Intelligent Information Management, 2009, 1, 89-97
doi:10.4236/iim.2009.12014 Published Online November 2009 (
Copyright © 2009 SciRes IIM
Optimization of Fused Deposition Modelling (FDM)
Process Parameters Using Bacterial Foraging
Samir Kumar PANDA1, Saumyakant PADHEE2, Anoop Kumar SOOD3, S. S. MAHAPATRA4
1Department of Mechanical Engineering, National Institute of Technology, Rourkela, India
2Department of Manufacturing Science and Technology,
Veer Surendra Sai University of Technology, Sambalpur, India
3Department of Manufacturing Science, National Institute of Foundry and Forge Technology, Ranchi, India
4Department of Mechanical Engineering, National Institute of Technology, Rourkela, India
Email: {Samirpanda.nitrkl, soumyakantpadhee2011,anoopkumarsood},
Abstract: Fused deposition modelling (FDM) is a fast growing rapid prototyping (RP) technology due to its
ability to build functional parts having complex geometrical shapes in reasonable build time. The dimen-
sional accuracy, surface roughness, mechanical strength and above all functionality of built parts are depend-
ent on many process variables and their settings. In this study, five important process parameters such as
layer thickness, orientation, raster angle, raster width and air gap have been considered to study their effects
on three responses viz., tensile, flexural and impact strength of test specimen. Experiments have been con-
ducted using central composite design (CCD) and empirical models relating each response and process pa-
rameters have been developed. The models are validated using analysis of variance (ANOVA). Finally, bacte-
rial foraging technique is used to suggest theoretical combination of parameter settings to achieve good
strength simultaneously for all responses.
Keywords: fused deposition modelling (FDM), strength, distortion, bacterial foraging, ANOVA, central
composite design (CCD)
1. Introduction
Reduction of product development cycle time is a major
concern in industries to remain competitive in the mar-
ketplace and hence, focus has shifted from traditional
product development methodology to rapid fabrication
techniques like rapid prototyping (RP) [1–5]. Although
RP is an efficient technology, full scale application has
not gained much attention because of compatibility of
presently available materials with RP technologies [6,7].
To overcome this limitation, one approach may be de-
velopment of new materials having superior characteris-
tics than conventional materials and its compatibility with
technology. Another convenient approach may be suita-
bly adjusting the process parameters during fabrication
stage so that properties may improve [8,9]. A critical
review of literature suggests that properties of RP parts
are function of various process related parameters and can
be significantly improved with proper adjustment. Since
mechanical properties are important for functional parts, it
is absolutely essential to study influence of various
process parameters on mechanical properties so that im-
provement can be made through selection of best settings.
The present study focus on assessment of mechanical
properties viz. tensile, flexural and impact strength of part
fabricated using fused deposition modelling (FDM)
technology. Since the relation between a particular me-
chanical property and process parameters related to it is
difficult to establish, attempt has been made to derive the
empirical model between the processing parameters and
mechanical properties using response surface methodol-
ogy (RSM). In addition, effect of each process parameter
on mechanical property is analysed. Residual analysis has
been carried out to establish validity of the model. De-
velopment of valid models helps to search the landscape
to find out best possible parametric combination resulting
in theoretical maximum strength which has not been ex-
plored during experimentation. In order to follow search
procedure in an efficient manner, latest evolutionary
technique such as bacteria foraging has been adopted due
to its superior performance over other similar random
search techniques [10]. First, bacteria foraging is easier to
implement because only few parameters need to be ad-
justed. Every bacterium remembers its own previous best
value as well as the neighbourhood best and hence, it has a
more effective memory capability than other techniques.
Bacteria foraging is more efficient in maintaining the
diversity of the swarm as all the particles use the infor-
mation related to the most successful particle in order to
improve themselves. In contrast, the worse solutions are
discarded and only the good ones are saved in genetic
algorithm (GA) [11]. Therefore, the population revolves
around a subset of the best individuals in GA. Particle
swarm optimization (PSO) can be considered as a good
option because of its fast convergence but it has the ten-
dency to get trapped at local optimum unless some pro-
cedure is adopted to escape [12].
