Detection and Analysis Tool Wear of Carbide Flat End Mills 2T, 3T, 4T for Surface-Rough Machining by Third Wave AdvantEdge Detection and Analysis Tool Wear of Carbide Flat End Mills 2T, 3T, 4T for Surface-Rough Machining by Third Wave AdvantEdgeTM Softwa
Detection and Analysis Tool Wear of Carbide Flat End Mills 2T, 3T, 4T
for Surface-Rough Machining by Third Wave AdvantEdgeTM Software
Simulation.
Arranged as a requirement to completing undergraduate program in mechanical
engineering, engineering faculty
Dony Irvan Siswanto
D200122001
MECHANICAL ENGINEERING DEPARTMENT
Engineering Faculty
UNIVERSITAS MUHAMMADIYAH SURAKARTA
2016
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Detection and Analysis Tool Wear of Carbide Flat End Mills 2T, 3T, 4T for Surface-Rough
Machining by Third Wave AdvantEdgeTM Software Simulation.
1
1
Dony Irvan Siswanto, 2Agus Dwi Anggono, 3Zou Ye
Bachelor Student, Dept. of Mechanical Engineering, Wuxi Institute of Technology, China, 2Mechanical Engineering
Universitas Muhammadiyah Surakarta, 3Mechanical Engineering Wuxi Institute of Technology.
donyirvan@gmail.com
ABSTRACT
Surface rough-machining is one of the important process before finishing process. For the finding the
best result of rough-machining’s quality criteria, least machining time and least remaining volume without
making worse of tool wear after roughing so need detection and analysis optimum parameter proses without
ignoring tool wear’s aspect. Cemented Carbide flat End-mill with 2, 3, 4 Flutes used as the main case parameter
study with Al 7075 T6 as the workpiece materials, by calculation and bring it to Third Wave Advant EdgeTM
Systems Software simulation some data become recommendation production parameters as the final conclusion.
Proses kekasaran permukaan merupakan salah satu proses penting sebelum proses finishing. Untuk
menemukan hasil terbaik dari kriteria kualitas kasar-mesin ini, waktu mesin setidaknya dan volume tersisa
sedikit tanpa membuat lebih buruk memakai alat setelah hidup seadanya sehingga perlu deteksi dan analisis
optimum Proses parameter tanpa mengabaikan aspek memakai alat ini. Semen Carbide datar End-pabrik
dengan 2, 3, 4 Flutes digunakan sebagai studi parameter kasus utama dengan Al 7075 T6 sebagai bahan benda
kerja, dengan perhitungan dan membawanya ke simulasi Gelombang Ketiga Advant EdgeTM Sistem Software
beberapa data menjadi parameter produksi rekomendasi sebagai kesimpulan akhir.
Key words: Rough-machining (proses kekasaran permukaan ), Cemented Carbide End-mill (Karbida
Endmill), Al 7075 T6, Third Wave Advant EdgeTM.
.
requirement of finishing product is not same with the
expected result, the duration of production take long time
and others some unexpected problems.
INTRODUCTION
In the modern era, development in the fields of
engineering, especially engineering technology is
progressing very rapidly. The development of science is a
major factor in developing of technology in the field of
engineering sciences. So, in the case the speed and accuracy
are the challenges that faced today.
Machining plays important role in producing products.
Milling is the most widespread metal removal process in
metalworking industry. Manufactured products qualities are
determined by their surface quality. The high friction
between tool and work piece leads to high temperatures, tool
wear, and poor surface quality [1].The end-milling process
is one of the most widely used material removal processes in
industry [2] Others, the operation of the machine with the
type of automated or computerized also needed skill or
knowledge in how to operate it. Beside that during the
production some problem happened, like tool broken, the
Some studies have observe the problem that face in the
field of production. Current machining processed and
cutting tool designs are slow and too conservative, leading
to high cost and significant waste [3]. Tool failure may
result in losses in surface finish and the dimensional
accuracy of a finishes part, or possible damage to the work
piece and machine [4]. Because of that needs some
optimization in every area.
Optimization in every area is one kind one rules that
should be done before, during and after production. Increase
material removal rate of existing CNC programs to lower
cost, bid new jobs more aggressively, balance a cell or
provide additional capacity without CAPX [5] is one kind of
study about optimization process . In general type of end1
mills are varied and have each purpose of every types to
solve diverse problem in production.
Agnew,P.J. said in his paper ―for example, when doing
a slotting operation, unless doing a light cut of about 2D or
less, it is best to use a two- or three-fluted end mill. The
general rule is use less flutes for deeper cuts, with four or
higher flutes for light cuts. The reason for this is the
venerability of chip packing that can lead to destruction of
the end mill [6]. Finally, all of that are basically interesting
to make research in this field of finding optimization and
simulation before machining processes.
There are some studies as the reference of this research.
Those are researches about optimization carbide end mill
with analyze and simulation. Design and developing of a
multi-purpose carbide end mill (MP-CE) to suit all kinds of
cutting proposes which will uncover the background
information on different kinds of end mill structure, tool
materials and various surface treatment on the cutter [8].
Generalized modeling of milling mechanics and
Dynamics – Helical End Mill is study about predicted and
measured cutting forces, surface roughness and stability
lobes for ball, helical tapered ball, and bull nosed end mills
are provided to illustrate the viability of the proposed
generalized end mill analysis [9].
The research by Kuttolamadom,M[10] is about
prediction of the wear and evolution of cutting tools in
carbide/Ti-6Al-4V machining Tribosystem by volumetric
tool wear characterization and modelling have proven that
straight tungsten carbide wear when machining Ti-6Al-4V is
mechanically-driven at slow surface speeds and thermallydriven at high surface speeds.
