative control-strategy which side-steps r a t h e r t ha n confronts the problems associated with a full conventional control-strategy.
2.1.2. Basic ideas of intelligent control Intelligent processing assumes at the outset that the parameters t hat lead to product variability are either too numerous, too difficult, or too expensive to bring under adequate control. Instead, the product is improved by intervening actively via a few first-order process variables, the influence of which on the material is well-understood, in order to count er a ct the influence of the secondorder variables which are either not controlled at all or are only controlled within easily achievable limits. To intervene meaningfully in this way, information must be available on, firstly, the cur r ent state of the material and, secondly, on the way the material responds to changes in the process variables. The first requirement is met by an extra set of sensors to monitor the material properties continuously during processing. The second requirement is met by an on-line computer model of the process to calculate appropriate values for process variables to steer the process towards a goal set in terms of material parameters.
2.2. Optimization through intelligent processing The success of a materials-processing operation depends both on the scientific and technical concepts underlying it and on the degree of control exerted over it. The primary reason for using computer models in intelligent processing is to optimize control over the process. Appropriate computer models, however, can be used also as a creative tool to optimize the process. The benefits to be gained from such an integrated approach of optimizing both the process and the control over it would seem evident. W het her such an integrated approach is considered central to the concept of IP or merely as a useful spin-off is a m a t t er of definition. Nevertheless, it should be remembered t hat any progress made along these lines c a nnot be a substitute for the development of new and better processing techniques.
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2.2.1. Optimization of control As outlined above, the motivation for turning away from conventional process-control is t h a t hidden variables are too laborious to uncover and bring under control. Turning to IP, however, does not automatically guarantee success: certain general conditions must be fulfilled. The most basic condition, one t h a t was taken for granted in the introduction, is that the technical aspects that are common on both CP and IP are not already a limiting factor in CP. Explicitly, this means that the precision and response time of sensors available for measuring process variables, and of the actuators for controlling them, must be adequate. If this is not the case then neither CP nor IP can be successful, and the only possible approach, apart from developing better sensors or actuators, is to make technical improvements to the process which will make it easier to control. The above condition is necessary for maintaining process variables at pre-selected values, but it may not be sufficient for active control. The possibility exists that actively steering the process against random- or hiddenvariables may make greater demands on sensors and actuators (e.g., response time, spatial resolution) than is required to maintain constant conditions. However, although it is known that, for example, the sensor/actuator must always act faster than fluctuations in the variable, it cannot necessarily be specified what this response time should be in an IP system. It is precisely because hidden variables are hidden, and because IP makes no attempt to uncover them, that the success of an IP system cannot be guaranteed. Unfortunately, this type of problem will be manifest only as a non-specific failure of the IP system to yield a better product. Fortunately, only certain types of low inertia or resonant processes-variables are likely to be troublesome in this respect, e.g., gas dynamic instabilities in coating processes. As experience is acquired, no doubt guidelines will be developed on the requirements for the control of various types of process variable. Thus, despite the promise that intelligent control holds for circumventing many traditional control-problems, it should 'nevertheless be born in mind that some process development may be necessary to realize these advantages.
2.2.2. Optimization of the process The intensive use of computers to model the process offers possibilities for improving the process. Whether a model can be used to optimize the process, however, depends on whether the model is empirical, or on whether it is an accurate physical and chemical description of the phenomena involved in the process. In the case of physically realistic models, the modelling abilities of an intelligent processing system may be used as a creative tool to optimize processes, and thus to improve materials, to levels beyond those achievable, even in theory, by conventional processing. In addition, there is the possibility that the ability to counteract unknown variables can be used to relax certain process conditions, e.g., quality of input materials, thereby achieving direct
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economic gains. Ultimately, intelligent processing may be expected to be integrated into computer-aided engineering of components. 2.3. Process models Process models are computer programs that relate materials properties to processing conditions. For process control they are formulated in terms of the measurable parameters (sensor inputs) using algorithms fast enough for online response. Models which describe the process in terms of parameters which are not accessible to sensor measurement or which use computationally intensive methods, e.g., finite-element calculations, are not suitable for on-line control but may be used for optimizing the process. 2.3.1. Models for process control The process models used in IP relate the way the material structure reacts to changes in process variables. Simply stated, the model receives signals from the material sensors, it calculates appropriate set-point values for process variables, and it sends these values to the (conventional) process controller to complete the control loop. This calculation occurs within a window of constraints imposed by: (a) equipment, which is of course restricted to a limited range of process variables; and (b) by economic considerations, such as minimization of process time. To steer a process against unpredictable fluctuations, a minimum product of accuracy of prediction and response time is required of the model. Within limits, either a crude and fast model or an accurate but slow model may be equally satisfactory. Because model development can be very time-consuming, and because a highly detailed knowledge of the process is contrary to the philosophy of IP, the starting point should be a simple model, providing closed-loop control is possible. Even empirical models can be satisfactory for the process control aspects of IP. An example of this is in hot forging [1] where the amount of deformation produced depends very sensitively on material temperature. In this application, measurement of the deformation produced by each hammer blow is measured and this information is used as the input to calculate the strength of the next blow. This empirical approach to IP in forging has the merits of not requiring an excessively large data base of material properties or of complicated algorithms to try to calculate temperature profile within the workpiece from surface temperature measurements. This example makes clear that IP is not simply the brute-force application of knowledge but that it depends critically on an input of natural intelligence. 2.3.2. External constraints for process-control models Even the most complex process-model is functionally equivalent to just a single equation in that it provides a unique output only if there is just one unknown. For simple processes such as brake-forming, where just one process variable is calculated, i.e., the length of the punch stroke, this is precisely what
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is required. For processes with more than one variable, however, the problem is faced that the process model can produce a multi-dimensional domain of possible process-conditions rather t h a n a unique set of values. For practical control a single set of values needs to be decided upon within the domain of possible values, i.e., a specific route to the goal must be selected from a range of possible routes. By definition, this decision cannot be based on physical or technical parameters within the model, but must be based on an externally imposed order of priorities. An example of such a decision is the choice of the time/temperature combination for a thermally-activated process. Whether a short-time/hightemperature treatment is chosen instead of a long-time/low-temperature treatment depends on an externally imposed hierarchy of priorities such as desired throughput rate, energy consumption, furnace life, etc., i.e., it depends on those factors which constitute the economic boundary-conditions within which the process is operated. The process model can thus be regarded as an invariant core of functional dependencies which operates within a transitory set of economic boundaryconditions. Artificial intelligence, or other rule-based software tools such as expert systems, which are not directly involved in the modelling of the physical and chemical phenomena of the process, are suitable for enforcing these boundary conditions. The external boundary-conditions are needed therefore not only to ensure that the model is applied to produce the best result judged according to external constraints, most of which are ultimately of an economic nature, but are needed for the process model to function at all.
2.3.3. Models for process optimization The disadvantage of empirical process-control models is that they contain little physics or chemistry, and therefore offer little insight into how a process may be improved. By analogy, a furnace controller with the empirical algorithm of: power on at temperatures below the set-point, and power off at higher temperatures (with the refinement of PID control); is sufficient to control any furnace but it offers no insight into how to build a better furnace. Yet the creative use of models is an important aspect of IP and only a quantitative model of all first-order physical- and chemical-phenomena will be of use for this purpose. A quantitative model is likely to run more slowly than an empirical model, and the on-line response time may be too slow to control the process. This will certainly be true of models which depend on computationally intensive techniques such as finite-element analysis or which model gas dynamics. Two ways of dealing with these computationally-intensive aspects of modelling are first, to use them only off-line in the initial design phase for the process, or second, to make results from such calculations available on-line to the control program from a data bank.
