Mechanical Systems and Signal Processing (1996) 10(3), 265–276

APPLICATION OF NEURAL NETWORKS TOFLANK WEAR PREDICTION

J. H. L

Department of Automation Design, Korea Academy of Industrial Technology 219-5,Kasan-Dong, Kumchon-Ku Seoul, 152-020, Korea

D. E. K S. J. L

Department of Mechancial Engineering, Yonsei University 134, Shinchon-Dong,Seodaemoon-Ku Seoul, 120-749, Korea

(Received April 1995, accepted September 1995)

Tool wear sensing plays an important role in the optimisation of tool exchange and tipgeometry compensation during automated machining in flexible manufacturing systems.

The focus of this work is to develop a reliable method to predict flank wear during aturning process. A neural network scheme is applied to perform one-step-ahead predictionof flank wear from cutting force signals obtained from a tool dynamometer.

Machining experiments conducted using the method presented in this paper indicate thatusing an appropriate force ratio, the flank wear can be predicted to within 8 per cent ofthe actual wear for various turning conditions.

7 1996 Academic Press Limited

1. INTRODUCTION

Flexible manufacturing systems which employ automated machine tools for cuttingoperations require reliable process monitoring systems to overlook the machiningoperations. Malfunctions in the production line can be minimised by using suchmonitoring systems, thus improving the operation efficiency by 10 to 65 per cent [1]. Thevariables that need to be monitored during machining may include machine tool condition,tool damage, tool wear, built-up-edge, workpiece dimensions, surface roughness, chatterand chip formation. Among these variables, gradual tool wear plays a critical role indictating the dimensional accuracy of the workpiece. Hence, there is a need for a reliabletool wear monitoring system that may be used in conjunction with adaptive controlschemes [2] to compensate for the accuracy jeopardised by tool wear. Due to thecomplexity of the metal cutting mechanism, a reliable commercial tool wear monitoringsystem is yet to be developed. The incentive of this work is to develop a reliable and costeffective method for monitoring tool wear in real time using neural network techniques.

Tool wear measurement may be divided into direct and indirect methods. Directmeasurement techniques include touch probe sensors and vision systems while indirectmethods include tool dynamometers, acoustic emission (AE), accelerometer, power meterand temperature sensors. For real time tool wear measurement indirect methods are usedbecause it is impossible to approach the tool tip for direct measurement during machining.Among the sensors that are used for indirect tool wear measurement in real time, the AEtechnique is known to be the most favourable, probably due to its superior sensitivity over

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tool dynamometers [3]. However, because of their high reliability and relatively low cost,tool dynamometers are used more frequently than AE sensors for monitoring tool wear.

The fact that tool wear affects the cutting force is well known. Tool wear sensing usingcutting force signals obtained from dynamometers during machining has been researchedover decades. Empirical methods have been employed extensively to identify theparameters that govern the cutting force changes generated by tool wear. As machiningtakes place crater wear tends to alter the tool tip geometry in such a way that the cuttingforce is reduced [4]. However, the overall cutting force, namely the vector sum of thrust,feed, and main cutting forces, typically increases over the machining time due to flankwear. It has been shown that for a 0.1 mm width of flank wear the main cutting forceincreases by 10 per cent, the feed force by 25 per cent and the thrust force by 30 per cent.

One of the difficulties in trying to correlate a single component of the cutting force withtool wear is that the cutting force also changes as cutting conditions such as cutting speedand depth of cut are altered. Rather than using a single force component, Mackinnon etal. [5] claim that using the ratio of the three cutting force components is a more effectiveapproach in predicting the tool wear. Furthermore, Shi and Ramalingam [6] show thatchanges in the cutting force ratio with respect to changes in cutting speed and depth ofcut are not significant. Thus, a reliable correlation between tool wear and cutting forceratio which is insensitive to the changes in the cutting conditions may be established.

In this paper a method to predict flank wear from cutting force signals using a neuralnetwork technique is presented. A neural network approach is employed in order to copewith the stochastic characteristic of the cutting forces as well as the varied cuttingconditions. The strategy is to develop a neural network model to predict flank wear fromforce ratio information obtained from a tool dynamometer.

2. WEAR BEHAVIOR OF CARBIDE TOOLS

The mechanisms of tool wear are quite varied depending on the workpiece and toolmaterials, cutting conditions, and tool geometry. Tool wear may be classified as craterwear, flank wear and notch wear according to the location of wear on the tool. These typesof wear are attributed to diffusion, abrasion, and adhesion phenomena that takes placeat the sliding interface [7, 8]. For a given type of wear any or a combination of these wearmechanisms may play a role. Typically, diffusive and adhesive wear are more profoundduring high speed machining where the cutting temperature is relatively high while abrasivewear is more common during low speed machining.

