Journal of Scientific & Industrial Research
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Vol. 68, June 2009, pp. 530-539
J SCI IND RES VOL 68 JUNE 2009
Metal cutting process parameters modeling: an artificial intelligence approach
Dejan Tanikic1*, Miodrag Manic2, Goran Radenkovic2, Dragan Mancic3
1
2
Technical Faculty Bor, VJ 12, 19210 Bor, Serbia
Faculty of Mechanical Engineering Niš, A. Medvedeva 14, 18000 Niš, Serbia
3
Faculty of Electronic Engineering Niš, A. Medvedeva 14, 18000 Niš, Serbia
Received 22 October 2008; revised 19 March 2009; accepted 17 April 2009
This study presents metal cutting process’ parameters modeling (cutting temperature, cutting force, and quality of machined
surface) using artificial neural networks, and hybrid, adaptive neuro-fuzzy systems. Proposed models can be used for metal
cutting process optimization, increasing productivity and reducing manufacturing costs.
Keywords: Artificial neural networks, Metal cutting process, Neuro-fuzzy system
Introduction
In metal cutting process, work used is consumed in
the process of plastic deformation of cutting layer and
in overcoming of friction that occurs in the contact area
of cutting tool (cutting insert) and work material
(workpiece)1. Heat generated in chip forming zone, as
well as in tool and chip contact zone and tool and
workpiece contact zone directly influences quality and
accuracy of machined surface and other phenomenon,
which occur in metal cutting process2,3. Amount of heat
generated in metal cutting process is expressed through
work done in this process and mechanical equivalent of
heat4. Theoretically, three zones5 (cutting, tool-chip
contact, and tool-workpiece contact) of heat generation
can be identified during turning. One novel approach is
to remove heat generated through a cooling cycle as in
interrupted cutting6.
In metal cutting process, penetration of cutting wedge
of tool into material of workpiece is enabled by cutting
force. Cutting force magnitude, or amplitude of dynamic
component of cutting force, can be used as control signal
in adaptive controlled machining system7. A large
number of models and theories for calculating cutting
force are based on orthogonal cutting model8,9, and also
a model, which includes strains, strain rates and cutting
temperature10.
*Author for correspondence
Tel: +381-30-424-555; Fax: +381-30-421-078
E-mail: [email protected]
Indicators of machined surface11, which identify quality
of machined surface, include physical-mechanical
characteristics of surface layer (micro hardness, residual
stresses) and geometrical characteristics of machined
surface (roughness, corrugation and shape deviation).
Surface roughness is irregularities of material resulted
from various machining operations12.
This study presents metal cutting process’ parameters
modeling (cutting temperature, cutting force, and quality
of machined surface) using artificial neural networks, and
hybrid, adaptive neuro-fuzzy systems. Main goals are
qualitative analysis of metal cutting process, identifying
and resolving most frequent problems, and improving
manufacturing productivity.
Experimental Details
Proposed model (Fig. 1) shows way of component
relations and information flow of material handling system,
and linked information system that processes obtained data.
Piezoelectric dynamometer with amplifier, for measuring
cutting force, is positioned on lathe. Temperature changing
of cutting tool and workpiece material is monitored by
infrared camera. After machining, height of roughness of
machined surface is measured by using an appropriate
tool. All measured data is passed to data acquisition system.
In data modelling system, data is modelled in two ways by
artificial neural networks (ANN modelling), and hybrid,
adaptive neuro-fuzzy system (NF modelling). After data
TANIKIC et al: METAL CUTTING PROCESS PARAMETERS MODELING USING ANN
531
Fig. 1—Component relations and information flow of material handling system
Table 1—Measuring equipment and accessories
No.
Measuring equipment and accessories
Name
Manufacturer
Type
1
2
Middle lathe
Cutting tool
3
4
5
6
7
8
Dynamometer
Amplifier
Computer system
Computer
Infrared camera
Tool for measuring surface
roughness
“Potisje”-Ada
Tool holder – “Sandvik”
Cutting insert – “Sandvik”
“Kistler”
“Kistler”
“Hewlett Packard”
PA-C-30
PCLNR
CNMG
9265A3
5007A
HP 9000/300
Pentium II
Varioscan 3021-ST
Surftest SJ-301
“Jenoptik”
“Mitutoyo”
modeling, it is necessary to perform verification of models
(model testing), and finally comparison of obtained data.