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Thus, it may prove best in practice to partially separate the functions of IP into process optimization, and active process control, based on 2 sets of models. Once this is done the practical constraint for the control model that it must be formulated to describe the process in terms of those parameters that will be measured by the sensors may be dropped. This separation of functions also suggests that, in practice, the fastest way to apply an IP system to an existing process would be to start with a simple empirical model. Empirical elements in the model can then be replaced progressively by physically realistic elements to gradually upgrade the model until it becomes a useful tool for optimizing the process.
2.4. Sensors Ideally a sensor should be a relatively inexpensive and robust piece of equipment th at measures relevant material properties directly, or if t hat is not possible, one that measures a related property from which the required property can be derived using an algorithm in a computer model of the process. However, the range of sensors that fall into the cheap and robust category is very restricted and is limited to measurement of typical process parameters such as temperature and pressure r a t h e r t han materials parameters such as grain size or chemical composition. The choice of sensors, as opposed to analytical instruments, for material microstructure is very restricted compared with sensors for process parameters. This restricted choice of sensor stems both from the fact that microstructure sensor development, e.g., ultrasonic measurement, is a new field (related to non-destructive evaluation) and from the fact that the measurements are intrinsically difficult to make. It is n o tewo r th y that successful examples of IP are related to shaping operations, i.e., bar forging, ring rolling and brake-forming, where it is the shape and size of the workpiece t ha t are measured and that this is done with conventional sensor technology. 2.5. Areas of application Areas of application for IP are decided according to three basic criteria: expected benefit, sensors, and models. Firstly, the short-fall between current practice and theory must be sufficient to justify the effort required to implement IP, and the reasons for the failure of CP to perform satisfactorily must be understood in order to estimate the amount of improvement expected from IP. Secondly, because the model must be formulated in terms of the measurable parameters, sensor characteristics are the starting point for model development. In practice the sensor criteria are very simple: either suitable sensors are available or they are not. Thirdly, each process requires unfort unat el y its own model and model development tends to be lengthy. IP therefore tends to be most suitable for processes t hat have reached a certain degree of maturity and are unlikely to change very much or to be supplanted. At present, the class of
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process that best meets these criteria is materials shaping. For most other processes suitable sensors are simply not yet available. 3. A p p l i c a t i o n o f i n t e l l i g e n t c o n t r o l t o b r a k e - f o r m i n g
3.1. Why brake-forming is suitable for intelligent control Brake-forming is one of the most common and simplest of all metal-forming processes. It is a process for making straight-line bends in sheet or strip by forcing the metal into a concave die using a punch, as shown in Fig. 2. Setting up a press to bend a sheet to a given angle tends to be a trial-and-error process, due to elastic spring-back of the sheet on removing the load. Reproducibility can also be poor because of batch-to-batch variation in the properties of nominally identical material. For example, the yield point of hot-rolled steel strip habitually varies by up to 20%. The amount of spring-back after bending therefore varies, thus affecting the reproducibility of the bend angle. Brakeforming is thus generally regarded as a simple and cheap but imprecise process. If a method could be found to compensate for the elastic spring-back of the sheet, then brake-forming could be transformed into a precise process. This objective can be achieved by the application of the concepts of intelligent processing. The two major requirements of IP are: (1) a process model; and (2) materials sensors; both of which can be met. The process model is based on: (1) a complete mathematical description of brakeforming [2-5]; and (2) on a mathematical description (constitutive law) of the elastic and strain-hardening properties of the sheet metal. Sensors are required to measure the linear displacement of the punch and the force it exerts on the workpiece. These requirements are easily met from the range of commercially available instruments. The basic idea of adding sensors and a computer is to upgrade a simple hydraulic press into a combination of a materials-characterization rig and
Punch
~
~
Sheet
e
Fig. 2. Schematic brake-forming operation.
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456
a processing rig in which each piece of metal is characterized while it is being bent. This real-time, in-situ characterization (elastic modulus, elastic limit, strain-hardening coefficients) is fully completed during the early stages of the bend so that the amount of spring-back, and the appropriate ram displacement to compensate for it, is calculated well in advance of the ram reaching the required position, i.e., an open control loop with one process variable is used.
3.2. Experimental 3.2.1. Equipment The experimental set-up is shown in Fig. 3. An existing 50 ton hydraulic press with a stationary upper platen and a moving lower platen was equipped with a linear displacement transducer to measure the displacement of the lower platen with respect to the frame. The punch was mounted on the upper platen and a load cell was installed under the die on the lower platen. After calibration against the load cell, the applied force could also be derived satisfactorily from the pressure in the hydraulic system. A die width of 20 mm and punch and die radii of 2 mm were chosen: this geometry permitted sheets of up to 5 mm thickness to be bent with internal angles in the range 70-120 degrees. The outputs from the sensors were connected to a 12-bit analogue-digital converter (ADC) and the manual press controls were bypassed by means of power relays operated remotely via signal relays. The connection between the control electronics and the computer was made using the Pheonix InterBus-S field bus: this consists of a card inserted in the PC and a cable to a local bus station adjacent to the ADC and relay modules. A 16 MHz 386 PC was used which also ran the process model software. The control exerted over the press was a simple sequence of: ram forward/stop/reverse.
field bus
(
(300kbits/s)
LI lu,
Press ~s: Control ~t
Unit it
linear displac~nt transducerj load cell Hydraulic Press
Fig. 3. Hardware for intelligent control of brake-forming.
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3.2.2. System characteristics The mode of operation is to set the press in motion, record data, calculate the ram reversal-point using the process model, and then simply monitor ram position until the target displacement is reached. This is an open control loop: the system acquires the data it needs, it makes a prediction (punch displacement) and then waits for t hat condition to be met. In contrast, a more conventional feedback control loop would require the ram to be reversed repeatedly to measure the bend angle in the unloaded condition: this would amount to no more t h an automated trial-and-error. The use of an open control loop, however, requires a highly accurate process model and constitutive law. A ram displacement of 4 mm was required to characterize both the elastic and plastic ranges of the material for the chosen die geometry. Data were recorded every 0.05 mm giving a total of 80 data points. Calculation of the target ram position took about 0.2 s corresponding to a further 1 mm of ram travel. The ram could therefore only be controlled at displacements greater t h a n 5 mm. A limit switch at 10 mm prevented the ram making hard contact with the die, in order to avoid overloading the load cell (maximum load 50 kN). The control space corresponding to these conditions is shown in Fig. 4 for a 4 mm thick strip of 316 stainless steel. The widest bend angle is about 120°, mainly due to the necessity of recording data over the first 4 mm of punch travel. By comparison, the calculation time is a relatively small overhead t hat could be reduced further by using a faster PC. Both the software for controlling the hardware and t h a t for the process model was written in QuickBasic. The process model algorithm is described in Section 3.3.1.
Punch
displacement (mm)
12
Time (s) (software limit switch at I0 mm)
I0
Z.O
1.5
Calculation ( 1.0
0.5
0
0.0 180
160
140
120
I00
80
Bend angle (degrees}
Fig. 4. The control space for the brake-forming of 4 mm thick 316 stainless steel.