Diffusive wear is known to occur as the carbon atoms on the rake face of the tool diffusetowards the workpiece at temperatures between 700 and 900°C, thus weakening the surfacelayer of the tool. Since diffusive wear is a function of temperature, it is commonly affectedby cutting speed and feedrate. Abrasive wear occurs when hard particles formed from workhardened chips or detached tool material abrade against the tool surface under highcontact pressure. Carbide and other tools which have relatively high hardness are lessprone to abrasive wear than high speed steel tools. Finally, adhesive wear is characterisedby strong adhesive junctions forming and breaking at the tool–workpiece sliding interfacepromoted by high affinity between the tool and the workpiece materials. In general, toolwear is an extremely complicated phenomena caused by a single or a combination of theabove mentioned wear mechanisms.

Prediction of tool wear from theoretical models is extremely difficult largely due to thenon-linear characteristics of the wear mechanisms. Varying cutting conditions andinhomogeneity in material properties are factors that contribute to the nonlinear wearphenomena. To consider the temperature effect as an example, high cutting speed causes

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the temperature to rise at the sliding interface thus causing diffusive wear, and furthermore,induces thermal softening of the tool surface allowing abrasive wear to occur. On the otherhand frictional force increase due to wear causes the sliding temperature to rise, thusinducing diffusive wear. Also, although the heat removed by the chip is about 50 per cent[8] when the metal removal rate is small, the percentage may drop to 10 to 15 per centwhen the metal removal rate is high. If such chips pile up at the rake face of the tool thetemperature at the sliding interface is apt to increase as well.

Due to the complications arising from the variations in temperature, material properties[9, 10], and microscopic tool tip geometry changes caused by built-up-edge and/or wear,the tool wear problem needs to be approached empirically from the stochastical point ofview. In the following sections a neural network technique, known for its effectiveness forhandling stochastic problems, as well as the tool wear experiments conducted to validatethe technique are presented.

3. PREDICTION OF FLANK WEAR

3.1.

The neural network utilised in this work is based on a multilayer perceptron modelwhich consists of input, hidden, and output layers, and uses error-back-propagationalgorithm for learning. Multilayer perceptron maps the input data and the desired valuesusing a non-linear sigmoid function [11] which can be shown as,

f(sum)=1

1+exp(−sum). (1)

Also, the units of each layer can be calculated by the following equation:

sum=wx+ u (2)

where x is the output vector of the previous layer, w is the weight matrix, and u is a biasvector that determines the location of the sigmoid function. Multilayer perceptron uses a

Figure 1. Neural network for one-step ahead wear prediction.

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Figure 2. Diagram of the experimental set-up.

cost function defined as the sum of squared error between the signal at the output layer,op , and the desired output signal, yp , to attain the optimal weighting factors for the model.The cost function is given as follows:

J=12

sp

( yp − op )2 (3)

where p is the index for the sampled data. Errors calculated from the output layerpropagates to the hidden layer where it updates the weights by the amount proportionalto the gradient of J,

Dw=−h1J1w

. (4)

A multilayer perceptron with one hidden layer, two input layer nodes, and one outputlayer node is employed to find the non-linear relationship between the cutting force ratios

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and tool wear. Using Taylor series expansion for the sigmoid function, the signal at theoutput layer, x(t), can be obtained by the following equation [12]:

x(t)= a0 + a1 x(t−1)+ a2 x(t−2)+ a3 x2(t−1 )+ a4 x2(t−2)

+ a5 x(t−1)x(t−2)+ a6 x3(t−1)+ a7 x3(t−2 )+ · · · (5)

where ai is a function of weight. Equation (5) shows that the multilayer perceptron modelincorporates the mean static cutting force expressed by a0, as well as higher order termswhich model the non-linear behaviour.

3.2. --

Despite the constant cutting condition maintained by the NC machine, the tool wearphenomena exhibit stochastic behavior largely due to the disturbance or noise originatingfrom various external sources. In order to deal with the noise problem in tool wearprediction a linear autoregressive exogeneous input (ARX) model with single input andsingle output is used. The output, x(t), input, u(t), and white noise whose mean is zero,e(t), are related by the following expression [13]:

x(t)= snx

j=1

axj x(t− j)+ snu

j=1

buj u(t− j)+ e(t). (6)

Also, the cutting time is an important factor that affects the temperature of the process,and therefore, contributes to the non-linearity of the wear mechanism. This matter canbe accounted for by using a non-linear ARX model expressed below [13]:

x(t)= f(x(t−1), . . . , x (t− nx ), u(t−1), . . . , u(t − nu ))+ e(t) (7)

where f( · ) is a non-linear function. In this paper the input, u(t) and output, x(t), representthe cutting force ratio and flank wear at time t, respectively. The white noise e(t), isgenerally within 10 per cent of the output signal.

