Abstract— In this paper back-propagation artificial neural network (BPANN) is employed to predict the wear of

the gear hobbing tools. The wear of high speed steel hobs during hobbing has been studied. The wear mechanisms

are strongly influenced by the choice of cutting speed. At moderate and high cutting speeds three major wear

mechanisms were identified: abrasion, mild adhesive and severe adhesive. The microstructure and wear behavior of

two high speed steel grades (M2 and ASP30) has been compared. In contrast, a variation in chemical composition or

microstructure of HSS tool material generally did not change the dominant wear mechanism. However, the tool

material properties determine the resistance against the operating wear mechanism and consequently the tool life.

The metallographic analysis and wear measurement at the tip of hob teeth included scanning electron microscopy

and stereoscope microscopy. Comparing experimental and BPANN results, an acceptable correlation was found.

Keywords— Back-propagation artificial neural network abrasion, adhesion, cutting speed, hobbing, wear

mechanism

1. Introduction Gear hobbing is a widely used method in mass gear

production. A hob has a large number of cutting edges

arranged spirally around the tool body. Gear hobbing

remains a cutting technology where high speed steel

continues to find wide application in modern

manufacturing practices [1], [2]. Gear hobbing has

complicated process kinematics; chip formation and

Tool wear mechanisms. Many researchers investigated

the wear behavior and tool life of turning and milling

cutting tools [3]-[6]. To understand the wear

mechanisms in Gear hobbing, it is necessary to have a

brief understanding of the Hobbing tribosystem includes

hob, Gear, Cutting operation and sever contact in the

tool-chip interface. Among the various hobbing

parameters cutting speed has the most effective role on

wear behavior [7].

In this study the effect of cutting parameters on the

wear mechanisms of HSS hobs has been investigated at

industrial conditions. The type of high speed steel

influences the speed that will be used and the wear of

hob. Two grades of HSS (AISI M2 and ASP30) are

selected for this purpose. Moreover back-propagation

artificial neural network (BPANN) is employed to

predict the wear of the gear hobbing tools

2. Experimental procedure The main shaft of a tractor (Fig.1) is selected as gear

blank. Table 1 shows the detail of hob, gear and

hobbing condition. The vertical hobbing machining

Rh6/1623 is used. Cutting speed that has direct

relationship with temperature can change the

predominant wear mechanisms and wear behavior of

hob as well as gear. Scanning electron microscopy

(SEM) was used to study the worn surface and

microstructure. Stereoscope (Nikon-type 104) was used

to measure the flank and crater wear on the flank and

rake face. Surface profilometery is used to measure the

roughness of gear that produced by high speed hobbing

process.

Prediction of Wear Mechanisms in High Speed Steel Hobs Using

Artificial Neural network

M.JALALI AZIZPOUR, H.MOHAMMADI MAJD

PTRI OF ACECR

IRAN

m_jpour@yahoo.com

SELECTED TOPICS in SYSTEM SCIENCE and SIMULATION in ENGINEERING

ISSN: 1792-507X 359 ISBN: 978-960-474-230-1

Fig1: Main shaft of a tractor as gear blank

Table1: Hobbing tribosystem

Gear Hob conditions

Material :21

NiCrMo2

Hardness :170 BHN

Spure Gear

dk=50.8 mm

Lcut =256 mm

Number of teeth: 18

ASP30& M2

DP = 10

1 st

GL= ∞

D0= 100 mm

L= 100 mm

Hardness: 65

RC

Cutting fluid

Telos

Continuous

shifting

Cut depth:

5.5mm

Axial feed rate :

2mm/rev

speed : variable

Climb

3. Result of experiments Among the variable hobbing parameters, only the

cutting speed can affect the wear behavior strangely [7].

Cutting speed has direct relationship with Tool- chip

interface temperature [7], [8]. At lower hobbing speed

where the temperature is not high enough, a stable built-

up edge (BUE) protects the cutting edge against the

abrasive and adhesive wear. However, the formation

and rebound mechanism of BUE Causes a sudden

failure in cutting edges in chipping form. The relative

motion between Tool and blank at such condition is

stick–slip. At higher cutting speeds, this relative motion

changes to slip so that the BUE will be unstable to play

a wear particle (debris) role, causes three body abrasion

wear. Fig.2 shows the SEM topography of BUE in the

cutting edge. The segregated carbides and hard partied

from tool and blank have the same effects.

As shown in Fig.3 increasing the hobbing speed

leads to high abrasion wear. The peak of flank wear is at

65 m/min. At V= 65m/min the flank wear is 0.3 mm. in

this condition each of the adhesive and abrasive wear is

present but the abrasion is the predominant wear

mechanism. Fig. 4 shows the SEM topography of the

adhesive wear that exists at each condition. This

adhesive component often is referred to as mild

adhesive wear.

