1. RSM OPTIMIZATION OF TURNING PROCESS PARAMETERS ON BRASS
WORKPIECE BY USING 3D PRINTED SINGLE POINT CUTTING TOOL
St. JOSEPH’S COLLEGE OF ENGINEERING
Chennai 119
Submitted by
M.A.ABDUL WAHAB
M.ANBARASAN
312316114001
312316114016
GUIDED BY:
N.SATHISHKUMAR M.E
ASSISTANT PROFESSOR
2. OBJECTIVE
• To design a suitable model of tool for machining purpose using
solid works software.
• To fabricate a 3d metal tool using the input from the solid work
design.
• To improve the efficiency of the tool we have used 3d printing
by which various properties of the tool will be developed as per
the need.
• Different output parameters are observed with multiple input
values.
• Parameters after machining is noticed and optimised
performance is found using response surface methodology
(RSM) software.
3. ABSTRACT
This project aims at combining the method of 3D printing with
machining process in lathe. The purpose of this study is to explore
the possibility of using 3D printed and Manufactured metal tool as
direct tool for metal below its strength.3D printing, also known as
Additive Manufacturing or Rapid Prototyping, is a novel
technology that allows to accelerate the production of the tool.
One of the main advantage of 3D printing method is the possibility
to create a more complicated size of tool accurately with less
consumption of time. With the help of this technology anisotropic
metals will be developed as per the need of machining purpose
which is more stronger and efficient then isotropic metals.
4. INTRODUCTION : 3D PRINTING OF METALS
• 3D printing is a new technology used to print the required
object to be produced.
• To this tool manufacturing Direct metal laser sintering (DMLS)
is used which is the one form of AM technology.
• It is a layer by layer manufacturing process, in which is the
molten powder is laid over and over to form the object.
• With the recent introduction of the Desktop AM machines,
availability, cost of the machine and raw materials are getting
cheaper.
• In metal additive manufacturing with the help of selecting the
Cartesian points the properties of strength and durability can be
arrested and anisotropic property will be obtained in the tool
manufactured.
• With the help of AM, various materials can be manufactured
using the same machine using different combinations.
5. LITERATURE SURVEY
S.
NO
AUTHOR YEAR PAPER NAME FINDINGS
1. Ian gibson,
David W.
Rosen &
Brent stucker
2010 A text book on
additive
manufacturing
technologies.
Additive manufacturing (AM)
technology came about as a result of
developments in a variety of different
technology sectors. Like with many
manufacturing technologies,
improvements in computing power
for processing the large amounts of
data typical of modern 3D computer-
aided design (CAD) models within
reasonable time frames.
2. Stratasys 2017-
18
Method and
apparatus for 3d
printing by
selective sintering
Selectively sintering the portion of the
layer that is defined by the mask to be
sintered and a stage the powder
delivery station, digital printing
station and sintering station to build a
plurality of layers that together form
the three dimensional object.
6. LITERATURE SURVEY
S.
NO
AUTHOR YEAR PAPER NAME FINDINGS
3. Gibson et al 2015 Additive
Manufacturing
Technologies
In detail the various aspects of joining
materials to form parts. A conceptual
overview of rapid prototyping and
layered manufacturing is
given, beginning with the
fundamentals so that readers can get
up to speed quickly. Unusual and
emerging applications such as micro-
scale manufacturing.
4. Muller and
sladojevic
2001 Rapid tooling
approaches for
small lot
production of
sheet-metal parts.
Using Rapid Prototyping Techniques
(RPTs) like Stereo lithography or
Selective Laser Sintering, the
automotive industry produces plastic
parts for prototypes faster and
cheaper compared to the techniques
used up to now.
7. LITERATURE SURVEY
S.
NO
AUTHOR YEAR PAPER NAME FINDINGS
5. Oudjene et al 2007 A methodology for
the 3D stress
analysis and the
design of layered
sheet metal
forming tools
joined by screws.
The results show the feasibility of the
developed procedure in the context of
industrial applications, the potential
interest of the optimization of the
screw positions is also outlined.