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Table 1 The error in a 90' bend resulting from a + 1% error in the listed parameter Parameter
Error in bend angle (%)
die width, L punch radius, rf, die radius, re sheet thickness, t friction coefficient, p
+ 1.2486 --0.1128 -0.1651 -0.2761 + 0.0002
The accu r acy of the bend angle depends on errors in geometry and friction, as shown in Table 1.
3.3. Press-brake control algorithm 3.3.1. Outline of the algorithm An algorithm developed by Stelson et al. [2-5] was used. The objective is to calculate how far the punch must travel in order to bend the metal through the chosen angle, 0T, taking into account the elastic spring-back of the sheet on unloading. For this calculation the dimensions of the sheet, the punch, and the die are required together with the coefficient of friction between the sheet and the die. It is necessary to know also the mechanical properties of the sheet. These are determined while the sheet is being bent and are derived from measurements of the punch force and displacement. The sheet is assumed to bend by a combination of elastic plus strain hardening plastic deformations. Three parameters a, h, and p suffice to characterize this deformation:
Y, = aF. + h (F.)~
(1)
To derive a bend angle for the sheet from the linear travel of the punch, the force-displacement relationship for the punch is converted to a moment-curv a t u re relationship for the sheet. The complete m o m e n t - c u r v a t u r e relationship can be established during the early stages of the bend so that the total punch travel required for the desired bend angle can be calculated in advance of the actual motion of the punch. The punch position is then monitored continuously until the calculated position is reached whereupon the hydraulic ram is reversed. The steps in this brake-forming process (see also the flow chart in Fig. 5) are: set the press in motion; briefly record force-displacement data for the punch; compensate the experimental data for geometric factors, i.e., eqns. (2) and (3); fit the compensated data to a linear plus power law model, i.e., eqn. (1);
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read default values: t, L, rr, ro, I~, 0r
1
I_ read sensors: Fp, Yp F
no
i
yes
I correct data: eqs(2,3) I [ data fit: a, h, p eq(1) [ [
calcMp:eq(4)I
[
read sensor: Yt
1
LI-nO
yes
I
s,o~o~
I
I
save data to disc
]
Fig. 5(a). Flow chart for the press control program.
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processing
a, h, p, Mp, t, L, re, rD, O,r
(to be obtained by iteration)
(bu = ~bL = OL = LF,old = 0
i Cu eq(6)
]_
Ou=¢u + ~u
F-
Oe eq(13)
no
I
¢L eq(12)
1
~bu eq(ll)
_1 v I
SF eq(lO)
yes
Fig. 5(b). Iterative loop for calculating the punch travel.
calculate the moment exerted by the punch on the sheet, i.e., eqn. (4); calculate the punch travel to achieve the required bend angle (eqns. (6-14)); reverse the punch when the set-value is achieved.
3.3.2. Compensationof experimental data The force and displacement experienced by the sheet differ from those exerted by the punch. The die half-width, L, effectively decreases as the sheet slides inwards over the radiused edges of the die. The vertical displacement, Ypm, is also reduced by the same phenomenon, see Fig. 6 (a). Friction between the sheet and the die reduces the downward force of the punch. The compensated punch force, Fpm, is given by:
L! t Fp=~ Fpm-t Ypm-½t L Fh
(2)
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Sheet ~ , ~
.
11 I
Centreline of sheet
/ OL
Yv
Fig. 6. (a) Effective die-width and punch-displacement as the sheet moves into the die; (b) geometry of the unsupported section of the sheet.
whilst the compensated punch displacement, Y,, is given by:
Y~=L~ gpm '
(3)
3.3.3. Calculation of the punch moment The material is fully characterized by the parameters a, h, and p in eqn. (1). The relationship between moment and curvature in terms of these 3 parameters is given by:
K=-~ 3a~+(2+p)h -~
(4)
When the sheet deforms plastically it wraps around the punch the arc of contact increasing with increasing load - so that the curvature under the punch is then determined by the radius of the punch, i.e., K
1
rp+t/2
(5)
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The moment exerted by the punch on the sheet is therefore a constant. Its value is obtained from eqn. (4) by iteration. First an approximate value is obtained by neglecting the elastic term which is relatively small:
L( KL2 ~"P M= \ ~ ] This approximation is then used to obtain an accurate value by iterating:
M,+,=L\(KL2-3aM,/L~ ~-~ / l:" i=1,2,3
. . . .
until convergence is obtained.
3.3.4. Calculation of punch travel The total punch travel, YPtargct, must be calculated by an iterative process because the geometry of the unsupported sheet in the free section between punch and die is not initially known, see Fig. 6(b). The iterative loop is shown in Fig. 5(b), where: ~u = 01-- Cu
(6)
~tL= ~tU( 1 3aMe(rp+t/2)) l LS
(7)
0L = ¢'c + ¢c
(8)
L - [(re + t/2)- 6L] sin ~hL--(rD + t/2) sin 0L
LF
COS ~/L \ ( p +2)(2p+ 1 ) / J
~bu
=
(2 +p) hSe (Me/LF (1 + p ) L 2
p SF /3aMe (2+p)h(Me/L)P'~ L ~-.~~-~ - k ---2~ "l~ )
(9)
(10) (11)
(12)
a_s~ Ye,.rgct=(rp+t/2)(1-cos CL)+ LFsin~L +6L COSCL+(rD +t/2)(1--COSCL)
(14)
3.4. Results and discussion The most distinctive characteristic of the type of IP system presented in this paper is the on-line characterization of each workpiece, and the resultant ability to compensate for minor variations in nominally uniform material.
S. Pickering/ Intelligent processing CONSTANT PUNCHSTROKE
INTELLIGENT CONTROL DEVIATION FROMMEAN ANGLE (DEGREES)
1.0
463
I I I I I I I I I I
I I I I I I I I I
0.8
0.6
-I4-
0.4
+ 0.2
+
•
0,0
•
4-
•
4-0.2
-0.4
-I-0.6
4-
-0.8
-I.0
8.00 PUNCH STROKE (mm) 7.95
O
7.90
O
O
O
O
O
O
O
O
O
7.85
7.80
I I I I I I I I I I
IIIIIIIIII
Fig. 7. Comparison of scatter in bend angle for intelligent control of punch stroke and for fixed punch stroke. Results for a 90 degree bend on 4 mm thick 316 stainless steel.