Optimal Selection of Tools for Rough Machining of
Sculptured Surfaces is another research that focused on
Evaluation of the effect of depth of cut and stepover, as
major machining parameters and their influence on
machining time and remaining volume and real time
experiments showed that simulation results are really close
to reality and thus CAM software is an adequate tool for
optimization purposes [11].
And the research which using TWS AdvantEdge for
finding optimization both in effective time reduce and also
tool wear. Simulating steel machining AdvantEdge user
defined and user defined yield custom material options [12]
and the path for improvement in Third Wave Software [13].
ANALYZE METHOD
Before input data to the TWS software. Analyze the all
specimens is necessary to approaching in the real condition.
Material that used in this experiment is Al-7075 T6
80x50x40 mm. and after getting final result, cheking the
data with real machining to real check the effect after
optimalization.
Figure speciment of workpiece material Al-7075 T6.
3.2.2 Carbide Flat Endmill
In the real machining after finding optimum parameter
processing, the cutting tools that use is ø8 mm with 3-flutes.
Figure Flat End-mill ø8 3T , Tool-length view. Flutes
view.
All of preparation and consideration are used to realize
the result of optimalization by simulation. And the real
machining treatment are used to become initial data
parameter for the next consideration. And all the equipment
beside those already explained above others are
Figure workpiece clamped in the Chuck,.cutting tool
with tool holder.
2
b
rpm
mm/min
mm
mm
s
ame---»
a
s
ame---»
F
15%---»
n
s
ame---»
2-Flutes-3Flutes4Flutes
Initial simulation. Exact effect of tool wear with
selected machining. The parameter above is used to looking
for the tool wear before optimization. So, it’s become first
data or common data of initial parameters. 3D plug milling
simulation is use to know the tool wear both of three types
of tools.
Simulation and experiment 1. Finding optimal feed rate
by TWS simulation. Because rough process are so
complicated path line, the simulation taken by using 3D
pocket milling.
3000
595
1
6
3000*
700*
1*
6*
3000
805
1
6
2-Flutes
ẘ
F
0,
5
000782
05,75
0,
5
00085
50,004
0,
5
00092
95
3-Flutes
ẘ
F
0,
80
000829
5
0,
82
00085
5,0061
0,
92
000954
5,75
s
ame---»
+
s
ame---»
15%--»
s
ame---»
ẘ= 0,00085 is in different area both on three tools. By
interpolation method feedrate value can be determined and
the result shown below
The value of F both 2,3,4 Flutes become consideration
in the next prediction. Final data in experiment 1 shown that
changes feed rate give effect on the total production time
and the increasing the number of flutes followed by
increasing feed rate value in same DOC and spindle speed
condition.
In this experiment, changing depth of cut value is used
to finding optimum DOC value without discharging tool
wear value (ẘ). DOC basically not changing cutting time
but total metal removing time (mm3/min). And the result are
shown by graph. It shown correlation of tool wear (ẘ) with
DOC (mm) . Another side is correlation between metal
removal rate (mm3/min) and DOC (mm).in order to finding
optimum depth of cut value, make cross-section ẘ per DOC
and MR per DOC. and the point is DOC=0.75, MR=3,xx
and ẘ=0,075.
4-Flutes
ẘ
F
0,
10
000823
64,613
0,
11
00085
00,008
0,
12
000946
24,304
Simulation and experiment 2. From the result of this
simulation can be used for finding optimal and effective
deep of cut (DOC). Other parameters are same and only
change number DOC to finding effect of DOC to another
consideration.
Simulation
and
experiment
3.
The
data
recommendation above use to predict the continues-changes
data to finding optimization parameter’s process without
ignoring tool life. And the approaching way to finding
continues parameter data are:
a
m
m
….
300
….
….1
0
…..
…..
2
As the same way, to finding depth of cut by
interpolation with tool wear value is 0,075. And the result
shown in table below
From the calculation result above the analysis going to
the simulation, the simulation parameter and the result are
a)
Parameters
- N-Flutes
=4
- Feed rate
= 1100 mm/min
- DOC
= 1,22 mm
b)
Result
b
m
m
--+25%---»
n
--- -15%---»
--- -5%---»
2-Flutes-3Flutes-4Flutes
rpm
F
mm/mi
….
….
….
.
---same---»
N
6
6*
6
---same---»
--- -25%---»
---+15%---»
--- +5%---»
Finally all the result collect to the one data base and
making comparison for making conclusion. Also from the
first simulation data until the last. Showing the comparison
as prove of optimizing is done.
DATA ANALYZE
In this experiment, the changes variable data is feed
rate value. Based on formula, feed rate give significant
effect to the total time, both estimate time and metal
removal millimeter cubic per minute. And the result are
shown by graph.
It shown correlation of cutting time (t) with tool wear
(ẘ). Another side is correlation between feed per tooth (f)
and tool wear (ẘ).in order to finding effective- safety feed
rate, make cross-section time per ẘ and f per ẘ. and the
point is t=8, f=0,1 and ẘ=0,00065.
Figure result of simulation . (a).temperature-contour.
(b) . X-Y-Z Force. (c). Power-Torque-Temperature.
The result shown that with using the parameters above
the cutting tool in the best condition for machining.
3
number has no effect on both total production time and metal
removal rate but giving effect on tool wear( stress and force)
in each teeth .
3.
Changing feedrate value give significant effect on the
total production time, and the increasing the number of flutes
followed by increasing feed rate value in same DOC and
spindle speed condition. Opposite with that, increasing depth
of cut value also followed by increasing metal removal rate as
well in the same spindle speed rotation condition.
4.