2. Literature Review
Fused deposition modelling (FDM) is one of the RP
processes that build part of any geometry by sequential
deposition of material on a layer by layer basis. The
process uses heated thermoplastic filaments which are
extruded from the tip of nozzle in a prescribed manner in
a semi molten state and solidify at chamber temperature.
The properties of built parts depend on settings of vari-
ous process parameters fixed at the time of fabrication.
Recently, research interest has been devoted to study the
effect of various process parameters on responses ex-
pressed in terms of properties of built parts. Studies have
concluded through design of experiment (DOE) ap-
proach that process parameters like layer thickness, raster
angle and air gap significantly influence the responses of
FDM ABS prototype [13,14]. Lee et al. [15] performed
experiments on cylindrical parts made from three RP
processes such as FDM, 3D printer and nano-composite
deposition (NCDS) to study the effect of build direction
on the compressive strength. Out of three RP technolo-
gies, parts built by NCDS are severely affected by the
build direction. Wang et al. [16] have recommended that
material used for part fabrication must have lower glass
transition temperature and linear shrinkage rate because
the extruded material is cooled from glass transition
temperature to chamber temperature resulting in devel-
opment of inner stresses responsible for appearance of
inter- and intra-layer deformation in the form of cracking,
de-lamination or even part fabrication failure. Belle-
humeur et al. [17] have experimentally demonstrated that
bond quality between adjacent filaments depends on en-
velope temperature and variations in the convective con-
ditions within the building part while testing flexural
strength specimen. Temperature profiles reveal that tem-
perature at bottom layers rises above the glass transition
temperature and rapidly decreases in the direction of
movement of extrusion head. Microphotographs indicate
that diffusion phenomenon is more prominent for adjacent
filaments in bottom layers as compared to upper layers.
Simulation of FDM process using finite element analysis
(FEA) shows that distortion of parts is mainly caused due
to accumulation of residual stresses at the bottom surface
of the part during fabrication [18]. The literature reveals
that properties are sensitive to the processing parameters
because parameters affect meso-structure and fibre-to-
fibre bond strength. Also uneven heating and cooling
cycles due to inherent nature of FDM build methodology
results in stress accumulation in the built part resulting in
distortion which is primarily responsible for week bond-
ing and thus affect the strength. It is also noticed that
good number of works in FDM strength modelling is
devoted to study the effect of processing conditions on
the part strength but no significant effort is made to de-
velop the strength model in terms of FDM process pa-
rameters for prediction purpose. The present study uses
the second order response surface model to derive the
required relationship among respective process parame-
ters and tensile, flexural and impact strength. The predic-
tive equations once validated can be used to envisage
theoretical possible best parameter settings to attain
maximum in response characteristic. Hence, the problem
becomes constrained optimization problem. In this study,
bacteria foraging approach has been adopted to deal with
the optimization problem.
The use of evolutionary algorithms to solve complex
optimization problems is very common these days be-
cause they provide very competitive results when solving
engineering design problems [19,20]. Furthermore,
swarm intelligence approaches have been also used to
solve this kind of problems [21,22]. However, most of
the work is centered on some algorithms such as Particle
Swarm Optimization [23], Ant Colony Optimization [24]
and Artificial Bee Colony [25]. Recently, another swarm-
intelligence-based model known as Bacterial Foraging
Optimization Algorithm (BFOA), inspired in the behav-
ior of bacteria E. Coli in its search for food, has been
proposed. Three behaviors were modeled by Passino in
his original proposal [26]: 1) Chemotaxis, 2) reproduc-
tion and 3) elimination-dispersal. BFOA has been suc-
cessfully applied to solve different type of problems like
forecasting [27], transmission loss reduction [28] and
identification of nonlinear dynamic systems [29].