6. Durgun 2015 The Use of Fused
Deposition
Modelled Tooling
in Low Volume
Production of
Stretch Formed
Double Curvature
Components.
The use of desktop 3D printing
technology in the production of a
large tool through assembly of smaller
printed hollow sections. The low cost
and production time for the additive
manufacturing tool solution is noted.
8. METHODOLOGY OF THE PROJECT
INVESTIGATION OF MACHING TOOL MADE BY 3D PRINTED METAL
LITERATURE SURVEY
3D CAD MODELLING
(MARAGING STEEL 3D PRINTED TOOL)
IDENTIFICATION OF HSS
TOOL FROM MARKET
2D STRUCTURE
(BUILD SOFTWARE:CATIA)
SOLID STRUCTURE
(BUILD STYLE:SOLID)
MEASUREMENT OF
DIMENSION
TECHNIQUE: REVERSE ENGINEERING
EXPERIMENTATION
• FABRICATION
TECHNIQUE: FUSED DEPOSITION MODELING
MATERIAL: MARAGING STEEL
MACHINING TYPE: TURING
EXPERIMENTATION
MACHINING TYPE: TURNING
INPUT: FEED,SPEED,DEPTH OF CUT OUPUT:MACHING TIME,MRR
OPTIMIZATION USING RSM SOFTWARE TO GET OPTIMIZED DESIRABILITY OF BOTH
9. SELECTION OF PROCESS
METAL 3D PRINTING
The 3D printing process builds
a three-dimensional object
from a computer-aided design
(CAD) model, usually by
successively adding material
layer by layer, which is why it
is also called additive
manufacturing
18. 5.1.1 SAMPLE CALCULATIONS
MATERIAL REMOVAL RATE:
MRR = (3.1415 * L * d * D)/T;
Where L = Cutting length of work piece (mm); d = Depth of cut (mm); D = diameter of work
piece (mm); T = Time (seconds); MRR = MATERIAL REMOVAL RATE (mm3/seconds);
D = D1+D2; D2 = D1-d;
Where D1 = Diameter before turning operation (mm);
D2 = Diameter after turning operation (mm);
1) MRR = (3.1415*30*0.45*42.55)/3.6
=501.026 mm3/sec
2) MRR = (3.1415*30*0.60*42.4)/2.64
= 907.7454 mm3/sec
3) MRR = (3.1415*30*0.60*42.4)/3.95
=606.695 mm3/sec
19. ANALYSING THE TOOL STEP-7
Response surface methodology
• In statistics, response surface methodology RSM explores the
relationships between several explanatory variables and one or
more response variables. The method was introduced by George E. P.
Box and K. B. Wilson in 1951.
• The main idea of RSM is to use a sequence of designed experiments to
obtain an optimal response. Box and Wilson suggest using a second-
degree polynomial model to do this.
• They acknowledge that this model is only an approximation, but they use
it because such a model is easy to estimate and apply, even when little is
known about the process.
• Statistical approaches such as RSM can be employed to maximize the
production of a special substance by optimization of operational factors.
