Process parameters optimization in sls process using design of experiments 2

INTERNATIONALMechanical Engineering and Technology (IJMET), ISSN 0976 –
International Journal of JOURNAL OF MECHANICAL ENGINEERING
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME
AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online) IJMET
Volume 4, Issue 2, March - April (2013), pp. 162-171
© IAEME: www.iaeme.com/ijmet.asp
Journal Impact Factor (2013): 5.7731 (Calculated by GISI) ©IAEME
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PROCESS PARAMETERS OPTIMIZATION IN SLS PROCESS USING
DESIGN OF EXPERIMENTS

Dr. T. Nancharaiah
Professor and Head, Department of Mechanical Engineering
DMSSVH College of Engineering, Machilipatnam-521002
A.P., INDIA.

M. Nagabhushanam
Associate Professor, Department of Mechanical Engineering
A.P., INDIA.

B. Amar Nagendram
Associate Professor, Department of Mechanical Engineering
A.P., INDIA.

ABSTRACT

Rapid prototyping (RP) is an additive manufacturing process which builds the parts
directly from CAD data sources. RP Techniques are increasingly being used to manufacture
complex precision parts for the automotive, aerospace and medical industries. One of the
popular RP processes is the Selective Laser Sintering (SLS) process which manufactures
parts by sintering metallic, polyoneric and ceramic powder under the effect of laser power.
This paper presents the effect of slice thickness and part orientation on total area of sintering
(TAS) and the laser energy. Using design of experiments (DOE) L9 orthogonal array was
selected and experiments were conducted. The experimental results were statistically
analyzed using ANOVA analysis, S/N ratio to find the contribution of each parameter and to
optimize the process parameters. The significance of each process parameter is further
strengthened by the correlation analysis. Finally confirmation tests were conducted for
optimum process parameters in SLS process.
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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

Keywords: Rapid Prototyping, additive manufacturing, selective laser sintering, laser
energy, ANOVA.

1. INTRODUCTION

Rapid prototyping (RP) is the most common name given to host of relation
technologies that are used to fabricate physical objects directly from three dimensions
CAD model. These methods are unique in that they add and bond materials in layers to
form objects. Such systems are also known by the general names free from fabrication
(FFF), solid free from fabrication (SFF) and layered manufacturing. The materials used
in rapid prototyping are numerous plastics, ceramics, metals ranging from stainless steel
to titanium and wood like paper.
There are different types of RP processes and are classified based on their layer
generation methods. Among these, most commonly used processes are Stereo
lithography (SLA), fused deposition modeling (FDM), Selective laser sintering (SLS),
laminated object manufacturing (LOM) and 3 D printings (3DP).
Among the different RP processes the SLS process has gained traction in the
manufacturing industry due to its capability to produce complex parts of any geometry
without the need for special tooling and support structures. SLS also able to manufacture
parts from materials such as metal and nylon which are difficult to fabricate using
traditional methods.
RP processes offer several advantages but have limitations like low productivity
(large build time), low part quality (dimensional accuracy) and low yield. A need thus
exist to carry out research and development on RP process to enable it to produce
functional parts of good quality with reduced production time and cost. The present work
primarily focuses on determining optimum slice thickness and part orientation for
minimal process energy consumption in selective layer sintering (SLS) process.
Selective laser sintering (SLS) is an additive manufacturing technique that uses a
high power laser (for example, a carbon dioxide laser) to fuse small particles of plastic,
metal (direct metal laser sintering), ceramic, or glass powders into a mass that has a
desired three-dimensional shape. The laser selectively fuses powdered material by
scanning cross-sections generated from a 3-D digital description of the part on the surface
of a powder bed. After each cross-section is scanned, the powder bed is lowered by one
layer thickness, a new layer of material is applied on top, and the process is repeated until
the part is completed. Figure (1) shows the basic process of SLS.
Compared with other methods of additive manufacturing, SLS can produce parts
from a relatively wide range of commercially available powder materials. These include
polymers such as nylon (glass-filled, or with other fillers) or polystyrene, metals
including steel, titanium, alloy mixtures, and composites and green sand. The physical
process can be full melting, partial melting, or liquid-phase sintering. Depending on the
material, up to 100% density can be achieved with material properties comparable to
those from conventional manufacturing methods. In many cases large number of parts can
be packed within the powder bed, allowing very high productivity.

163


Fig: (1) Selective Laser Sintering Process

2. LITERATURE REVIEW

RP in general and SLS in particular have recently gained popularity as a main stream
of manufacturing process for producing functional parts in bulk quantities. However, there
has been very little reported research on understanding the relation between the part shape,
process parameters and energy consumption in SLS process. Traditionally, researches have
concentrated on the physical, chemical and mechanical changes involved in the creation of
slices in different RP process. Phatak and pande (1) designed a modular system and
implemented to find the optimum orientation of the CAD part quality model in RP process
using generic algorithm technique for improvements in manufacture. Canellidis et al (2)
proposed methodology using genetic algorithm, to get optimum part orientation using a multi
criteria objective function comprising of the estimated build time, post processing time and
the average surface roughness of the part.
Nelson et al (3) analyzed the SLS process and developed a one-dimensional thermal
model to predict the laser energy required for the complete sintering of bisphenol – A
polycarbonate powder. They studied the effect of laser scan speed, laser power, and powder
size and powder bed temperature among various other parameters on the development of the
sintered layers. Eho et al(4) used a genetic algorithm (GA) method to optimize the SLA
process by analyzing the dimensional errors in SLA parts and correlating them to the laser
power used for creating the slices. LW et al (5) investigated the environmental effects of
three RP process: SLA, SLS and FDM and calculated the life cycle energy utilization in
these processes. No systematic work has however, been directed towards findings as optimal
process parameters for minimum laser energy consumption in SLS process. The work
reported in this paper is an attempt in this direction.

