Study on Parametric Optimization of Fused Deposition Modelling (FDM) Process

By: Sood, Anoop KumarContributor(s): Mahapatra, S S [Supervisor] | Ohdar, R K [Supervisor] | Department of Mechanical EngineeringMaterial type: TextTextLanguage: English Publisher: 2011Description: 207 pSubject(s): Engineering and Technology | Mechanical Engineering | Machine DesignOnline resources: Click here to access online Dissertation note: Thesis (Ph.D)- National Institute of Technology, Rourkela Summary: Rapid prototyping (RP) is a generic term for a number of technologies that enable fabrication of physical objects directly fro m CAD data sources. In contrast to classical methods of manufacturing such as milling and forging which are based on subtractive and formative principles respectively, these processes are based on additive principle for part fabrication. The biggest advant age of RP processes is that an entire 3 - D (three - dimensional) consolidated assembly can be fabricated in a single setup without any tooling or human intervention; further, the part fabrication methodology is independent of the complexity of the part geomet ry. Due to several advantages, RP has attracted the considerable attention of manufacturing industries to meet the customer demands for incorporating continuous and rapid changes in manufacturing in shortest possible time and gain edge over competitors. Ou t of all commercially available RP processes, fused deposition model l ing (FDM) uses heated thermoplastic filament which are extruded from the tip of nozzle in a prescribed manner in a temperature controlled environment for building the part through a layer by layer deposition method. Simplicity of operation together with the ability to fabricate parts with locally controlled properties resulted in its wide spread application not only for prototyping but also for making functional parts. However, FDM process has its own demerits related with accuracy, surface finish, strength etc. Hence, it is absolutely necessary to understand the shortcomings of the process and identify the controllable factors for improvement of part quality. In this direction, present stu dy focuses on the improvement of part build methodology by properly controlling the process parameters. The thesis deals with various part quality measures such as improvement in dimensional accuracy, minimization of surface roughness, and improvement in m echanical properties measured in terms of tensile, compressive, flexural, impact strength and sliding wear. The understanding generated in this work not only explain the complex build mechanism but also present in detail the influence of processing paramet ers such as layer thickness, orientation, raster angle, raster width and air gap on studied responses with the help of statistically iv validated models, microphotographs and non - traditional optimization methods. For improving dimensional accuracy of the part , Taguchi‟s experimental design is adopted and it is found that measured dimension is oversized along the thickness direction and undersized along the length, width and diameter of the hole. It is observed that different factors and interactions control th e part dimensions along different directions. Shrinkage of semi molten material extruding out from deposition nozzle is the major cause of part dimension reduction. The oversized dimension is attributed to uneven layer surfaces generation and slicing const raints. For recommending optimal factor setting for improving overall dimension of the part, grey Taguchi method is used. Prediction models based on artificial neural network and fuzzy inference principle are also proposed and compared with Taguchi predict ive model. The model based on fuzzy inference system shows better prediction capability in comparison to artificial neural network model. In order to minimize the surface roughness, a process improvement strategy through effective control of process parame ters based on central composite design (CCD) is employed. Empirical models relating response and process parameters are developed. The validity of the models is established using analysis of variance (ANOVA) and residual analysis. Experimental results indi cate that process parameters and their interactions are different for minimization of roughness in different surfaces. The surface roughness responses along three surfaces are combined into a single response known as multi - response performance index ( MPI ) using principal component analysis. Bacterial foraging optimisation algorithm (BFOA), a latest evolutionary approach, has been adopted to find out best process parameter setting which maximizes MPI . Assessment of process parameters on mechanical propertie s viz. tensile, flexural, impact and compressive strength of part fabricated using FDM technology is done using CCD. The effect of each process parameter on mechanical property is analyzed. The major reason for weak strength is attributed to distortion wit hin or between the layers. In actual practice, the parts are subjected to various types of loadings and it is necessary that the fabricated part must withhold more than one type of loading simultaneously. v To address this issue, all the studied strengths ar e combined into a single response known as composite desirability and then optimum parameter setting which will maximize composite desirability is determined using quantum behaved particle swarm optimization (QPSO). Resistance to wear is an important consi deration for enhancing service life of functional parts. Hence, present work also focuses on extensive study to understand the effect of process parameters on the sliding wear of test specimen. The study not only provides insight into complex dependency of wear on process parameters but also develop a statistically validated predictive equation. The equation can be used by the process planner for accurate wear prediction in practice. Finally, comparative evaluation of two swarm based optimization methods su ch as QPSO and BFOA are also presented. It is shown that BFOA, because of its biologically motivated structure, has better exploration and exploitation ability but require more time for convergence as compared to QPSO. The methodology adopted in this study is quite general and can be used for other related or allied processes, especially in multi input, multi output systems. The proposed study can be used by industries like aerospace, automobile and medical for identifying the process capability and further improvement in FDM process or developing new processes based on similar principle.
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Thesis (Ph.D/M.Tech R) Thesis (Ph.D/M.Tech R) BP Central Library
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Thesis (Ph.D)- National Institute of Technology, Rourkela

