RESEARCHES REGARDING CUTTING TOOL CONDITION MONITORING Marinela Ință1*, Achim Muntean1, and Sorin -Mihai Croitoru2 1Lucian Blaga University of Sibiu… [601243]

RESEARCHES REGARDING CUTTING TOOL
CONDITION MONITORING
Marinela Ință1*, Achim Muntean1, and Sorin -Mihai Croitoru2
1Lucian Blaga University of Sibiu , Industrial Engineering and Management Department , Romania,
Sibiu, B -dul Victoriei, no.10 , 550025
2 Politeh nica University of Bucharest, IMST Faculty, Romania, Bucharest, Spl. Independentei, no 313
Abstract . The paper main purpose is monitoring of tool wear in metal
cutting using neural networks due to their ability of learning and adapting
their self , based on experiments. Monitoring the cutting process is difficult
to perform on -line because of the complexity of tool wear process , which
is the most important parameter that defines the tool state at a certain
moment. Most of the researches appraise the tool wea r by indirect factors
such as forces, consumed power, vibrations or the surface quality. In this
case, it is important to combine many factors for increasing the accuracy of
tool wear prediction and establish the admissible size of wear. For this ,
paper bo th the theoretical data obtained from FEM analyze and
experimental ones are used and compared in order to appreciate the
reliability of the results .
1 Introduction
The most updated techniques for intelligent tool condition monitoring which are based on
inform ation from different sensors integrated with a neural network [1]. The artificial
neural networks (NN) consist of many massive interconnections of rather simple neurons
simulating the biological nervous system. These networks are also referred to as pa rallel
distributed processing. Based on the modification of weights , which came from the
experience, the network can learn from its past experience, so as to simulate the human
brain. So we can say these type of networks is intelligent, [2].
In any machini ng process, the survey of the tool state plays a central role in the process
diagnosis system. During this process the tool also loses the capacity of cutting, which is
referred as tool wear. The size of the tool wear is the common way to evaluate the tool
state.
The monitoring of gradual wear requires the development of sensitive, accurate, and
reliable devices. On-line monitoring and compensation of the tool wear would be of a great
help to avoid the increase in cutting force [3] loss of accuracy, deteri oration in surface
finish, increase in cutting temperature and increase in vibration due to tool wear.

* Corresponding author: [anonimizat]

The main types of monitoring the tool wear are direct and indirect methods. Although,
both methods use different sensors to monitor the tool wear, but t hey differ in some aspects.
In the direct method [4], sensors directly measures the tool wear, such as optical
scanning technique, electrical resistance, radioactive technique, measurement of tool
geometry, change in work piece size, and analysis of tool wear particles in the chips. The
difficulties of direct methods lead to the indirect measuring techniques to measu re
accessible process variables ( machine tool vibrations, acoustic emission, temperature, noise
etc.), which are related to tool wear and corr elating the changes in these parameters to the
change in the tool wear. The static methods use some static characteristics of the
monitoring signal such as the mean the RMS [5]. Unfortunately, static methods are often
too sensitive to the variation of the cutting conditions and not suitable for on -line
monitoring . Therefore, the dynamic m ethods are developed, which use the dynamic
characteristics of the monitoring signal to identify and predict the tool condition.
The majority of the techniques discussed [6] above for tool wear sensing are limited due
to influence of external disturbances unrelated to tool wear, and inability of instrumentation
to operate reliably in the immediate v icinity of the cutting process . Investigations have been
carried out by sever al researchers on various tool wear sensors based on radioactive
isotopes. The input settings of the process are selected by evaluating, on -line, a set of
feasible alternatives with respect to several criteria. Relevant performance measures such as
process cost and production rate can be directly influenced in this approach by estab lishing
appropriate definitions for the decision criteria.
2 Monitoring of cutting tool state
Monitoring of the machining process has an important role in avoiding to long idle time of
the machine or preventing undesired events like loss of accuracy, excessive tool wear or
even the failure of the cutting tool.
The researches looking the conception of any tool state monitoring systems is
continuing for a while and even if the te chnology of sensors and computers has evolved, the
use in industry is poor , [7]. As [1] “suggested “, despite more a decade of intensive
scientific research, the development of the monitoring systems of th e tool wear is still an
attempt in course of develop ment”, even if a big variety of monitoring techniques have
been reported and many publications prese nted satisfactory performances. Thus, the
success of a monitoring system of the tool state depends in a great measure upon the
capacity of the system to ide ntify any anomaly and to answer on -line with a corresponding
action.
Measuring and analyze on purpose of diagnosis and monitoring assumes the conception
of certain hierarchic structures in a modular construction so that each module has to solve
the necessa ry input data for the next modules based on the data from previous ones. This
could be achieve based on information for each module and by inclusion in it of a
specialized monitoring function depending on the possible defect to appear.
For realizing an on -line tool condition monitoring system, we assume that:
 Normal function of the process depends directly on the tool condition; bad function
means an altered condition of the tool, that could be detected by the system;
 After detecting of an aberrant signal, the system must analyze and decide in accordance
with imposed conditions. The answer is send to the machine tool system for execution
of the received order;
 Uses of artificial intelligence by means of neural networks/ fuzzy logic increase the
accuracy and reliability of the system.
From the earlier researches, one can notice that the main parameters us ed to monitor the
tool wear are the cutting forces, vibrations, acoustic emission and the power consumed [8].