• In contrast to conventional methods, the interaction among process
variables can be determined by statistical techniques
24. Source
Sum of
Squares
Degree of
freedom
Mean
Square
F
Value
p-value
Prob > F
Model 21.93992 6 3.656653 30.1283199 < 0.0001 significant
A-speed 4.485363 1 4.485363 36.95632549 < 0.0001
B-feed 4.998575 1 4.998575 41.18483584 < 0.0001
C-depth of cut 0.001965 1 0.001965 0.016186806 0.9007
AB 2.148357 1 2.148357 17.70099294 0.0010
AC 0.170934 1 0.170934 1.408377449 0.2566
BC 0.398498 1 0.398498 3.283354487 0.0931
Residual 1.577801 13 0.121369
Lack of Fit 0.684118 8 0.085515 0.478439605 0.8309 not significant
Pure Error 0.893683 5 0.178737
Cor Total 23.51772 19
Std. Dev. 0.348381 R-Squared 0.93291
Mean 3.928 Adj R-Squared 0.901946
C.V. % 8.869169 Pred R-Squared 0.851468
PRESS 3.493124 Adeq Precision 19.41423
ANOVA RESULTS FOR RESPONSE SURFACE MODEL FOR 3D PRINTED TOOL
MACHINING TIME
25. Source
Sum of
Squares
Degree of
freedom
Mean
Square
F
Value
p-value
Prob > F
Model 629384.6 6 104897.4 40.0127872 < 0.0001 significant
A-speed 104420.6 1 104420.6 39.83088706 < 0.0001
B-feed 87642.92 1 87642.92 33.43110847 < 0.0001
C-depth of cut 150473.9 1 150473.9 57.39776467 < 0.0001
AB 2385.439 1 2385.439 0.909918056 0.3575
AC 3146.017 1 3146.017 1.200037897 0.2932
BC 4086.844 1 4086.844 1.558913326 0.2338
Residual 34080.77 13 2621.598
Lack of Fit 23507.15 8 2938.393 1.389491791 0.3731 not significant
Pure Error 10573.63 5 2114.725
Cor Total 663465.4 19
Std. Dev. 51.20154 R-Squared 0.948632
Mean 488.2583 Adj R-Squared 0.924924
C.V. % 10.48657 Pred R-Squared 0.876463
PRESS 81962.44 Adeq Precision 22.1964
MRR
27. DERIVED EQUATION FOR 3D PRINTED TOOL:
Final Equation in Terms of Actual Factors:
1. Time = +23.34251-0.027262 * speed-0.25344 * feed+4.31478 * depth of cut+3.56558E-
004 * speed * feed+6.41753E-003 * speed * depth of cut
-0.16803* feed * depth of cut
2. MRR = -692.13727+0.98855 * speed+9.41551 * feed-593.01889 * depth of cut-
0.011881* speed * feed+0.87063 * speed * depth of cut+17.01653 * feed * depth of cut
1 DERIVED EQUATIONS FOR HSS TOOL:
Final Equation in Terms of Actual Factors:
1. TIME = +19.92828-0.013822 * speed-0.15704 * feed-11.87291 * depth of cut+5.35055E-
005 * speed * feed+0.012090 * speed * depth of cut+0.060743 * feed * depth of cut.
2. MRR = +212.09814-0.34792 * speed-3.85645 * feed-1036.01052 * depth of
cut+7.30408E-003 * speed * feed+1.32457 * speed * depth of cut+21.43962 * feed *
depth of cut.
28. GRAPHS
Design-Expert® Software
time
Color points by value of
time:
6.02
2.3
Internally Studentized Residuals
Normal
%
Probability Normal Plot of Residuals
-2.19 -1.20 -0.22 0.77 1.75
1
5
10
20
30
50
70
80
90
95
99
Normal Plot of Residuals for Time
Design-Expert® Software
time
Color points by value of
time:
6.02
2.3
Predicted
Internally
Studentized
Residuals
Residuals vs. Predicted
-3.00
-1.50
0.00
1.50
3.00
2.15 3.11 4.06 5.02 5.98
Residual vs Predicted for Time
HSS TOOL
MACHINING TIME
29. Design-Expert® Software
time
Color points by value of
time:
6.02
2.3
Run Number
Internally
Studentized
Residuals
Residuals vs. Run
-3.00
-1.50
0.00
1.50
3.00
1 4 7 10 13 16 19
Design-Expert® Software
time
Color points by value of
time:
6.02
2.3
Actual