3. METHODOLOGY

This section presents a methodology to analyze the energy utilization in the SLS
process by modeling the virtual manufactures of a part and correlating the energy to the slice
thickness and part orientation. The overall methodology for calculations the laser energy for
manufactures a part in SLS is shoes in fig (2). The part is first modeled in a CAD system and
the CAD model is exported to the STL file format. The part orientation and slice thickness
are selected and the STL file is sliced. For each slice, the sintering area is calculated and the
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total area of sintering (TAS) is calculated by adding up the sintering areas for all the slices.
The total laser energy is than calculated from the TAS and correlated to the orientation and
slice thickness.

STL file as the part

Fix build orientation and slice
thickness

For each slice, calculate sintering
area using connect hull approach

Sum sintering areas for all slices to
calculate total area of sintering (TAS)

Calculate laser energy from TAS

Repeat for different values of build
orientation and slice thickness

Fig (2): Over all Methodology to
calculate laser energy

4. DESIGN OF EXPERIMENTS

Design of experiments and analysis of results are engaging the attention of the
Research Scholars and also practicing engineers. Many statistical tools are being used in the
recent past. Present day competition in the industry is pushing for more and more emphasis
on quality. Improved quality and enhancement in the market share can be achieved through
preventive action rather than inspection and process control techniques. Design of
experiments is one such quality improvement process which builds quality into products and
process as that eliminates expensive controls and inspection. It is a valuable tool to optimize
product and process design, to accelerate development cycle and to reduce development cost.
This will also improve easy transition of products from R & D stage to manufacture.

4.1 Selection of Process Parameters in SLS Process

When preparing to build SLS parts, many fabrication parameters are needed in the
software. To achieve optimum quality, these parameters are set differently according to
requirements of applications. Therefore, the first step in the experiment was to identify the
process control parameters that are likely to affect the laser energy in SLS process. The two
process parameters are selected at three different levels as shown in table (1).

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Table (1) Selection of Process parameters and their levels

Process parameters Level 1 Level 2 Level 3
Slice thickness in mm (A) 0.03 0.05 0.10
Part orientation in degrees (B) 0 30 45

4.2 Orthogonal Arrays (OA)

An experiment in which all possible combination of factor levels are used is called
‘full factorial experiments’. If an experiment consists of ‘n’ number of factors and each factor
at ‘X’ levels. Number of trials possible (treatment combinations) = Xn. As the number of
factors considered at multi levels increases, it becomes increasingly difficult to conduct the
experiment with all treatment combinations. In this situation, orthogonal arrays are at our
rescue (which are highly fractionalized factorial layouts), becomes useful in reducing the
number of trials.

4.3 Selection of Orthogonal Array

The first step in selecting the correct standard OA involves counting the total degrees
of freedom (dof) in the study. This count fixes the minimum number of experiments that must
be run to study the factors involved. In counting the total dof, the investigator commits 1 dof
to the overall mean of the response under study. This begins the dof count as 1.
The number of dof associated with each factor under study equals one less than the number of
treatment levels available for that factor. One determines the total dof in the study as follows:
if nA and nB represent the number of treatments available for two factors A and B
respectively, Then

1 = dof to be used by the overall mean
nA – 1 = dof for A
nB – 1 = dof for B

An example will illustrate this procedure. If a design study involves three 3-level factors (A
and B), then the dof would be as follows:

Source of dof required dof
Overall mean 1
A,B 2(3 – 1) = 4
Hence,
Total dof = 1 + 4 = 5
Therefore, in this example, one must conduct at least 5 experiments to be able to estimate the
desired main effects. The corresponding OA must therefore have at least 5 rows. Therefore
L9 orthogonal array is selected for experimentation and shown in table (2).

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Table (2) L9 orthogonal array

Expt. Columns
No. 1 2 3 4
1 1 1 1 1
2 1 2 2 2
3 1 3 3 3
4 2 1 2 3
5 2 2 3 1
6 2 3 1 2
7 3 1 3 2
8 3 2 1 3
9 3 3 2 1

5 EXPERIMENTAL RESULTS

A trial run was performed in which a series of samples were built on the SLS
machine. Totally 9 samples were produced by SLS according to the L9 array. The dimensions
of the sample specimen shown in figure (3)

Fig (3): CAD model of the Part

5.1 Results

The study involved 9 sample components produced by SLS machine. Experimental
results for laser energy were shown in the table (3) and in figure (4). From graphs it was
found that the slice thickness increases as laser energy required sintering the part decreases
and part orientation effects moderately.