Two body abrasion wear is counteracted with high

yield strength (high hardness) as well as large carbide

volume of the HSS for three body abrasion [7],[10]. At

such condition also, the segregated BUE have a scratch

action. The presence of BUE as a thermal barrier layer

protects the rake face against the high temperature. At

cutting speeds higher than 65 m/min wear particles

(debris) begin to soften, and therefore lose their

abrasive role at flank wear. Declining in the flank wear

curve is because of phenomenon described above.

Softening and rebounding of the thermal barrier layer

leads to heat transfer from cutting zoon to rake face that

softens the hob tooth and forms a crater in rake face.

This adhesive component often is referred to as mild

adhesive wear. If the hob is used upper than its limit of

heat resistance, sever adhesive wear may result a large

scale plastic flow of surface material in direction of the

chip flow. Severe adhesive wear are primarily resisted

by HSS material through its high yield strength at

elevated temperatures (High hot hardness). At V=90

m/min the severe adhesion wear is predominant.

Adhesion wear mechanism is identified by deep

craters. The depth of crater increases always

proportionally with cutting speed. It will be useful to

find an optimum condition in which, the flank wear is

decreased but the thermal barrier role of BUE remains

exist. We can select V=50 m/min or higher cutting

speed such as V=95 m/min for economic aspects.

Macroscopic fracture of the tool can occur is a rather

scarce event. More common it localized chipping of tool

edge. Smooth tool surface, high fracture Toughness

promoted by fine grained structure of both matrix and

hard phased is counteractive tool properties against this

wear mechanism. This type of wear can occurs at low or

high cutting speeds.

Fig.2: SEM topography of built-up edge

SELECTED TOPICS in SYSTEM SCIENCE and SIMULATION in ENGINEERING

ISSN: 1792-507X 360 ISBN: 978-960-474-230-1

Fig. 3: Flank and crater wear vs. cutting speed

Fig.4: SEM topography of adhesion wear mechanisms

Fig.5 shows a micrograph of the microstructure from

ASP30 hobs. Fig.6 shows a micrograph of the

microstructure from AISI M2 steel used as hob material.

As can be seen, the microstructure consists of hard

particles dispersed in a soft matrix. ASP30 that is the

product of Anti Segregation Process has fine carbides as

well as homogeneous distribution of carbides through

the matrix. Then it is difficult for the particles to

segregate from the matrix and hence there will be some

debris at the interface. For this reason, abrasive wear in

M2 hobs is more severe compared with ASP30 hobs

(see Fig.7). High degree of C/V proportion in the

chemical composition (such as M42) leads to a

reduction in the hardness difference between the matrix

and dispersed carbides. It has the same effect on the

abrasion as well as adhesion wear. The critical cutting

speed in M2 hobs is reported to be 60 m/min [11].

The hot hardness of ASP30 hob is because of high

amount of cobalt in the chemical composition. It means

that the ASP30 hobs can be used at high speed hobbing.

Moreover, at high cutting speeds, another wear criterion

exists. Increasing the surface roughness (Fig.8) of the

gear produced at such conditions, limits the cutting

speed to be used. Therefore, for optimizing the cutting

operation it is necessary to consider the surface

roughness of gears as an important criterion. For high

speed hobbing, we may use rigid machine tool and

blanks with low length to diameter (L/D) proportion.

Fig. 5: SEM micrograph of ASP30 hob microstructure

Fig. 6: SEM micrograph of M2 hob microstructure

Fig.7: SEM topography of severe abrasion wear

mechanisms of M2 hob

4. Neural networks An artificial neural network is a parallel distributed

information processing system. It stores the samples

with distributed coding, thus forming a trainable

nonlinear system. The main idea behind a neural

network approach resembles the human brain

functioning. Given the input and the expected outputs,

the program is self adaptive to the environment so as to

respond to different inputs rationally. The objective of

this paper is to investigate the prediction of wear in gear

hobbing, by training a BPANN.

SELECTED TOPICS in SYSTEM SCIENCE and SIMULATION in ENGINEERING

ISSN: 1792-507X 361 ISBN: 978-960-474-230-1

Fig.8: Surface roughness of gear (µm) vs. cutting speed

The neuron can be classified into three types: input,

output, hidden neurons. Input neurons are the ones that

receive input from the environment, such as cutting speed

in this study (Table.2). Output neurons are those that

send the signals out of the system, like flank wear, crater

wear and wear mechanisms. As the activation function,

Sig activation function has been used, which is

continuous, nonlinear, monotonic non-decreasing and S

shaped: [12]

( )xe

xfβ−+

=1

1

In this study, the back propagation, which is a widely

used algorithm, is used in the training step. Back

propagation is a systematic method for training multilayer

artificial neural networks. It has a strong mathematical

foundation based on gradient descent learning. Elman BP

network train with the back propagation algorithm is used.