3. Experimental Procedure
The tensile test and three-point bending tests were per-
formed using Instron 1195 series IX automated material
testing system with crosshead speeds of 1mm/s and
2mm/s respectively in accordance with ISO R527:1966
and ISO R178:1975 respectively. Charpy impact test
performed in Instron Wolpert pendulum impact test ma-
chine is used to determine the impact strength of speci-
men in accordance with ISO 179:1982. During impact
testing, specimen is subjected to quick and intense blow
by hammer pendulum striking the specimen with a speed
of 3.8m/s. The impact energy absorbed is measure of the
Copyright © 2009 SciRes IIM
Copyright © 2009 SciRes IIM
toughness of material and it is calculated by taking the
difference in potential energy of initial and final position
of hammer. Impact energy is converted into impact
strength using the procedure mentioned in the standard.
where ij
and are coded and actual value of th
level of th factor respectively.
Apart from high and low levels of each factor, zero
level (center point) and ±α level (axial points) of each
factor is also included. To reduce the number of levels
due to machine constraints, face centred central compos-
ite design (FCCCD) in which α=1 is considered. This
design locates the axial points on the centres of the faces
of cube and requires only three levels for each factor.
Moreover, it does not require as many centre points as
spherical CCD. In practice, two or three centre points are
sufficient. In order to get a reasonable estimate of ex-
perimental error, six centre points are chosen in the pre-
sent work. Table 1 shows the factors and their levels in
terms of uncoded units as per FCCCD. For change in
layer thickness, change of nozzle is needed. Due to un-
availability of nozzle corresponding to layer thickness
value at centre point as indicated by Equation 1, modi-
fied centre point value for layer thickness is taken. Half
factorial 25 unblocked design having 16 experimental run,
10 (2K, where K=5) axial run and 6 centre run is shown
in Table 2 together with the response value for tensile,
flexural and impact strength for each experimental run.
Three specimens per experimental run are fabricated
using FDM Vantage SE machine for respective strength
measurement. The 3D models of specimen are modelled
in CATIA V5 and exported as STL file. STL file is im-
ported to FDM software (Insight). Here, factors as shown
Table 1 are set as per experiment plan (Table 2). All tests
are carried out at the temperature 23±2ºC and relative
humidity 50±5% as per ISO R291:1977. Mean of each
experiment trial is taken as represented value of respec-
tive strength and shown in Table 2. The material used for
test specimen fabrication is acrylonitrile butadiene sty-
rene (ABS P400).
4. Experimental Plan
It is evident from the literature that strength of FDM
processed component primarily depend process parame-
ters. Therefore, five important control factors such as
layer thickness (A), part build orientation (B), raster an-
gle (C), raster width (D) and raster to raster gap (air gap)
(E) are considered in this study. Other factors are kept at
their fixed level. 5. Bacteria Foraging Optimization Algorithm
In order to build empirical model for each tensile
strength, flexural strength and impact strength, experi-
ments were conducted based on central composite design
(CCD) [30]. The CCD is capable of fitting second order
polynomial and is preferable if curvature is assumed to
be present in the system. To reduce the experiment run,
half factorial 2K design (K factors each at two levels) is
considered. Maximum and minimum value of each factor
is coded into +1 and -1 respectively using Equation 1 so
that all input factors are represented in same range.
As other swarm intelligence algorithms, BFOA is based
on social and cooperative behaviors found in nature. In
fact, the way bacteria look for regions of high levels of
nutrients can be seen as an optimization process. Each
bacterium tries to maximize its obtained energy per each
unit of time expended on the foraging process and
avoiding noxious substances. Besides, swarm search
assumes communication among individuals. The swarm
of bacteria, S, behaves as follows:
ij i
(1) 1) Bacteria are randomly distributed in the map of nu-
and 21ii
2) Bacteria move towards high-nutrient regions in the
map. Those located in regions with noxious sub-
stances or low-nutrient regions will die and disperse,
respectively. Bacteria in convenient regions will re-
produce (split).
