Tool Wear Prediction When Maching Ti6Al4V Using Different Tool

Materials

Y. H. Fan1, a, M. L. Zheng1,b, M. M. Cheng1,c and D. Q. Zhang1,d

1,Harbin University of science and technology, Harbin150080, China ayihang1911@16.3.com, bminli@hrbust.edu.cn, c chengmingming23@163.com

Keywords: Tool material, BP neural network, Tool wear, Wear prediction model

Abstracts. The tool life is different when using different tool materials machine Ti6Al4V under

different cutting conditions. Based on the experiments of cutting Ti6Al4V using different tool

materials, the tool wear prediction model is established with BP neural network theory. The wear

prediction model is verified by further experiments. The results show that there is a valid prediction

interval when using BP neural network predicts tool wear. When the cutting speed is in the vicinity

of the training sample data, the relative error of the tool wear prediction and experimental results is

less than 10%, which can meet the actual production. The tool wear prediction model is helpful for

cutting tool material selection, cutting speed optimization and predomination the time of changing

tool accurately when cutting titanium alloy.

Introduction

Tool wear and breakage have been get attentions in titanium machining. Especially in modern

precision machining process, tool wear not only affect the machining accuracy of workpiece, and

even lead to the workpiece void. Therefore, tool wear prediction is of great significance in the

cutting process. As the American scholar B.M. Karmer said in CIRP 35th Annual Meeting: in

productivity enhancement of Computer Integrated Manufacturing System, no technology is more

important than accurate estimating tool life [1]. For decades, scholars have dedicated to this

research, and there are a lot of tool wear prediction studies in many articles, but their main concern

focused on the changing of the stress tool suffered from theoretical calculation to monitor tool wear

state, etc [2,3,4]. These methods are discussed theoretically and difficult to change for the actual

production conditions.

Since the 1990s, intelligent manufacturing technology has become the key technology in

manufacturing. Intelligent detection, fault diagnosis and prediction techniques of artificial neural

network have been used successfully. Artificial neural network has a very strong learning ability,

generalization ability, and nonlinear approximation ability. It can avoid the traditional complex

process of modeling. More importantly, it can be applied to the changing conditions of production

practice [5].

Titanium alloy Ti6Al4V is difficult cutting material. Because of the diversity of tool selection in

cutting Ti6Al4V, different tool material show different lifetime characteristics under different

cutting conditions, which gives great difficulties to determine the tool life. BP network is used to

predict wear of different tool material in this paper and it achieved good results.

Advanced Materials Research Vol. 188 (2011) pp 325-329Online available since 2011/Mar/29 at www.scientific.net© (2011) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMR.188.325

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Data needed by prediction model acquisition

Experiments. The input parameters of wear prediction model are tool material, cutting speed v and

cutting time t, and output parameter is flank wear VB. To ensure the prediction accuracy of the

model, training data should be not less than 20 groups, and should distribute uniformly or regularly.

In this paper, complete experimental method is used in this paper to provide training dates for the

network model.

Table 1 illustrates the tool materials and cutting conditions. As the cutting speed is the main

factor affecting tool wear, while titanium is precious metals, in order to reduce the test volume,

cutting speed of the three cutting elements is selected as an input for prediction model.

Table 1 Tool materials and cutting conditions

Tool type

Parameters SNMG120408N-UP SNMG120408N-UP SNMG120408N-UP

Tool

materials AC830P AC520U G10E

Coating TiCN+Al2O3+TiN(CVD) TiAlN+AlCrN(PVD) —

Cutting

parameters

v=40-140m/min

f=0.1mm/r

ap=1mm

v=40-140m/min

f=0.1mm/r

ap=1mm

v=40-140m/min

f=0.1mm/r

ap=1mm

Cooling

method Dry

Workpiece Ti6Al4V

Experimental results. Fig.1 and Fig.2 show the different VB and tool life respectively with

cutting speed v. These results will serve as a training data for prediction model.

