A neural network thrust force controller to minimize delamination during drilling of graphite-epoxy laminates

Inl, J. Mach. Tools Manure. Vol. 36, No. 9. pp. 985-1003, 1996 Copyright 0 1996 Publirdled by Elsevier Science Ltd Printed in Great Britain. All righB reserved 0890--6955/96515.00 + .00

Pergamon

PII: S0890-6955(96)00013-2

A NEURAL NETWORK THRUST FORCE CONTROLLER TO MINIMIZE DELAMINATION DURING DRILLING OF GRAPHITEEPOXY LAMINATES R. STONE'I" and K. KRISHNAMURTHY*t (Original received 3 July 1995; in final form 3 January 1996)

Abstract--Delamination is a well-recognized problem associated with drilling fiber-reinforced composite materials (FRCMs). The most noted problems occur as the drill enters and exits the FRCM. Since drilling is often a final operation during assembly, any defects introduced in parts through the drilling process that result in the part being rejected represent an expensive loss. Studies based on linear-elastic fracture mechanics theory have proposed critical cutting and thrust forces in the various drilling regions that can be used as a guide in preventing crack growth or delamination. Using these critical force curves as a guide, a thrust force controller was developed to minimize the delamination while drilling a graphite-epoxy laminate. A neural network control scheme was implemented which required a neural network identifier to model the drilling dynamics and a neural network controller to learn the relationship between feed rate and the desired thrust force. Experimental results verifying the validity of this control approach as well as the robustness of the design are presented. Visual measurements of the delamination zones were used to quantify the benefits of the thrust force controlled drilling process versus the conventional constant feed rate drilling process. Copyright © 1996 Published by Elsevier Science Lid

NOMENCLATURE E J(.) F^, F^. c,~,

modulus of elasticity function thrust and critical thrust forces Fc, Fc.... tangential and critical tangential cutting forces Fp peeling force F:. c,t, F:. ,~,~,~,critical. desired and maximum thrust forces gz. tru~

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critical energy release rate for delamination (mode I fracture) depth of uncut material under the drill thickness of the composite laminate parameter relating Fc and Fp helix angle at the drill tip coefficient of friction between the drill and work piece Poisson's ratio I.

INTRODUCTION

Composite materials are well known for their superior mechanical properties in certain orientations. However, due to their anisotropic, inhomogeneous and abrasive nature, machining composites produces challenges not encountered in machining conventional materials. Excessive tool wear, poor machined surface finishes and separation of one ply from another (known as delamination), are common problems Ill. These problems are a result of cutting extremely stiff fibers encased in a relatively pliable matrix. Instead of the fibers being sheared as the tool cutting surface makes contact, the fibers may be pulled out of the surrounding matrix because of a shear failure of the matrix. This action is known as fiber pull out and adversely affects surface finish while creating a delamination zone. Any delamination is highly undesirable as it creates a surface flaw and a potential point of origin for failure. The need to reduce delamination or avoid it altogether is great, since machining is

*Author to whom correspondence should be addressed. tDepartment of Mechanical and Aerospace Engineering and Engineering Mechanics. University of MissouriRolla, Rolla, 65409-0050, Missouri. U.S.A. 985