1iK ; 12 j
Table 1. Factors and their levels (*modified)
Factor Symbol Unit
Low Level
Centre point
High Level
Layer thickness A mm 0.1270 0.1780* 0.2540
Orientation B degree 0.0000 15.000 30.000
Raster angle C degree 0.0000 30.000 60.000
Raster width D mm 0.4064 0.4564 0.5064
Air gap E mm 0.0000 0.0040 0.0080
Table 2. Experimental data obtained from the FCCCD runs
Factor (Coded units)
1 -1 -1 -1 -1 +1 15.6659 34.2989 0.367013
2 +1 -1 -1 -1 -1 16.1392 35.3593 0.429862
3 -1 +1 -1 -1 -1 9.1229 18.8296 0.363542
4 +1 +1 -1 -1 +1 13.2081 24.5193 0.426042
5 -1 -1 +1 -1 -1 16.7010 36.5796 0.375695
6 +1 -1 +1 -1 +1 17.9122 38.0993 0.462153
7 -1 +1 +1 -1 +1 18.0913 39.2423 0.395833
8 +1 +1 +1 -1 -1 14.0295 22.2167 0.466667
9 -1 -1 -1 +1 -1 14.4981 27.6040 0.342708
10 +1 -1 -1 +1 +1 14.8892 34.5569 0.429167
11 -1 +1 -1 +1 +1 11.0262 20.0259 0.379167
12 +1 +1 -1 +1 -1 14.7661 25.2563 0.450001
13 -1 -1 +1 +1 +1 15.4510 36.2904 0.375000
14 +1 -1 +1 +1 -1 15.9244 37.3507 0.437785
15 -1 +1 +1 +1 -1 11.8476 22.9759 0.419792
16 +1 +1 +1 +1 +1 15.9328 28.8362 0.482292
17 -1 0 0 0 0 13.4096 27.7241 0.397222
18 +1 0 0 0 0 15.8933 33.0710 0.44757
19 0 -1 0 0 0 14.4153 34.7748 0.402082
20 0 +1 0 0 0 9.9505 25.2774 0.388539
21 0 0 -1 0 0 13.7283 27.5715 0.382986
22 0 0 +1 0 0 14.7224 30.0818 0.401388
23 0 0 0 -1 0 13.5607 28.9856 0.401041
24 0 0 0 +1 0 13.8388 28.8622 0.395833
25 0 0 0 0 -1 13.6996 28.8063 0.405555
26 0 0 0 0 +1 13.8807 29.0359 0.409028
27 0 0 0 0 0 14.4088 29.7678 0.407292
28 0 0 0 0 0 13.0630 31.6717 0.396373
29 0 0 0 0 0 13.8460 30.1584 0.406558
30 0 0 0 0 0 13.8727 31.0388 0.397712
31 0 0 0 0 0 13.5914 29.1475
32 0 0 0 0 0 13.2189 31.9426 0.410686
3) Bacteria are located in promising regions of the map
of nutrients as they try to attract other bacteria by gener-
ating chemical attractants.
4) Bacteria are now located in the highest-nutrient re-
5) Bacteria now disperse as to look for new nutrient
regions in the map.
Three main steps comprise bacterial foraging behavior:
1) Chemotaxis (tumble and swimming), 2) reproduction
and 3) elimination-dispersal. Based on these steps, Pass-
ino proposed the Bacterial Foraging Optimization Algo-
rithm which is summarized in pseudo-code as follows:
Pseudo-Code for BFOA
Initialize input parameters: number of bacteria (Sb),
chemotactic loop limit (Nc,), swim loop limit (Ns), re-
production loop limit (Nre), number of bacteria for re-
production (Sr), elimination-dispersal loop limit (Ned),
Copyright © 2009 SciRes IIM
step sizes (Ci) depending on dimensionality of the prob-
lem and probability of elimination dispersal (Ped).
Create a random initial swarm of bacteria θi(j, k, l) i
i = 1, . . . , Sb
Evaluate f(θi(j, k, l)) , i = 1, . . . , Sb
For l=1 to Ned Do
For k=1 to Nre Do
For j=1 to Nc Do
For i=1 to Sb Do
Perform the chemotaxis step (tumble-swim or tum-
ble-tumble) for bacteria θi(j, k, l)
End For
End For
Perform the reproduction step by eliminating the Sr
(half) worst bacteria and duplicating the other half
End For
Perform the elimination-dispersal step for all bacteria
θi(j, k, l) , i = 1, . . . , Nb with probability 0 Ped 1
End For
The chemotactic step was modeled by Passino with the
generation of a random direction search (Equation 2)
() ()
where (i)n is a randomly generated vector with ele-
ments within the interval [1, 1]. After that, each bacteria
θi(j, k, l) modifies its positions as indicated in Equation 3.
