A fuzzy-nets in-process (FNIP) system for tool-breakage monitoring in end-milling operations

Pergmon Int. J. Mach. Tools Manufact. Vol. 37. No. 6. pp. 783-800. 1997

© 1997 Elsevier Science Ltd All right~ reserved. Printed in Caeat Britain

0890-6955/97517.00 + .00

PH: S0890-6955(96)00023..5

A FUZZY-NETS IN-PROCESS (FNIP) SYSTEM FOR TOOL- BREAKAGE MONITORING IN END-MILLING OPERATIONS

J.C. CHEN*? and JT. BLACK~

(Received 15 August 1994; in final form 14 February 1996)

Abstract--The fuzzy-nets in-process (FNIP) system is proposed for monitoring tool breakage in end-milling operations. The FNIP system consists of two components: (1) the fuzzy search classifier Cr-'SC), which maps a state vector into a recommended action using fuzzy pattern recognition; and (2) the fuzzy adaptive controller (FAC), which maps a state vector and a failure signal into a scalar grade that indicates state integrity. The FAC also produces the output action value, p, to upgrade FSC mapping according to the variation of the input state. By coupling fuzzy logic control systems and neural networks into the fuzzy-nets system, a self-learning capability (ability to generate rule bases and to fine-tune the membership functions of each linguist variable to the appropriate level of granularity) was developed. With this on-line learning capability, the fuzzy rule bases of FSC and FAC are established by fine-tuning the parameters in the FNIP system. After establishing all the fuzzy rule bases, the performance of the FNIP system is examined for an end-milling operation. Experiments have shown that the FNIP system is able to detect tool breakage in the end-milling operation "on-line', approaching a real-time basis. © 1997 Elsevier Science Ltd.

1. INTRODUCTION

Tool breakage in the metal cutting process is a catastrophic failure condition that requires immediate recognition and retraction of the cutting tool. The problem of tool breakage can result in defects of the workpiece or damage to the machine tool. In past decades many approaches have been developed to detect tool breakage in a manufacturing process. Recent research in sensors which monitor the cutting process indirectly (through acoustic emission [1], dynamometer, spindle sensors [2], vibration and sound intensity) has been successful. Iwata and Moriwaki [3] proposed the use of acoustic emission in monitoring tool wear and breakage in the turning operation. Their experiments demonstrate that the acoustic emission intensity increases as the tool wear progresses, and high-amplitude acoustic emission bursts are produced as the tool breaks. Tlusty and Ismail [4] classified cutting force and spindle motor sensors as the primary sensors necessary for adaptive control. They noted that even table-type dynamometers provide a contradictory frequency response and are suited for a tool breakage or force adaption control system. Based on this survey, a table-type dynamometer is used to measure the cutting forces in the proposed tool-breakage detection model.

Various approaches of tool-breakage detection algorithms have been proposed. Lan and Naerheim [5] demonstrated an interesting approach to monitor the cutting forces of a milling machine using a very high-order autoregressive, AR(I 5), time-series filter to detect tool failures. Altintas et al. [6] revealed that a low-order series tool-breakage detection model can be developed using the periodic characteristics of the cutting forces, and synch- ronizing the measurement sampling and tool frequencies in milling. However, these methods rely on tool-breakage templates which are recorded in the trial machining of the first workpiece in a batch. Such a trial cut may not be suitable for a flexible manufacturing environment where the lot size may be small. Thus recent researchers have attempted to develop a more complex and adaptive tool-breakage detection model which can detect tool failure without a trial cut.

*Department of Industrial Education and Technology, Iowa State University, 211 I. Ed. II, Ames IA 5001 !- 3130 U.S.A.

?Author to whom correspondence should be addressed. ,Department of Industrial Engineering, 307 Dunstan Hall, Auburn University, Auburn AL 36849-5352 U.S.A.

783

784 J.C. Chen and JT. Black

Neural network approaches have been proposed [7, 8] to provide a tool-breakage detection model with an adaptive capability. The neural network methods do not need any on- line modeling during the sampling of data, since they can estimate the condition of the tool by inspecting the data of any two tool resolutions. A neural network, such as the adaptive resonance theory (ART2)-type network, can be trained on experimental data and used to monitor the machining process. However, this approach has three drawbacks: (1) it is very time-consuming and expensive to collect data of a good tool and a broken tool at all possible cutting conditions and for all different types of tools; (2) it requires a very large memory space and some of these data may conflict with each other; and (3) the parameters must be selected to achieve optimum performance. These parameters are selected by trial-and-error methodology which is very time-consuming [7].

All of the neural network models attempt to achieve satisfactory performance through a dense interconnection of many simple computational elements or nodes, which perform in a fashion similar to the way neurons perform in a human being. Consequently these elements, or nodes, used in neural nets linked with variable weights, are nonlinear and analogical. Neural-network models can be classified depending on whether they learn with supervision (pattern-class information) and whether they contain closed synaptic loops or feedback loops. Basically there are three processing units in the neural network: the input, hidden and output layer nodes. The input layer nodes receive the information from the controlled system, and the output layer nodes provide the controlled system with a recommended action. The hidden layer nodes form the internal representation of the pattern presented at the input layer.