In the same spindle speed value 3000 rpm, the
optimization rouh-machining process parameter (safetyeffective production) for 2T are 550 mm/min feed-rate value
with 2,45 mm depth of cut, 825 mm/min for 3T with 1,63 mm
depth of cut, 1100 mm/min for 4T with 1,2 mm depth of cut.
And the changes the value of spindle rotation be followed by
changing feedrate and depth of cut respectively.
Finally the data above to finding continues data
changes of parameter cutting process. In this graph shown
that the recommendation of choosing value of each
parameters, such as spindle speed, feed rate, depth of cut
and also the tool with the n-number of flutes. And the range
of optimum parameter which good in tool wear are check in
the Third Wave System AdvantEdge software.
Graph Correlation between spindle speed, feed rate
and Depth of cut
REFERENCES
[1]
p. Chockalingam and l. H. Wee, ―surface
roughness and tool wear study on milling of aisi 304
stainless steel using different cooling conditions,‖ int. J.
Eng. Technol. Vol. 2 no. 8, august, 2012. Melaka malaysia ,
vol. 2, no. 8, pp. 1386–1391, 2012.
[2]
b. . Damazo, m. . Dutterer, and m. . Kennedy,
―a summary of micromilling studies,‖ 1st int. Conf. Gen.
Meet. Eur. Soc. Precis. Eng. Nanotechnology,bremen, ger.,
2001.
[3]
c. Dr. Troy d.marusich, sustainable
manufacturing_u.s. doe advanced manufacturing office peer
review meeting. Clifornia santa barbara: third wave systems
inc., 2014.
[4]
x. Li, b. S. Lim, j. H. Zhou, s. Huang, s. J.
Phua, k. C. Shaw, and m. J. Er, ―fuzzy neural network
modelling for tool wear estimation in dry milling operation,‖
annu. Conf. Progn. Heath manag. Soc., pp. 1–11, 2009.
[5]
o. Gilbert, ―force balancing an advanced
toolpath.‖ Connecticut center for advanced technology,inc.,
usa.
[6]
p.j. agnew, ―what to consider when
evaluating solid carbide end mill machining.‖ .
[7]
y. Yang, x. Li, p. Jiang, and l. Zhang,
―prediction of surface roughness in end milling with gene,‖
proc. 41st int. Conf. Comput. Ind. Eng., pp. 441–446, wuhan
china.
[8]
h. Ku and w. C. Chia, ―design of multipurpose carbide end mill,‖ vol. 67, no. 2, pp. 34–40, 2006.
[9]
s. Engin and y. Altintas, ―generalized
modeling of milling mechanics and dynamics: part i - helical
end mills,‖ univ. Br. Columbia dep. Mech. Eng., no. 1997,
1998.
[10]
m. A. Kuttolamadom, s. Hamzehlouia, and m.
L. Mears, effect of machining feed on surface roughness in
0
1000
2000
3000
4000
5000
6000
20
4500
18
4000
16
3500
14
3000
12
2500
10
2000
8
1500
6
1000
4
500
2
0
0
1000
F-2T
2000
a-T2
F-3T
F-4T
3000
4000
Spinde speed (Rpm)
a-T3
a-T4
ẘ5000
DOC (mm)
5000
F (mm/min)
Corelation between Spindle Speed, Feedrate
ẘ ( and
.mm.K.α/oK.min)
DOC
0
6000
One of prove that the calculation and the result can be
recomendation are shown by simulation and real machining
analyze ( 3-flutes with different parameter).
CONCLUSION
1.
Optimization helps in determining the parameters that
result in both improved production rate and enhanced surface
quality before finishing process. In the present work
influences of end milling parameters namely Spindle speed,
Feed Rate, Depth of cut, Step over and Machining time (MT)
are studied with tool wear consideration.
2.
Increasing n-flutes number is directly proportional
with feedrate value and inversely proportional with feed per
tooth, if the number of feed rate are same increasing n-flute
4
cutting 6061 aluminum. Clemson university - international
center for automotive research copyright, 2010, pp. 1–19.
[11]
p. Spanoudakis, n. Tsourveloudis, and i.
Nikolos, ―optimal selection of tools for rough machining of
sculptured surfaces,‖ proc. Int. Multiconference eng.
Comput. Sci. 2008 vol ii imecs 2008, 19-21 march, 2008,
hong kong, vol. Ii, pp. 19–21, 2008.
[12]
t. H. C. Childs, ―simulating steel maching
advantedge user defined and user defined yield custom
material options.‖
[13]
troy d. Marusich, ―the path for improvement
in third wave software,‖ 2010.
[14]
c. Saikaew and p. Baowan, ―surface finish
improvement in ball nose end milling by optimizing
operating conditions for different cutting times,‖ indian j.
Eng. Mater. Sci., vol. 22, no. February, pp. Pp.38–50, 2015.
[15]
j. Kechagias, p. Kyratsis, and n. Mastorakis,
―on prediction of surface roughness of al7075alloy during
slot milling using nn modeling,‖ recent adv. Mech. Mater.
Mech. Eng. Chem. Eng., pp. 98–107.
[16]
v. S. Jatti, r. Sekhar, and r. K. Patil, ―study of
ball nose end milling of lm6 al alloy : surface roughness
optimisation using genetic algorithm,‖ int. J. Eng. Technol.,
vol. 5, no. 3, pp. 2859–2865, 2013.
[17]
w. D. S. M. Ponnala and k. L. N. Murthy,
―modeling and optimization of end milling machining
process,‖ ijret int. J. Res. Eng. Technol., pp. 430–447, 2012.