θi(j+1, k, l) = θi(j, k, l)+C(i)(i) (3)
Equation 2 represents a tumble (search direction) and
Equation 3 represents a swim. The swim will be repeated
Ns times if the new position is better than the previous one:
f(θi(j + 1, k, l)) < f(θi(j, k, l)). The reproduction step con-
sists on sorting bacteria in the population θi(j, k, l) i
, i =
1, . . . , Nb based on their objective function value f(θi(j, k,
l)) and to eliminate half of them with the worst value. The
remaining half will be duplicated as to maintain a fixed
population size. The elimination-dispersal step consists
on eliminate each bacteria θi(j, k, l) , i = 1, . . . , Nb with
a probability 0 Ped 1. It should be noted that BFOA
requires 7 parameters and the n step sizes depending of
the number of variables of the problem to be fine-tuned by
the user.
5. Results and Discussions
Analysis of the experimental data obtained from FCCCD
design runs is carried out using MINITAB R 14 software
for full quadratic response surface model as given by
Equation 4.
iiiiiiiji j
xxx x
 
 
 
where is the response,
is th factor i
For significance check, F value given in ANOVA table
is used. Probability of F value greater than calculated F
value due to noise is indicated by p value. If p value is
less than 0.05, significance of corresponding term is es-
tablished. For lack of fit, p value must be greater the 0.05.
An insignificant lack of fit is desirable because it indi-
cates any term left out of model is not significant and
developed model fits well. Based on analysis of variance
(ANOVA), full quadratic model is found to be suitable
for tensile strength, flexural strength and impact strength
(Table 3) with regression p-value less than 0.05 and lack
of fit more then 0.05. For tensile strength, all the terms
are significant where as square terms are insignificant for
Table 3. Analysis of variance (ANOVA) table
Tensile strength Flexural strength Impact strength
Source DOF
Regression 20 112.482 5.6241 11.65 0.000799.05839.95314.960.0000.0293 0.00146 16.720.000
Linear 5 64.373 12.8750 26.66 0.000 611.818 122.36 45.810.0000.0258 0.00515 58.880.000
Square 5 14.966 2.9932 6.20 0.0064.470 0.894 0.3300.8820.0019 0.00038 4.30 0.021
Interaction 10 33.143 3.3143 6.86 0.002182.77118.2776.8400.0020.0016 0.00016 1.85 0.164
Residual 11 5.312 0.4829 29.3832.671 0.0010 8.8E-05
Lack of fit 6 4.116 0.6861 2.870 0.13423.2453.8743.1600.1140.0008 0.00013 4.03 0.074
Pure error 5 1.196 0.2392 6.138 1.228 0.0002 3.3E-05
Total 31 117.794 828.442 0.0302
DOF= degree of freedom; SS= sum of square; MS= mean sum of square
Copyright © 2009 SciRes IIM
Figure 1. Normal probability plot of residual at 95% of confidence interval (a) Tensile strength (b) Impact strength (c) Flex-
ural strength
flexural strength and interaction terms do not impart sig-
nificant effect on impact strength.
The t-test was performed to determine the individual
significant term at 95% of confidence level and final
response surface equations in uncoded form for tensile
strength (TS), flexural strength (FS) and impact strength
(IS) are given from Equation 5 to Equation 7 respectively
in terms of coded units. The coefficient of determination
(R2) which indicates the percentage of total variation in
the response explained by the terms in the model is
95.5%, 96.5% and 96.8% for tensile, flexural and impact
strength respectively.