986

R. Stone and K. Krishnamurthy

necessary to provide a means of assembly. Drilling in particular becomes important in the manufacturing process. Unfortunately, drilling often results in delamination around the drill hole site. In the aircraft industry, for example, drilling-associated delamination accounts for 60% of all part rejections during final assembly of an aircraft [2]. The economic impact of this is significant considering the value associated with the part when it reaches the assembly stage. Additionally, conventional drills including carbide tipped tools exhibit excessive tool wear. To overcome the wear problem associated with conventional tools, diamond tipped tools have been developed and show superior cutting ability and tool life. The major difficulty encountered in designing a control system for drilling is that the dynamics of the drilling process are not fully understood and therefore cannot be accurately modeled mathematically [3]. Empirical models exist which relate feed rate to thrust force and torque for various materials and tool geometries. These models of the drilling process offer a convenient form, but have coefficients that themselves are variables of the work piece material, cutting conditions and tool wear. Such empirical models are not easily formed for composite materials due to the anisotropic nature of the laminate and that a large number of parameters influence the material properties. Along with conventional drilling, other nontraditional machining techniques have been applied to composite materials. Abrasive water jets have been successfully used in cutting slots and piercing holes in metal and ceramic composites; however, surface defects were noted and the machined edges were not knife-edge smooth. Thin graphite--epoxy laminates (<2.54 mm) machined at slow cutting speeds (<25.4 mm s-1) with an abrasive water jet show acceptable edge conditions. Delamination, however, becomes unacceptable at greater thicknesses and at faster cutting speeds [4, 5]. Laser machining, practically an industry standard now, has been applied to composite materials. Through a highly focused beam of light 0.1 mm in diameter with power in excess of 10s W cm -2, lasers cut by locally vaporizing material, leaving behind a minimally affected heat zone. The Nd:YAG (neodymium/yttrium-aluminum-garnet) laser has proved effective for machining metallic composites while CO2 lasers are best for cutting materials containing an organic resin (matrix) [4]. The obvious advantage of the aforementioned nontraditional machining methods is the absence of excessive tool wear. This enables these methods to be automated. Unfortunately disadvantages also exist. Nontraditional methods often cannot produce the same shape changes as traditional (milling, drilling, turning, etc.) methods. Furthermore, as composites achieve widespread use outside the aerospace and defense industries, less expensive means of machining are required. The objective of this study was to minimize the delamination associated with drilling in composite materials with thickness on the order of the diameter of the desired hole. A thrust force controller was used to accomplish this objective. A neural network control scheme was chosen since a dynamic model of the drilling process could not easily be obtained. The neural network control scheme consisted of a neural identifier which modeled the drilling dynamics and a neural controller which specified the feed rate for a desired thrust force. Recurrent neural networks were used for both the neural identifier and controller. This study extended a neural network force controller previously developed for end milling operations of conventional materials to the drilling of composite laminates [6]. 2. PREVIOUSRESEARCH

2.1. Drilling in composites Difficulties associated with drilling almost exclusively relate to delamination. Previous research has noted that tool geometry and thrust force affect the amount of delamination [7, 8]. Furthermore, theoretical and experimental studies have shown the entrance and exit regions to be most susceptible to delamination [2, 9]. Also, theoretical relations for the critical thrust force as a function of uncut material thickness have been developed [10]. By drilling at thrust forces below this critical value, delamination could be minimized.

Delamination during Drilling of Graphite-Epoxy Laminates

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However, a control scheme has not yet been implemented to achieve this thrust force profile. Before attempting to develop a control scheme, it was beneficial to understand the mechanisms by which delamination occurs. The two mechanisms of delamination associated with drilling are known as peel-up at entrance and push-out at exit [10]. In practice, it has been found that the delamination associated with push-out is more severe than that of peel-up [2]. Peel-up occurs as the drill enters the laminate and is shown schematically in Fig. l(a). After the cutting edge of the drill makes contact with the laminate, the flute tends to pull away the upper laminas before they are completely severed. This upward force results in the top laminas separating from each other, forming a delamination zone. Push-out is the delamination mechanism occurring as the drill reaches the exit side of the material and is shown schematically in Fig. l(b). As the amount of uncut material beneath the drill is reduced, it becomes more susceptible to deformation. Eventually the thrust force on the drill will exceed the inter-laminar bonds causing an exit delamination zone as the tool pierces through the exit side. Both experimental and theoretical approaches have attempted to identify in delamination of composites the relationships between the parameters associated with the drilling process. Experimental studies have used video pictures to observe the delamination radius around drill sites as it varies with thrust force, feed rate, spindle speed and laminate depth [11]. Additionally, tool geometry influences have been investigated for a hollow grinding tool and a standard twist drill [7]. Theoretical studies have used a fracture mechanics approach to determine the critical forces below which crack growth (delamination) will not occur [7, 10, 12]. The fracture mechanics approach of Ho-Cheng and Dharan [10] was adopted in this work to provide reference values for the thrust force controller to follow such that delamination was minimized. Ho-Cheng and Dharan [10] assumed that the mechanisms causing delamination in composites could be accurately modeled by a linear-elastic fracture mechanics approach (LEFM). This approach provided an elegant yet powerful development of the critical forces associated with peel-up and push-out delamination. The critical forces which produce crack propagation during peel-up and push-out were found to be the following:

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where Fc. crit is the critical tangential cutting force at which peel-up occurs, FA. crit is the critical thrust force at which push-out occurs, Gtc is the critical energy release rate for delamination (mode I fracture), E and v are the material constants modulus of elasticity and Poisson's ratio, respectively, h is the depth of material remaining to be cut under the drill, H is the thickness of the composite, k=-f(~t, k), ~t is the coefficient of friction between the drill and work piece and ~, is the helix angle at the drill tip. Noting that the mechanism describing the peel-up force at the drill entrance to the composite is analogous to the action of a power screw, the peeling force Fp can be related to the cutting force as

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Note that Equations (1) and (2) demonstrate that the critical force values are functions of material properties and the uncut thickness below the drill. Simplifications used in this model included the use of the longitudinal value of E from a unidirectional composite. Even though, in general, composites are anisotropic, this simplification gave results which were on the conservative side since, for the same amount of energy release in fracture, the higher modulus of elasticity required a lower force to balance the energy equation. Also, the critical energy release rate for mode I fracture was used since it was easily measured, even though plane strain conditions were not always present. Again, this value was lower than that for the plane stress case, resulting in a conservative prediction [10]. Jain and Yang [7] have taken a more detailed approach in the area of the directionally varying modulus of elasticity. Their results indicated that the unidirectional case was the lower limit for the critical thrust force. Therefore the above analysis should give a conservative result in multi-directional laminates. Although the peel-up model dealt with the cutting force and peel-up force, the thrust force was the desired force to use in a control scheme. This was because, from an implementation standpoint, it was desirable to use only one controller type throughout the drilling process. While it was possible to have one cutting force controller for entrance and a second thrust force controller for exit, that approach was more complicated and could potentially result in undesirable transient responses at controller switchover. Therefore, in an effort to develop a single thrust force controller, it was assumed that the critical thrust force to prevent peel-up at the entry surface of the laminate was equal to the critical peelup force as predicted by Equations (l) and (3). In fact, it can be shown by considering the forces acting in the axial direction that this represents a conservative estimate of the critical thrust force. Therefore, one could ensure that delamination did not occur if the applied thrust force did not exceed the critical force values given by Equations (1)-(3). 2.2. Control schemes for drilling Several approaches have been examined for controlling the drilling process in general [3, 13]. The major obstacle is that dynamics of the chilling process are not fully understood and therefore cannot be accurately modeled mathematically. Empirical models of the drilling process offer a convenient form, but have coefficients that themselves are variables of work piece material, cutting conditions and tool wear. Attempts at applying traditional control techniques to an inaccurate/time-varying model would bear few fruits.

Delamination during Drilling of Graphite-Epoxy Laminates

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To date, the typical approach to drilling is to drive the tool at a constant, or nearly constant, feed rate. Under such conditions, the cutting force and torque can vary significantly when the spindle speed decreases as hole depth increases. The problem arises in that these variations may reach such proportions as to cause tool breakage and subsequent damage to the work piece. Additionally, work piece finish is adversely affected by force variations during the drilling process. Perhaps the most common exhibition of poor work piece finish is the presence of burrs on the exit side of the work piece. Large, tightly attached burrs require time consuming and expensive finishing work. For these reasons, traditional drilling approaches often produce unsatisfactory results in metals and always produce delamination in composites. Various methods of force control have been successfully applied to machining operations such as turning and end milling [14]. However, very little attention has been given to force or torque control of drilling operations. One approach by Furness et al. [3] demonstrated the effectiveness of a torque controller over standard feed and speed controllers. They used an experimentally determined dynamic model between feed and torque to design a Smith-Predictor torque controller based on proportional-plus-integral control. The implemented controller was able to maintain torque within 25% of the desired value. One of the reasons why force or torque control of drilling processes has received very little attention is because the benefits to be gained have been regarded as minimal since 486 PC z-Axis Drive Module Signal 3-Axis SAJO VF54 Vertical Milling Machine