Lathe, used for examining and measuring, is located in
the Laboratory for Production Engineering, at the Faculty
of Mechanical Engineering in Niš. Workpiece material
(cylindrical bars, dimensions φ45x250 mm) was not
previously thermally treated steel, with AISI designation
4140. This steel belongs to the group of doped, decent,
cold drown steels, with strength of Rm=1050 N/mm2, and
measured hardness of 205 HB. Measuring equipment with
accessories, needed for determining cutting temperature,
cutting forces and surface roughness during turning
process is denominated in Table 1. Metal cutting process
is carried out without using any coolant and lubrication
fluids, which can obstruct directly recordings of cutting
process. Pausing between two experiments enables system
to cool down to room temperature.
According to recommendations of manufacturer and
experience of machinist, SANDVIK Coromant cutting
tool [tool holder PCLNR 32 25 P12, and cutting insert
CNMG 12 04 08 (grade 235)] has been chosen.
Results and Discussion
Cutting Temperature
Because of stabilization of process temperature,
measuring should be done shortly after beginning of
cutting process 13 (30 s is enough for temperature
stabilization). Room temperature before start of
experiments was 28°C. Following parameters of cutting
process were adopted: cutting speed V (80, 95, 110, 125
and 140 m/min), feed rate f (0.071, 0.098, 0.196 and
0.321 mm/rev), and depth of cut a (0.5, 1, 1.5 and
2 mm). Minimal measured cutting temperature
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J SCI IND RES VOL 68 JUNE 2009
(214.82°C) occurs at: V, 80 m/min; f, 0.071 mm/rev; and
a, 0.5 mm. Maximal measured cutting temperature
(594.02°C) was measured at: V, 140 m/min; f, 0.321 mm/
rev; and a, 2 mm.
Increasing feed rate causes increase in temperature
of cutting; particularly at low cutting speeds. For example,
during machining (V, 80 m/min) for change in f from
0.071 mm/rev to 0.321 mm/rev, temperature rises about
89%, while with same conditions, during machining
(V, 140 m/min), temperature rises about 29% (a=0.5 mm).
Larger values of a cause an increase in cutting forces,
thereby resulting in increase in cutting temperature
[increasing of cutting temperature between depth of cut
(0.5-2 mm) is relatively monotonous, and takes values of
31-38%]. Cutting temperature is closely connected to
cutting speed. For example, while depth of cut is constant
(a=0.5 mm), increasing of V (80-140 m/min) causes
temperature increase of 70% (f, 0.071 mm/rev) and only
16% (f, 0.321 mm/rev).
Cutting Forces
Largest component of cutting force in all experiment
setups is main cutting force (F1), while radial force (F2)
and axial force (F3) are quite minor. Resulting cutting
force has minimal value of 5.234 kN (V, 80 m/min; f,
0.071 mm/rev; and a, 0.5 mm) and maximal value of
68.446 kN (V, 140 m/min; f, 0.321 mm/rev; and a, 2 mm).
Monitoring ratio of components of cutting force, rather
than monitoring magnitude of individual cutting force
component, can successfully fulfill state of cutting tool
monitoring14. During various cutting conditions, F1/F2 =
1.26-2.59, F1/F3 = 1.417-4.283, and F2/F3 = 0.603-2.355.
While cutting with constant parameters (f, 0.071 mm/
rev; V, 140 m/min; a, <1.25 mm), magnitude of F2 is larger
than magnitude of F3, while cutting with a >1.25 mm,
magnitude of F2 is smaller than magnitude of F3. From
this consideration, it can be concluded that general
relation, which exactly defines ratio between F1, F2 and
F3, does not exist. This ratio varies from case to case,
and depends on general cutting conditions as well as
adopted cutting regimes.
Components of cutting force and resulting cutting force
are in narrow relationship with feed rate. Increase of
resulting cutting force (at changing feed rate, 0.071-0.321
mm/rev) is 179-289%. Cutting forces directly depends
on depth of cut. Increasing depth of cut (0.5-2 mm)
increases cutting force by 180-340%; at larger values of
feed rates change is more intensive. Finally, cutting forces
are least sensitive to changes in cutting speed. In the
range of speed changes from 80-140 m/min, increase of
resulting cutting force is 1-40%.
Quality of Machined surface
For arithmetic mean deviation (AMD, Ra) modeling,
same input parameters as in temperature and force
modeling are adopted but cutting speed takes values of
80, 110 and 140 m/min. AMD directly depends on feed
rate. So, increasing feed rate increases AMD; more
intensive at higher cutting speeds. For example, for
machining (a, 0.5 mm), on increasing f from 0.071 mm/
rev to 0.321 mm/rev at V= 140 m/min, Ra increases by
211%, while for same cutting conditions and at
V= 80 m/min, Ra increases only by 42%.
Increasing depth of cut increases Ra,. In machining (f,
0.071 mm/rev), at a increasing from 0.5 mm to 2 mm, Ra
increases by 173%, while at machining with f= 0.321
mm/rev, Ra increases by only 4% (V, 140 m/min).