TS = 13.5625 + 0.7156 A - 1.3123 B + 0.9760 C
+ 0.5183 E + 1.1671 A2 - 1.3014 B2 - 0.4363 (A×C)
+ 0.4364 (A×D) - 0.4364 (A×E) + 0.4364 (B×C)
+ 0.4898 (B×E) - 0.5389(C×D) + 0.5389 (C×E)
- 0.5389 (D×E) (5)
FS = 29.9178 + 0.8719 A - 4.8741 B + 2.4251 C
- 0.9096 D + 1.6626 E - 1.7199 (A×C)
+ 1.7412 (A×D)- 1.1275 (A×E) + 1.0621 (B×E)
+ 1.0621 (C×E) + 1.0408 (D×E) (6)
IS = 0.401992 + 0.034198 A + 0.008356 B + 0.013673 C
+ 0.021383 A2 + 0.008077 (B×D) (7)
Anderson-Darling (AD) normality test results are
shown in Figure 1 for respective strength residue. P-
value of the normality plot is more than 0.05 and signi-
fies that residue follows the normal distribution.
Once the empirical models are validated for tensile,
flexural, and impact strength of FDM built parts, next
step is to search the optimization region for finding out
suitable parameter settings that maximize responses be-
yond the experimental domain. Here, the objective func-
tion to be maximized is given as:
Maximize (T
S or FS or IS ). (8)
Subjected to constraints:
Amin < A < A
max (9)
Bmin < B < B
max (10)
Cmin < C < C
max (11)
Dmin < D < D
max (12)
Emin < E < E
max (13)
The min and max in constraints 9-13 show the lowest
and highest control factors settings (control factors) used
in this study (Table 1). The constraints to be selected
must be relevant to the response as shown in Equations
5-7. In order to apply BFOA, initial parameters of the are
set as: number of bacteria, Sb =50, chemotactic loop limit,
Nc =30, swim loop limit, Ns =100, reproduction loop limit,
Nre =20, number of bacteria for reproduction, Sr =50%,
elimination-dispersal loop limit, Ned =50, step sizes, Ci
Copyright © 2009 SciRes IIM
=1% of the difference of the maximum and minimum
value of the variable, and probability of elimination dis-
persal, Ped =0.05. The algorithm is coded in MATLAB
and run on IBM Pentium IV desktop computer. The
convergence curve for different responses is shown in
Figure 2. The response values and optimal parameter
settings are shown in Table 4. It can be observed from it
that the response values significantly (in the tune of three
to four times of experimental values) improve only set-
ting the parameters within available range. Layer thick-
ness (A) should be maintained at lower limit for improv-
ing tensile and flexural strength whereas it is to be set at
higher range for the improvement of impact strength.
Same trend of setting has also been observed for part ori-
entation (B). Raster angle (C) and raster width (D) must
be set at higher ranges for improving all responses. How-
ever, air gap (E) should be set at higher and medium
range for improvement of tensile and flexural strength
respectively whereas it does not play significant role for
improvement of impact strength Lower layer thickness
and air gap helps to occur diffusion in an effective man-
ner causing better bonding among layers and better heat
6. Conclusions
In this work, functional relationship between process
parameters and strength (tensile, flexural and impact)
forFDM process has been developed using response sur-
face methodology for prediction purpose. The process pa
rameters considered are layer thickness, orientation, raster
angle, raster width and air gap. The empirical models
Figure 2. Convergence curves
Table 4. Optimal parameter settings with response values
Responses Value
Layer thickness
Raster angle
Raster width
Air gap
Tensile strength 174.3177 0.1318 9.6100 59.9937 0.4196 0.0074
Flexural strength 126.4818 0.1278 4.9504 54.7311 0.4960 0.0034
Impact strength 1.6056 0.2531 29.9963 59.9951 0.5063 Not applicable
Copyright © 2009 SciRes IIM
developed here have passed all the known statistical tests
and can be used in practice. Since FDM process is a
complex one, it is really difficult to obtain good func-
tional relationship between responses and process pa-
rameters. A latest evolutionary approach such as bacterial
foraging has been used to predict optimal parameter set-
tings. It predicts parameters in universal range of values
that may not exist in the experimental system set up.
Therefore, this technique suggests alternative system
settings so as to produce optimum results in the process.