A general schematic of the neural network with delay or lagging inputs is shown inFig. 1. The flank wear prediction is made from feedforward neural networks where thenumber of input units is obtained from lagged u(t) and x(t), both of which are terms of

Figure 3. Stochastic property of flank wear (v=100m/min; f=200mm/min; d=0.8mm).

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Figure 4. Three cutting force components for 80, 140 and 200m/min cutting speeds. r, Main cutting force;w, feed cutting force; q, thrust cutting force.

ARX, and the number of output units is determined by the number of predicting steps.In this work, one-step-ahead prediction is used, and therefore, the number of output unitsis one.

4. EXPERIMENTS

Tool wear experiments for turning process were conducted using an NC lathe (DaewooPAN-20) with carbide tools (P20) and initial workpiece dimensions of 80 mm diameter and250 mm length. The three cutting force components were measured with a tooldynamometer (Kistler 9257B) and the analog signals were stored in a tape recorder forpost processing with a data acquisition computer system. The schematic of theexperimental set-up is illustrated in Fig. 2. Following the cutting experiment tool wear wasmeasured using an automated vision system coupled with an optical microscope. Thismeasurement device has proven to be effective for flank wear measurement with highaccuracy and without subjective error that may be introduced by the user [14]. In all

Figure 5. Force ratio (Fr) obtained from Fig. 4.

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T 1

Results of the force ratio for various cutting conditions

Depth Approach Nose ChipItem Speed Feed of cut angle radius breaker Workpiece

Maximum 0.759 0.820 0.687 0.519 0.674 0.707 0.738Minimum 0.679 0.496 0.622 0.437 0.598 0.535 0.540Difference 0.080 0.324 0.065 0.082 0.076 0.172 0.198

machining experiments care was taken to ensure that chip formation did not affect thecutting forces. The chips were removed during the turning process to prevent chip roll-up.

In order to identify a force ratio that is sensitive only to tool wear regardless of thecutting condition, preliminary experiments were conducted using the following cuttingconditions:

Cutting speed 50, 80, 110, 140, 170, 200 m/minFeed rate 0.08, 0.16, 0.24, 0.32, 0.40 mm/minDepth of cut 0.4, 0.8, 1.2, 1.6, 2.0 mmNose radius 0.4, 0.8, 1.2 mmChipbreaker No breaker, MG, TC, MH, KEApproach angle 1, 15, 45°Cutting fluid not usedWorkpiece materials SS41B, SM45C, SCM4

A statistical analysis was performed using the data obtained from the preliminary teststo select the force ratio that is least sensitive to the changes in the cutting conditions shownabove. The force ratio obtained from this analysis is used as the input variable of the neuralnetwork model as explained in Section 5.1.

As for the flank wear experiments, the following machining conditions were used:

Test 1:Cutting speed=140 m/min, Depth of cut=1.2 mmTest 2:Cutting speed=180 m/min, Depth of cut=1.2 mmTest 3:Cutting speed=220 m/min, Depth of cut=1.2 mmTest 4:Cutting speed=140 m/min, Depth of cut=1.6 mmTest 5:Cutting speed=180 m/min, Depth of cut=1.6 mmTest 6:Cutting speed=220 m/min, Depth of cut=1.6 mmTest 7:Cutting speed=140 m/min, Depth of cut=2.0 mmTest 8:Cutting speed=180 m/min, Depth of cut=2.0 mmTest 9:Cutting speed=220 m/min, Depth of cut=2.0 mm

Feed: 0.3 mm/revInsert tip: ANMG 12408 (P20 no coating)Tool holder: PSBNR2525M (Approach angle: 15°)Overhang: 56 mmWorkpiece: SM45CCutting fluid: not usedTraining file: Test 1, 5, 9Test file: Test 2, 3, 4, 6, 7, 8

The force components were sampled at 50 Hz for 60 mm of cutting distance followedby flank wear measurement with the vision system. Figure 3 shows the flank wear data

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Figure 6. Measured flank wear for tests 1 to 9 ( f=0.3mm/rev). Test: 1, w; 2, q; 3, r; 4, e; 5, p; 6, +;7, W; 8, R; 9 Q.

as a function of the number of 60 mm cuts for five machining experiments conducted underidentical conditions. Such stochastic and non-linear properties of flank wear are modeledusing the neural network scheme presented in the previous sections.

5. RESULTS AND DISCUSSION

5.1.

Parameters affecting the three cutting force components are tool geometry, depth of cut,cutting speed, feedrate, workpiece properties, cutting fluid and tool wear. For example,the variations in the three cutting force components as functions of cutting speed are shownin Fig. 4. Indirect estimation of tool wear from cutting force measurements requires acomplete understanding of the effects of these parameters on the cutting forces. The keypoint is to identify the expression for the ratio of cutting forces that is most sensitive totool wear while being robust to other parameters [5].