Because all these parameters are indirect indic ators of tool wear the use of single or only
two parameters causes a bad accuracy and reliability of the system. In a few cases , also the
temperature of the tool is considered , but due to difficulties encountered at the practical
measure of that , it is not practically useful.
This paper analyzes the possibility of using temperature of the cutting tool to be one of
the three parameters to indicate the state of wear of the cutting tool: main cutting force,
temperature and acoustic emission.
The researchers are focused on introducing the temperature as the third monitored
parameter of the wear , because all analytical models for the tool wear contain the
temperature as main parameters and everybody assumes that the main causes of toll wear
are mechanical and thermal loads. Also , the prediction of tool wear has the form of
temperature field in FEM analyze.
3 Tool wear simulation with FEM
Concerning the topic of the present paper the introduction of the FEM in the field of
deformation in elasto plastic media made possible the use of this method in the metal cutting
process . For establishing a theoretical model for the cutting temperature and forces, D eform
2D Machining software is used , for modeling and simulating of the cutting process , using
the assu mptions of o rthogonal cutting.
The software can simulate the cutting process using different cutting paramete rs like cutting
speed, feed and depth of cut for computing the cutting forces, temperature of cutting zone,
stress and strain state in chip and work piece, ap preciation of chip form and estimation of
the tool wear and tool life.
The data referring to the force, temperature and tool wear prediction (based on the Usui
model ) obtained from the simulation with FEM are used for preliminary training of the NN.
In th is way , we can obtain a theoretical function of the tool state , which links the tool wear
with the three parameters monitored.
Based on U sui Model (eq. 1) a program for solving the tool wear was developed and the
theoretical values of wear were calculate d:



TC
sev Cdtdw2
1
(1)
where
dtdw – wear rate;
C1, C2 – coefficients depending on tool and piece materials
sv
– chip shear velocity , [m/min],
T
– cutting temperature

– normal pressure
In order to obtain some input data for the algorithm of calculus of tool wear, referring at
load, forces, temperature a series of simulations were performed in DEFORM 2D software.
For the process of simulation there was used a to ol made of hard metal (WC), covered
with a layer of 5 microns of titanium carbo -nitrid (TiCN), and for the work piece material a
C45 steel annealed and a 50 mm in diameter specimen. Cutting parameters are presented in
table 1 together with working conditio ns that are introduced in pre – processor.
Simulation with FEM is made for determine the size of forces in order to obtain a model
of forces for the cutting process , taking into consideration the tool and process parameters
and also to determine the temper ature of the tool . In case of a new one and considering
different degrees of the tool wear, making the assumption that the temperature increase

with the degree of wear due to the change in tool geometry and increase of the friction
forces.
Table 1 . Input d ata for simulation
Nr. Tool geometry Feed,
[mm/rot] Depth of cut,
[mm] Cutting speed,
[m/min]
1.
4,056
00


r
0,07 0,5 52
2. 0,14 1,0 100
3. 0,28 1,5 200
4.
4,056
00


r
0,07 0,5 52
5. 0,14 1,0 100
6. 0,28 1,5 200
7.
000
102,0 ;56

fw

0,07 0,5 52
8. 0,14 1,0 100
9.
0,28 1,5 200

Consequently, to the run of the program of finite elements used for the simulation of the
turning process the following values for output parameters were obtained:
 the temperature distribution field in the cutting zone (figure 1);
 the isotherms of temperature field in the cutting tool, very useful for the estimation
of the tool wear;
 plane stress state in work piece and chip with effective stress;
 the load on X and Y for the chip elements in N.