The parameter settings seem to be logical from the prac-
tical point of view. Number of layers in a part depends
upon the layer thickness and part orientation. If number
of layers is more, it will result in high temperature gra-
dient towards the bottom of part. This will increase dif-
fusion between adjacent rasters and strength will im-
prove. Small raster angles are not preferable as they will
results in long rasters which will increase the stress ac-
cumulation along the direction of deposition resulting in
more distortion and hence weak bonding. Thick rasters
results in high temperature near the boding surfaces
which may improve the diffusion and may result in
strong bond formation. The study can also extended in
the direction of studying compressive strength, fatigue
strength and vibration analysis.
[1] B. Wiedemann and H. A. Jantzen, “Strategies and ap-
plications for rapid product and process development in
Daimler-Benz AG. Computers in Industries,” Vol. 39,
No. 1, pp. 11–25, 1999.
[2] S. Upcraft and R. Fletcher, “The rapid prototyping
technologies”, Rapid Prototyping Journal, Vol. 23, No.
4, pp. 318–330, 2003.
[3] S. Mansour and R. Hauge, “Impact of rapid manufac-
turing on design for manufacturing for injection mould-
ing,” Journal of Engineering Manufacture, Part B, Vol.
217, No. 4, pp. 453–461, 2003.
[4] N. Hopkinson, R. J. M. Hagur, and P. H.Dickens,
“Rapid manufacturing: An industrial revolution for the
digital age,” John Wiley and Sons Ltd., England, 2006.
[5] A. Bernarand and A. Fischer, “New trends in rapid
product development,” CIRP Annals-Manufacturing
Technology, Vol. 51, No. 2, pp. 635–652, 2002.
[6] G. N. Levy, R. Schindel, J. P. Kruth, and K. U. Leuven,
“Rapid manufacturing and rapid tooling with layer
manufacturing (LM) technologies-State of the art and
future perspectives,” CIRP Annals-Manufacturing Te-
chnologies, Vol. 52, No. 2, pp. 589–609, 2003.
[7] A. Pilipović, P. Raos, M. Šercer, “Experimental analy-
sis of properties of materials for rapid prototyping,”
International Journal of Advanced Manufacturing
Technology, Vol. 40, No. 11–12, pp. 105–115, 2009.
[8] P. M. Pandey, P. K. Jain, and P. V. M.Rao, “Effect of
delay time on part strength in selective laser Sintering,”
International Journal of Advanced Manufacturing Te-
chnology, Vol. 43, No. 1–2, pp. 117–126, 2009.
[9] K. Chockalingama, N. Jawahara, and U. Chandrasekhar,
“Influence of layer thickness on mechanical properties
in stereolithography,” Rapid Protyping Journal, Vol. 12,
No. 6, pp. 106–113, 2006.
[10] Y. Liu, K. M. Passino, and M. A. Simaan, “Biomimicry
of social foraging bacteria for distributed optimization:
Models, principles, and emergent behaviors,” Journal
of Optimization Theory and Applications, Vol. 115, No.
3, pp. 603–628, 2002.
[11] D. E. Goldberg, “Genetic algorithm in search, optimi-
zation and machine learning,” Addison-Wesley Long-
man Publishing Co., Inc. Boston, MA, 1989.
[12] S. Biswas and S. S. Mahapatra, “An improved meta-
heuristic approach for solving the machine loading
problem in flexible manufacturing systems,” Interna-
tional Journal of Services and Operations Management,
Vol. 5, No. 1, pp. 76–93, 2009.
[13] S. H. Ahn, M. Montero, D. Odell, S. Roundy, and P. K.
Wright, “Anisotropic material properties of fused
deposition modelling ABS,” Rapid Prototyping Journal,
Vol. 8, No. 4, pp. 248–257, 2002.
[14] Khan, Z.A., Lee, B.H., and J. Abdullah, “Optimization
of rapid prototyping parameters for production of
flexible ABS object,” Journal of Material Processing
Technology, Vol. 169, pp. 54–61, 2005.