Increasing V also influences Ra. Increasing V from
80 m/min to 140 m/min (a, 0.5 mm), Ra increases 138%,
while Ra increases only 55% when a = 2 mm.
Big influence on height of roughness has built up edge
(BUE), as well as constant presence of frictional forces,
which cause interrupted character of cutting process. At
higher cutting speeds, this effect is almost completely
eliminated. So, real value of roughness approaches its
theoretical value. Real value of roughness depends on
geometric factors, as well as cutting speed, workpiece
characteristics, cutting tool angles, coolant and lubricant
fluids, elastic properties of workpiece, state of tool (sharp
or worn) etc.
Data Modeling using Artificial Neural Networks (ANN)
In ANN structure15,16 (Fig. 2), number of hidden layers
was set to 1 (with 2 neurons), after that number of hidden
layers and neurons in them was increased, with tracking
errors and possibility of generalization of appropriate
network architecture. Number of training cycles was
varied, too. Testing was done on ANNs (Fig. 3) with one
hidden layer of neurons with 2, 3 and 5 neurons (Ns=2,
Ns=3, Ns=5), and with two hidden layers of neurons with
2+2 and 3+2 hidden neurons respectively (Ns1=2 Ns2=2;
Ns1=3 Ns2=2). ANN with two hidden layers (with 3 and
2 neurons) has been found most accurate, and data
generalization has been successfully performed. So,
adopted ANN architecture is 3-3-2-1. Matlab software
was used for ANN modeling. Adopted learning algorithm
(Levenberg-Marquardt) provides best convergence in
approximation of an unknown function (function
prediction).
TANIKIC et al: METAL CUTTING PROCESS PARAMETERS MODELING USING ANN
533
Fig. 2—Schematic structure of artificial neural network (ANN)
Fig. 3—Error of different networks architectures subjected to number of training cycles
Three independent systems were created: I) ANN
for cutting temperature prediction (ANNTEMP); ii) ANN
for cutting force prediction (ANNFORCE); and iii) ANN
for arithmetic mean deviation of machined surface
prediction (ANNSURF). ANNs for cutting temperature
and cutting speed prediction were trained with training
set of 80 experimentally obtained data. Rest 31 data set
was used for verification (testing) of ANN. ANNSURF
was trained with training set of 48 experimentally obtained
data, and, after that tested with remaining 25 data set.
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Fig. 4—Architecture of characteristic adaptive neuro-fuzzy system
Data Modelling using Adaptive Neuro-Fuzzy Systems (ANFIS)
ANFIS17,18 contains a few number of layers with nodes
(neurons), each of which performs a particular function
(node function) on the incoming signals as well as a set
of parameters pertaining to this node. The type of the
function, which the node performs, may vary from node
to node, and a choice of node function depends on overall
input-output function that network simulates19. In general,
this system represents the way for adjusting existential
base of rules, using learning algorithm, which is based on
assembly of input-output pairs, used for training. In
architecture of ANFIS (Fig. 4), it is supposed that system
has just two input values x and y (Level 1), and one output
value z (Level 5). In case, rule base consists just two
fuzzy IF-THEN rules (Takagi-Sugeno type), as shown in
Level 2:
Rule 1: IF x is A1 AND y is B1,
THEN f1 = p1x + q1y + r1
Rule 2: IF x is A2 AND y is B2,
THEN f2 = p2x + q2y + r2
First part of fuzzy rule (after IF part of rule) is called
premise, while second part of the rule (after THEN part
of rule) is called consequent. From ANFIS system
architecture, it is obvious that for given values of premise
parameters, output value can be presented as linear
combination of consequent parameters20 as
f =
w1
w2
f1 +
f = w1 f1 + w 2 f 2 =
w1 + w 2
w1 + w 2 2
( )
( )
( ) ( )
(
)
( )
= w1 x p1 + w1 y q1 + w1 r1 + w 2 x p 2 + w 2 y q 2 + w 2 r2
…(1)
Operation of hybrid algorithm is composed from one
forward and one backward pass. In forward pass signals
are running until Level 4, and consequent parameters
are identified by least squares estimate. In backward pass,
error rates propagate backward and premise parameters
are updated by gradient descent.
On data modeling computer system, Matlab software
is installed (toolbox for modeling data using hybrid,
adaptive neuro-fuzzy systems). Based on this system,
appropriate application is created (called NeuroFuzzy),
which enables post processing of data about cutting
temperature, resulting cutting force and arithmetic mean
deviation of machined surface using experimentally
obtained data. Three neuro-fuzzy systems (NF) were
created: First one for NFTEMP, second for NFFORCE
and third for NFSURF.
