Predicting Flank Wear of Cemented Carbide ISO P10

Predicting Flank Wear of Cemented Carbide ISO P10
I. INTRODUCTION
Manufacturing companies similar to any other business aim at reducing the cost of
production in order to expand the profit margin in the current competitive business world.
The companies purchase machines which make it possible to produce finished products. For
example, an iron rod manufacturing company must purchase a cutting tool which they expect
to use for some time before replacing the components. In this sense, tool wear results in
replacement meaning making new orders and thus spending more. In this paper, a case of a
company dealing in turning of AISI 1045 using ISO P10 tool is considered for the analysis.
Tool wear calls for a replacement since degraded tools require more energy to operate and
might result in increased energy consumption. Thus, the economic consequence of replacing
a cutting tool comes with economic decision-making aspects [11].
According to Lopes [7], wear is undesirable deterioration of an element through the
removal of particles from a workpiece. Tool deformation is a tribological phenomenon
common with frequent machining resulting from increasing in surface irregularity [7]. The
parameters which affect the product quality and production cost include feed rate, cutting
speed and depth of cut. The major reason for optimizing turning process is to minimize
vibrations thus energy generated which cause increased tool wear [8]. Therefore, it is of
importance for the turning technique to attain optimal levels of the three parameters (cutting
speed, cutting depth and feed rate) in order to simultaneously achieve desired product quality,
reduced idle time and reduced cost of production [10]. According to [14], [12] and [4], the
cost of machining has a strong positive relationship with tool wear specifically in HSS cutting
tool.

II. METHOD AND DATA
AISI 1045 is medium tensile steel supplied in normalized condition or the black hot
rolled condition with has a tensile strength of between 570 - 700 MPa and Brinell hardness
ranging between 170 and 210 [1]. The approach used in the prediction of flank wear of AISI
1045 replicates the work done by Okokpujie et al. [16] titled “Experimental data-set for
prediction of tool wear during turning of Al-1061 alloy by high-speed steel cutting tools".
The data used for this project was obtained from the experimental data performed by Lopes et
al. [7]. The experiment collected data of end finishing end milling operations of AISI steel.
Therefore, instead of predicting Al-1061 tool wear the data on AISI 1045 is used to predict
flank wear of HSS used in the end finishing of AISI 1045.
The milling experiments were performed in a FADAL vertical machining center,
model VMC 15, with a maximum spindle rotation of 7500 RPM. The main motor of the
machine is the rate at 15 kW of power [7]. The cutting fluid used was synthetic oil Quimatic
MEII, and the tool overhang was 60 mm. A positive end mill tool, code R390-025A25-11M
with an entering angle of 90% and 25mm diameter. The tool had a medium step with three
inserts. In the experiment, three rectangular inserts with an edge length of 11 cm each were
used (Selvaraj & Chandramohan, 2010). The material of the tool used was cemented carbide
ISO P10 coated with TiN and TiCN through the PVD process with coating hardness of
approximately 3000 HV3 [7]. The tool's substrate hardness was estimated at 1650HV3 with
grain size less than 1μm. The work material used for the experiment was a rectangular block
with square cross-section 100mm and length of 300mm AISI 1045 medium tensile steel with
an estimated hardness of 180 HB [7].
The experiment was performed at the Federal University at Itajubá, Laboratory of
Mechanics. Measurements of the tool flank wear (VBmax) (fw) were captured with an optical
microscope (magnification 45X) with images acquired by a coupled digital camera. The

criteria adopted as the end of tool life was flank wear of approximately VBmax = 0.30 mm
[7]. The entire data set for 82 experiments was used to develop the prediction model. The
machining parameters such as feed rate of 0.01, 0.10, 0.15, 0.20 and 0.29 mm/tooth, and
radial depth of cut of 12.26, 15, 16.50, 18.00, and 20.74 mm and cutting speed of 254,300,
325, 350, and 396 m/min, was used as control factors during the finishing end milling
operation of AISI 1045 steel in order to predict flank wear, and to determine the effects of the
cutting parameters on the tool wear.
In this paper, the results were used to develop a model for the prediction of the tool
flank wear. Similar to Okokpujie et al. [16] data brief, least squares were used to estimate the
mathematical model presented in equation (1).

III. MATHEMATICAL MODEL AND COMPOSITION OF THE MATERIALS
According to Ramesh, Karunamoorthy & Palanikumar [13], the relationship between
tool wear and cutting parameters takes the form shown in equation (1).
TW max =K . V x . F y . Rz (1)
Where:
K is a constant
x, y, and z are the power equations
V is the cutting speed
F is the feed rate
R is the radial depth of cut.
Equation (1) is in the non-liner form thus least squares method would not be possible
therefore following the transformation procedure used by Okokpujie & Okonkwo [9]
equation is transformed using base 10 logarithm to equation (2).
log T W max=log K + x . log V + y . log F+ z . log R (2)
For simplification purposes, the equation is further transformed to
Y = β0 + β1 X1 + β2 X2 + β3 X3 +e (3)
Where:
Y =log TW max , β0=log K , β1=x , β2= y∧β3=z
X1 =log V , X 2=log F , ¿ X3=log R
e are the residuals from the regression.
The assumption made with this model is that the residuals are normally distributed with zero
mean and constant variance. In order to estimate the parameters of the model, the sums of
squares for the residuals are minimized. The minimization process involves minimizing
equation (4).