However, while neural-network models cannot directly encode structured knowledge, fuzzy systems can directly encode structured knowledge in a numerical framework [9]. It has been shown that fuzzy logic systems demonstrate a great potential for application in intelligent manufacturing applications. Additionally, the fuzzy control system is capable of estimating functions and control systems with only a partial description of the systems' behavior. This is very difficult to construct by simply using neural-network models. There- fore the neural networks and fuzzy systems are married into a so-called fuzzy-nets (FN) system to facilitate a simple training technique for a complex system such as a machining process. In addition, as a result of the hidden layer nodes with fuzzy sets, the fuzzy-nets system requires less memory and has the capability to adapt.

A fuzzy-nets in-process (FNIP) system for tool-breakage detection in end-milling operations is proposed. Tool breakage is detected through the fuzzy associative memory (FAM) rules that solve the nonlinear behavior of the machining process. With an available leaming rule and self-provided training samples (input--output associative data), the FNIP system may generate and adjust weights, control parameters, fuzzy rules and membership functions. After the establishment of the rule bases, the performance of the FNIP system is examined for end-milling operations.

2. THE ARCHITECTURE OF THE FNIP SYSTEM

Generally, decision-making about the existence of tool breakage is dependent upon the criteria (how the cutting signal patterns are classified and interpreted). The cutting force signal and the variation of the cutting parameters are the input state of the FNIP system. Given the input state which is fed back from the process, the FNIP selects an action (shut down or do nothing) by implementing an inference scheme based on a fuzzy-nets classifier. The architecture of the FNIP system for tool breakage monitoring is schematically shown in Fig. 1. It has two components:

1. The fuzzy search classifier (FSC), which maps a state vector into a recommended action using fuzzy pattern recognition.

2. The fuzzy adaptive controller (FAC), which maps a state vector and a failure signal into a scalar grade that indicates state integrity. The FAC also produces the output active value, p, to upgrade FSC mapping according to the variation of the input state.

The encoding state is fed back into the FNIP, along with the failure signal. Learning

Fuzzy-nets in-process system for tool-breakage operations

. .~_ - _ Failure Fuzzy Signal

( A d a p t i v e ~ ~ r---~ IController ] ~ I

[ ~ " ~ ~P IMachininq I ~ " l~4kl sF~:~ [Recomme~]ff r ° ees s / ' A ' 1 Classifier I I

T I ( " ' " ) I '°"°° I Input State

Fig. 1. The architecture of the FNIP system.

785

is accomplished by fine-tuning the parameters in the fuzzy-nets systems (FSC and FAC). Consequently the parameters describing the fuzzy membership function in the FSC are changed, and in the FAC the weights (the fuzzy active value) are adjusted. By using this adaptive function, the FNIP system can realize the change of the machining condition and upgrade the classifier for proper functioning.

The FSC and FAC work as fuzzy-nets (FN) systems, which have an architecture as shown in Fig. 2(a) and (b), respectively. The membership functions in layer two and the fuzzy rule base in layer three of the FN systems are highly dependent upon the character- istic of each individual machine. Therefore five-step learning procedures are proposed for generating FAM rules for the FSC and FAC.

2.1. The fuzzy search classifier (FSC)

The objective of the FSC is to map an input state vector into a recommended action using fuzzy pattern recognition. The input state vector consists of the condensed force signal, and the recommended action is to either shut down the machine when tool breakage is detected or to do nothing. The learning procedure for the FSC consists of five steps and is summarized as follows.

2.1.1. Step 1: divide the input space into fuzzy regions. The input feature vector consists of the difference between the resultant force magnitude of each tooth period. Note that the resultant force is generated by a dynamometer and encoded with a magnitude of voltage. In this study, five successive peak forces (n+l=5) are encoded asf~,f2 ..... fs. The successive force difference is calculated as

([VFi-, VF~ + 1, [VF~-,VFf ] ..... [VFf, VF + 1) (1)

The input feature vector is given as

([VFi-, VFI+], [VF/-, VF2 + ] ..... [VF~, VF+]) (2)

where k=n, and this vector is a n-dimensional feature space. Assume that the domain intervals of the input feature vector x are

([VF/, VFl + ], [VFf,VF~ ] ..... [VF~, VF + ]) (3) where "domain intervals" means that the variable will most likely lie in this interval. Each interval is divided into 2N+1 regions, which arc denoted by SN (small number N) ..... S1 (small number 1), MD (medium), L1 (large number 1) ..... and LN (large number N). The shape of each membership function is triangular, and the width of the spread of each triangular function is the same. For example, the spread of an input feature VF~ is defined as

786

(a)

J.C. Chen and JT. Black

-1 I

f

VF. I ~

NR: N o r m a l Tool

Output Action

B r o k e n Tool

Layer 1 LaYeL L Layer 3 Layer 4 Layer 5

(b)

Contro,d IP -

¢

f r l

Layer I Layer 2 Layer 3 Layer 4 Layer 5

Fig. 2. (a) The fuzzy-nets architecture for the FSC; (b) the fuzzy-nets architecture for the FAC.

VFf ~ - V F ? (4) s(VFl) - 2N

The center points o f each linguistic variable (SN, .... S1, MD, L1 ..... LN) of VF~ are

(VFi- ..... VFI(N1)'sVFI, VF~ + -VFi-

2 ,VFt + (NI).s(VF0 ..... VF~ + ) (5)

respectively. For example , assume that the domain interval o f VF~ is defined as [ - 1.5 V,

Fuzzy-nets in-process system for tool-breakage operations 787

1.5 V]. Then the width of the each spread, denoted as s(VF0, is 0.75 V. Figure 3 shows how the domain interval of VFt is divided into five regions.