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DESIGN OF THRUST FORCE CONTROLLER

3.1. Neural network control scheme The present study employed recurrent neural networks and a recursive least-squares training algorithm [6, 16]. This approach was investigated for improved performance and to avoid the long training times associated with the commonly used feed forward neural network. In addition to the inter-layer connection weights, the recurrent neural network included intra-layer connection weights and the nodes in the hidden layers formed a dynamical system. The neural network control scheme for the drilling process required two neural networks, one for modeling the dynamic process, termed the neural identifier (NI), and the second for control, termed the neural controller. Therefore, training was a two-step process. First, the neural identifier was trained to model the dynamics of the drilling process. Then the trained neural identifier was used to modify the neural controller weights. This was accomplished by holding the neural identifier weights constant, back propagating the process output error through the neural identifier and then updating the neural controller weights such that the output error was minimized. The mathematical details and procedures for training the neural identifier and neural controller were presented in Ref. [6]. 3.2. Experimental setup The experimental setup for verifying the neural network control scheme is shown in Fig. 2. Note that while the setup described here may sound excessive for a drilling operation, it also served as a test bed to study advanced control algorithms for three-dimensional end milling operations. A SAJO Model VF54 three-axis vertical milling machine with a 7.5 h.p. spindle drive was used. The milling machine had been retrofitted so that the axes were actuated by Superior Electric MllI-FF206-C5 motors with 500 count revolution -~ encoders. These motors were driven by Superior Electric SS2000 D6 Slo-Syn drive modules which were in turn driven by a Motion Engineering MEI PC/DSP three-axis motion controller board. The milling machine was controlled by a 486-66 PC which served as a tool for software development. The PC included an Alacron AL860 co-computer board for performing the necessary neural network computations and a Data Translation DT2839 high channel count-high speed board (data acquisition board). The x-, y- and z-cutting forces were measured by a Kistler Model 9257B three-component dynamometer together with Kistler Model 5004 charge amplifiers. While it is typical to use a four-component dynamometer for drilling (where the fourth signal is the torque),

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only a three-component dynamometer was available. This was not viewed as a limitation since the laminate thickness considered was of the order of one hole diameter and the torque does not become excessive in such a case [17]. Only the thrust force, viz. force in the z-direction, was of interest here. The sampled force signal from the milling machine varied as the drill rotated due to the mechanics of the drilling process. Consequently, the z-direction force signal was averaged over one spindle revolution. It was this average thrust force that was controlled and henceforth this will be just referred to as thrust force, for convenience. The calculation of the average force values was facilitated by a proximity sensor together with in-house built hardware to indicate the completion of a spindle revolution. In this study, the feed rate was updated every three spindle revolutions. The rationale behind this choice of update was: (i) the force data must be collected for at least two revolutions to enable detection of the starting and ending points for averaging; and (ii) to allow enough time for carrying out the necessary neural network computations. The force

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signal was sampled at 8000 samples s-~. A detailed description of the experimental setup can be found in Ref. [18]. The SAJO vertical milling machine used in this study was equipped with a coolant system which was utilized to remove the graphite-epoxy chips. A water-soluble oil served as the coolant fluid. The coolant nozzle was directed at the drill such that the coolant would encompass any chips expelled during the drilling process. This measure was deemed necessary as the graphite-epoxy chips formed during drilling have diameters on the order of a few microns and pose a potential health hazard if inhaled [19]. No absorption of the coolant fluid by the graphite-epoxy laminate was observed during the relatively short drill time. 3.3. Material and drill specifications The thrust force controller for drilling application was developed for use with AS4/35016 graphite-epoxy laminate. Specifically, the composite was a 48-ply quasi-isotropic (0 °, +45 °, 90 °) laminate approximately 6.35 mm thick. Two straight flute polycrystalline diamond tipped (DT) drills of diameters 6.65 and 8.33 mm were used at spindle speeds of 652 and 822 rpm. These drills were obtained from CJT Koolcarb, Inc., Addison, IL. No backing-up material was used in this study. This present study attempts to minimize delamination under the worst case conditions. 4.

