USING COLD ROLLED STEEL 1018

appliancepartΤεχνίτη Νοημοσύνη και Ρομποτική

19 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

61 εμφανίσεις

Manuel R. Piña
Monarrez

RELIABILITY ANALYSIS FOR TOOL WEAR
USING COLD ROLLED STEEL 1018
SPECIMEN AND DOE DATA

The 15th Annual International Conference on Industrial Engineering Theory,
Applications & Practice


1.
Introduction

2.
Process Description

3.
Analysis

4.
Conclusions

5.
References


Out line

1

t


Introduction

Actually,

one

of

the

problems

in

the

machining

industry

is

related

to

the

cutting

tool

wear
.

The

nature

of

tool

wear

in

metal

cutting

is

complex

and

is

not

clear

enough

besides

of

numerous

investigations

[
1
]
.


Here

we

analyze

if

the

tool

wear

behavior,

presents

different

flank

wear

depending

if

the

turning

process

starts

with

a

worn

tool

which

is

removed

of

the

tool

holder

but

used

later

in

a

new

cutting

process

and

when

the

turning

process

starts

with

a

new

tool

which

is

not

removed

of

the

tool

holder

during

all

cutting

process
.



Predictors

variables

cutting

speed

and

feed

rate


Response

variable

was

the

flank

wear



1

t


Introduction

In

this

work,

the

behavior

of

the

tool

wear

stages

was

analyzed

when

the

cutting

tool

was

remove

of

the

tool

holder

and

used

it

later

with

a

tool

wear

degree

(worn

tool)

in

a

new

cutting

process
.



According

to

the

problem

beforehand

mentioned,

following

questions

can

be

done
.


1
.
-
How

is

the

β

behavior

in

the

different

stages

of

Cutting

Tool

Wear

when

it

is

removed

of

the

tool

holder

and

used

it

later?


2
.
-
Is

there

difference

significant

between

the

only

model

to

predict

the

tool

wear

when

it

is

not

removed

of

the

tool

holder

during

all

cutting

process

and

the

models

used

to

predict

the

tool

wear

in

the

different

of

tool

wear

stages

when

the

cutting

tool

is

removed

of

the

tool

holder

and

used

it

later?

1

t


Process Description

The

material

used

was

specimens

of

1

inch

diameter

of

cold

rolled

steel

1018
.;

The

chemical

properties

are

C

(
0
.
15
-
0
.
2
),

Mn
(
0
.
6
-
0
.
9
),

P(
0
.
4

max)

and

S(
0
.
05

max)
.

The

cutting

tool

was

KC
730

carbide

inserts

mounted

on

the

tool

holder

MCLNR
-
124
BNJ
7
.







For each trial first we used a new cutting tool, and the tool wear stages were
identified as V1, V2 and V3. The specimens were processed on a CNC lathe
with coolant, the amount of flank wear was measured off
-
line with a tool
microscope with a 50X lens at several random periods of time.

1

t

Process Description

DOE

data

was

recollected

using

a

factorial

design

with

the

following

levels

and

five

replicates







The flank wear was used as indicator of tool life.




According to the ISO 3685, the critical tool wear is 0.20mm.




This amount of wear is used to estimate the pseudo
-
times

1

t


Analysis

The

experimentation

time

was

randomly

selected
.

Complete

data

are
:

1

t

Analysis

Anova

analysis

for

V
1

vs

Tool

Wear


Anova

analysis

for

V
2

vs

Tool

Wear


1

t

Analysis

Anova

analysis

for

V
3

vs

Tool

Wear

Since

for

V
1
,

V
2

and

V
3
,

the

regression

equations

are

significant,

they

are

used

for

relate

the

machining

time

with

the

tool

wear

as

follows

1

t

Analysis

Correlation

analysis

is

as

follows

1

t

Analysis


Since

the

regression

analysis

is

significant

(p<
0
.
05
)

We

used

these

models

for

estimating

for

each

wear

stage,

the

corresponding

pseudo

times

(time

for

which

the

corresponding

tool

wear

is

exactly

0
.
20
mm)
.



For Example by established the tool wear equal to 0.20mm for the stage v1
given in Table 3, the first
pseudotime

is:

1

t

Analysis

The

seudo
-
times

and

its

weibull

parameters

are

we could conclude that:



S
tage V1 presents an increasing hazard function and the highest wear rate.



Stage V2, has the lower beta value, decreasing the hazard function and the
higher MTTF value.



Stage
V
3, although has an increasing hazard function, because it has a
reasonable beta value, appears to be the most efficient machining and stable
process.