The analysis of the preliminary experiments to determine the appropriate inputparameter for the neural network model resulted in the selection of the followingexpression of the force ratio among several candidates:

Fr =z(feed force2 + thrust force2)

main cutting force(8)

Figure 7. Photograph of flank wear measurement using the vision system.

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Figure 8. Results of the neural network learning using data from tests 1, 5 and 9. ——, Estimated; q,measured.

Figure 5 shows the variation in Fr with respect to the cutting speed corresponding tothe data given in Fig. 4. The difference between the maximum and the minimum Fr valuesobtained for various cutting speeds and other cutting conditions are given in Table 1. Itshows that Fr is relatively insensitive to speed, depth of cut, approach angle, and noseradius while the ratio is affected by feedrate, chip breaker type, and workpiece material.Thus, the Fr may be considered to be robust with respect to all cutting conditions testedexcept for the three variables where the spread of Fr is significant. One way to compensatefor this problem is to gather a separate database for Fr and feedrate, chip breaker type,and workpiece material to be used in combination with the flank wear prediction modelpresented here. In this work flank wear prediction using Fr as the model input is performedwith varying cutting speeds and depths of cut.

5.2.

The increase in flank wear as a function of the number of cuts for tests 1 to 9 givenin Section 4 are plotted in Fig. 6. A photograph of typical wear data obtained from thevision system is presented in Fig. 7. As shown in Fig. 6, the flank wear varies significantlywith-varying cutting speed and depth of cut. However, since Fr is insensitive to thesevariables, a neural network model using Fr as the input data can be employed to predictflank wear under varying cutting speed and depth of cut conditions. To accomplish this,a neural network consisting of an input layer with four units, a hidden layer with four units,and an output layer with one unit was used. Thus, x(t), representing flank wear is theoutput and [u(t−1), u(t−2), x(t−1), x(t−2)], which includes two lagged flank wearx(t−1) and x(t−2), and two lagged force ratio u(t−1) and u(t−2), constitutes theinput vector.

Results from tests 1, 5 and 9 were selected arbitrarily as the learning data for the neuralnetwork. The learning conditions [11, 12] are as follows: learning rate=1; increase inlearning rate=1.05; decrease in learning rate=0.7; momentum=0.95; errorratio=1.04. The results of network learning are shown in Fig. 8. Comparison betweenthe actual data and estimated values from the trained model shows good correlation, andtherefore, the weighting factors between the perceptron layers must have convergedadequately.

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Figure 9. Results of one-step-ahead flank wear prediction for (a) tests 2 and 3; (b) tests 4 and 6; (c) tests 7and 8. Estimated value tests 2, 4 and 7, ——; measured value tests 2, 4 and 7, w. Estimated value tests 3, 6and 8, –––; measured value tests 3, 6 and 8, q.

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Figure 10. Overall flank wear prediction error.

5.3. --

Figure 9 shows the one-step-ahead flank wear prediction capability of the trained neuralnetwork model for tests 2, 3, 4, 6, 7 and 8. The data shows that the maximum deviationof the predicted flank wear from the actual value is about 0.02 mm. The prediction errorcan be calculated using an error criterion expressed as follows:

error criterion=csN

t

(x(t)− x(t))2

sN

t

x2(t)

. (9)

Figure 10 gives the results of the overall averaged error calculations using the errorcriterion for various cutting conditions (tests 2, 3, 4, 6, 7 and 8). The range of percentageerror is within 6 to 9.5 per cent with the average being about 8 per cent. Thus, with constantfeedrate, chip breaker type and workplace material, the neural network model presentedin this paper is capable of predicting flank wear with reasonable accuracy for varyingcutting speed and depth of cut.

6. CONCLUSIONS

A neural network technique was used to predict flank wear for turning processes undervarious cutting conditions. Using an appropriate force ratio as the model input thefollowing conclusions can be made regarding the results of this work:

(1) Cutting force ratio can be used for robust prediction of flank wear for variouscutting conditions. However, variations in feedrate, chip breaker type, and workpiecematerials should be accounted for by other methods.

(2) The neural network model can be used to map the relationship between the forceratio and flank wear for varying cutting speed and depth of cut. The one-step-aheadprediction of flank wear resulted in an overall error of about 8 per cent.

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APPENDIX A: NOMENCLATURE

ai coefficient of Taylor expansione(t) white noiseexp ( · ) exponential functionFr force ratio for model inputf( · ) activation functionJ cost functionnx number of lagged outputnu number of lagged inputN number of dataop output vector of each layersum net input vector for p-th training datau(t) input variable (force ratio)w weight matrix between two units of different layersx(t) output variable (flank wear)x(t) estimated outputx outputs from previous layer or input vectoryp training targetu bias vector of each unith learning rate