2.1.2. Step 2: generate fuzzy rules from input-output given data pairs. The signal obtained from the cutting process generates the input feature vector x (Equation (2)). The output (y) indicates the condition of the tool: it either remains in good condition or breaks one or more teeth. This is determined by observing the condition of the tool after cutting. The desired input--output data pairs are

[VF~i), VF~) ..... V/a/), y(i), /~(y")(o] (6)

where i denotes the number of the training data set, y denotes the output class and /.ty denotes a degree of this data set assigned by a human expert which represents the useful- ness of the data pair. The input-output data pairs define the fuzzy classification rules for the knowledge base of the fuzzy logic system as

IF {(VF, is A,, AND VF2 is B 2 . . . . . AND VFN is NO} THEN {the output class is Yl} (7)

The degrees of each feature of the input vector are determined in different regions. The function is given as

1

~"~XC, xs(Xi) =

I

- - , xl ~ [Xo, X~ + X3 Ix,-Xol

Xs IXo-x,I

X~ , x i~(Xc-Xs, Xc]

0, Otherwise

(8)

where Xc and Xs indicate the center point and the spread width of the linguistic level X, respectively. For example, in Fig. 4(a), VF~ 1) has 0.2 ° in MD, 0.8 ° in L1 and 0 ° in all other regions. This can be expressed as

~ ~"" 1 )'~ $.LMDC. MD$(VF~ I)) = 0.8; ~"~LIC, LIS( r~ / = 0.2 (9)

Similarly, in Fig. 4(b) V/a, I) has 1.0 ° in the MD, and 0 ° in all the other regions. After all of the input elements have been assigned degrees in all of the regions, each element is assigned to the region which has the maximum degree. For example, VF~ ') is assigned to be in the LI (0.8 °) and VF ~I) is assigned to be in the MD (I°). Then, one rule from one pair of the desired input-output pair is assigned, for example:

~(~,)

1.0

0.0

$2 SI

( - 1 . 5 )

MD LI 1.2

(~) Is.5) VF, +

r

VFt (vol~|e)

Fig. 3. The domain interval of VFi divided into five regions.


~,(vr,)

1.0

. 2

0.0

$2 SI MD .LI L2

! -

VFt¢ t) Vl~l +

(a)

v V~

g(vv,)

1.0 ~ -

0.0

$2 SI

Vl~-

MD LI L2

vF- + v

v ~

(b)

Fig. 4. Examples of finding the degree of each input variable in the FSC system.

[VF~I)(0.8E L1, max) ..... VFt2)(1 ~MD, max),

y°)(NORMAL), /z.~t,, = 0 .9 ]~ [V~ i), VF~ i) ..... VF~j ), y"),l~y%q~(wheni= 1)

Rule 1: IF VF~" is L1, /~ .... /~ F~n l) is MD; THEN y(l) is (NORMAL)

(lO)

where/k denotes the logic "and" used in the control logic. This means that the conditions of the IF part must be met simultaneously in order for the result of the THEN part to occur. After an adequate number of data pairs are established, the FAM rules are generated.

2.1.3. Step 3: avoid conflicting rules. It is highly likely that there will be conflicting rules, i.e. rules that have the same IF part, but a different THEN part. Top-down and bottom-up methodologies are proposed to resolve this conflict. Top-down methodology assigns a degree to each rule. The degree of the following rule "IF x~ is A and x2 is B, THEN y is C" is defined as

d(Rule) = IZA(XOIXB(X2)IZc(y)Ixd (11)

where/~a is the data pair degree assigned by the human expert. Note that A, B and C are linguistic values for the input vector and the output. An example of two conflicting rules (k and j) is

rule no. k: "IF xt is A and x2 is B, THEN y is C"

rule no. j: "IF x~ is A and x2 is B, THEN y is D"

The degree of each rule is


d(rule no. k) = ~A(Xl)~,LB(X2)~,Lc(y)~.Ld

d(rule no. j) = ~.LA(XI)~LB(X2)~,LD(y)~.Ld

The following strategy is used to resolve the conflicting rules. If the magnitude of the deviation is [d(rule no. k)-d(rule no. j)l>8~, where 0<By<0.1, then the rule with the maximum active value will be chosen as the winner. 8y is a user-defined parameter. Other- wise, the bottom-up procedure is required to resolve the conflict.

In the bottom-up methodology, two more regions are added to one feature of the input vector. For example, VFI is initially set up for five regions. If the differential degree of rule k andj is less than 8, then VFi is extended to seven regions. Thus all of the previously trained input-output data pairs must be retrained. If any other rules conflict, the region number of the next input feature (VF2 ..... VFn) is extended sequentially until all of the conflicting situations are resolved.

2.1.4. Step 4: develop a combined fuzzy rules base. In order to illustrate the development of a fuzzy rule base, a two-dimensional example is given (Fig. 5). The following strategy summarizes how the cells of the fuzzy rules base are filled. Since the linguistic rule is an "and" rule in our case, only one rule will fill a cell. For example, suppose we obtain a linguistic rule from a data pair "IF xi is L1 and x2 is MD, THEN y is NR" for the rule base; then NR will fill the cell indicated in Fig. 5.