[18]
a. A. Syah, e. Sutikno, r. Raharjo, j. Teknik,
m. Fakultas, and t. Universitas, ―pengaruh feed rate dan
depth of cuts terhadap surface roughness pada proses milling
dengan,‖ jurusan teknik mesin fakultas teknik universitas
brawijaya.
[19]
ja. Schey, introduction to manufacturing
process 2nd, second ed. New york, 1987.
[20]
g. S. I. Hesler, ―investigating the mechanisms
of diamond tool wear cutting ferrous materials using a
quantitative study of machining parameters.‖
[21]
b. Sick, ―review on-line and indirect tool
wear monitoring in turning with artificial neural networks,‖
a rev. More than a decad. Res. Mech. Syst. Signal process. ,
pp. 16(4), 487–546,, 2002.
[22]
j. Qu and a. J. Shih, ―analytical surface
roughness parameters of a theoretical profile consisting of
elliptical arcs,‖ in machining science and technology, vol. 7,
no. 2, united states, 2003, pp. 281–294.
[23]
t. G. Stoebe and a. West, ―iron and steel —
properties and applications.‖ 2009.
[24]
alcoa inc., ―alcoa mill products-alloy 7075
plate and sheet.‖
[25]
sede legale e amministrativa/headquarters and
administration office, aluminum 7075-t6; 7075-t651.
Modena: crp meccanica s.r.l.
[26]
a. D. Isadare, b. Aremo, m. O. Adeoye, o. J.
Olawale, and m. D. Shittu, ―effect of heat treatment on some
mechanical properties of 7075 aluminium alloy,‖ mater.
Res., vol. 16, no. Sep,3rd, pp. 190–194, feb. 2013.
[27]
d. Singla and s. R. Mediratta, ―evaluation of
mechanical properties of al7075-fly ash composite
material,‖ int. J. Innov. Res. Sci. Eng. Technol., vol. 2, no.
April,4th, pp. 951–959, 2013.
[28]
v. Songmene, r. Khettabi, i. Zaghbani, j.
Kouam, and a. Djebara, ―machining and machinability of
aluminum alloys,‖ canada, 1983.
[29]
a. Gatto, e. Bassoli, and l. Iuliano,
―performance optimization in machining of aluminium
alloys for moulds production : hsm and edm,‖ university of
modena and reggio emilia-politecnico di torino,italy, 2010.
[30]
h.-t. Young, l.-c. Chuang, k. Gerschwiler, and
s. Kamps, ―a five-axis rough machining approach for a
centrifugal impeller.‖
[31]
j. G. Parmar and p. A. Makwana, ―prediction
of surface roughness for end milling process using artificial
neural network,‖ int. J. Mod. Eng. Res., vol. 2, no. 3, pp.
1006–1013, 2012.
[32]
chia wee chong, ―research and development
of multi purpose carbide end mill,‖ 2005.
[33]
t. Conical cutting tool, ―end mill attributes
and terminology commonly used industry language and
definisions.‖
[online].
Available:
http://www.conicalendmills.com/wpcontent/uploads/2014/10/end_mill_attributes_terminology.p
df. [accessed: 15-mar-2016].
[34]
m. Kuttolamadom, ―prediction of the wear &
evolution of cutting tools in a carbide / ti-6al-4v machining
tribosystem by volumetric tool wear,‖ 2012.
[35]
rao, manufacturing technology vol-ii 2e. Tata
mcgraw-hill education. Isbn 978-0-07-008769-9., 2009, p.
30.
[36]
j. R. Davis, asm international. Handbook
committee (1995). Tool materials. Isbn 978-0-87170-545-7.,
1995, p. 289.
[37]
k. Kadirgama, b. Mohammad, h. Al-ani, and
m. M. Noor, ―cutting force prediction model by fea and rsm
when machining hastelloy c-22hs with 90 ° holder,‖ j. Sci.
Ind. Res., vol. 67, no. June, pp. 421–427, 2008.
[38]
tws_user manual, third wave system
advantedge user manual version 4.8 (third wave
system,minneapolis). 2007, pp. 8–20.
[39]
c. Constantin, s. Croitoru, g. Constantin, and
e. S. T. R. Ă. Jescu, ―fem tools for cutting process modelling
and simulation,‖ u.p.b. sci. Bull., ser. D , vol. 74, 2012.
[40]
tws_advantedge 4.4, third wave systems
releases version 4 . 4 of advantedge tm classic, no. 1.
Minneapolis, mn 55349 usa: third wave systems inc., 2003,
p. 55439.
5
[41]
saurabh r. Virkar a, ―numerical simulations
and analysis of ductile mode micro,‖ kalamazoo, michigan,
2010.
[42]
tecplot inc., tecplot.360 2011 user ’ s manual.
Bellevue u.s: tecplot,inc., 2011.
[43]
copyright © 1995-2015 third wave systems.,
third wave advantedge user’s manual. Advantedge © third
wave systems windows © microsoft corporationnx®siemens
product lifecycle management software inc., 2015.
[44]
uddeholm
corrax,
―cutting
data
recomendations.‖
[online].
Available:
http://www.uddeholm.com/files/cutting_data_corrax_eng.pd
f.
[accessed:
08-mar-2016].
6
for Surface-Rough Machining by Third Wave AdvantEdgeTM Software
Simulation.
Arranged as a requirement to completing undergraduate program in mechanical
engineering, engineering faculty
Dony Irvan Siswanto
D200122001
MECHANICAL ENGINEERING DEPARTMENT
Engineering Faculty
UNIVERSITAS MUHAMMADIYAH SURAKARTA
2016
APPROVAL SHEET
ta clrrcrd *ce6d rod kgdi:sd ty secsviss,
lffi,iDg ty facgd af Iledcd E&gi"€Eiry Dcp4hd Wui ldi@ ef
Tecfutogr ad l{rsd of tr&ciakal F'ghnarng d@ffi L}uhcrsies
Thii find Rcpqt
h.