Fig.1 v-VB Fig.2 v-T

Test data acquisition. Test data obtained is the basis for filtering the network model established

with training data. Except tool material, cutting speed and cutting time as input parameters, other

experimental conditions should be consistent. In order to ensure the accuracy of prediction model,

the number of test data should distribute regularly and it requires a certain amount of test data exists

in the edge of the training data so as to verify the extension performance of tool wear prediction

model.

Data pretreatment. As the values and dimension of input dates are different, the neural

network's internal parameters adjustment (weights and thresholds) vary greatly between each other

when training neural network prediction model. The weights and threshold value adjustment of

neural network make convergence slow, which is not conducive to get a better model, therefore, the

data need pretreatment to reduce the effects.

The normalized method is selected for data pretreatment. Normalized is linear mapping between

the raw data according to the value to get dimensionless new value distribute in [0, 1].

326 High Speed Machining

Neural network prediction of tool wear

BP neural network. BP neural network is used to establish tool wear prediction model and model

selection in this paper. BP neural network is the most commonly used neural network model. It

consists of input and output layer, hidden layer. Each layer contains a number of neurons. The input

layer and output layer neurons need the real issues to decide. The input layer neurons are 3 and

output layer neurons is 1. The number of hidden layers and number of neurons should be

determined by the actual situation. Weight and thresholds values are the parameters describe the

relationship between each layer and node. The global adjustment is adopted in BP network to

approach the target. The training process of BP neural network is the process of correction and

optimization weights and threshold values, as shown in Fig.3 [6].

Tool material

Cutting speed

Cutting timel

Flank wear

Input layer

Hidden layer

Output layer

No

Yes

Training over

Adjust the weight &

threshold value and

OK?

Fig.3 Training process of BP neural network

Setting main parameters of BP neural network extracting tool wear model. (1) Setting main

parameters of BP neural network

The main parameters setting of BP neural network include that input and output layer design,

model structure determination, the choice of hidden nodes and transfer function, and selection of the

training model. The parameters of tool wear prediction model are obtained according to practical

requirements, as shown in Table 2.

Table 2 Parameters of tool wear prediction model

Input

layer

Output

layer

Transfer

function 1

Transfer

function 2

Hidden

layer

Training data Hidden

nodes

3 1 tansig purelin 1 Normalized data 12

(2) Extracting tool wear model

According to the parameters of prediction model and weights and threshold values which have

been identified, it can exports mathematical function model between cutting speed, different tool

materials, cutting time, and tool wear VB when turning Ti6Al4V.

4

2 ( ( ( ) 1( , ) ) 1( ))

12 2( 2( ) ( 1)) 2

1 j

x j iw i j b ii

VB lw i b

e− × × +∑

= × − +

+

∑ (1)

Where VB is prediction values of tool wear, X(j) is input parameters of cutting speed, tool

material and cutting the time after normalization, iw1,lw2 are coefficient matrix and vector

respectively, and b1.,b2 are both constants.

Advanced Materials Research Vol. 188 327

0.2285 0.1186 -0.2998

0.0066 0.0002 0.1115

0.3650 0.3273 0.5293

-0.4869 0.2193 0.3763

0.0104 -0.1907 0.9868

0.0928 0.0674 0.401= iw

79

0.5641 -0.4837 0.4977

0.4228 -0.0819 -0.4491

0.2859 -0.2932 0.7153

-0.0284 -0.4710 -0.1001

-0.7808 -0.3306 -0.3001

-0.3539 -0.3902

0.1723

-0.9542

0.1858

-0.2378

-0.1357

0.3455; 1

0.5996

-0.3995

-2.3092

0.2293

0.5193

0.6458 0.4458

b

=

,

2 [0.0550 0.4114 -0.6305 0.0006

-1.3271 -0.8140 0.0032 -0.0114

-0.0406 -0.4357 -0.1586 0.0239];

2 -0.2967

lw

b

=

= (2)

Verify wear prediction model

Further experiments are carried out to verify the wear prediction model. Experiments were

conducted in CAK6150Di and tool selection shows in Table 1. Cutting speed v=30, 70, 110, 130,

150m/min, cutting time t=1,2,3,4min, feed f=0.1mm/r, and cutting depth ap=1mm.