2.1.5. Step 5: determine a mapping based on the fuzzy rule base. This step, a so-called defuzzification step, converts the fuzzy values of output variables into a space of nonfuzzy (crisp) action. For most control applications, in order to generate a crisp control value a defuzzifier is required to determine the best output action. The FSC is a classification application and therefore the final decision of the FSC system is made by a maximum selector. The maximum selector picks the point with the maximum value of output among all possible distributions.

2.2. The fuzzy adaptive controller (FA C)

The objectives of the FAC are to evaluate the input information and to predict the reinforcement associated with different input states. The input of the FAC is a variation of each critical parameter of the machining condition, e.g. rotational speed (Ns) or feed rate ~) . The output of FAC is a fuzzy active value (denoted by p), which indicates the integrity of the input state. The value p is used to fine-tune the output action suggested from the FSC and to upgrade the FSC mapping according to the variation of the input state, p is the ratio of the variation of the average peak-force (V~ from the input fr and Ns to the trained FSC cutting condition (fr---.~-6 ipm, Ns=550 rpm). The variation is given as

L2

X 2

LI

MD NR

Sl

$2

$2 St MD L1 L2

Xl

Fig. 5. An example of a two-dimensional rule base.


1 n + l n + l

~7/~(6 ipm. 550 r p m ) - n + 1 [ ~ f / ( t + 1)- ~f , - (t)] (12) i = 1 i = 1

where f~ indicates the peak-force of the tooth period i, and n is the number of teeth of the cutting tool. Therefore p is given as

V~'(fr, Ns) (13) p~ , Ns) - V~'(6 ipm, 550 rpm>

The cutting condition is changed when p does not equal 1. Therefore p enforces a change in the internal domain of each parameter. The magnitude of each interval domain changes to

~TF~ new ~-" p.(VFT, old); VF;. + new = P'(VFi +, o~d), where i = 1 ..... n (14)

Since the interval domain of each input is changed, the membership functions of each linguistic variable are also changed. I fp is too far away from the center, then the detection output action from the FSC will skip and detect another set of signals with the new membership functions. The proposed training procedure of the FAC is similar to that of the FSC and is summarized as follows.

2.2.1. Step 1: divide the input and output spaces into fuzzy regions. The input vector of the FAC consists of the rotational speed (N~) and table feed-rate ~). In this research the range of the rotation speed (N~) is assumed to be 530-730 rpm and the range off~ is assumed to be 5-7 ipm. Thus the input feature vector and "domain intervals" are given as

x = [xl, X2]T,Vx1E[530, 730 rpm],Vx2~[5, 7 ipm] (15)

Note that the input vector is a two-dimensional feature. Then each input variable is divided into five regions (N=2, 2N+1=5), which are denoted by $2, S1, MD, L1 and L2. Also, the shape of each membership function is triangular. The width of the spread of each triangular function is the same and is given as follows:

f~+ - f 2 (8 -6 ) ipm s~) 2N 4 - 0.5 ipm

N2-N~- (730-530) rpm s(Ns)- - 2N - 4 = 50 rpm (16)

The center points of each linguistic variable ($2, S1, MD, L1, L2) of Ns are (530, 580, 630, 680, 730) rpm, and for fr are (5, 5.5, 6, 6.5, 7) ipm.

The output value (p) has a domain interval of [0.5, 1.1], which is determined by per- forming various cutting experiments within the domain intervals of the input variables. The output is divided into five regions. Therefore the spread of the output, denoted as s(p), is 0.15. The center points of each linguistic variable ($2, S1, MD, L1, L2) o f p are (0.5, 0.65, 0.80, 0.95, 1.1). Figure 6 shows that the domain intervals of Ns, f~ and p are divided into five regions.

2.2.2. Step 2: generate fuzzy rules from given data pairs through experiment. The desired input--output data pairs are given as

[N(si), f~o, p(O, 1.6~o] (17)

where i denotes the number of the training data set, p denotes the output value and /zd


~Nt)

t $2 $1 MD LI L2 "°1 r - i

0.0 Hi" N Ns (rpm) 530 630 730

(a)

791

u ( t , )

SI MD LI 1,2 $2 1.0

0.8 - - - -

°:o fd" G¢ I) • f , ( ipm) 5.0 6.0 7.0

(b)

~(p)

t SI MD LI 1,2 $2

i' "0.0

p - pC p p_value 0.5 0.8 1.1

(e)

Fig. 6. Examples of finding the degree of input and output variables in the FAC system.

denotes a degree of the data set assigned by a human expert. The input-output data pairs define the fuzzy classification rules for the knowledge base of the system as

IF {(Ns, is N! A N D f n is FI)} THEN the output is Pl (18)

The degrees of each feature of the input vector are determined in different regions. The function of each input variable is given as described in Equation (8). In the FSC system, the rule base is generated as the cutting condition at 6 ipm of table feed-rate, with 550 rpm of rotation speed and a p value of 1, which indicates that the condition is the same as the role base of the FSC. For example, if the cut in the first experiment has been set at a table feed-rate of 6.8 ipm and a rotational speed of 600 rpm, the p value is obtained through the experiment and is given as


V~"(6.4 ipm, 600 rpm) P(6.4 iprn, 600 rpm) VF(6 ipm, 550 rpm) (19)

Assume that the experiment signal shows that P(6.8 ipm, 600 rpm) is 0.83. In Fig. 6(a) N~,, 1) has 0.6 ° in S1, 0.4 ° in MD and 0 ° in all the other regions. Similarly, in Fig. 6(b) f~l ) has 0.8 ° in L2, 0.2 ° in L1 and 0 ° in all the other regions. This can be expressed as