1

t


Conclusions

the

related

reliability

indicators

for

the

tool

wear

are

1

t


Conclusions



If

we

have

a

reliable

transference

function

and

a

critical

value,

it

is

posible

to

use

this

function

to

extrapolate

and

determine

seudotimes
.



It

is

clear

that

the

tool

wear

behavior

is

a

function

of

a

set

of

environmental

factors

(
covariables
)

and

due

that

a

more

complex

analysis

could

be

carried

out

by

incorporating

a

proportional

hazard

models

analysis
.



Also

is

worthy

to

consider

that

because

of

the

lower

pseudo
-
time

data,

we

could

make

a

more

accurate

goodness

of

fit

analysis

and

determine

if

another

probability

function

is

more

suitable

as

could

be,

in

this

case,

the

lognormal

model
.



We

consider

that

this

approach

is

reasonably

accurate,

mainly

because

the

fit

of

the

regression

polynomial

models

used

for

relate

the

factors

with

the

wear

tool

in

the

analysis,

present

a

multiple

correlation

index

close

to

one

and

neither

of

them

present

a

difference

higher

of

5
%

between

R
-
Sq

and

R
-
Sq(
adj
)

index

(see

Table

3
)

and

VIF

lower

than

ten
.



1

t


References

1. Abernethy R. B. (2009). The New Weibull Handbook. Fifth edition.
Abernrthy

North Palm Beach Florida. ISBN:0
-
9653062
-
3
-
2

2.
Choudhury

S. K,
Bartarya

G. (2003). Role of temperature and surface
finish in predicting tool wear using neural network and design of
experiments.
International Journal of Machine Tool & Manufacture
Vol

43
pp.747
-
753.

3. De Belie N.,
Monteny

J. and
Taerwe

L. (2002). Apparatus for Accelerated
Degradation Testing of Concrete Specimens. Materials and Structures /
Mat~riaux

et Constructions, 35: 427
-
433.

4.
Gertsbakh

(2000). Reliability Theory with Applications to Preventive
Maintenance, Springer
-
Verlag
, Berlin Heidelberg.

5.
Gonzalez

D. S., Piña M. R.,
Cheang

A. (2008).
Improvement of Concrete
Structural Elements MTTF by Applying a Polymeric Coating with
Vynilester

Seal.
International Journal of Industrial Engineering Theory Application and
Practice
, Special Issue Cancun Conference, pp. 428
-
435.

1

t


References

6.
Kopač

J,
Šali

S. (2001). Tool wear monitoring during the turning process.
Journal of Materials Processing Technology
Vol.113, pp.312
-
316.

7. Lin J. T., Bhattacharyya D,
Kecman

V. (2003). Multiple regression and
neural networks analysis in composites machining,
Composites Sciences and
Technology

V. 63, pp. 539
-
548.

8.
Modarres

M.,
Kaminskiy

M,
Krivtsov

V. (1999). Reliability Engineering and
Risk Analysis a Practical Guide.
Marcel
Dekker

Inc., New York
Basel
.

9.
Nahmias

S. (1999). Análisis de la Producción y las Operaciones, Santa
Clara
University
. Ed. Continental, S.A. de C.V., 1a Edición, México.
pp

687
-
745.

10. Nelson, W. (1990). Accelerated Testing: Statistical Models,
Test Plans and
Data Analyses.

Wiley
. New York.

11. Piña M., Rodríguez M., Díaz J. J.. (2005). Superioridad de la regresión
general Ridge sobre mínimos cuadrados.
CULCyT
// Enero
-
Febrero, Año 2, No
6,
pp

21
-
26.


1

t


References

12. Rico L. Noriega S. Aguirre J. J.
Diaz

J.J. and
Sanchez

J. 2009.
Tool Wear
Behavior in Turning Processes using a Worn cutting Tool.
International
Journal of Applied Engineering Research
,
vol

4, No.1, pp.15
-
24.

13.
Shelemyahu

Zacks

(1992). Introduction to Reliability Analysis Probability
Models and Statistical Methods. Springer
-
Verlag

New York Inc. ISBN:0
-
387
-
97718
-
X.

14.
Shyur

H,
Elsayed

E.,
Luxhøj

J. (1999). A general hazard regression model
for accelerated life testing. © J.C.
Baltzer

AG, Science Publishers.
Annals of
Operations Research,
91:263

280.

15. Valery
Marinov
, Experimental study on the abrasive wear in metal
cutting, Wear 197(1996) 242
-
245.

16. Wang K.,
Ke

J., Lee W. (2007). Reliability and Sensitivity Analysis of a
Repairable System with Warm Standbys
andRunreliable

Service Stations.
Springer
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1232.


Thanks!!!

The 12th Annual International Conference on Industrial Engineering Theory,
Applications & Practice