Mottr',.'qr& &ra&atr-
Ccrydbt :kylrre S&rEe rytn!al36lyl0*fI!00f
Psdi! i
&y : Tucrday
ib :22i,i620r6
Ss1rvixr I
Sqerviw'll
--re,
.4>
\ta'r
rt+
zJvou-tue,
{ASlrDri tirEee, Sf-ltLErE;.,ltJ
)
KrEig,
Itrad of MecSakal Frg
t,livlniue
DEp{lnd
iemndlnh Su*ztr
rrri lUiitorb Brru XlnilL $r, ,l,,s.- Pl-D)
I&ad of
fde&ical Fag Disd{eftt
YrD.i
VALIDAIION SHEET
Tnb Fiflf Rcpo.r
tc
bao ec*rce(
ecpd
ad tgdizrd fu .rytlvir*r,
D!t[!sd XIrBi brdrd! of
ll'l,t*iqg by }&ad qf M(E[uiGrl *"ffi
Techologr r6d }lcad of l,ktui.d E4lEiq
Muhmmdiyah
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Detection and Analysis Tool Wear of Carbide Flat End Mills 2T, 3T, 4T for Surface-Rough
Machining by Third Wave AdvantEdgeTM Software Simulation.
1
1
Dony Irvan Siswanto, 2Agus Dwi Anggono, 3Zou Ye
Bachelor Student, Dept. of Mechanical Engineering, Wuxi Institute of Technology, China, 2Mechanical Engineering
Universitas Muhammadiyah Surakarta, 3Mechanical Engineering Wuxi Institute of Technology.
donyirvan@gmail.com
ABSTRACT
Surface rough-machining is one of the important process before finishing process. For the finding the
best result of rough-machining’s quality criteria, least machining time and least remaining volume without
making worse of tool wear after roughing so need detection and analysis optimum parameter proses without
ignoring tool wear’s aspect. Cemented Carbide flat End-mill with 2, 3, 4 Flutes used as the main case parameter
study with Al 7075 T6 as the workpiece materials, by calculation and bring it to Third Wave Advant EdgeTM
Systems Software simulation some data become recommendation production parameters as the final conclusion.
Proses kekasaran permukaan merupakan salah satu proses penting sebelum proses finishing. Untuk
menemukan hasil terbaik dari kriteria kualitas kasar-mesin ini, waktu mesin setidaknya dan volume tersisa
sedikit tanpa membuat lebih buruk memakai alat setelah hidup seadanya sehingga perlu deteksi dan analisis
optimum Proses parameter tanpa mengabaikan aspek memakai alat ini. Semen Carbide datar End-pabrik
dengan 2, 3, 4 Flutes digunakan sebagai studi parameter kasus utama dengan Al 7075 T6 sebagai bahan benda
kerja, dengan perhitungan dan membawanya ke simulasi Gelombang Ketiga Advant EdgeTM Sistem Software
beberapa data menjadi parameter produksi rekomendasi sebagai kesimpulan akhir.
Key words: Rough-machining (proses kekasaran permukaan ), Cemented Carbide End-mill (Karbida
Endmill), Al 7075 T6, Third Wave Advant EdgeTM.
.
requirement of finishing product is not same with the
expected result, the duration of production take long time
and others some unexpected problems.
INTRODUCTION
In the modern era, development in the fields of
engineering, especially engineering technology is
progressing very rapidly. The development of science is a
major factor in developing of technology in the field of
engineering sciences. So, in the case the speed and accuracy
are the challenges that faced today.
Machining plays important role in producing products.
Milling is the most widespread metal removal process in
metalworking industry. Manufactured products qualities are
determined by their surface quality. The high friction
between tool and work piece leads to high temperatures, tool
wear, and poor surface quality [1].The end-milling process
is one of the most widely used material removal processes in
industry [2] Others, the operation of the machine with the
type of automated or computerized also needed skill or
knowledge in how to operate it. Beside that during the
production some problem happened, like tool broken, the
Some studies have observe the problem that face in the
field of production. Current machining processed and
cutting tool designs are slow and too conservative, leading
to high cost and significant waste [3]. Tool failure may
result in losses in surface finish and the dimensional
accuracy of a finishes part, or possible damage to the work
piece and machine [4]. Because of that needs some
optimization in every area.
Optimization in every area is one kind one rules that
should be done before, during and after production. Increase
material removal rate of existing CNC programs to lower
cost, bid new jobs more aggressively, balance a cell or
provide additional capacity without CAPX [5] is one kind of
study about optimization process . In general type of end1
mills are varied and have each purpose of every types to
solve diverse problem in production.
Agnew,P.J. said in his paper ―for example, when doing
a slotting operation, unless doing a light cut of about 2D or
less, it is best to use a two- or three-fluted end mill. The
general rule is use less flutes for deeper cuts, with four or
higher flutes for light cuts. The reason for this is the
venerability of chip packing that can lead to destruction of
the end mill [6]. Finally, all of that are basically interesting
to make research in this field of finding optimization and
simulation before machining processes.
There are some studies as the reference of this research.
Those are researches about optimization carbide end mill
with analyze and simulation. Design and developing of a
multi-purpose carbide end mill (MP-CE) to suit all kinds of
cutting proposes which will uncover the background
information on different kinds of end mill structure, tool
materials and various surface treatment on the cutter [8].