4.1.

RESULTS FOR DRILLING GRAPHITE-EPOXY LAMINATES

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The neural identifier was trained to accept three inputs: the current and previous command feed rate, and the previous measured thrust force. The output of the neural identifier was the current thrust force. Training data was obtained by drilling with the 6.65 mm drill at a spindle speed of 652 rpm and at various feed rates and recording the feed rate and the measured thrust force. The training and testing sets are described in Table 1. Eight data sets obtained from triangular feed rate profiles, each with 80 data pairs, were used to train the neural identifier. One data set obtained from a sinusoidal feed rate profile, again with 80 data points, was used to test the generalization ability of the neural identifier. Figure 3(a) shows the thrust force predicted by the neural identifier for data set three after just five training cycles. Figure 3(b) shows that the trained neural identifier accurately

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predicted the desired thrust force for the testing set (sinusoidal feed rate profile). It is clear that the neural identifier has been well trained over a wide range of thrust force and feed rates, and has good generalization capability. The neural identifier was then used to train the neural controller. Inputs to the neural controller consisted of the previous sample of feed rate and current sample of the thrust force error (the difference between the desired and actual thrust force). The output of the neural controller was the current feed rate. To train the neural controller, a desired thrust force curve was generated from the LEFM critical force equations. The critical thrust force is given by Equations (1)-(3) where the critical thrust force related to peel-up was assumed to equal the critical peel-up force. The depth of cut for the intersection of the critical thrust force curves related to peel-up and to push-out occurs at half the thickness of the material. Assuming E=144.795 GPa, v=0.3 and G~c=200 J m -2, the maximum critical thrust force F~. ca, was found to be over 4000 N. This value was excessive for the present application and the critical thrust force equations were modified to produce the following piece-wise defined desired thrust force curve:

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Fz, desired

~ [

h'~ 0_
and F z

oe~i,ed
(4)

Ft, max,otherwise where F:. max is some value well below F~, . , . A sample desired thrust force curve, as described by Equation (4) taking F~ ,,~x as 444.82 N (100 lb), is shown in Fig. 4. For comparison, this figure also shows the desired thrust force curve using Equations (1)-(3). Figure 5(a) shows the training results for the neural controller after 30 passes through the 150 data points of the training set. The training set consisted of three desired force curves, as generated by Equation (4), with three different values for Ft. m~, of 444.82 N (100 lb), 556.03 N (125 lb) and 333.62 N (75 lb). The controller was trained in an unsuper-

Delamination during Drilling of Graphite-EpoxyLaminates

997

i- i

I I| hn

nnnnnnnnnnn

Fig. 10. Hole quality for 6.65 mm DT drill with constant feed rate: (a) entrance; (b) exit. vised fashion and no penalty was placed on the control effort. This approach led to the neural controller predicting negative feed rate values, which corresponded to the drill backing up as the desired force curve dropped down to its minimum value. However, during implementation of the neural controller, the feed rate was limited to a minimum value of 25.4×10 -4 mm s -~ so that the drill always proceeded in the forward direction and no adverse effects on the control of the process were observed. Additionally, during training of the neural controller random noise was introduced into the thrust force error to model the thrust force signals received from the dynamometer. A testing set was used to check the generalization of the neural controller and is shown in Fig. 5(b). The testing set was also generated from Equation (4) with two desired force curves with maximum thrust force values of 333.62 N (75 lb) and 444.82 N (100 lb). No noise was added to the testing set. From Fig. 5 it is clear that the neural controller can specify the appropriate feed rate to obtain the desired thrust force. The neural identifier was trained by initializing the connection weights to random values between _+0.I, and the learning rate and forgetting factor were set to 0.15 and 0.98, respectively. Two hidden layers with two neurons each were used. The neural controller was trained by initializing the connection weights to random values between _+0.1, the learning

998

R. Stone and K. Krishnamurthy

i I i i| i, ,,,R

..,, .. ,

Fig. 11. Hole quality for 6.65 mm DT drill with neural controller: (a) entrance; (b) exit.

rate and forgetting factor were set to 0.2 and 0.98, respectively, and employing four and three neurons, respectively, in each of the two hidden layers. Note that no special effort was made in optimizing the neural network parameters for the neural identifier or the neural controller. 4.2.