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Table (3) Experimental results for Laser Energy

Expt. TAS in Laser Energy in
No. mm2 Kilo Joules
1 8958.8 21.67
2 8977.4 21.72
3 8964.9 21.69
4 5386.0 13.03
5 5399.7 13.06
6 5389.5 13.04
7 2707.6 6.54
8 2707.6 6.55
9 2709.1 6.55

Slice thickness Vs Laser energy Part orientation Vs Laser energy

25 13.775
Las er energy
Laser energy

13.77
20 13.765
15 13.76
10 13.755
5 13.75
0 13.745
0 1 2 3 4 0 1 2 3 4
Slice thickness in mm Part orientation

Fig (4): Variation of Laser Energy with respect to Slice thickness and Part orientation

5.2 Analysis

A. Signal to noise (S/N) ratio

The signal to noise ratio measures the sensitivity of the quality characteristic being
investigated to those uncontrollable external factors. To minimize the problem, the governing
relationships for the S/N ratio in terms of the experimentally measured values of laser energy,
i.e., S/N ratio = -10 log 10 MSD
Where MSD = ∑(yi - ) / y)2 /n, y the target value that is to be achieved, the number of
samples. The S/N ratio values obtained for the trials are listed in Table (4). From the results
optimum laser energy value obtained at level 3 of slice thickness and level 1 of part
orientation.

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Table (4) S/N ratio for optimization of Laser energy

Factor Level 1 Level 2 Level 3 Max. – Min.

Slice thickness -26.728 -22.30 -16.317 10.41

Part build orientation -21.77 -21.79 -21.78 0.02

B. ANOVA analysis

ANOVA analysis provides significance rating of the various factors analyzed in this
study. Based on the above rating, factors, which influence the objective function
significantly, could be identified and proper control measures adopted. In a similar way, those
factors with minimum influence could be suitably modified to suit economic considerations.
The ANOVA computations are carried out based on procedure out lined in ref (10) and listed
table in (5). A variable possessing the maximum value of variance is said to have the most
significant effect on the process under consideration. When the contribution of any factor is
small, then the sum of squares, (SS) for that factor is combined with the error (SSe). This
process of disregarding the contribution of a selected factor and subsequently adjusting the
contributions of the other factors is known as pooling.

Table (5) ANOVA analysis

Factor Sum of Degree of Variance Percentage F – test f-table
squares freedom of value
(SS) (dof) contribution

Slice thickness 352.028 2 176.014 97.79 260.76 99.3

Part orientation 5.539 2 2.7695 1.538 4.103 18.33

Error 2.699 4 0.675 0.749 -- --

Total 359.966 8 -- -- -- --

C. Correlation analysis

In process control, the aim is to control the characteristics of the output of the process
by controlling a process parameter. One succeeds if the parameters are chosen correctly. The
choice is usually based on judgment and knowledge of the concerned technology. A
correlation is assumed between a variable product characteristic and a variable process
parameter.

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In the present study, a relationship is assumed between the slice thickness (process
parameter) and laser energy, part orientation and laser energy. Slice thickness is the property
which significantly affects the quality of the prototypes in SLS process. This is proved by the
contribution at 99% level of significance. The correlation coefficient (r) obtained from the
results is - 0.9477 for slice thickness. The range of values for (r) lies between 1 and -1. The
experimental value indicates a reasonably strong negative (indirect) relation. Therefore, as
slice thickness increases, the laser energy decreases. The correlation coefficient (r) obtained
is 0.242 for part orientation. The experimental value indicates moderate positive (direct)
relation. Therefore, as part orientation increases, the laser energy value increases moderately.

6 CONFORMATION TESTS

Once the optimal level of design parameters has been selected, the confirmation tests
were conducted. The experimental results confirm the prior design and analyses for
optimizing he process parameters.

Table (6) Results of the confirmation experiments for laser energy

Optimal process parameters
Prediction Experimental
Level A3B1 A3 B 1
Laser energy in KJ 6.54 6.499

7 CONCLUSIONS

This paper presents the effect of slice thickness and part orientation on laser energy of
a part manufactured in the SLS process. Nine sample parts were virtually manufactured and
their laser energies were calculated for different sets of slice thickness and part orientations
and the results are presented. From the results, it can be concluded that the slice thickness in
inversely proportional to the total laser energy building the part, and the effect of part
orientation on laser ene4rgy is dependant upon the geometry of the part. In this study the
design of experiments and S/N ratio provides a systematic and efficient methodology for the
design optimization of the process parameters. From the ANOVA analysis it was found that
the effect of slice thickness on laser energy is 97.79% and part orientation is 1.538%. This
significance is further strengthened by correlation analysis. The confirmation experiments
were conducted to verify the optimal process parameters.

7.1 Scope of future work

In this paper, only the SLS laser energy has been analyzed while other energy
components such as platform energy, energy for heating the bed etc., have been neglected.
This work can be extended considering other energies. This work can also be extending to
other process parameter incorporating in addition to these two process parameter for
optimization.

170


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