Generalized modeling of milling mechanics and
Dynamics – Helical End Mill is study about predicted and
measured cutting forces, surface roughness and stability
lobes for ball, helical tapered ball, and bull nosed end mills
are provided to illustrate the viability of the proposed
generalized end mill analysis [9].
The research by Kuttolamadom,M[10] is about
prediction of the wear and evolution of cutting tools in
carbide/Ti-6Al-4V machining Tribosystem by volumetric
tool wear characterization and modelling have proven that
straight tungsten carbide wear when machining Ti-6Al-4V is
mechanically-driven at slow surface speeds and thermallydriven at high surface speeds.
Optimal Selection of Tools for Rough Machining of
Sculptured Surfaces is another research that focused on
Evaluation of the effect of depth of cut and stepover, as
major machining parameters and their influence on
machining time and remaining volume and real time
experiments showed that simulation results are really close
to reality and thus CAM software is an adequate tool for
optimization purposes [11].
And the research which using TWS AdvantEdge for
finding optimization both in effective time reduce and also
tool wear. Simulating steel machining AdvantEdge user
defined and user defined yield custom material options [12]
and the path for improvement in Third Wave Software [13].
ANALYZE METHOD
Before input data to the TWS software. Analyze the all
specimens is necessary to approaching in the real condition.
Material that used in this experiment is Al-7075 T6
80x50x40 mm. and after getting final result, cheking the
data with real machining to real check the effect after
optimalization.
Figure speciment of workpiece material Al-7075 T6.
3.2.2 Carbide Flat Endmill
In the real machining after finding optimum parameter
processing, the cutting tools that use is ø8 mm with 3-flutes.
Figure Flat End-mill ø8 3T , Tool-length view. Flutes
view.
All of preparation and consideration are used to realize
the result of optimalization by simulation. And the real
machining treatment are used to become initial data
parameter for the next consideration. And all the equipment
beside those already explained above others are
Figure workpiece clamped in the Chuck,.cutting tool
with tool holder.
2
b
rpm
mm/min
mm
mm
s
ame---»
a
s
ame---»
F
15%---»
n
s
ame---»
2-Flutes-3Flutes4Flutes
Initial simulation. Exact effect of tool wear with
selected machining. The parameter above is used to looking
for the tool wear before optimization. So, it’s become first
data or common data of initial parameters. 3D plug milling
simulation is use to know the tool wear both of three types
of tools.
Simulation and experiment 1. Finding optimal feed rate
by TWS simulation. Because rough process are so
complicated path line, the simulation taken by using 3D
pocket milling.
3000
595
1
6
3000*
700*
1*
6*
3000
805
1
6
2-Flutes
ẘ
F
0,
5
000782
05,75
0,
5
00085
50,004
0,
5
00092
95
3-Flutes
ẘ
F
0,
80
000829
5
0,
82
00085
5,0061
0,
92
000954
5,75
s
ame---»
+
s
ame---»
15%--»
s
ame---»
ẘ= 0,00085 is in different area both on three tools. By
interpolation method feedrate value can be determined and
the result shown below
The value of F both 2,3,4 Flutes become consideration
in the next prediction. Final data in experiment 1 shown that
changes feed rate give effect on the total production time
and the increasing the number of flutes followed by
increasing feed rate value in same DOC and spindle speed
condition.
In this experiment, changing depth of cut value is used
to finding optimum DOC value without discharging tool
wear value (ẘ). DOC basically not changing cutting time
but total metal removing time (mm3/min). And the result are
shown by graph. It shown correlation of tool wear (ẘ) with
DOC (mm) . Another side is correlation between metal
removal rate (mm3/min) and DOC (mm).in order to finding
optimum depth of cut value, make cross-section ẘ per DOC
and MR per DOC. and the point is DOC=0.75, MR=3,xx
and ẘ=0,075.
4-Flutes
ẘ
F
0,
10
000823
64,613
0,
11
00085
00,008
0,
12
000946
24,304
Simulation and experiment 2. From the result of this
simulation can be used for finding optimal and effective
deep of cut (DOC). Other parameters are same and only
change number DOC to finding effect of DOC to another
consideration.
Simulation
and
experiment
3.
The
data
recommendation above use to predict the continues-changes
data to finding optimization parameter’s process without
ignoring tool life. And the approaching way to finding
continues parameter data are:
a
m
m
….
300
….
….1
0
…..
…..
2
As the same way, to finding depth of cut by
interpolation with tool wear value is 0,075. And the result
shown in table below
From the calculation result above the analysis going to
the simulation, the simulation parameter and the result are
a)
Parameters
- N-Flutes
=4
- Feed rate
= 1100 mm/min
- DOC
= 1,22 mm
b)
Result
b
m
m
--+25%---»
n
--- -15%---»
--- -5%---»
2-Flutes-3Flutes-4Flutes
rpm
F
mm/mi
….
….
….
.
---same---»
N
6
6*
6
---same---»
--- -25%---»
---+15%---»
--- +5%---»
Finally all the result collect to the one data base and
making comparison for making conclusion. Also from the
first simulation data until the last. Showing the comparison
as prove of optimizing is done.
DATA ANALYZE
In this experiment, the changes variable data is feed
rate value. Based on formula, feed rate give significant
effect to the total time, both estimate time and metal
removal millimeter cubic per minute. And the result are
shown by graph.
It shown correlation of cutting time (t) with tool wear
(ẘ). Another side is correlation between feed per tooth (f)
and tool wear (ẘ).in order to finding effective- safety feed
rate, make cross-section time per ẘ and f per ẘ. and the
point is t=8, f=0,1 and ẘ=0,00065.
Figure result of simulation . (a).temperature-contour.
(b) . X-Y-Z Force. (c). Power-Torque-Temperature.