Rapid prototyping (RP) is a generic term for a number of technologies
that enable fabrication of physical objects directly fro
m CAD data sources. In
contrast to classical methods of manufacturing such as milling and forging
which are based on subtractive and formative principles respectively, these
processes are based on additive principle for part fabrication. The biggest
advant
age of RP processes is that an entire 3
-
D (three
-
dimensional)
consolidated assembly can be fabricated in a single setup without any tooling
or human intervention; further, the part fabrication methodology is
independent of the complexity of the part geomet
ry. Due to several
advantages, RP has attracted the considerable attention of manufacturing
industries to meet the customer demands for incorporating continuous and
rapid changes in manufacturing in shortest possible time and gain edge over
competitors. Ou
t of all commercially available RP processes, fused deposition
model
l
ing (FDM) uses heated thermoplastic filament which are extruded from
the tip of nozzle in a prescribed manner in a temperature controlled
environment for building the part through a layer
by layer deposition method.
Simplicity of operation together with the ability to fabricate parts with locally
controlled properties resulted in its wide spread application not only for
prototyping but also for making functional parts. However, FDM process
has
its own demerits related with accuracy, surface finish, strength etc. Hence, it
is absolutely necessary to understand the shortcomings of the process and
identify the controllable factors for improvement of part quality. In this
direction, present stu
dy focuses on the improvement of part build
methodology by properly controlling the process parameters. The thesis deals
with various part quality measures such as improvement in dimensional
accuracy, minimization of surface roughness, and improvement in m
echanical
properties measured in terms of tensile, compressive, flexural, impact
strength and sliding wear. The understanding generated in this work not only
explain the complex build mechanism but also present in detail the influence
of processing paramet
ers such as layer thickness, orientation, raster angle,
raster width and air gap on studied responses with the help of statistically
iv
validated models, microphotographs and non
-
traditional optimization
methods.
For improving dimensional accuracy of the part
, Taguchi‟s
experimental design is adopted and it is found that measured dimension is
oversized along the thickness direction and undersized along the length, width
and diameter of the hole. It is observed that different factors and interactions
control th
e part dimensions along different directions. Shrinkage of semi
molten material extruding out from deposition nozzle is the major cause of
part dimension reduction. The oversized dimension is attributed to uneven
layer surfaces generation and slicing const
raints. For recommending optimal
factor setting for improving overall dimension of the part, grey Taguchi method
is used. Prediction models based on artificial neural network and fuzzy
inference principle are also proposed and compared with Taguchi predict
ive
model. The model based on fuzzy inference system shows better prediction
capability in comparison to artificial neural network model.
In order to minimize the surface roughness, a process improvement
strategy through effective control of process parame
ters based on central
composite design (CCD) is employed. Empirical models relating response and
process parameters are developed. The validity of the models is established
using analysis of variance (ANOVA) and residual analysis.
Experimental
results indi
cate that process parameters and their interactions are different for
minimization of roughness in different surfaces.
The surface roughness
responses along three surfaces are combined into a single response known
as multi
-
response performance index (
MPI
)
using principal component
analysis. Bacterial foraging optimisation algorithm (BFOA), a latest
evolutionary approach, has been adopted to find out best process parameter
setting which maximizes
MPI
.
Assessment of process parameters on mechanical propertie
s viz.
tensile, flexural, impact and compressive strength of part fabricated using
FDM technology is done using CCD. The effect of each process parameter on
mechanical property is analyzed. The major reason for weak strength is
attributed to distortion wit
hin or between the layers. In actual practice, the
parts are subjected to various types of loadings and it is necessary that the
fabricated part must withhold more than one type of loading simultaneously.
v
To address this issue, all the studied strengths ar
e combined into a single
response known as composite desirability and then optimum parameter
setting which will maximize composite desirability is determined using
quantum behaved particle swarm optimization (QPSO).
Resistance to wear is an important consi
deration for enhancing service
life of functional parts. Hence, present work also focuses on extensive study
to understand the effect of process parameters on the sliding wear of test
specimen. The study not only provides insight into complex dependency of
wear on process parameters but also develop a statistically validated
predictive equation. The equation can be used by the process planner for
accurate wear prediction in practice. Finally, comparative evaluation of two
swarm based optimization methods su
ch as QPSO and BFOA are also
presented. It is shown that BFOA, because of its biologically motivated
structure, has better exploration and exploitation ability but require more time
for convergence as compared to QPSO.
The methodology adopted in this study
is quite general and can be
used for other related or allied processes, especially in multi input, multi
output systems. The proposed study can be used by industries like
aerospace, automobile and medical for identifying the process capability and
further
improvement in FDM process or developing new processes based on
similar principle.

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