Just like with modeling using ANNs, input parameters
adopted are V, f and a, while output values are NFTEMP,
NFFORCE and NFSURF. After analytical consideration
of different architectures of ANFIS behaviors,
TANIKIC et al: METAL CUTTING PROCESS PARAMETERS MODELING USING ANN
535
a)
b)
c)
Fig. 5—Cutting temperature in relation to: a) cutting speed; b) depth of cut; and c) feed rate
parameters adopted were number of membership function
of each input value (2), type of membership function
(gbellmf), type of output membership functions
(constant), method of optimization used for modeling
(hybrid), and number of training epochs (60 for NFTEMP
and NFFORCE, and 30 for NFSURF).
Comparative Analysis
Fig. 5 gives comparative results of exploitation of
ANNTEMP and NFTEMP, as well as measured values
of cutting temperatures. Measured values of resulting
cutting force, and predicted values obtained by
ANNFORCE and NFFORCE are shown in Fig. 6.
Comparative results of exploitation of ANNSURF and
NFSURF, and measured values of arithmetic mean
deviation of machined surface are shown in Fig. 7. From
Figs 5-7, it could be observed that all measured and
predicted values match. Besides, measured and modeled
values, trendlines (a graphic representation of trends in
data sets) are shown in following figures. In this particular
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J SCI IND RES VOL 68 JUNE 2009
a)
b)
c)
Fig. 6—Resulting cutting force in relation to: a) cutting speed; b) depth of cut; and c) feed rate
case, polynomial trendline was used (this type of trendline
uses following equation to calculate least squares fit
through y = b + c1 x + c2 x 2 , where b, c 1 and c2 are
constants).
Analysis of maximum as well as average mean errors
for every single case (Table 2) indicates that ANFIS gives
a bit more accurate values than ANN. In almost all
situations (accept maximum error in cutting force
prediction), error was minor in case of using ANFIS.
System for Tool State Prediction
Main constraint factors, which occur during analysis
of tool state prediction, are related to: i) Providing required
quality of machined surface; and, ii) Maximum exploitation
of used tool. So, successful system must take into
consideration all relevant factors, as well as their overall
influence on current state of the tool. Systems for cutting
temperature, cutting force and surface quality prediction,
based on ANNs and ANFIS technologies, represent subsystems of this global system (Fig. 8). ANFIS, showing
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TANIKIC et al: METAL CUTTING PROCESS PARAMETERS MODELING USING ANN
a
b)
Fig. 7—Arithmetic mean deviation in relation to: a) cutting speed; b) feed rate
better characteristics, were adopted for sub-systems of
main system. First variant represents “parallel” connection
of this sub-systems where input parameters of all models
are cutting regimes, while output values, together with
other relevant parameters goes in artificial intelligence
based system for prediction of cutting tool. In second
variant “serial” outputs from foregoing subsystem
together with cutting regimes, represents inputs in
subsequent sub-system (or global system). At least, output
from last sub-system, together with other relevant factors,
goes into global (identical to previous case) system, which
gives prediction of cutting tool state. Principally, second
variant is a little bit complex, but accuracy of such a
system would be better (because of more precise
definition of input parameters of each sub-system). Global
(main) system can be based on ANNs, ANFIS
technologies, or some other artificial intelligence based
system. The output from this system is evaluation of
cutting tool state. This signal could be a significant and
Table 2 — Maximal and mean errors of used systems
Model
Max. error, %
Mean error, %
Cutting temperature
ANNTEMP
NFTEMP
14.057
8.773
3.496
3.396
Cutting force
ANNFORCE
NFFORCE
ANNSURF
NFSURF
13.637
17.843
4.388
3.876
Arithmetic mean deviation
12.628
6.932
11.321
6.310
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Fig. 8—Possible variant solutions of tool state prediction system
objective cutting tool state identifier, and could be further
used for projection of intelligent manufacturing.
2
Conclusions
This work shows possibility of implementation of
artificial intelligence based systems in metal cutting
process. In first phase, for different values of input
parameters (cutting speed, feed rate and depth of cut),
data acquisition is performed for cutting temperature,
cutting force and arithmetic mean deviation of machined
surface. Modeling of cutting process was performed in
next phase, using experimentally obtained data, and
artificial intelligence based approach (ANNS and ANFIS
systems). For some unknown values of input parameters,
system can predict some output parameters of interest.
For proposed experiment setup, ANFIS system showed
slightly better performances, and have priority in the
modeling of this type of data. Finally, global system for
predicting state of the cutting tool was proposed (with
sub-systems for cutting temperature, cutting force and
arithmetic mean deviation prediction).
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