The result shown that with using the parameters above
the cutting tool in the best condition for machining.
3
number has no effect on both total production time and metal
removal rate but giving effect on tool wear( stress and force)
in each teeth .
3.
Changing feedrate value give significant effect on the
total production time, and the increasing the number of flutes
followed by increasing feed rate value in same DOC and
spindle speed condition. Opposite with that, increasing depth
of cut value also followed by increasing metal removal rate as
well in the same spindle speed rotation condition.
4.
In the same spindle speed value 3000 rpm, the
optimization rouh-machining process parameter (safetyeffective production) for 2T are 550 mm/min feed-rate value
with 2,45 mm depth of cut, 825 mm/min for 3T with 1,63 mm
depth of cut, 1100 mm/min for 4T with 1,2 mm depth of cut.
And the changes the value of spindle rotation be followed by
changing feedrate and depth of cut respectively.
Finally the data above to finding continues data
changes of parameter cutting process. In this graph shown
that the recommendation of choosing value of each
parameters, such as spindle speed, feed rate, depth of cut
and also the tool with the n-number of flutes. And the range
of optimum parameter which good in tool wear are check in
the Third Wave System AdvantEdge software.
Graph Correlation between spindle speed, feed rate
and Depth of cut
REFERENCES
[1]
p. Chockalingam and l. H. Wee, ―surface
roughness and tool wear study on milling of aisi 304
stainless steel using different cooling conditions,‖ int. J.
Eng. Technol. Vol. 2 no. 8, august, 2012. Melaka malaysia ,
vol. 2, no. 8, pp. 1386–1391, 2012.
[2]
b. . Damazo, m. . Dutterer, and m. . Kennedy,
―a summary of micromilling studies,‖ 1st int. Conf. Gen.
Meet. Eur. Soc. Precis. Eng. Nanotechnology,bremen, ger.,
2001.
[3]
c. Dr. Troy d.marusich, sustainable
manufacturing_u.s. doe advanced manufacturing office peer
review meeting. Clifornia santa barbara: third wave systems
inc., 2014.
[4]
x. Li, b. S. Lim, j. H. Zhou, s. Huang, s. J.
Phua, k. C. Shaw, and m. J. Er, ―fuzzy neural network
modelling for tool wear estimation in dry milling operation,‖
annu. Conf. Progn. Heath manag. Soc., pp. 1–11, 2009.
[5]
o. Gilbert, ―force balancing an advanced
toolpath.‖ Connecticut center for advanced technology,inc.,
usa.
[6]
p.j. agnew, ―what to consider when
evaluating solid carbide end mill machining.‖ .
[7]
y. Yang, x. Li, p. Jiang, and l. Zhang,
―prediction of surface roughness in end milling with gene,‖
proc. 41st int. Conf. Comput. Ind. Eng., pp. 441–446, wuhan
china.
[8]
h. Ku and w. C. Chia, ―design of multipurpose carbide end mill,‖ vol. 67, no. 2, pp. 34–40, 2006.
[9]
s. Engin and y. Altintas, ―generalized
modeling of milling mechanics and dynamics: part i - helical
end mills,‖ univ. Br. Columbia dep. Mech. Eng., no. 1997,
1998.
[10]
m. A. Kuttolamadom, s. Hamzehlouia, and m.
L. Mears, effect of machining feed on surface roughness in
0
1000
2000
3000
4000
5000
6000
20
4500
18
4000
16
3500
14
3000
12
2500
10
2000
8
1500
6
1000
4
500
2
0
0
1000
F-2T
2000
a-T2
F-3T
F-4T
3000
4000
Spinde speed (Rpm)
a-T3
a-T4
ẘ5000
DOC (mm)
5000
F (mm/min)
Corelation between Spindle Speed, Feedrate
ẘ ( and
.mm.K.α/oK.min)
DOC
0
6000
One of prove that the calculation and the result can be
recomendation are shown by simulation and real machining
analyze ( 3-flutes with different parameter).
CONCLUSION
1.
Optimization helps in determining the parameters that
result in both improved production rate and enhanced surface
quality before finishing process. In the present work
influences of end milling parameters namely Spindle speed,
Feed Rate, Depth of cut, Step over and Machining time (MT)
are studied with tool wear consideration.
2.
Increasing n-flutes number is directly proportional
with feedrate value and inversely proportional with feed per
tooth, if the number of feed rate are same increasing n-flute
4
cutting 6061 aluminum. Clemson university - international
center for automotive research copyright, 2010, pp. 1–19.
[11]
p. Spanoudakis, n. Tsourveloudis, and i.
Nikolos, ―optimal selection of tools for rough machining of
sculptured surfaces,‖ proc. Int. Multiconference eng.
Comput. Sci. 2008 vol ii imecs 2008, 19-21 march, 2008,
hong kong, vol. Ii, pp. 19–21, 2008.
[12]
t. H. C. Childs, ―simulating steel maching
advantedge user defined and user defined yield custom
material options.‖
[13]
troy d. Marusich, ―the path for improvement
in third wave software,‖ 2010.
[14]
c. Saikaew and p. Baowan, ―surface finish
improvement in ball nose end milling by optimizing
operating conditions for different cutting times,‖ indian j.
Eng. Mater. Sci., vol. 22, no. February, pp. Pp.38–50, 2015.
[15]
j. Kechagias, p. Kyratsis, and n. Mastorakis,
―on prediction of surface roughness of al7075alloy during
slot milling using nn modeling,‖ recent adv. Mech. Mater.
Mech. Eng. Chem. Eng., pp. 98–107.