T h e r e s u l t s p r e s e n t e d h e r e i l l u s t r a t e t h e benefits t h a t c a n be o b t a i n e d by this approach. R e s u l t s are g i v e n in Fig. 7 for the s c a t t e r in b e n d a n g l e on 20 s u c c e s s i v e s p e c i m e n s of 4 m m t h i c k 316 s t a i n l e s s steel strip b e n t t h r o u g h 90 degrees. T h e s e s p e c i m e n s w e r e cut f r o m a single l e n g t h of strip a n d w e r e t h u s n o m i n a l l y
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identical. The first 10 specimens were bent using the IP control-system. The consequence of using IP control was that the punch stroke was different for each specimen to compensate for difference in properties between specimens and that the scatter band for the bend angle was 0.4 degrees wide. For the second series of 10 specimens the IP system was switched off and the press was run with a fixed punch stroke equal to the median stroke length used for the first 10 specimens. The resultant scatter band (dots) was about 3 times wider. For comparison, the process model was used to calculate what the scatter would have been on the first 10 specimens if the same fixed punch stroke would have been used. These calculated values are shown as crosses ( + ) and they fall within the same scatter band as the experimental results. The difference in the width of the two scatter bands is attributable to differences in mechanical properties between specimens. The process model described these properties in terms of the parameters a, h, and p of eqn. (1). Linear regression analysis of the plot of punch stroke against each of these parameters for the first l0 specimens revealed a significant correlation (r=0.94) with a, the elastic modulus, but only weak correlations with h (r = 0.45) and p ( r = 0.46). These correlations were taken to indicate that the main difference in properties between specimens and therefore the extra scatter under fixed stroke conditions compared with IP was attributable to differences in the elastic modulus. It was concluded that these experiments on stainless steel strip demonstrate that the IP control-system with variable punch stroke length reduced scatter in bend angle by a factor of 3 compared with a fixed punch stroke length. This improvement was attributed to the in-situ characterization of the material during processing. These results were obtained under conservative conditions, i.e., with nominally identical specimens: greater improvement might be expected using specimens of the same specification from different batches or from different suppliers. 4. C o n c l u s i o n s In this paper intelligent processing is presented as a form of on-line process control with the aim of steering the process towards a goal specified in terms of the properties of the processed material. This goal is achieved by the use of sensors to measure properties of the mateial during processing and process models to relate the materials properties to processing conditions. For a process with just one process variable, e.g., brake-forming, the process model, which is a physical description of the process, is sufficient for intelligent process-control. With more than one process variable, however, a functionally distinct software capability is also required in order to choose between different possible paths to the goal. This choice is based on a set of priorities external to the process model. Generally speaking, open-loop control requires a more physically realistic and accurate model of the process than does closed-loop control with rapid feed-back.
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So far the ability of I P s y s t e m s h a s b e e n d e m o n s t r a t e d o n l y for a limited n u m b e r of a p p l i c a t i o n s : t h e s e are m a i n l y m a t e r i a l s - s h a p i n g o p e r a t i o n s in w h i c h s e n s o r s for process v a r i a b l e s could be used to deduce m a t e r i a l s properties. T h e a v a i l a b i l i t y of s u i t a b l e s e n s o r s for m a t e r i a l s p r o p e r t i e s r e m a i n s a m a j o r o b s t a c l e for the w i d e r a p p l i c a t i o n of IP. T h e d e v e l o p m e n t of process models also tends to be v e r y t i m e - c o n s u m i n g a n d p r o b a b l y c o n s t i t u t e s the m o s t e x p e n s i v e i t e m of I P d e v e l o p m e n t s . T h e model is m o r e t h a n j u s t a m a t h e m a t i c a l d e s c r i p t i o n of the process: it is a d e s c r i p t i o n of the process in t e r m s of quantities t h a t c a n be m e a s u r e d w i t h the sensors t h a t are a v a i l a b l e , and this description m u s t be f o r m u l a t e d for r a p i d real-time response. M o r e o v e r , models seem to be process-specific so the o p p o r t u n i t i e s to benefit f r o m existing a p p l i c a t i o n s is limited. T h e c o m b i n e d c o n s t r a i n t s of s e n s o r a v a i l a b i l i t y and process-model d e v e l o p m e n t f a v o u r a p p l i c a t i o n to simple, slow process. N e v e r t h e l e s s , as d e m o n s t r a t e d in this p a p e r for the case of b r a k e - f o r m i n g , o n c e b o t h sensors and a p r o c e s s - c o n t r o l a l g o r i t h m a r e a v a i l a b l e , the implement a t i o n of i n t e l l i g e n t c o n t r o l c a n b r i n g c o n s i d e r a b l e benefits for a v e r y modera t e cost.
References [1] E. Siemer, P. Nieschwitz and R. Kopp, Quality optimized process control of open die forging, in: H.N.G. Wadley, P.A. Parish, B.B. Rath and S.M. Wolf (Eds.), Proc. Symp. on Intelligent Processing of Materials and Advanced Sensors, Orlando, Florida, The Metallurgical Society, 1986, pp. 157 170. [2] K.A. Stelson and D.C. Gossard, An adaptive pressbrake control using an elastic-plastic material model, J. Eng. Ind., 104 (1982) 389-393. [3] K.A. Stelson, Real time identification of workpiece material characteristics from measurements during brakeforming, J. Eng. Ind., 105 (1983) 45-53. [4] K.A. Stelson, An adaptive pressbrake control for strain hardening materials, J. Eng. Ind., 108 (1986) 127 132. [5] S. Kim and K.A. Stelson, Finite element method for the analysis of a material property identification algorithm for pressbrake bending, Trans. ASME, 115 (1988) 218- 222.
447
Elsevier
Intelligent processing of materials S. P i c k e r i n g
Institute for Advanced Materials, Joint Research Centre, Commission of the European Communities, P.O. Box 2, 1755 ZG Petten, The Netherlands (Received March 17, 1992; accepted July 7, 1992)
Industrial S u m m a r y This paper reviews the background to intelligent processing and presents a case study to illustrate the benefits achievable through intelligent process-control. Intelligent processing is most suitable for processes where the reasons for the failure of conventional processcontrol are well understood, where suitable sensors are available, and where a certain degree of maturity has been reached so that the process is unlikely to change drastically or to be supplanted. Materials-shaping processes meet these criteria best, and brake-forming provides an excellent example. The implementation of intelligent processing to brake-forming, based on an analysis of the process developed by Stelson, is demonstrated successfully in this paper.
Notations rp rD L t
Y. Fp aFp h(Fp)" L' 0 r
punch radius die radius half width of die sheet thickness punch travel punch force represents elastic deformation represents power-law strain-hardening e f f e c t i v e d i e h a l f - w i d t h = L - rD s i n 0 f l a n k a n g l e = s i n - 1 [(r/L) c o s fl] +/? t a n -1 [ - - ( r - - Ypm)/L]
rp+t+ro
Correspondence to: Dr. S. Pickering, Institute for Advanced Materials, Joint Research Centre, Commission of the European Communities, P.O. Box 2, 1755 ZG Petten, The Netherlands. 0924-0136/93/$06.00 © 1993 Elsevier Science Publishers B.V. All rights reserved.
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H.
Pickeril~g/lntelligentprocessing
measured punch force measured punch displacement effective sheet displacement = Y~m- rD (1 -- COS0) horizontal force component including friction
F pm
Y'p.~ Fh
{ sin O- p cos O~ coefficient of friction between sheet and die sheet curvature punch moment wrap-around angle under the punch, unloaded condition wrap-around angle under the punch, loaded condition flank angle, unloaded condition = OT flank angle, loaded target flank angle free angle, unloaded free angle, loaded beam span of the free section arc length of the free section deflection of the free section Moment under the punch
K M
~u ~L Ou OL Ot LF ~L M~
1. I n t r o d u c t i o n
The term "intelligent processing" (IP) refers to an innovation in process control made possible by recent advances in computer- and sensor-technology. Process control basically comprises comparing sensor signals with set-point values and adjusting actuator signals accordingly. In terms of control circuitry, if conventional processing (CP) may be represented as a single controlloop (see Fig. 1) then intelligent control circuitry is characterized by a loop
H~te~ia)s Str~cture s~c~i~¢ation
.............i _ n
~-~
I
Process
(a)
.........
I
t
~ate~iat
~.-
~
Sensor
Material
~
.IA
i Mater,als ~
Proces~cu
(b)
Fig. 1. Schematic control circuits for conventional (a) and intelligent (b) processing.