The comparisons of experimental and predicted results are

shown in Fig.4. The number 1-3 represents tool material

AC830P, AC520U, and G10E respectively. The number 1-5

represent cutting speed 30, 70, 110, 130, 150m/min.

According to the comparisons of experimental and

predicted results, we can get the results that there is a valid

prediction interval when using BP neural network predicts tool

wear. When the cutting speed is in the vicinity of the training

sample data, the relative error of the tool wear prediction and

experimental results is less than 10%, which can meet the

actual production. However, when cutting speed is 130,

150m/min, the relative error is about 20%, and the model

cannot predict the value of tool wear well.

The big relative error can be explained by following reasons:

(1) The training number of neural network. Over-training will increase error of the network, but

too little also increase errors. How to select the training number scientifically and rationally should

be further analysis.

(2) As the scope limitations of training sample data, there exist bigger errors in the range far

from the sample data. This is the own weaknesses of BP network prediction.

(3) In establishing the prediction model, some complex parameters, such as cutting force, cutting

temperature, vibration and so on, which impact tool wear, did not taken into account. Especially

when cutting speed is higher, the parameters have greater impacts on tool wear. Therefore, the

greater the cutting speed, the relative error is bigger.

Conclusions

The tool life is different when using different tool materials machine Ti6Al4V under different

cutting conditions. Based on the experiments of cutting Ti6Al4V using different tool materials, the

tool wear prediction model is established with BP neural network theory. The wear prediction

model is verified by further experiments. The results show:

Fig.4 Comparisons of experimental

and predicted results

328 High Speed Machining

(1) There is a valid prediction interval when using BP neural network predicts tool wear. When

the cutting speed is in the vicinity of the training sample data, the relative error of the tool wear

prediction and experimental results is less than 10%, which can meet the actual production.

(2) Because of the effects of training number of neural network, own weaknesses of BP network

prediction, and as some complex parameters, such as cutting force, cutting temperature, vibration

and so on, which impact tool wear, did not taken into account when establish model, especially

when cutting speed is higher, the parameters have greater impacts on tool wear, the greater the

cutting speed, the relative error is bigger.

(3) The tool wear prediction model is helpful for cutting tool material selection, cutting speed

optimization and predomination the time of changing tool accurately when cutting titanium alloy.

Acknowledgement

This work was supported by “The Science and Technology Major Projects of China for High-grade

CNC Machine Tools and Basic Manufacturing Equipments (2009ZX04014-042).

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Advanced Materials Research Vol. 188 329

High Speed Machining 10.4028/www.scientific.net/AMR.188 Tool Wear Prediction when Maching Ti6Al4V Using Different Tool Materials 10.4028/www.scientific.net/AMR.188.325

DOI References

[3] KURAPATI VENKATESH, MENGCHU ZHOU, and REGGIE J. CAUDILL. Design of rtificial neural

networks for tool wear monitoring. Journal of Intelligent Manufacturing (1997) , 215-226.

doi:10.1023/A:1018573224739 [5] C. Chen, J. L. Xu, J. L. Huang. Chinese Journal of Mechanical Engineering, Vol.38 (2002), p.135-138.

doi:10.3901/JME.2002.03.113 [3] KURAPATI VENKATESH, MENGCHU ZHOU, and REGGIE J. CAUDILL. Design of artificial neural

networks for tool wear monitoring. Journal of Intelligent Manufacturing (1997) 8, 215-226.

doi:10.1023/A:1018573224739 [5] C. Chen, J. L. Xu, J. L. Huang. Chinese Journal of Mechanical Engineering, Vol.38 (2002), pp.135-138.

doi:10.3901/JME.2002.03.113