[16]
v. S. Jatti, r. Sekhar, and r. K. Patil, ―study of
ball nose end milling of lm6 al alloy : surface roughness
optimisation using genetic algorithm,‖ int. J. Eng. Technol.,
vol. 5, no. 3, pp. 2859–2865, 2013.
[17]
w. D. S. M. Ponnala and k. L. N. Murthy,
―modeling and optimization of end milling machining
process,‖ ijret int. J. Res. Eng. Technol., pp. 430–447, 2012.
[18]
a. A. Syah, e. Sutikno, r. Raharjo, j. Teknik,
m. Fakultas, and t. Universitas, ―pengaruh feed rate dan
depth of cuts terhadap surface roughness pada proses milling
dengan,‖ jurusan teknik mesin fakultas teknik universitas
brawijaya.
[19]
ja. Schey, introduction to manufacturing
process 2nd, second ed. New york, 1987.
[20]
g. S. I. Hesler, ―investigating the mechanisms
of diamond tool wear cutting ferrous materials using a
quantitative study of machining parameters.‖
[21]
b. Sick, ―review on-line and indirect tool
wear monitoring in turning with artificial neural networks,‖
a rev. More than a decad. Res. Mech. Syst. Signal process. ,
pp. 16(4), 487–546,, 2002.
[22]
j. Qu and a. J. Shih, ―analytical surface
roughness parameters of a theoretical profile consisting of
elliptical arcs,‖ in machining science and technology, vol. 7,
no. 2, united states, 2003, pp. 281–294.
[23]
t. G. Stoebe and a. West, ―iron and steel —
properties and applications.‖ 2009.
[24]
alcoa inc., ―alcoa mill products-alloy 7075
plate and sheet.‖
[25]
sede legale e amministrativa/headquarters and
administration office, aluminum 7075-t6; 7075-t651.
Modena: crp meccanica s.r.l.
[26]
a. D. Isadare, b. Aremo, m. O. Adeoye, o. J.
Olawale, and m. D. Shittu, ―effect of heat treatment on some
mechanical properties of 7075 aluminium alloy,‖ mater.
Res., vol. 16, no. Sep,3rd, pp. 190–194, feb. 2013.
[27]
d. Singla and s. R. Mediratta, ―evaluation of
mechanical properties of al7075-fly ash composite
material,‖ int. J. Innov. Res. Sci. Eng. Technol., vol. 2, no.
April,4th, pp. 951–959, 2013.
[28]
v. Songmene, r. Khettabi, i. Zaghbani, j.
Kouam, and a. Djebara, ―machining and machinability of
aluminum alloys,‖ canada, 1983.
[29]
a. Gatto, e. Bassoli, and l. Iuliano,
―performance optimization in machining of aluminium
alloys for moulds production : hsm and edm,‖ university of
modena and reggio emilia-politecnico di torino,italy, 2010.
[30]
h.-t. Young, l.-c. Chuang, k. Gerschwiler, and
s. Kamps, ―a five-axis rough machining approach for a
centrifugal impeller.‖
[31]
j. G. Parmar and p. A. Makwana, ―prediction
of surface roughness for end milling process using artificial
neural network,‖ int. J. Mod. Eng. Res., vol. 2, no. 3, pp.
1006–1013, 2012.
[32]
chia wee chong, ―research and development
of multi purpose carbide end mill,‖ 2005.
[33]
t. Conical cutting tool, ―end mill attributes
and terminology commonly used industry language and
definisions.‖
[online].
Available:
http://www.conicalendmills.com/wpcontent/uploads/2014/10/end_mill_attributes_terminology.p
df. [accessed: 15-mar-2016].
[34]
m. Kuttolamadom, ―prediction of the wear &
evolution of cutting tools in a carbide / ti-6al-4v machining
tribosystem by volumetric tool wear,‖ 2012.
[35]
rao, manufacturing technology vol-ii 2e. Tata
mcgraw-hill education. Isbn 978-0-07-008769-9., 2009, p.
30.
[36]
j. R. Davis, asm international. Handbook
committee (1995). Tool materials. Isbn 978-0-87170-545-7.,
1995, p. 289.
[37]
k. Kadirgama, b. Mohammad, h. Al-ani, and
m. M. Noor, ―cutting force prediction model by fea and rsm
when machining hastelloy c-22hs with 90 ° holder,‖ j. Sci.
Ind. Res., vol. 67, no. June, pp. 421–427, 2008.
[38]
tws_user manual, third wave system
advantedge user manual version 4.8 (third wave
system,minneapolis). 2007, pp. 8–20.
[39]
c. Constantin, s. Croitoru, g. Constantin, and
e. S. T. R. Ă. Jescu, ―fem tools for cutting process modelling
and simulation,‖ u.p.b. sci. Bull., ser. D , vol. 74, 2012.
[40]
tws_advantedge 4.4, third wave systems
releases version 4 . 4 of advantedge tm classic, no. 1.
Minneapolis, mn 55349 usa: third wave systems inc., 2003,
p. 55439.
5
[41]
saurabh r. Virkar a, ―numerical simulations
and analysis of ductile mode micro,‖ kalamazoo, michigan,
2010.
[42]
tecplot inc., tecplot.360 2011 user ’ s manual.
Bellevue u.s: tecplot,inc., 2011.
[43]
copyright © 1995-2015 third wave systems.,
third wave advantedge user’s manual. Advantedge © third
wave systems windows © microsoft corporationnx®siemens
product lifecycle management software inc., 2015.
[44]
uddeholm
corrax,
―cutting
data
recomendations.‖
[online].
Available:
http://www.uddeholm.com/files/cutting_data_corrax_eng.pd
f.
[accessed:
08-mar-2016].
6