PLMDc. MDs(/~s 1)) = 0.4; /ZSlc, s~,(N~ ") = 0.6, ~LI, Llc, Lls(f~ 1)) = 0.2; #(LL2c, L2sQe~ I)) ----- 0.8 (20)

Also, as shown in Fig. 6c, P(6.8 ipm. 600 rpm) has 0.80 ° in MD, 0.2 ° in L1 and 0 ° in the other regions. After all of the input elements have been assigned degrees in all the regions, each element is assigned to the region with the maximum degree. For example, N~ ~) is assigned to S1 (1°), f~) is assigned to L2 (0.8 °) and p is assigned to MD (0.8°). Then one rule from one pair of the desired input--output pair is assigned, for example:

[A~i), f~i), p(i), /x<i),)]~(when i = 1),

[/¢~l)(0;6eS1, max), f~l)(0.8eL2, max), p°)(0.8EMD, max), ~aqt) = 0 .9 ]~

Rule 1: IF (N~ j) is S1 A f~) is L2) THEN (#1) is MD)

(21)

where A denotes logic "and", and is used in the control logic. This means that the conditions of the IF part must be met simultaneously in order for the result of the THEN part to occur. After an adequate number of data pairs are established, the FAM rules are generated.

2.2.3. Step 3: avoid conflicting rules. Similarly, in the rule base of the FAC, it is possible to have rules that conflict with each other. Top-down and bottom-up methodologies used in the FSC are also applied to resolve this conflict.

2.2.4. Step 4: develop a combined fuzzy rule base. The fuzzy rule base of the FAC is a two-dimensional matrix. A five-region FAM is given in Fig. 7. The following example explains how the cells of the fuzzy rule base of the FAC are filled. Assume that rule i, described in Step 3, is the winner. Thus a linguistic rule "IF Ns is S 1 and f~ is L2, THEN p is MD" is obtained and should be fired in the rule base, and then MD will fill the cell indicated in Fig. 7.

L2

LI

Ns MD

SI MD

SR

$2 S1 MD LI L2

f~ Fig. 7. An example of a five-region FAM bank in the FAC system.


2.2.5. Step 5: determine a mapping based on the fuzzy rule base. Finally, the following defuzzification strategy is used to determine the output control p for the inputs (Ns, fr). First, for given inputs (Ns, f0, the antecedents of the ith fuzzy rule use the product operation [Equation (6)] to determine the degree, /~Outpot, of the output control responding to the input. For example,

i i / .LOutput ---- /Jqnput~cs(Ns)/ . /~inpUt~r(fr) (22)

where Output i denotes the output regions of Rule i, and Input i denotes the input region of Rule i of N~ and fr. For example, Step 3 of Rule 1 gives

j L I . h D ( p ) ~- ~,LSI I ( N s ) ~ - g L 2 1 ( f r ) (23)

and then the centroid defuzzification is applied to determine the output:

j • . ~-/~output/C d

Y _ j = 1 (24) m

J '~ ]']~outputj

j = l

where c i denotes the center value of region Output i and m is the number of fuzzy rules in the combined fuzzy rule base.

This five-step procedure is a one-pass build-up procedure that does not require time- consuming training of the FNIP system to contain the rule base. Additionally, the fuzzy- nets approach has the same advantages as those of the fuzzy control system--the fuzzy control system is capable of estimating functions and control systems with a partial description of the system behavior. This would be very difficult to be constructed by simply using neural networks models. Therefore the neural networks and fuzzy systems are married into a so-called fuzzy-nets system to facilitate a simple training technique for a complex system such as machining process. To understand the implementation in a practical setup for the FNIP system, a physical experimental setup and test are required to verify the approach.

3. THE EXPERIMENTAL SETUP AND RESULTS

The performance of the FNIP system is examined for an end-milling operation. The experimental setup consists of hardware and software. In this section the hardware of the experiment is described first, followed by a discussion on the development of software. Finally, experimental cuts are performed to develop the rule bases of the FSC and FAC systems to replace the FNIP system for practical implementation.

3.1. Hard~:are setup

The experimental setup is shown in Fig. 8. The basic components of the setup include the following:

• vertical CNC milling machine; • Kistler 9257A three-component dynamometer; • COMPAQ portable III microcomputer (286); • Omega WB-ASC analog/digital card; • charge amplifier with three channels; • various 3/4 in four-flute double end mills; • probe for revolution count; • workpieces.

KTN 37-6-¢


t

L ~ 3 Rotating Sensor

Workpiece

Dynamometer

Milling Table

CNC

Controller

Classifier Input I IBM-XT ~"

I (FNIP ,._ System)

CNC "-- ----

Parameters -"= Force(s)

(This block was not adapted in this research)

Fig. 8. The experimental setup for the FNIP system.

A/D

The cutting force signal is measured by a Kistler 9257A three-component dynamometer. The dynamometer is mounted on the table of the CNC milling machine with the workpiece mounted on it. The output voltage signal of the charge amplifier is collected by a COM- PAQ portable III microcomputer. The COMPAQ PC has an Omega WB-ASC data acqui- sition card installed in one expansion slot to sample the data on-line. The Omega WB- ASC card connected three channels: two for x- and y-forces and one for the probe signal synchronous with the spindle rotation. The speed of the Omega WB-ASC card for three synchronous channels is approximately 400 Hz. Experimental data is collected using four- flute (3/4 in diameter) high-speed steel (HSS) end mills for machining of 6061 aluminum blocks. The cutting geometry is shown in Fig. 9. Figures 10 and 11 show an example of force signals generated by the FSC system with a normal-tool cut and a broken-tool cut, respectively.