Predicted
Predicted vs. Actual
2.10
3.10
4.10
5.10
6.10
2.15 3.12 4.08 5.05 6.02
Residual vs Run for Time Predicted vs Actual for Time
30. Design-Expert® Software
MRR
Color points by value of
MRR:
907.745
210.226
Internally Studentized Residuals
Normal
%
Probability
Normal Plot of Residuals
-1.63 -0.72 0.19 1.10 2.00
1
5
10
20
30
50
70
80
90
95
99
Design-Expert® Software
MRR
Color points by value of
MRR:
907.745
210.226
Predicted
Internally
Studentized
Residuals
Residuals vs. Predicted
-3.00
-1.50
0.00
1.50
3.00
209.28 388.83 568.38 747.94 927.49
Normal Plot of Residuals for MRR Residual vs Predicted for MRR
MRR
31. Design-Expert® Software
MRR
Color points by value of
MRR:
907.745
210.226
Run Number
Internally
Studentized
Residuals
Residuals vs. Run
-3.00
-1.50
0.00
1.50
3.00
1 4 7 10 13 16 19
Design-Expert® Software
MRR
Color points by value of
MRR:
907.745
210.226
Actual
Predicted
Predicted vs. Actual
200.00
382.50
565.00
747.50
930.00
209.28 388.83 568.38 747.94 927.49
Residual vs Run for MRR Predicted vs Actual for MRR
32. 3D PRINTED TOOL-MARAGING STEEL
Design-Expert® Software
Time
Color points by value of
Time :
6.28
2.26
Internally Studentized Residuals
Normal
%
Probability
Normal Plot of Residuals
-1.44 -0.50 0.43 1.36 2.30
1
5
10
20
30
50
70
80
90
95
99
Design-Expert® Software
Time
Color points by value of
Time :
6.28
2.26
Predicted
Internally
Studentized
Residuals
Residuals vs. Predicted
-3.00
-1.50
0.00
1.50
3.00
2.27 3.27 4.27 5.27 6.27
Normal Plot of Residuals for Time Residual vs Predicted for Time
MACHINING TIME
33. Design-Expert® Software
Time
Color points by value of
Time :
6.28
2.26
Run Number
Internally
Studentized
Residuals
Residuals vs. Run
-3.00
-1.50
0.00
1.50
3.00
1 4 7 10 13 16 19
Design-Expert® Software
Time
Color points by value of
Time :
6.28
2.26
Actual
Predicted
Predicted vs. Actual
2.20
3.23
4.25
5.28
6.30
2.26 3.26 4.27 5.28 6.28
Residual vs Run for Time Predicted vs Actual for Time
34. MRR
Design-Expert® Software
MRR
Color points by value of
MRR:
11127.3
2413.4
Internally Studentized Residuals
Normal
%
Probability
Normal Plot of Residuals
-1.29 -0.46 0.37 1.21 2.04
1
5
10
20
30
50
70
80
90
95
99
Design-Expert® Software
MRR
Color points by value of
MRR:
11127.3
2413.4
3
2
2
3
3
Predicted
Internally
Studentized
Residuals
Residuals vs. Predicted
-3.00
-1.50
0.00
1.50
3.00
2149.97 4386.09 6622.21 8858.33 11094.45
Normal Plot of Residuals for MRR Residual vs Predicted for MRR
35. Design-Expert® Software
MRR
Color points by value of
MRR:
11127.3
2413.4
Run Number
Internally
Studentized
Residuals
Residuals vs. Run
-3.00
-1.50
0.00
1.50
3.00
1 4 7 10 13 16 19
Design-Expert® Software
MRR
Color points by value of
MRR:
11127.3
2413.4
5
5
5
5
5
Actual
Predicted
Predicted vs. Actual
2100.00
4375.00
6650.00
8925.00
11200.00
2149.97 4394.31 6638.65 8883.00 11127.34
Residual vs Run for MRR Predicted vs Actual for MRR
36. CONTOUR AND 3D SURFACE PLOT FOR HSS TOOL
MACHINING TIME
600.00 675.00 750.00 825.00 900.00
40.00
46.70
53.41
60.11
66.82
time
A: speed
B:
feed
2.26943
2.94603
3.62263
4.29923
4.97583
6
6
6
6
6
6
are
600.00
675.00
750.00
825.00
900.00
40.00
46.70
53.41
60.11
66.82
1.5
2.55
3.6
4.65
5.7
time
A: speed
B: feed
Design-Expert® Software