Experimentalresults

Once the neural controller training was completed, the thrust force controller was tested on the graphite--epoxy laminate. The 6.65 mm drill was used to drill holes with a spindle speed of 652 rpm following the desired thrust curve given by Equation (4). Figure 6(a) shows the actual thrust force, desired thrust force and feed rate versus time and Fig. 6(b) shows the same variables versus hole depth. Except for an overshoot of about 25%, the controller was able to maintain the thrust force within 5% of the desired value. The relatively large overshoot was due to two reasons. First, the feed rate was well beyond the range of the training data, and second, the feed rate was updated fairly slowly, every 0.276 s (three spindle revolutions). Overall, the neural controller performed quite well. From Fig. 6(b) it may be seen that the desired thrust force again became a constant at a depth of around 6.35 mm. This was the point where the drill first pierced the exit plane

Delamination during Drilling of Graphite-EpoxyLaminates

999

lIB

| | | | I

,,.

"

Ibi,ilmnm,n,m, ,mnmi

i i iI 1k n W W . . n . n . . . . . . Fig. 12. Hole quality for 8.33 mm DT drill with constant feed rate: (a) entrance; (b) exit. of the graphite--epoxy laminate. Thrust force control was discontinued at this point in order to avoid driving the drill at excessive feed rates as the actual thrust force quickly decreased to zero. The drilling process was completed at an arbitrarily chosen constant feed rate of 0.0254 mm s- ~ and the desired thrust force was no longer updated. Also note that the thrust force history shown in Fig. 6(a) corresponds to the thrust force history shown in Fig. 6(b) to a depth of only about 7.11 mm. Although the neural identifier was not used after training the neural controller, for comparison the thrust force predicted by the neural identifier is shown in Fig. 6(c). The neural identifier can be seen to predict the thrust force quite well, further verifying its generalization capability. To quantify the improvements obtained with the neural controller, the graphite-epoxy laminate was drilled using a constant feed rate. Figure 7 shows the large variation of thrust force when drilling with a constant feed rate of 1.27 mm s-]. This value for the constant feed rate was chosen based on commonly used values from other experimental studies [11, 20]. Here the thrust force increased sharply at entrance and then decreased sharply as the drill pierced through the exit plane. It was the large thrust force values in the vicinity of the entrance and exit surfaces that were responsible for delamination.

1000

R. Stone and K. Krishnamurthy

lira

D-

| | |

r

I I I

t,.B.,,,,,,,a,...,,,.,.,,

Fig. 13. Hole quality for 8.33 mm DT drill with neural controller: (a) entrance; (b) exit.

To check the robustness of the neural controller without updating the connection weights or changing the spindle speed, a 8.33 mm drill was used to drill through the graphiteepoxy laminate. Figure 8 shows that the controller was again able to maintain the thrust force within 5% of the desired value after an initial overshoot of 16%. Additionally, again without updating the connection weights, the neural controller was tested with the 6.65 mm drill at a spindle speed of 822 rpm. Figure 9 shows that the neural controller was still effective; the overshoot was about 12% and the thrust force was maintained within 5% of the desired value. Thus, the neural controller was robust to changes in the drilling parameters. While it was evident that the neural controller could drill with a desired thrust force, Table 2. Performance comparison with constant feed rate and neural controller Hole diameter (mm)

6.65 8.33

Delamination diameter (mm) With constant feed rate With neural controller 8.4 9.9

6.9 8.4

Delamination during Drilling of Graphite-Epoxy Laminates

|

i,.,:

U

1001

+

llilonnonnnnon

Fig. 14. Exit hole quality for 6.65 mm CT drill: (a) with constant feed rate; (b) with neural controller.