3.2. Software setup After the hardware has been properly set up, the system must be examined to ensure

its performance. The software setup consists of the following:

• CNC programs for the vertical CNC milling machine; • data collection program for the FSC system; • rule base and conflict resolving program for the FSC system; • data collection program for the FAC system; • rule base and conflict resolving program for the FAC system; • FNIP detection program.

All the programs except the CNC programs are developed in Borland C++. The functions of each program are summarized as follows.


la

i

"-- ~ Machined surface

I

I

Rotation of the spindle (Ns)

Z Direction

4-fluted HSS 3/4" end mill

- Table feed rate (fr) Workpiece (6061 aluminium)

Ix Y

Mill table coordinate

2.0

~" 1.5

o > 1 .0

O

" 0.5

0.0 0.0

Fig. 9. Cutting geometry with the same axial and radial depth of cut in the FSC system.

3.0

500.0 1000.0 Data Point

1500.0

Fig. 10. The force signals generated by the FSC system with normal-tool cut,

I

2.0

1.0 #.

' I

- - CuRing Force ......... Rotation Pedod

I ° 0 " '

0.0 500.0 1000.0 1500.0 Data Point

Fig. 11. The force signals generated by the FSC system with a broken-tool cut.


3.2.1. Collection program for the FSC. The data collection program performs two main functions: (1) it receives the force signal and performs the filter function to reduce the noise; and (2) it detects the resolution and peak forces, and computes the variation of the peaks. The data pairs are generated as the input-output data pair form specified in Equation (6), and the data are collected during the milling operation with a rotational speed at 550 rpm and a feed rate at 6 ipm with the axial depth of cut at 0.015 in. Note that the tool condition (y) is specified by observation after the cutting process, and the degree of each data pair (~d) is specified by engineers. A total of 328 data pairs are collected for the training procedure of the FSC system [10]. One half of the data are generated by normal-tool cutting conditions, with the other half being generated by broken- tool cutting conditions.

3.2.2. Base and conflict resolving program for the FSC. The rule base and conflict resolving program performs three functions: (1) it generates the rules; (2) it performs the top-down methodology; and (3) it performs the bottom-up methodology. After accomplishing these functions the program generates the fuzzy rule base of the FSC system. The trained rule base and the rule degrees are listed in Table 1. Note that there are only 48 rules out of 34 (81) rules that were generated with fuzzy rules. Note that blanks

Table 1. The FSC rule base before and after the testing experiments

Rule number VF~ VF2 VF3 ~rF 4 Output degree Rule

1 SM SM SM SM NR 0.9628 2 SM SM SM MD NR 3 SM SM SM LG NR 0.3378 4 SM SM MD SM NR 0.2546 5 SM SM MD MD BK 0.5681 6 SM SM MD LG NR 0.4134 7 SM SM LG SM BK 0.3541 8 SM SM LG MD NR 0.1236 9 SM SM LG LG 10 SM MD SM SM 11 SM MD SM MD 12 SM MD SM LG 13 SM MD MD SM NR 0.5512 14 SM MD MD MD BK 0.2897 15 SM MD MD LG BK 0.2573 ! 6 SM MD LG SM BK 17 SM MD LG MD BK 0.1848 18 SM MD LG LG 19 SM LG SM SM NR 0.2607 20 SM LG SM MD BK 21 SM LG SM LG BK 0.1564 22 SM LG MD SM NR 0.5679 23 SM LG MD MD NR 0.6532 24 SM LG MD LG 25 SM LG LG SM NR 26 SM LG LG MD 27 SM LG LG LG BK 28 MD SM SM SM NR 0.5527 29 MD SM SM MD BK 0.5975 30 MD SM SM LG BK 0.5634 31 MD SM MD SM BK 32 MD SM MD MD BK 0.3975 33 MD SM MD LG NR 0.3895 34 MD SM LG SM BK 0.4528 35 MD SM LG MD BK 0.5786 36 MD SM LG LG 37 MD MD SM SM NR 0.5512 38 MD MD SM MD NR 0.6051 39 MD MD SM LG 40 MD MD MD SM NR 0.8677 41 MD MD MD MD NR 0.4039 42 MD MD MD LG NR 0.4562


Table 1. Continued

797

Rule number VF~ VF 2 VF3 VF4 Output degree Rule

43 MD MD LG SM BK 0.2574 44 MD MD LG MD NR 0.3468 45 MD MD LG LG 46 MD LG SM SM 47 MD LG SM MD NR 0.4123 48 MD LG SM LG BK 0.4875 49 MD LG MD SM BK 0.1745 50 MD LG MD MD NR 0.1646 51 MD LG MD LG NR 52 MD LG LG SM 53 MD LG LG MD 54 MD LG LG LG NR 0.3642 55 LG SM SM SM NR 0.3367 56 LG SM SM MD NR 0.4212 57 LG SM SM LG BK 58 LG SM MD SM NR 0.4251 59 LG SM MD MD NR 0.3254 60 LG SM MD LG 61 LG SM LG SM NR 62 LG SM LG MD 63 LG SM LG LG BK 0.3214 64 LG MD SM SM BK 65 LG MD SM MD NR 0.4112 66 LG MD SM LG 67 LG MD MD SM NR 0.2399 68 LG MD MD MD NR 0.2563 69 LG MD MD LG 70 LG MD LG SM 71 LG MD LG MD 72 LG MD LG LG 73 LG LG SM SM 74 LG LG SM MD 75 LG LG SM LG BK 0.1024 76 LG LG MD SM BK 77 LG LG MD MD 78 LG LG MD LG 79 LG LG LG SM BK 80 LG LG LG MD NR 0.3546 81 LG LG LG LG NR 0.2154