Fig. 1. Evolution of temperature in the turning process (no. 3 from table 1)
With data from FEM simulation , a software product named “calcul_uzura.m” in Matlab
software . After running , the program and considering a real cutting time data were obtained
and presented in table 2.

Table 2. Calculated data ( theoretical values)
The calculated wear after a certain time cycle could be represented and used in the
monitoring sys tem, as the neural network learning curve.
4 Experimental test
In order to estimate tool wear by measuring noise experimental tests were carried out on the
same machine tool and under the same cutting conditions as in the case of attempts to force ,
tempera ture and noise .
Starting from the parameters of data acquisition board National Instruments USB6008,
three signals were acquisitioned (F, T and Z) with the sampling frequency imposed by
hardware, these being the instant values for input in the NN. The three acquisitioned signals
are performed using:
 workpiece made of C 45 steel with length 500 mm having diameter 30 mm with the
following characteristics: R p02 = 380 daN/mm2; Rm = 640 daN/mm2; HB 218;
 measure of the temperature, T variation in cutting area, p erformed by a natural
thermocouple tool -work -piece method;
 cutting forces, F it has been measured by dynamometer in turning;
 Measurement of noise, Z was made using Sonometer Quest Technologies 2100 .
4.1 Tool temperature measurement using natural thermocou ple
For measurement and acquisition on -line of cutting tool, temperature there was realized an
experimental stand and virtual instrument developed in LabView 8.5 software. It is well
known that the main problem encountered in tool temperature using the nat ural
thermocouple is its calibration. Temperature
[0C] Force F y
[N] Wear
[mm] Temperature
[0C] Force F y
[N] Wear
[mm]
20.00 0.00 0.00 298.00 93.82 0.50
111.00 9.79 0.05 302.00 109.22 0.55
153.00 10.70 0.10 306.00 122.42 0.60
202.00 36.82 0.15 307.00 135.62 0.65
227.00 56.47 0.20 315.00 148.82 0.70
259.00 56.57 0.25 326.00 162.02 0.75
265.00 70.45 0.30 338.00 175.22 0.80
278.00 81.91 0.35 349.00 186.20 0.85
283.00 88.56 0.40 353.00 232.50 0.90
290.00 92.10 0.45 357.00 298.76 0.95
298.00 93.82 0.50 359.00 338.56 1.00
302.00 109.22 0.55 361.00 387.95 1.05
306.00 122.42 0.60 363.00 432.99 1.10
307.00 135.62 0.65 367.00 547.32 1.15

That problem is caused because of the different materials used for the cutting tool and
for the work pieces that are normally different in each operation and product [9]. Using the
stand for different natural thermoco uple calibration presented in [10] which is able to
calibrate simultaneous three types of natural thermocouples with a reference K type
thermocouple we were able to calibrate the used couple of materials. The following
calibration equation for the couple P 30 – AISI 1045 steel was obtained:
x y  382.078.9
(2)
Where:
– y is temperature in [°C] corresponding to the measured voltage x in µV.
For obtaining the mathematical model of temperature was used a factorial experiment
type 32, and the follow ing mathematical model was derived:
22
010331.0003 8452.1 0155.0 00776.1 0035.2 4105.36
fv E fv f v T
  
(3)
Using the ANOVA analysis of the temperature model obtained, we can find influences of each
parameter and its concordance. Temperature dependen ce of the cutting parameters is
illustrated by th e graphic below (fig. 2).