the question that must be addressed was whether better hole quality, i.e. smaller delamination zone, was obtained. The present results clearly indicated that better hole quality was obtained with the neural controller. Visual measurements of hole delamination diameter were made for holes drilled with a constant feed rate and with the neural controller. The holes were photographed using a Panasonic GP-CD40 CCD camera connected to a Macintosh Quadra 660AV. The built-in Macintosh audio--visual digitizer was used to capture a frame from the camera to produce the images. Figures 10 and 12 show the entrance and exit surfaces of holes drilled with a constant feed rate while Figs 11 and 13 show the surfaces when drilled with the neural controller for both the 6.65 and 8.33 mm drills. Note that in Figs 10--13 (a) refers to entry and (b) to exit. The massive delamination shown in Fig. 10 corresponds to the thrust force curve shown in Fig. 7. The much improved hole appearance shown in Fig. 11 corresponds to the thrust force curve of Fig. 6. Similarly, the hole in Fig. 13 corresponds to the thrust force curve of Fig. 8. Comparing the holes shown in Figs 12 and 13, it is clear that the neural network control scheme resulted in an improved exit surface. However, note that even the holes drilled with the neural controller have a slightly ragged exit surface due to the drill not cutting the final fibers at the hole edge. Finally, the average measured delamination diameters for holes drilled with a MIM 36:9-B

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R. Stone and K. Krishnamurthy

constant feed rate and the neural controller are tabulated in Table 2 for the 6.65 and 8.33 mm drills. Each delamination diameter was determined by averaging the values from four holes. Furthermore, the drilling scheme was chosen randomly for the eight holes to eliminate tool wear as a factor. The use of DT drills resulted in smaller thrust forces compared to, for example, singlepoint drills. For such single-point drills, the effectiveness of the neural network control scheme was more pronounced. Figure 14 shows the quality of the hole (exit side) with a constant feed rate and the neural controller when a 6.65 m m carbide tipped (CT) drill with a 118 ° standard point angle also supplied by CJT Koolcarb, Inc. was used. The neural controller for this case was trained following the same procedure used for the DT drill. It may be noted that the DT drills produced better quality holes for the constant feed rate cases than did the CT drills. This was due to the sharper cutting surface of the DT drills. Other point geometries such as four or eight facet split points could have been considered. However, the two types of drills considered here were probably at the extremes of the performance envelope and very clearly showed the improvements achievable using the neural network control scheme. 5. CONCLUDINGREMARKS This study has successfully shown that the neural network control scheme discussed and implemented can drill at desired thrust force profiles within an acceptable error margin and can minimize the delamination of a graphite--epoxy laminate. The robustness of the controller was demonstrated by varying some of the drilling parameters, spindle speed and drill diameter. The fracture mechanics derived critical force equations developed in Ref. [ 10] are good approximations and serve as useful guides in generating a desired thrust force profile. Although testing was carried out with one type of graphite-epoxy laminate, similar results could be expected for other types of graphite--epoxy laminates or other composite materials. One enhancement to the neural network control scheme presented here would be the inclusion of an acoustic emission system to monitor delamination. Correlation of acoustic emission to delamination has been shown [12] and could represent another input to the neural controller. Acknowledgements--This study was funded in part by the National Science Foundation under Grant Number