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449
within a loop. Conventional process-control is concerned exclusively with process variables, e.g., temperature, pressure, etc: the sensors measure process variables and the set-points are pre-determined values of these variables. In contrast, IP is concerned with the control of material variables. The inner control-loop is the same as the conventional processing-loop and the two extra components that are indispensable for IP, namely, materials-property sensors and process models, are located in the outer loop. The purpose of the outer control-loop is to continuously adjust the process variable set-points for the inner control-loop in response to signals from the material sensors. This adjustment is effected by the process model by comparing signals from the materials-property sensors with its own set-points: these set-points are effectively the specification for the processed material. The process model thus constitutes an "intelligent" core which, by relating materials properties to processing conditions, can run the process without pre-determined processing conditions. IP is therefore distinguished from conventional processing not only in the technology used but also in its objective, which is to actively steer the process towards a goal defined in terms of the properties desired for the processed material. The active steering of the process motivates use of the adjective "intelligent". To steer the process towards its goal, relevant material properties are monitored continuously during processing in a way that amounts to in-situ quality assurance. Intelligent processing therefore achieves quality by making the product correctly in the first place rather than by the conventional practice of rejecting defective parts on the basis of inspection and testing; a method which is not only wasteful but unreliable. The purpose of this paper is to present the application of intelligent processing to brake-forming to illustrate the benefits available through intelligent process-control. To this end the principles of intelligent processing are first reviewed. The specific application including the control algorithm is then presented. Finally, conclusions are drawn on both the problem areas, and on the benefits, offered by intelligent processing.
2. The principles of intelligent processing 2.1. Strategies of process control 2.1.1. Limitations of conventional process-control Process control is the key to maximizing the quality of processed material and the reason why materials-processing techniques routinely fail to yield the quality of material of which they are inherently capable is that conventional process control is inadequate. In fact, conventional control-methods are intrinsically unsuited for obtaining optimum results in materials processing and a different approach to process control is required.
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The root of the problem with conventional process-control is that it represents an essentially deterministic view of the process, which in turn encourages a confrontational approach to problems. According to this approach, wellcharacterized input materials yield a reproducible and predictable product when subjected to well-controlled, pre-determined process conditions. Variation in the properties of the product is ascribed to random- or hiddenvariables. Therefore, to reduce variations in product properties, random variables must be identified and eliminated, and hidden variables must be discovered and controlled. Unfortunately, in many cases such a head-on approach can be technically diificult, time-consuming, and expensive. Batch-to-batch variations in the starting material is a typical case of a variable t hat is difficult to c o u n t e r a c t using conventional process-control. Intelligent process-control has emerged as an alternative control-strategy which side-steps r a t h e r t ha n confronts the problems associated with a full conventional control-strategy.
2.1.2. Basic ideas of intelligent control Intelligent processing assumes at the outset that the parameters t hat lead to product variability are either too numerous, too difficult, or too expensive to bring under adequate control. Instead, the product is improved by intervening actively via a few first-order process variables, the influence of which on the material is well-understood, in order to count er a ct the influence of the secondorder variables which are either not controlled at all or are only controlled within easily achievable limits. To intervene meaningfully in this way, information must be available on, firstly, the cur r ent state of the material and, secondly, on the way the material responds to changes in the process variables. The first requirement is met by an extra set of sensors to monitor the material properties continuously during processing. The second requirement is met by an on-line computer model of the process to calculate appropriate values for process variables to steer the process towards a goal set in terms of material parameters.
2.2. Optimization through intelligent processing The success of a materials-processing operation depends both on the scientific and technical concepts underlying it and on the degree of control exerted over it. The primary reason for using computer models in intelligent processing is to optimize control over the process. Appropriate computer models, however, can be used also as a creative tool to optimize the process. The benefits to be gained from such an integrated approach of optimizing both the process and the control over it would seem evident. W het her such an integrated approach is considered central to the concept of IP or merely as a useful spin-off is a m a t t er of definition. Nevertheless, it should be remembered t hat any progress made along these lines c a nnot be a substitute for the development of new and better processing techniques.
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2.2.1. Optimization of control As outlined above, the motivation for turning away from conventional process-control is t h a t hidden variables are too laborious to uncover and bring under control. Turning to IP, however, does not automatically guarantee success: certain general conditions must be fulfilled. The most basic condition, one t h a t was taken for granted in the introduction, is that the technical aspects that are common on both CP and IP are not already a limiting factor in CP. Explicitly, this means that the precision and response time of sensors available for measuring process variables, and of the actuators for controlling them, must be adequate. If this is not the case then neither CP nor IP can be successful, and the only possible approach, apart from developing better sensors or actuators, is to make technical improvements to the process which will make it easier to control. The above condition is necessary for maintaining process variables at pre-selected values, but it may not be sufficient for active control. The possibility exists that actively steering the process against random- or hiddenvariables may make greater demands on sensors and actuators (e.g., response time, spatial resolution) than is required to maintain constant conditions. However, although it is known that, for example, the sensor/actuator must always act faster than fluctuations in the variable, it cannot necessarily be specified what this response time should be in an IP system. It is precisely because hidden variables are hidden, and because IP makes no attempt to uncover them, that the success of an IP system cannot be guaranteed. Unfortunately, this type of problem will be manifest only as a non-specific failure of the IP system to yield a better product. Fortunately, only certain types of low inertia or resonant processes-variables are likely to be troublesome in this respect, e.g., gas dynamic instabilities in coating processes. As experience is acquired, no doubt guidelines will be developed on the requirements for the control of various types of process variable. Thus, despite the promise that intelligent control holds for circumventing many traditional control-problems, it should 'nevertheless be born in mind that some process development may be necessary to realize these advantages.