Fig. 2. Interacting factors
We can observe that, the temperature increase more at small feeds that for the greater
feeds, where the chip thickness is bigger.
4.2 Cutting f orces measurement
To determine the influence of the cutti ng forces on tool wear an inductive dynamometer for
measuring cutting forces in turning was used.
To find out a model of the cutting force a full factorial experiment type 33 for the three
factors and three levels of variation was used, which results in 27 experiments. After data
processing with Design of Experiment software the following mathematical model was
obtained for the main cutting force:

vavf af v a f F
pp p y

82.428.23 86. 1119 26.7 33.153 07. 190876.386 (4)
The model obtained was analyzed using statistical tests ANOVA and Fisher, which gi ve
us the influence of each factor, reliability and diagnostics for each factor. In this case , the
degree of confidence used is 95%.
Fisher coefficient calculated for testing the suitability of the proposed model shows that the
main cutting force model is completely having factors interactions not considered in the model
and influence in a significant way values resulting from measurements.
Table 3. Significance testing factors
Source of variation Sum of squares DF Fcalc Prob>F
A factor – feed
83891.70 1 4.35 0.0574
B factor -depth of cut 4.90 e005 1 25.40 0.0002 The most significant
C factor – cutting speed
59155.32 1 3.06 0.1036
AB factor 46563.01 1 2.41 0.1444
AC factor 1.177 e005 1 6.10 0.0282  significant
BC factor 1.145 e005 1 5.93 0.0300  significant
Comparing coefficients calculated by Fisher's test for F y component, it appears that, B
factor (depth of cut) is the most significant parameter.
To be able to achieve a prediction of wear (VB) in terms of cutting force, F y we assume
that the fo rces measured during the experiments are distributed equally over the entire area
of contact between tool and workpiece. Also in this case it were made a series of
measurements on the same cutting conditions, keeping constant cutting conditions and
using d ifferent wears of inserts. To obtain the mathematical model a factorial experiment of
type 33 was developed, and after analysis with software Design -Expert the following
mathematical model:
VBf VBpafpa VB fpa Fy

53. 3333 11. 145965.234 24. 1307 21. 1017 44.42080.415
(5)
Analyzing the obtained results the following conclusions can be drawn:
 analysis of cutting force signal (sudden increase or decrease) one can detect a
collision, breaking or other undesirable random events;
 for achieving a correlation wear -force a mathematical model was derived both for a
new tool and a worn one having a wear rate VB = 0.25-1.2 mm;
 in the initial experiment the parameter cutting speed was suppressed due to the low
influenc e and replaced with parameter wear, VB that allows to determine a correlation
force -wear;
 in the new model the order of influence in the output is VB, a p, f that indicating that
force could be used as monitored parameter of the wear process;
4.3 Noise measurement
The results of the measurements were processed using a virtual instrument in Lab View 8.5
software presented in figure 3. To obtain a clearer signal and for eliminating other factors to
intervene in the cutting process a measurement chain of noise band -pass filter was used,
which will completely attenuate any signal outside the desired range.

Fig. 3. Virtual instrument for noise analyze
Analyze and interpretation of measurement results for the cases mentioned wear were
perfo rmed using the software Sigview .
The foll owing features were obtained and analyzed:
– noise amplitude (Figure 4),
– analyze FFT (Fast Fourier Transform);
– highest 5 peaks ;
– determining the fundamental harmonic signals , etc.


Fig.4.Variation of noise amplitude in time
In order to use the theoretical instead of the experimental data for training the neural
network those were compared to the experimental values obtained for forces and
temperature with the ones obtained in FEM:
– The temperature difference between the maximum temperature measured and that
obtained by finite element simulation is greater (about 30%) , because in experiment
we measured the average temperature of tool – chip contact and in FEM it was the
maximum tool temperature on a discrete element .
– If we consider the average chip -tool tempe rature, model accuracy is much better, with
less than 3.5 % error.