MSS-9216479 and the Missouri Department of Economic Development through the Manufacturing Research and Training Center-UMR. REFERENCES [1] S. Abrate and D. A. Walton, Machining of composite materials. Part 1: traditional methods, Composite Manfufact. 3. 75 (1992). [2] T. L. Wong, S. M. Wu and G. M. Croy, An analysis of delamination in drilling composite materials, 14th National SAMPE Technology. Conf., p. 471. Atlanta, GA. (1982). [3] R. J. Furness, A. G. Ulsoy and C. L. Wu, Feed, speed, and torque controllers for drilling, Proc. Amer. Cont. Conf., p. 1947 (1993). [4l A. Sadat, Machining of composites, Encyclop. Compos. 3, 95 (1990). [5] G. Hamatani and M. Ramulu, Machinability of high temperaturecomposites by abrasive waterjet, Machining Composites, ASME Winter Annual Meeting (edited by M. Taya and M. Ramulu), PED-Vol. 35/MDVol. 12, p. 49. ASME, New York (1988). [6] X. Qu, K. Krishnamurthy, W. Lu and B. McMillin, A neural network controller for force control in end milling operations, Dynamic Systems and Control, Int. Mechanical Engineering Congress and F~PO.(edited by C. Radcliffe), DSC-Vol. 55, p. 563. ASME, New York (1994). [7l S. Jain and D. C. H. Yang, Delamination-freedrilling of composite laminates, Processing Fabrication and Manufacturing of Composite Materials, ASME Winter Annual Meeting (¢xftted by T. Srivatsan and E. Lavernia), MD-Vol. 35, p. 45. ASME, New York (1992). [8] V. Chandrasekharan, S. G. Kapoor and R. E. DeVor, A mechanistic approach to predicting the cutting forces in drilling: with application to fiber-reinforcedcomposite materials, Machining of Advanced Composites, ASME Winter Annual Meeting (edited by M. Ramulu and R. Komanduri), MD-Vol. 45/PED-Voi. 66, p. 33. ASME, New York (1993). [9] A. B. Sadat, W. S. Chan and B. B. Wang, Delaminationof graphite/epoxylaminate during drilling operation, ASME J. Energy Resourc. Technol. 114, 139 (1992). [10] H. Ho-Cheng and C. K. H. Dharan, Delamination during drilling in composite laminates, Machining Composites, ASME Winter Annual Meeting (edited by M. Taya and M. Ramulu), PED-Vol. 35/MD-Vol. 12, p. 39. ASME, New York (1988).

[ 1i ] G. Dipaolo, S. G. Kapoor and R. E. DeVor, An experimentalinvestigation of the crack growth phenomenon

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for drilling of fiber-reinforced composite materials, Machining of Advanced Composites, ASME Winter Annual Meeting (edited by M. Ramulu and R. Komanduri), MD-Vol. 45/PED-Vol. 66, p. 15. ASME, New York (1993). [12] C. L. Jiaa, H. Ho-Cbeng and C. K. H. Dharan, Correlation between acoustic emission and delamination in drilling of composite laminates, Monitoring and Control for Manfacturing Processes, ASME Winter Annual Meeting (edited by S. Liang and T. Tsao), PED-Vol. 44, p. 177. ASME, New York (1990). [13] R. J. Fumess, A. G. Ulsoy and C. L. Wu, Supervisory control of drilling, Proc. American Control Conf., p. 1952, Dallas, TX. (1993). [ 14] A. G. UIsoy and Y. Koren, Control of machining processes, ASME J. Dyn. Sys, Meas. Cont. 115, 301 (1993). [I 5] K. S. Narendra and K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Netw. 1, 4 (1990). [16] Q. Xu, K. Krishnamunhy, B. McMillin and W. Lu, A recursive least squares training algorithm for multilayer recurrent neural networks, Proc. American Control Conf., p. 1712 (1994). 1171 W. H. Cubberly and R. Bakerjian, Tool and Manufacturing Engineers Handbook, SME, Dearborn, MI ( 1989 ). [181 T. Luo0 Q. Xu, K. Krishnamunhy, W. Lu and B. McMillin, Force control in two-dimensional end milling operations using recurrent neural networks, Proc. ASME Dynamic Systems and Control Division, Int. Mechanical and Engineering Congress and Expo. (edited by T. E. Albens), DSC-Vol. 57-2, p. 773. ASME, New York ( 1995 ). [19] W. K6nig and S. Rummenh611er, Technological and industrial safety aspects in milling FRPS, Machining Advanced Composites, ASME Winter Annual Meeting (edited by M. Ramulu and R. Komanduri), MD-Vol. 45/PED-Vol. 66, p. 1. ASME, New York (1993). [20] N. Bhatnagar, N. K. Naik and N. Ramakrishnan, Experimental investigations of drilling on CFRP composites, Mater. Manufact. Proc. 8, 683 (1993).