time
6.02
2.3
X1 = A: speed
X2 = C: depth of cut
Actual Factor
B: feed = 55.00
600.00 675.00 750.00 825.00 900.00
0.20
0.32
0.45
0.57
0.70
time
A: speed
C:
depth
of
cut 2.39255
2.8156
3.23866
3.66172
4.08478
Design-Expert® Software
time
6.02
2.3
X1 = A: speed
X2 = C: depth of cut
Actual Factor
B: feed = 55.00
600.00
675.00
750.00
825.00
900.00
0.20
0.32
0.45
0.57
0.70
1.9
2.575
3.25
3.925
4.6
time
A: speed
C: depth of cut
Design-Expert® Software
time
Design Points
6.02
2.3
X1 = B: feed
X2 = C: depth of cut
Actual Factor
A: speed = 750.00
40.00 46.70 53.41 60.11 66.82
0.20
0.32
0.45
0.57
0.70
time
B: feed
C:
depth
of
cut
2.46864
2.93694
3.40524
3.87354
4.34183
6
6
6
6
6
6
Design-Expert® Software
time
6.02
2.3
X1 = B: feed
X2 = C: depth of cut
Actual Factor
A: speed = 750.00
40.00
46.70
53.41
60.11
66.82
0.20
0.32
0.45
0.57
0.70
2
2.925
3.85
4.775
5.7
time
B: feed
C: depth of cut
40. In this study RSM method is used to find the optimal value for the TIME and MRR for both the
3D printing maraging steel tool and HSS tool machined with the BRASS material and the result
are as follows:
The Optimal Time and MRR value obtained from the HSS tool machining with the brass
tool is
TIME - 2.300004181 s
MRR - 291.5409765 mm^3/s
DESIRABILITY- 0.939905122
WHERE THE MAXIMUM AND MINIMUM VALUE OBTAINED FROM THE HSS TOOL IS:
MAX FOR TIME - 6.97 S
MIN FOR TIME - 2.3 S
MAX FOR MRR - 907.7454 mm^3/s
MIN FOR MRR - 210.226 mm^3/s
FINAL EXPERIMENT OUTPUT
41. The Optimal Time and MRR value obtained from the 3D printed maraging steel tool
machining with the brass tool is
TIME - 2.259927001 s
MRR -315.9226908 mm^3/s
DESIRABILITY- 0.886692496
WHERE THE MAXIMUM AND MINIMUM VALUE OBTAINED FROM THE
HSS TOOL IS:
MAX FOR TIME - 7 S
MIN FOR TIME - 2.26 S
MAX FOR MRR - 843.8197 mm^3/s
MIN FOR MRR - 172.386 mm^3/s
42. COMPARISON OF MRR AND MACHINING TIME BETWEEN HSS AND 3D
PRINTED TOOL
43. CONFORMATORY TEST RESULTS
SPEED FEED DEPTH OF CUT TIME MRR
3D PRINTED MARAGING STEEL
900 51.58 0.2 2.300004181 292.88762
SPEED FEED DEPTH OF CUT TIME MRR
HSS TOOL
893.05 40 0.2 2.259927001 311.88765
44. CONCLUSION
THE CONCLUSION OF THE WORK DONE IN THIS STUDY AND ARE LISTED AS FOLLOWS
• Literature surveys on various issues of additive manufacturing in tooling were carried out.
• After a detailed review among various additive manufacturing techniques, a suitable AM
technique was selected.
• Among the various AM materials in, a suitable material (MARAGING STEEL) is manufactured
using 3D printing.
• Modelling of structure were made in 2d and 3d in CATIA software.
• Modelled structures were converted into .STL (Standard Triangulation Language) file format.
• Through the reverse engineering technique the dimensions were measured successfully.
• Turning operation was successfully executed with brass work piece using both the HSS tool
and 3D printed tool.
• From the input the machining time and the mrr are calculated both the outputs were
optimised with RSM software.
• The optimized values were similar with mrr and time with the tool material.
• The final percentage difference with both the material was 3%.
• Anistropic behaviour is obtained by the 3d printed tool.
• Thus 3D printing can be used as tooling applicaton.