[15] C. S. Lee, S. G. Kim, H. J. Kim, and S. H. Ahn,
“Measurement of anisotropic compressive strength of
rapid prototyping parts,” Journal of Material Process-
ing Technology, Vol. 187–188, pp. 637–630, 2007.
[16] T. M. Wang, J. T. Xi, and Y. Jin, “A model research for
prototype warp deformation in the FDM process,” In-
ternational Journal of Advanced Manufacturing Tech-
nology, Vol. 33, No. 11–12, pp. 1087–1096, 2007.
[17] C. T. Bellehumeur, P. Gu, Q. Sun, and G. M. Rizvi,
“Effect of processing conditions on the bonding quality
of FDM polymer filaments,” Rapid Prototyping Journal,
Vol. 14, No. 2, pp. 72–80, 2008.
[18] K. Chou and Y. Zhang, “A parametric study of part
distortion in fused deposition modeling using three di-
mensional element analysis,” Journal of Engineering
Manufacture, Part B, Vol. 222, pp. 959–967, 2008.
[19] E. Mezura-Montes and C. A. Coello Coello, “Con-
strained optimization via Multiobjective Evolutionary
Algorithms,” In J. Knowles, D. Corne, and K. Deb,
editors, Multiobjective Problem Solving from Nature,
Springer, Heidelberg, pp. 53–75, 2008.
[20] Z. Michalewicz and M. Schoenauer, “Evolutionary al-
gorithms for constrained parameter optimization prob-
lems,” Evolutionary Computation, Vol. 4, No. 1, pp.
1–32, 1996.
[21] S. He, E. Prempain, and Q. H. Wu, “An improved par-
Copyright © 2009 SciRes IIM
ticle swarm optimizer for mechanical design optimiza-
tion problems,” Engineering Optimization, Vol. 36, No.
5, pp. 585–605, 2004.
[22] T. Ray and K. Liew, “Society and civilization: an opti-
mization algorithm based on the simulation of social
behavior,” IEEE Transactions on Evolutionary Com-
putation, Vol. 7, No. 4, pp. 386–396, 2003.
[23] J. Kennedy and R. C. Eberhart, “Swarm intelligence,”
Morgan Kaufmann, UK, 2001.
[24] G. Leguizamón and C. Coello-Coello, “A boundary
search based ACO algorithm coupled with stochastic
ranking,” In 2007 IEEE Congress on Evolutionary
Computation (CEC’07), Singapore, September 2007.
IEEE Press, pp. 165–172, 2007.
[25] D. Karaboga and B. Basturk, “On the performance of
artificial bee colony (ABC) algorithm,” Applied Soft
Computing, Vol. 8, No. 1, pp. 687–697, 2008.
[26] K. Passino, “Biomimicry of bacterial foraging for dis-
tributed optimization and control,” IEEE Control Sys-
tems Magazine, Vol. 22, No. 3, pp. 52–67, 2002.
[27] R. Majhi, G. Panda, G. Sahoo, P. Dash, and D. Das,
“Stock market prediction of s&p 500 and DJIA using
bacterial foraging optimization technique,” In Pro-
ceedings of the IEEE Congress on Evolutionary Com-
putation (CEC’07), Singapore, IEEE Service Center, pp.
2569-2575, September 2007.
[28] S. Mishra, G. D. Reddy, P. E. Rao, and K. Santosh,
“Implementation of new evolutionary techniques for
transmission loss reduction,” In Proceedings of the
IEEE Congress on Evolutionary Computation (CEC’
07), Singapore, IEEE Service Center, pp. 2331–2336,
September 2007.
[29] B. Majhi and G. Panda, “Bacteria foraging based iden-
tificacion of nonlinear dynamic system,” In Proceed-
ings of the IEEE Congress on Evolutionary Computa-
tion (CEC’07), Singapore, IEEE Service Center, pp.
1636–1641, September 2007.
[30] D. C. Montgomery, “Design and Analysis of Experi-
ments”, Fifth Edition, John Wiley and Sons Pvt. Ltd.,
Singapore, 2003.
Copyright © 2009 SciRes IIM