2.2.2. Optimization of the process The intensive use of computers to model the process offers possibilities for improving the process. Whether a model can be used to optimize the process, however, depends on whether the model is empirical, or on whether it is an accurate physical and chemical description of the phenomena involved in the process. In the case of physically realistic models, the modelling abilities of an intelligent processing system may be used as a creative tool to optimize processes, and thus to improve materials, to levels beyond those achievable, even in theory, by conventional processing. In addition, there is the possibility that the ability to counteract unknown variables can be used to relax certain process conditions, e.g., quality of input materials, thereby achieving direct
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economic gains. Ultimately, intelligent processing may be expected to be integrated into computer-aided engineering of components. 2.3. Process models Process models are computer programs that relate materials properties to processing conditions. For process control they are formulated in terms of the measurable parameters (sensor inputs) using algorithms fast enough for online response. Models which describe the process in terms of parameters which are not accessible to sensor measurement or which use computationally intensive methods, e.g., finite-element calculations, are not suitable for on-line control but may be used for optimizing the process. 2.3.1. Models for process control The process models used in IP relate the way the material structure reacts to changes in process variables. Simply stated, the model receives signals from the material sensors, it calculates appropriate set-point values for process variables, and it sends these values to the (conventional) process controller to complete the control loop. This calculation occurs within a window of constraints imposed by: (a) equipment, which is of course restricted to a limited range of process variables; and (b) by economic considerations, such as minimization of process time. To steer a process against unpredictable fluctuations, a minimum product of accuracy of prediction and response time is required of the model. Within limits, either a crude and fast model or an accurate but slow model may be equally satisfactory. Because model development can be very time-consuming, and because a highly detailed knowledge of the process is contrary to the philosophy of IP, the starting point should be a simple model, providing closed-loop control is possible. Even empirical models can be satisfactory for the process control aspects of IP. An example of this is in hot forging [1] where the amount of deformation produced depends very sensitively on material temperature. In this application, measurement of the deformation produced by each hammer blow is measured and this information is used as the input to calculate the strength of the next blow. This empirical approach to IP in forging has the merits of not requiring an excessively large data base of material properties or of complicated algorithms to try to calculate temperature profile within the workpiece from surface temperature measurements. This example makes clear that IP is not simply the brute-force application of knowledge but that it depends critically on an input of natural intelligence. 2.3.2. External constraints for process-control models Even the most complex process-model is functionally equivalent to just a single equation in that it provides a unique output only if there is just one unknown. For simple processes such as brake-forming, where just one process variable is calculated, i.e., the length of the punch stroke, this is precisely what
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is required. For processes with more than one variable, however, the problem is faced that the process model can produce a multi-dimensional domain of possible process-conditions rather t h a n a unique set of values. For practical control a single set of values needs to be decided upon within the domain of possible values, i.e., a specific route to the goal must be selected from a range of possible routes. By definition, this decision cannot be based on physical or technical parameters within the model, but must be based on an externally imposed order of priorities. An example of such a decision is the choice of the time/temperature combination for a thermally-activated process. Whether a short-time/hightemperature treatment is chosen instead of a long-time/low-temperature treatment depends on an externally imposed hierarchy of priorities such as desired throughput rate, energy consumption, furnace life, etc., i.e., it depends on those factors which constitute the economic boundary-conditions within which the process is operated. The process model can thus be regarded as an invariant core of functional dependencies which operates within a transitory set of economic boundaryconditions. Artificial intelligence, or other rule-based software tools such as expert systems, which are not directly involved in the modelling of the physical and chemical phenomena of the process, are suitable for enforcing these boundary conditions. The external boundary-conditions are needed therefore not only to ensure that the model is applied to produce the best result judged according to external constraints, most of which are ultimately of an economic nature, but are needed for the process model to function at all.
2.3.3. Models for process optimization The disadvantage of empirical process-control models is that they contain little physics or chemistry, and therefore offer little insight into how a process may be improved. By analogy, a furnace controller with the empirical algorithm of: power on at temperatures below the set-point, and power off at higher temperatures (with the refinement of PID control); is sufficient to control any furnace but it offers no insight into how to build a better furnace. Yet the creative use of models is an important aspect of IP and only a quantitative model of all first-order physical- and chemical-phenomena will be of use for this purpose. A quantitative model is likely to run more slowly than an empirical model, and the on-line response time may be too slow to control the process. This will certainly be true of models which depend on computationally intensive techniques such as finite-element analysis or which model gas dynamics. Two ways of dealing with these computationally-intensive aspects of modelling are first, to use them only off-line in the initial design phase for the process, or second, to make results from such calculations available on-line to the control program from a data bank.
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Thus, it may prove best in practice to partially separate the functions of IP into process optimization, and active process control, based on 2 sets of models. Once this is done the practical constraint for the control model that it must be formulated to describe the process in terms of those parameters that will be measured by the sensors may be dropped. This separation of functions also suggests that, in practice, the fastest way to apply an IP system to an existing process would be to start with a simple empirical model. Empirical elements in the model can then be replaced progressively by physically realistic elements to gradually upgrade the model until it becomes a useful tool for optimizing the process.
2.4. Sensors Ideally a sensor should be a relatively inexpensive and robust piece of equipment th at measures relevant material properties directly, or if t hat is not possible, one that measures a related property from which the required property can be derived using an algorithm in a computer model of the process. However, the range of sensors that fall into the cheap and robust category is very restricted and is limited to measurement of typical process parameters such as temperature and pressure r a t h e r t han materials parameters such as grain size or chemical composition. The choice of sensors, as opposed to analytical instruments, for material microstructure is very restricted compared with sensors for process parameters. This restricted choice of sensor stems both from the fact that microstructure sensor development, e.g., ultrasonic measurement, is a new field (related to non-destructive evaluation) and from the fact that the measurements are intrinsically difficult to make. It is n o tewo r th y that successful examples of IP are related to shaping operations, i.e., bar forging, ring rolling and brake-forming, where it is the shape and size of the workpiece t ha t are measured and that this is done with conventional sensor technology. 2.5. Areas of application Areas of application for IP are decided according to three basic criteria: expected benefit, sensors, and models. Firstly, the short-fall between current practice and theory must be sufficient to justify the effort required to implement IP, and the reasons for the failure of CP to perform satisfactorily must be understood in order to estimate the amount of improvement expected from IP. Secondly, because the model must be formulated in terms of the measurable parameters, sensor characteristics are the starting point for model development. In practice the sensor criteria are very simple: either suitable sensors are available or they are not. Thirdly, each process requires unfort unat el y its own model and model development tends to be lengthy. IP therefore tends to be most suitable for processes t hat have reached a certain degree of maturity and are unlikely to change very much or to be supplanted. At present, the class of
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process that best meets these criteria is materials shaping. For most other processes suitable sensors are simply not yet available. 3. A p p l i c a t i o n o f i n t e l l i g e n t c o n t r o l t o b r a k e - f o r m i n g
3.1. Why brake-forming is suitable for intelligent control Brake-forming is one of the most common and simplest of all metal-forming processes. It is a process for making straight-line bends in sheet or strip by forcing the metal into a concave die using a punch, as shown in Fig. 2. Setting up a press to bend a sheet to a given angle tends to be a trial-and-error process, due to elastic spring-back of the sheet on removing the load. Reproducibility can also be poor because of batch-to-batch variation in the properties of nominally identical material. For example, the yield point of hot-rolled steel strip habitually varies by up to 20%. The amount of spring-back after bending therefore varies, thus affecting the reproducibility of the bend angle. Brakeforming is thus generally regarded as a simple and cheap but imprecise process. If a method could be found to compensate for the elastic spring-back of the sheet, then brake-forming could be transformed into a precise process. This objective can be achieved by the application of the concepts of intelligent processing. The two major requirements of IP are: (1) a process model; and (2) materials sensors; both of which can be met. The process model is based on: (1) a complete mathematical description of brakeforming [2-5]; and (2) on a mathematical description (constitutive law) of the elastic and strain-hardening properties of the sheet metal. Sensors are required to measure the linear displacement of the punch and the force it exerts on the workpiece. These requirements are easily met from the range of commercially available instruments. The basic idea of adding sensors and a computer is to upgrade a simple hydraulic press into a combination of a materials-characterization rig and
Punch
~
~
Sheet
e
Fig. 2. Schematic brake-forming operation.
St Pickering/Intelligent processb~g
456
a processing rig in which each piece of metal is characterized while it is being bent. This real-time, in-situ characterization (elastic modulus, elastic limit, strain-hardening coefficients) is fully completed during the early stages of the bend so that the amount of spring-back, and the appropriate ram displacement to compensate for it, is calculated well in advance of the ram reaching the required position, i.e., an open control loop with one process variable is used.