SM--smail, MD=middle, LG=large, NR--normal tool, BK=broken tool.

in the rule base (Table 1) indicate that rules have not been specified. This is because the process may not be sensitive to these rules or that the number of the training data pairs is insufficient. In cases where 6y is specified as 0.01, no conflicting rules occurred during the training procedure.

3.2.3. Collection program for the FAC. The data collection program for the FAC performs two main functions: (1) it receives the force signal and performs the filter function to reduce the noise; and (2) it detects the resolution of the spindle, identifies the peak forces and computes the p value [Equation (13)]. The data pairs are generated as the input- output data pair form specified in Equation (17), and the data are collected during the milling operation with various rotational speeds and feed rates, as listed in Table 2, and with the same axial and radial depth of cut as specified in Fig. 9. Various combinations of the cutting conditions, rotational speeds and table feed-rates have been set as the input- output pairs for training the FAC system. A total of 70 data pairs are generated and listed in Table 2.

3.2.4. Base and conflict resolving program for the FAC. Similar to the rule base and conflict resolving program for the FSC, the rule generation and conflict resolving program for the FAC performs three functions: (1) it generates the rules; (2) it performs the top- down methodology; and (3) it performs the bottom-up methodology. After accomplishing


Table 2. Seventy input--output data pairs for training the FAC system

Data set N~ (rpm) f~ (ipm) p value Data set N~ (rpm) f, (ipm) p value

1 550.0 6.0 1.0000 36 720.0 6.2 0.7437 2 530.0 7.0 1.0955 37 720.0 6.7 0.8471 3 555.0 7.0 0.9043 38 720.0 6.7 0.8059 4 530.0 6.5 0.8734 39 720.0 7.0 0.8186 5 555.0 6.5 0.8346 40 620.0 5.1 0.7498 6 530.0 5.5 0.8831 41 620.0 5.1 0.6017 7 550.0 5.5 0.8866 42 620,0 5.5 0.7122 8 530.0 6.8 1.0687 43 620.0 5.5 0.7113 9 555.0 6.8 1.0196 44 620.0 6.0 0.8224 10 550.0 6.0 1.0000 45 620.0 6.0 0.9077 11 530.0 6.0 1.0275 46 620.0 6.4 0.8143 ! 2 600.0 6.0 0.7534 47 620.0 6.4 0,8394 13 590.0 6.0 0.7262 48 620.0 6.8 0.8636 14 600.0 6.8 0.8292 49 590.0 5.0 0.7235 15 600.0 5.3 0.6923 50 590.0 5.0 0.6629 16 590.0 5.3 0.6953 51 590.0 5.3 0.7667 17 650.0 5.0 0.5322 52 590.0 5.3 0.6882 18 680.0 5.0 0.6594 53 590.0 5.7 0.7277 19 650.0 5.6 0.6146 54 590.0 5.7 0.8853 20 680.0 5.6 0.6499 55 590.0 6.1 0.8060 21 650.0 6.3 0.8035 56 590.0 6.1 0.7518 22 680.0 6.3 0.7170 57 590.0 6.5 0.7864 23 650.0 6.9 0.7185 58 590.0 6.9 0.7932 24 690.0 6.9 0.7117 59 570.0 5.0 0.8420 25 690.0 6.3 0.7668 60 540.0 5.0 0.7911 26 690.0 6.3 0.7270 61 540.0 5.4 0.9518 27 690.0 5.6 0.6834 62 570.0 5.4 0.9508 28 690.0 5.6 0.8490 63 540.0 5.8 0.9239 29 690.0 5.1 0.6471 64 570.0 5.8 0.7665 30 690.0 5.1 0.6302 65 570.0 6.2 1.0442 31 720.0 5.1 0.6420 66 540.0 6.2 0.9421 32 720.0 5.1 0.7402 67 570.0 6.6 1.0293 33 720.0 5.5 0.6875 68 540.0 6.6 0.8821 34 720.0 5.5 0.6261 69 540.0 7.0 1.0318 35 720.0 6.2 0.7795 70 570.0 7.0 1.0965

these functions, the program then generates the fuzzy rule base of the FAC (Fig. 12). Note that only 23 rules out of 52 (25) rules in the rule base were fired and no conflicting rule occurred in the training procedure (8 was specified as 0.01).

3.2.5. FNIP detection program. The FNIP program, which consists of the FSC and FAC programs and rule bases, is developed for testing. This program performs the detecting loop indicated in the architecture of the FNIP system (Fig. 1). Thus the FNIP is ready to perform various tests to evaluate the performance of the system.