Elman networks are back propagation networks, with the

addition of a feedback connection from the output of the

hidden layer to its input. This feedback path allows Elman

networks to learn to recognize and generate temporal

patterns, as well as spatial patterns [13]. For an Elman to

have the best chance at learning a problem it needs more

hidden neurons in its hidden layer than are actually

required for a solution by another method.

This model has four layers including, an input layer,

two hidden layer and an output layer. In this work,

different number of hidden units has been employed to

obtain the optimum number of hidden units. The

experiments show that number of 20 units in the hidden

layer is enough to reach the desired accuracy.

Training of the neural network was done in

MATLAB, using Sig and TRAINLM function.

TRAINLM is a network training function that updates

weights and bias values in a back propagation algorithm

according to Levenberg–Marquardt optimization.

Levenberg–Marquardt algorithm is a highly efficient

method for solving non-linear optimization problems

[14], [15].

5. Conclusions

In this work, a two-layer back propagation network

is developed to best fit this nonlinear engineering

problem. Through comparison between the targeted

value and training results with different neuron numbers

in the hidden layers, an appropriate number of 20 is

suitable to set up this network. For this nonlinear

engineering problem, the appropriate algorithm is

Levenberg -Marquardt because it can reach high

accuracy. The error between the predicted value and

targeted one is little. Using this network can save much

time. To obtain the most proper network a net with

different structures has been trained. For this purpose

first a 1-20-20-3 network with 100 epochs was selected

to modify the weights. The average of mean square

error (MSE) was recorded. Other networks have been

investigated. According to comparison among the

structures it was be seen that the best network is 1-20-

20-3. After 160 epochs the mean of errors converged to

2.3e-6. (Table.3). 70 percent of experimental data was

used for training the network, 15 percent for test and 15

percent to verify the trained network.

Wear mechanisms of HSS hobs has been

investigated. Tribological and metallurgical analyses are

employed for this purpose. A summery of conclusions is

as follow:

• In HSS hobs at moderate cutting speed, the

predominant mechanism is abrasion wear at the

flank of cutting edge. The maximum of flank

wear is at V=65m/min

• Adhesive wear can exist at each hobbing

condition but at high speed hobbing the

predominant wear mechanism is severe

adhesive wear.

• The presence or absence of built-up edge is

important to change the predominant wear

mechanism.

• The hob made by ASP30 has high hardness,

excellent hot hardness and wear resistance

compared with M2.

• The grade of tool material can not change the

wear mechanism, but wear resistance of hob can

be improved by correct selecting of tool

material considering cutting condition to be

used and machining economy.

SELECTED TOPICS in SYSTEM SCIENCE and SIMULATION in ENGINEERING

ISSN: 1792-507X 362 ISBN: 978-960-474-230-1

• The error between the predicted value and

targeted one is near to zero and the network can

predict the wear with high accuracy.

Table2: Experimental results

min/m mm

V B

mm

KT Predominant

Wear mechanisms

10 0.05 0.005 Chipping

15 0.05 0.005 Chipping

20 0.08 0.005 Chipping

25 0.09 0.005 Adhesion(BUE)

30 0.1 0.005 Abrasion

35 0.12 0.007 Abrasion

40 0.15 0.01 Abrasion

45 0.17 0.01 Abrasion

50 0.2 0.015 Abrasion

55 0.22 0.02 Abrasion

60 0.25 0.025 Severe abrasion

62 0.25 0.03 Severe abrasion

64 0.28 0.03 Severe abrasion

65 0.3 0.03 Severe abrasion

67 0.27 0.03 Severe abrasion

70 0.25 0.035 Severe abrasion

75 0.23 0.04 Severe abrasion

80 0.21 0.04 Adhesion

85 0.18 0.05 severe adhesion

88 0.13 0.05 severe adhesion

90 0.13 0.05 severe adhesion

95 0.1 0.06 severe adhesion

98 0.1 0.06 severe adhesion

100 0.07 0.07 severe adhesion

Table3: Test conditions

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Determination of barreling curve in upsetting process by

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[15]M.T.HAGEN,"training feed forward network with the

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Network

output

BV

Network

output

TK

Crater wear

TK

Flank

wear

BV

cut

speed

(input)

0.0365 -0.00378 0.005 0.05 10

0.0798 0.004054 0.005 0.08 20

0.99 0.006915 0.005 0.10 30

0.1305 0.008219 0.010 0.15 40

0.18 0.018519 0.015 0.20 50

0.24 0.023029 0.025 0.25 60

0.29 0.034289 0.035 0.25 70

0.3 0.043573 0.040 0.21 80

0.15 0.049961 0.050 0.130 90

0.09 0.0699 0.070 0.070 100

SELECTED TOPICS in SYSTEM SCIENCE and SIMULATION in ENGINEERING

ISSN: 1792-507X 363 ISBN: 978-960-474-230-1