3.2. Experimental 3.2.1. Equipment The experimental set-up is shown in Fig. 3. An existing 50 ton hydraulic press with a stationary upper platen and a moving lower platen was equipped with a linear displacement transducer to measure the displacement of the lower platen with respect to the frame. The punch was mounted on the upper platen and a load cell was installed under the die on the lower platen. After calibration against the load cell, the applied force could also be derived satisfactorily from the pressure in the hydraulic system. A die width of 20 mm and punch and die radii of 2 mm were chosen: this geometry permitted sheets of up to 5 mm thickness to be bent with internal angles in the range 70-120 degrees. The outputs from the sensors were connected to a 12-bit analogue-digital converter (ADC) and the manual press controls were bypassed by means of power relays operated remotely via signal relays. The connection between the control electronics and the computer was made using the Pheonix InterBus-S field bus: this consists of a card inserted in the PC and a cable to a local bus station adjacent to the ADC and relay modules. A 16 MHz 386 PC was used which also ran the process model software. The control exerted over the press was a simple sequence of: ram forward/stop/reverse.
field bus
(
(300kbits/s)
LI lu,
Press ~s: Control ~t
Unit it
linear displac~nt transducerj load cell Hydraulic Press
Fig. 3. Hardware for intelligent control of brake-forming.
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3.2.2. System characteristics The mode of operation is to set the press in motion, record data, calculate the ram reversal-point using the process model, and then simply monitor ram position until the target displacement is reached. This is an open control loop: the system acquires the data it needs, it makes a prediction (punch displacement) and then waits for t hat condition to be met. In contrast, a more conventional feedback control loop would require the ram to be reversed repeatedly to measure the bend angle in the unloaded condition: this would amount to no more t h an automated trial-and-error. The use of an open control loop, however, requires a highly accurate process model and constitutive law. A ram displacement of 4 mm was required to characterize both the elastic and plastic ranges of the material for the chosen die geometry. Data were recorded every 0.05 mm giving a total of 80 data points. Calculation of the target ram position took about 0.2 s corresponding to a further 1 mm of ram travel. The ram could therefore only be controlled at displacements greater t h a n 5 mm. A limit switch at 10 mm prevented the ram making hard contact with the die, in order to avoid overloading the load cell (maximum load 50 kN). The control space corresponding to these conditions is shown in Fig. 4 for a 4 mm thick strip of 316 stainless steel. The widest bend angle is about 120°, mainly due to the necessity of recording data over the first 4 mm of punch travel. By comparison, the calculation time is a relatively small overhead t hat could be reduced further by using a faster PC. Both the software for controlling the hardware and t h a t for the process model was written in QuickBasic. The process model algorithm is described in Section 3.3.1.
Punch
displacement (mm)
12
Time (s) (software limit switch at I0 mm)
I0
Z.O
1.5
Calculation ( 1.0
0.5
0
0.0 180
160
140
120
I00
80
Bend angle (degrees}
Fig. 4. The control space for the brake-forming of 4 mm thick 316 stainless steel.
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Table 1 The error in a 90' bend resulting from a + 1% error in the listed parameter Parameter
Error in bend angle (%)
die width, L punch radius, rf, die radius, re sheet thickness, t friction coefficient, p
+ 1.2486 --0.1128 -0.1651 -0.2761 + 0.0002
The accu r acy of the bend angle depends on errors in geometry and friction, as shown in Table 1.
3.3. Press-brake control algorithm 3.3.1. Outline of the algorithm An algorithm developed by Stelson et al. [2-5] was used. The objective is to calculate how far the punch must travel in order to bend the metal through the chosen angle, 0T, taking into account the elastic spring-back of the sheet on unloading. For this calculation the dimensions of the sheet, the punch, and the die are required together with the coefficient of friction between the sheet and the die. It is necessary to know also the mechanical properties of the sheet. These are determined while the sheet is being bent and are derived from measurements of the punch force and displacement. The sheet is assumed to bend by a combination of elastic plus strain hardening plastic deformations. Three parameters a, h, and p suffice to characterize this deformation:
Y, = aF. + h (F.)~
(1)
To derive a bend angle for the sheet from the linear travel of the punch, the force-displacement relationship for the punch is converted to a moment-curv a t u re relationship for the sheet. The complete m o m e n t - c u r v a t u r e relationship can be established during the early stages of the bend so that the total punch travel required for the desired bend angle can be calculated in advance of the actual motion of the punch. The punch position is then monitored continuously until the calculated position is reached whereupon the hydraulic ram is reversed. The steps in this brake-forming process (see also the flow chart in Fig. 5) are: set the press in motion; briefly record force-displacement data for the punch; compensate the experimental data for geometric factors, i.e., eqns. (2) and (3); fit the compensated data to a linear plus power law model, i.e., eqn. (1);
S. P i c k e r i n g / Intelligent processing
read default values: t, L, rr, ro, I~, 0r
1
I_ read sensors: Fp, Yp F
no
i
yes
I correct data: eqs(2,3) I [ data fit: a, h, p eq(1) [ [
calcMp:eq(4)I
[
read sensor: Yt
1
LI-nO
yes
I
s,o~o~
I
I
save data to disc
]
Fig. 5(a). Flow chart for the press control program.
459
460
S, P i c k e r i n g / l n t e l l i g e n t
processing
a, h, p, Mp, t, L, re, rD, O,r
(to be obtained by iteration)
(bu = ~bL = OL = LF,old = 0
i Cu eq(6)
]_
Ou=¢u + ~u
F-
Oe eq(13)
no
I
¢L eq(12)
1
~bu eq(ll)
_1 v I
SF eq(lO)
yes
Fig. 5(b). Iterative loop for calculating the punch travel.
calculate the moment exerted by the punch on the sheet, i.e., eqn. (4); calculate the punch travel to achieve the required bend angle (eqns. (6-14)); reverse the punch when the set-value is achieved.
3.3.2. Compensationof experimental data The force and displacement experienced by the sheet differ from those exerted by the punch. The die half-width, L, effectively decreases as the sheet slides inwards over the radiused edges of the die. The vertical displacement, Ypm, is also reduced by the same phenomenon, see Fig. 6 (a). Friction between the sheet and the die reduces the downward force of the punch. The compensated punch force, Fpm, is given by:
L! t Fp=~ Fpm-t Ypm-½t L Fh
(2)
S. Pickering/Intelligentprocessing |
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Sheet ~ , ~
.
11 I
Centreline of sheet
/ OL
Yv
Fig. 6. (a) Effective die-width and punch-displacement as the sheet moves into the die; (b) geometry of the unsupported section of the sheet.
whilst the compensated punch displacement, Y,, is given by:
Y~=L~ gpm '
(3)
3.3.3. Calculation of the punch moment The material is fully characterized by the parameters a, h, and p in eqn. (1). The relationship between moment and curvature in terms of these 3 parameters is given by:
K=-~ 3a~+(2+p)h -~
(4)
When the sheet deforms plastically it wraps around the punch the arc of contact increasing with increasing load - so that the curvature under the punch is then determined by the radius of the punch, i.e., K
1
rp+t/2
(5)
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462
The moment exerted by the punch on the sheet is therefore a constant. Its value is obtained from eqn. (4) by iteration. First an approximate value is obtained by neglecting the elastic term which is relatively small:
L( KL2 ~"P M= \ ~ ] This approximation is then used to obtain an accurate value by iterating:
M,+,=L\(KL2-3aM,/L~ ~-~ / l:" i=1,2,3
. . . .
until convergence is obtained.