5 The model of neural network (NN) in monitoring
For better on -line monitoring of machining processes, it is necessary to have the actual
process of tool wear. Tool wear can be estimat ed from the signals of sensors.
The use of NN for monitoring the tool wear involves a series of measures linked with
data acquisition, processing and input of the measured values in the NN. The main steps
are:
 data acquisition of the signals for forces, tem perature (theoretical values) and noise
(real values);
 normalization of the data;
 transmission of the data in the input layer of NN.
The Neural network is composed by 3 layers using the inner product DOTPROD of bias
functions, the net input function NETSU M and the transfer function TANSIG. The
designed network for wear prediction is composed as follows: 3 neurons on the input layer
(for force, temperature and noise), 3 neurons in the hidden layer and a neuron for the output
(normalized value of the wear). Due to the training back propagation algorithm needs that
activation functions must be limited and differentiabl e a virtual instrument has been
realized for the activation function as sigmoid type both for the neurons in the hidden layer
and for the output level.
For data normalization a program in Microsoft Visual Basic was developed. It takes the
acquired data in an time interval from Excel Cells in 3 vectors as described in the program
code presented below . The result of this processing is that it keeps a constant activity level
at the level of a neuron layer. Because of the heterogeneous 3 values of the input data their
scales are completely different and that must be eliminated. The normalized data for NN
training having the values between 0 and 1 are p resented in figure 6 and the values were
used as example for the network training.

Fig. 6. Template learning using input data
With proposed neural network, model and theoretical data obtained from FEM a NN
training was done using theoretical wear funct ion as output and adding the third parameter
as measured noise considering that as an improvement of accuracy.
6 CONCLUSIONS
The main goal of the paper was to show that introducing tool temperature as input factor in
neural network training besides forces and noise or vibrations improve accuracy and

stability of the results. Important researches were performed for improving the method of
natural thermocouple for measuring the variation of tool temperature.
Considering both theoretical results and experimen tal ones, we can obtain an ideal set of
input data for neural network training for different sets of tool and workpiece materials. The
complex function obtained can be tuned in function of real conditions for each cutting
operation, taking into considerati on the measured parameters from on line sensors.
The difference between the measured and computed temperature is inside a reasonable limit
(approx. 3% for average temperature ) and this can be explained by the fact that the natural
thermocouple measures th e average temperature of tool -piece interface. Achieved small
difference between theoretical and experimental values justify the use of the calculated
values instead of experimental and can thus generate databases for preliminary training
neural networks
The present paper aims to contribute to the integration of neural network capabilities in
the Lab View environment f or improving computer -aided control and monitoring of
machining processes.
Further researches are proposed for improving the accuracy, reliability and
communication between the tool state monitoring and the NC of the machine tools.
References
1. B. Stick. On line an indirect tool wear monitoring in turning with artificial neural networks . A
review of more than a decade of research, Mechanical Systems an Signal Processing, 16(4) , 487 –
546, (2002 ).
2. R. J. Schalkoff. Artificial neural networks . NewYork:McGraw – Hill Inc., ISBN 0 -07-057118 -X,
(1997 ).
3. A. Salimi , M. Zadshakouyan. Tool condition monitoring using cutting and thrust forces and
fuzzy logic technique . Australian Journal of Basic and Applied Sciences, 3(1), 283-290, ISSN
1991 -8178, (2009 ).
4. T. Özel , Y. Karp at Predictive modeling of surface roughness and tool wear in hard turning using
regression and neural networks , International Journal of Machine Tools and Manufacture, 45,
2005, pp. 467 -479, (2005 ).
5. R. Allen, H. Shi. Tool wear monitoring using a combinatio n of cutting force and vibration
signals , International Journal of COMADEM, 4(1), 26-32, (2001 ).
6. C. Byrne, D. Dornfeld, I.I.G. Ketteler, W. Konig, R. Teti. Tool condition Monitoring – the status
of research and industrial application , Annals of the CIRP, 44, 541 -567, (1995 ).
7. L. Huajie , J. Dong. Dispatching rule selection using artificial neural networks for dynamic
planning and scheduling . Journal of Intelligent Manufacturing , 7, Number 3, 243 -250,
Springerlink, (1996) .
8. C. Scheffer, H.,Katz, P.S. Heyns, F. Klocke. Development of a tool wear monitoring system for
hard turnin g, International Journal of Machine Tool and Manufacture, 43, 973 -985, (2003 ).
9. U. Zuperl, F. Cus, B. Mursec, T.A. Ploj. Hybrid analytical -neural network approach to the
determination of o ptimal cutting conditions , Journal of Materials Processing Techn ology, 157-
158, 82-90, (2004) .
10. A. Muntean M., Ință,. Optimization of cutting parameters for special alloys – zircaloy 2 ,
Academic Journa l of Manufacturing Engineering, 11 Issue 2, 93-98, (2013).