3.3. Experimental tests and results

After the FNIP system is well trained, tests can be conducted to evaluate the performance of the system. Since all the rules are generated at the same depth of cut (0.015 in) and full immersion (Fig. 9), all the tests are performed at the same cutting condition with various rotational speeds and table feed-rates. A series of tests with different tool conditions (broken or normal tools), rotational speeds (530-730 rpm), and table feed-rates (5-7 ipm) are performed, and the results are listed in Table 3. Note that the first test of each test combination is investigated when the end mill starts to cut the material. This can be considered as a cutting condition with a variable radial depth of cut.

It is observed that the FNIP model can reasonably perform tool breakage detection with a successful rate of approximately 90% within the limited domain interval. Additionally, the tests modified the FNIP system so that it had a more stable rule base (Table 1). Note also that the rules, as underlined in Table 1, were generated during the testing experiments.


Ns

L2 SI SI MD SI

LI SI MD MD MD

MD St LI $1 MD

SI LI SI MD MD L2

L1 MD LI L2 L2 $2

S2 SI MD L1 L2

f~

Fig. 12. The FAM matrix bank of the FAC system.

Table 3. The testing combination and results with same depth of cut (0.015 in)

Test number Speed (Ns) rpm Feed rate ~ ) ipm Tool condition Detected result

1 550 6.0 Good 3, yes a 2 550 6.0 Broken 2, nob; 1, yes 3 600 5.5 Good 3, yes 4 600 5.5 Broken 2, yes; 1, no c 5 650 6.5 Good 3, yes 6 650 6.5 Broken 3, yes 7 700 6.3 Good 3, yes 8 700 6.3 Broken 3, yes 9 530 5.0 Good 3, yes 10 530 5.0 Broken 3, yes

aYes indicates the FNIP system is successful. bNo indicates the FNIP system is a failure. c2, yes, 1, no indicates that two out of three tests are successful, and one of the three tests is a failure.

This proves that the FNIP system has the capability to upgrade the rule base while the machining process takes place.

4. CONCLUSIONS

The FNIP system was developed and tested to detect tool breakage in an end-milling operation. From the testing results, the FNIP system was observed to be able to detect tool breakage in an end-milling operation "on-line", approaching a real-time basis. The use of fuzzy systems and neural networks to sense tool breakage for end-milling operations at various rotational speeds and table feed-rates are presented in the following points:

(a) The FNIP system is operated by using a vertical CNC milling machine which has been observed to have spindle run-out and rigidity problems. Kadiyala [11] demonstrated that the machine has approximately 0.004 in of spindle runout in the free cut condition (the spindle turns, but no material is cut) at the cutting edge of the cutter. Consequently, the ranges of rotational speed and feed rate in the experiments are limited. The failure tests shown in Table 3 might be affected due to this limitation. (b) The FNIP system requires hardware implementation to increase its decision-making speed. (c) The same training procedure and rule generating scheme can be applied to include more variables in the system, including workpiece material, variation of the radial and axial depths of cut, and the different number of teeth of the cutter.


(d) The force signal generated in this research is collected by a dynamometer which is relatively expensive and inconvenient for industrial applications. Other types of sensors may be used to replace the dynamometer. For example, accelerometers may be used for the FNIP system because of their cost effectiveness and ease of installation. However, the encoding system for the accelerometers should be identified before utiliz- ing the advantages of the FNIP system.

The main features and advantages of the new method developed in this paper are: (a) the proposed training procedure has the capability to resolve conflicting problems and to adapt the parameters of the system; and (b) fuzzy-nets can experience successful control for some cases whereas control cannot be attained using either a pure neural network control or a pure fuzzy control.

REFERENCES

[1] M.S. Lan and D.A. Dornfeld, In-process tool fracture detection, ASME J. Engng Mater. Technol. 106, 111 (1984).

[2] K. Matsushima, P. Bertok and T. Sata, In-process detection of tool breakage by monitoring spindle motor current of a machine tool. Measurement and Control for Batch Manufacturing, Winter Ann. Meet., ASME, Phoenix, Adz., p. 14 (1982).

[3] K. Iwata and T. Moriwaki, An application of acoustic emission to in-process sensing of tool wear, Ann. CIRP 26(1), 21 (1977).

[4] J. Tlusty and F. Ismail, Special aspects of chatter in milling, J. Engng Ind. ASME 105, 24 (1983). [5] M.S. Lan and Y. Naerheim, In-process detection of tool breakage, Z Engng Ind. ASME 108, 191 (1986). [6] Y. Altintas, I. Yeliowley and J. Tlusty, The detection of tool breakage in milling operations, J. Engng Ind.

ASME 110, 271 (1988). [7] I.N. Tansel and C. McLaughlin, Detection of tool breakage in milling operation--part II. The neural network

approach, Int. J. Mach. Tools Manufact. 33, 545 (1993). [8] Y,S. Tarng, Y.W. Hseih and S.T. Hwang, Sensing tool breakage in face milling with a neural network,

Int. J. Mach. Tools Manufact. 34, 341 (1994). [9] B. Kosko, Neural Network and Fuzzy Systems. Prentice Hall, Englewood Cliffs, N.J. (1992).

[10] J.C. Chen, Pokayoke systems in unmanned manufacturing cells. Ph.D. Dissertation, Auburn University, Auburn, Ala. (1994).

[11] S. Kadiyala, Private communication, Ph.D. candidate in the Mechanical Engineering Department, Auburn University, Ala. (1994).

A fuzzy-nets in-process (FNIP) system for tool-breakage monitoring in end-milling operations

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