3.3.4. Calculation of punch travel The total punch travel, YPtargct, must be calculated by an iterative process because the geometry of the unsupported sheet in the free section between punch and die is not initially known, see Fig. 6(b). The iterative loop is shown in Fig. 5(b), where: ~u = 01-- Cu
(6)
~tL= ~tU( 1 3aMe(rp+t/2)) l LS
(7)
0L = ¢'c + ¢c
(8)
L - [(re + t/2)- 6L] sin ~hL--(rD + t/2) sin 0L
LF
COS ~/L \ ( p +2)(2p+ 1 ) / J
~bu
=
(2 +p) hSe (Me/LF (1 + p ) L 2
p SF /3aMe (2+p)h(Me/L)P'~ L ~-.~~-~ - k ---2~ "l~ )
(9)
(10) (11)
(12)
a_s~ Ye,.rgct=(rp+t/2)(1-cos CL)+ LFsin~L +6L COSCL+(rD +t/2)(1--COSCL)
(14)
3.4. Results and discussion The most distinctive characteristic of the type of IP system presented in this paper is the on-line characterization of each workpiece, and the resultant ability to compensate for minor variations in nominally uniform material.
S. Pickering/ Intelligent processing CONSTANT PUNCHSTROKE
INTELLIGENT CONTROL DEVIATION FROMMEAN ANGLE (DEGREES)
1.0
463
I I I I I I I I I I
I I I I I I I I I
0.8
0.6
-I4-
0.4
+ 0.2
+
•
0,0
•
4-
•
4-0.2
-0.4
-I-0.6
4-
-0.8
-I.0
8.00 PUNCH STROKE (mm) 7.95
O
7.90
O
O
O
O
O
O
O
O
O
7.85
7.80
I I I I I I I I I I
IIIIIIIIII
Fig. 7. Comparison of scatter in bend angle for intelligent control of punch stroke and for fixed punch stroke. Results for a 90 degree bend on 4 mm thick 316 stainless steel.
T h e r e s u l t s p r e s e n t e d h e r e i l l u s t r a t e t h e benefits t h a t c a n be o b t a i n e d by this approach. R e s u l t s are g i v e n in Fig. 7 for the s c a t t e r in b e n d a n g l e on 20 s u c c e s s i v e s p e c i m e n s of 4 m m t h i c k 316 s t a i n l e s s steel strip b e n t t h r o u g h 90 degrees. T h e s e s p e c i m e n s w e r e cut f r o m a single l e n g t h of strip a n d w e r e t h u s n o m i n a l l y
464
,% Pwkcring/lntelliget~! processit~g
identical. The first 10 specimens were bent using the IP control-system. The consequence of using IP control was that the punch stroke was different for each specimen to compensate for difference in properties between specimens and that the scatter band for the bend angle was 0.4 degrees wide. For the second series of 10 specimens the IP system was switched off and the press was run with a fixed punch stroke equal to the median stroke length used for the first 10 specimens. The resultant scatter band (dots) was about 3 times wider. For comparison, the process model was used to calculate what the scatter would have been on the first 10 specimens if the same fixed punch stroke would have been used. These calculated values are shown as crosses ( + ) and they fall within the same scatter band as the experimental results. The difference in the width of the two scatter bands is attributable to differences in mechanical properties between specimens. The process model described these properties in terms of the parameters a, h, and p of eqn. (1). Linear regression analysis of the plot of punch stroke against each of these parameters for the first l0 specimens revealed a significant correlation (r=0.94) with a, the elastic modulus, but only weak correlations with h (r = 0.45) and p ( r = 0.46). These correlations were taken to indicate that the main difference in properties between specimens and therefore the extra scatter under fixed stroke conditions compared with IP was attributable to differences in the elastic modulus. It was concluded that these experiments on stainless steel strip demonstrate that the IP control-system with variable punch stroke length reduced scatter in bend angle by a factor of 3 compared with a fixed punch stroke length. This improvement was attributed to the in-situ characterization of the material during processing. These results were obtained under conservative conditions, i.e., with nominally identical specimens: greater improvement might be expected using specimens of the same specification from different batches or from different suppliers. 4. C o n c l u s i o n s In this paper intelligent processing is presented as a form of on-line process control with the aim of steering the process towards a goal specified in terms of the properties of the processed material. This goal is achieved by the use of sensors to measure properties of the mateial during processing and process models to relate the materials properties to processing conditions. For a process with just one process variable, e.g., brake-forming, the process model, which is a physical description of the process, is sufficient for intelligent process-control. With more than one process variable, however, a functionally distinct software capability is also required in order to choose between different possible paths to the goal. This choice is based on a set of priorities external to the process model. Generally speaking, open-loop control requires a more physically realistic and accurate model of the process than does closed-loop control with rapid feed-back.
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So far the ability of I P s y s t e m s h a s b e e n d e m o n s t r a t e d o n l y for a limited n u m b e r of a p p l i c a t i o n s : t h e s e are m a i n l y m a t e r i a l s - s h a p i n g o p e r a t i o n s in w h i c h s e n s o r s for process v a r i a b l e s could be used to deduce m a t e r i a l s properties. T h e a v a i l a b i l i t y of s u i t a b l e s e n s o r s for m a t e r i a l s p r o p e r t i e s r e m a i n s a m a j o r o b s t a c l e for the w i d e r a p p l i c a t i o n of IP. T h e d e v e l o p m e n t of process models also tends to be v e r y t i m e - c o n s u m i n g a n d p r o b a b l y c o n s t i t u t e s the m o s t e x p e n s i v e i t e m of I P d e v e l o p m e n t s . T h e model is m o r e t h a n j u s t a m a t h e m a t i c a l d e s c r i p t i o n of the process: it is a d e s c r i p t i o n of the process in t e r m s of quantities t h a t c a n be m e a s u r e d w i t h the sensors t h a t are a v a i l a b l e , and this description m u s t be f o r m u l a t e d for r a p i d real-time response. M o r e o v e r , models seem to be process-specific so the o p p o r t u n i t i e s to benefit f r o m existing a p p l i c a t i o n s is limited. T h e c o m b i n e d c o n s t r a i n t s of s e n s o r a v a i l a b i l i t y and process-model d e v e l o p m e n t f a v o u r a p p l i c a t i o n to simple, slow process. N e v e r t h e l e s s , as d e m o n s t r a t e d in this p a p e r for the case of b r a k e - f o r m i n g , o n c e b o t h sensors and a p r o c e s s - c o n t r o l a l g o r i t h m a r e a v a i l a b l e , the implement a t i o n of i n t e l l i g e n t c o n t r o l c a n b r i n g c o n s i d e r a b l e benefits for a v e r y modera t e cost.
References [1] E. Siemer, P. Nieschwitz and R. Kopp, Quality optimized process control of open die forging, in: H.N.G. Wadley, P.A. Parish, B.B. Rath and S.M. Wolf (Eds.), Proc. Symp. on Intelligent Processing of Materials and Advanced Sensors, Orlando, Florida, The Metallurgical Society, 1986, pp. 157 170. [2] K.A. Stelson and D.C. Gossard, An adaptive pressbrake control using an elastic-plastic material model, J. Eng. Ind., 104 (1982) 389-393. [3] K.A. Stelson, Real time identification of workpiece material characteristics from measurements during brakeforming, J. Eng. Ind., 105 (1983) 45-53. [4] K.A. Stelson, An adaptive pressbrake control for strain hardening materials, J. Eng. Ind., 108 (1986) 127 132. [5] S. Kim and K.A. Stelson, Finite element method for the analysis of a material property identification algorithm for pressbrake bending, Trans. ASME, 115 (1988) 218- 222.