Batch-7 Project cdcdcwcwcxewxxReport.pdf

A PROJECT REPORTON
OPTIMIZATION OF 3D PRINTING PARAMETERS IN FUSED
DEPOSITION MODELLING FOR IMPROVING PART QUALITY
AND MECHANICAL STRENGTH
Submitted in partial fulfillment of the requirements for the award of the degree of
BACHELOR OF TECHNOLOGY
IN
MECHANICAL ENGINEERING
BY
M.V.K. RAHUL 21P35A0312
CH. ASHISH RAM 20P31A0317
K.N.S. MANI KIRAN 20P31A0332
K. SRINIVAS 20P31A0333
Under the guidance of
Dr. CH.V.V.M.J. SATISH M.Tech , Ph.D
ASSOCIATE PROFESSOR
DEPARTMENT OF MECHANICAL ENGINEERING
ADITYA COLLEGE OF ENGINEERING & TECHNOLOGY
(Permanently Affiliated to JNTUK, Kakinada, Approved by AICTE, New Delhi, Accredited by
NAAC-UGC)
Recognized by UGC Under Section (2f) and 12(B) of UGC Act 1956
Aditya Nagar, ADB Road, Surampalem-533437
2020-2024

ADITYA COLLEGE OF ENGINEERING & TECHNOLOGY
(Permanently Affiliated to JNTUK, Kakinada, Approved by AICTE, New Delhi, Accredited by
NAAC-UGC)
Recognized by UGC Under Section (2f) and 12(B) of UGC Act 1956
Aditya Nagar, ADB Road, Surampalem-533437
Department of Mechanical Engineering
CERTIFICATE
This is to certify that the project work entitled “OPTIMIZATION OF 3D PRINTING
PARAMETERS IN FUSED DEPOSITION MODELLING FOR IMPROVING PART QUALITY AND
MECHANICAL STRENGTH" is a being submitted byM.V.K.Rahul,21P35A0312,CH.AshishRam,20P31A0317,
K.N.S.ManiKiran,20P31A0332,K.Srinivas,20P31A0333,in partial fulfillment of the requirements for the
award of Bachelorof Technology degree in Mechanical Engineering, during the academic year
2020-2024. The results embodied in this project report have not been submitted to any other
institute or university for the award of any degree.
PROJECT GUIDE
Dr. CH.V.V.M.J. Satish M.Tech, Ph.D.
Associate Professor
Dept. of Mechanical Engineering
Aditya College of Engineering &Technology
HEAD OF THE DEPARTMENT
Dr. Puli Danaiah, M.Tech, Ph.D.
Professor and Head
Dept. of Mechanical Engineering
Aditya College of Engineering &Technology
EXTERNAL EXAMINER

DECLARATION
Here we declare that this project titled "OPTIMIZATION OF 3D PRINTING PARAMETERS
IN FUSED DEPOSITION MODELLING FOR IMPROVING PART QUALITY AND MECHANICAL
STRENGTH" has been under taken by us. This work has been submitted to Aditya College of
Engineering & Technology, Surampalem, in partial fulfillment for the award of Degree of Bachelor
of Technology in Mechanical Engineering.
We further declare that this project work has not been submitted in full or partfor the
award of any degree of this on any other educational institutions.

ACKNOWLEDGEMENT
We are thankful to our beloved Dr. CH.V.V.M.J. Satish, M.Tech, Ph.D, Associate
Professor of Dept. of Mechanical Engineering for being a project guide and for his constant
support and encouragement throughout the project.
We are very thankful to Dr. Puli Danaiah, M.Tech, Ph.D Professor & Head, Dept. of
Mechanical Engineering, ADITYA COLLEGE OF ENGINEERING & TECHNOLOGY, and Surampalem
for his constant support and encouragementthroughout the project.
We also offer our sincere thanks to our beloved Dr. Dola Sanjay. S M.Tech , Ph.D Principal,
ADITYA COLLEGE OF ENGINEERING & TECHNOLOGY, for his cooperation and help in completion
ofourproject andthroughoutourcourse.
Finallywealsothank toall thestaffmembers ofthe Department ofMechanicalEngineering
for rendering co-operation all throughtheperiodof project. We also wishto thank our management
and friends for have been constant source of inspiration.
With sincere regards,

Aditya College of Engineering & Technology
Aditya Nagar, ADB Road, Surampalem – 533437
DEPARTMENT OF MECHANICAL ENGINEERING
Course Outcome mapping with PO’s and PSO’s
Course Name: B. Tech Class: IV B. Tech ME-A
Faculty Name: Ch. V. V. M. J. Satish Regulation: R20
Academic
Year:
2023-24 Semester: II
Title of the
Project:
Optimization of 3D Printing Parameters in Fused Deposition Modelling
for improving Part Quality and Mechanical Strength
COURSE OUTCOMES (COs):
Upon completion of the course, students will be able to:
CO# Course Outcomes
Blooms Taxonomy
level
CO1 Identify the area of the project Remember
CO2 Illustrate the literature of the project and problem identified Understand
CO3 Determine the plan of action, Methodology Apply
CO4
Identify the Printing parameters and their Levels for 3D printing
for finding the optimal levels of parameters
Remember
CO5 Designing and Experimentation Create
CO6
Results and analysis Conclusion, Scope for future work and
documentation
Evaluate
CO-PO/PSO MATRIX:
PO
1
PO
2
PO
3
PO
4
PO
5
PO
6
PO
7
PO
8
PO
9
PO1
0
PO1
1 PO12 PSO1 PSO2 PSO3
CO1 3 2 3 2 3
CO2 3 3 2 3 2 1
CO3 3 1 2 2 3 3 1 1 2
CO4 3 3 3 2 3 1 2 1 1
CO5 3 2 3 2 3 1 3 2 2 3 2
CO6 3 3 3 2 3 3 2 2 3
Course 3 2.4 2.75 2 3 2 2 1.5 1.6 3 2

Faculty signature
PO1 Engineering Knowledge PO7 Environment & Sustainability
PO2 Problem Analysis PO8 Ethics
PO3 Design / Development of Solutions PO9 Individual & Team Work
PO4 Conduct Investigations of complex
problems
PO10 Communication Skills
PO5 Modern Tool usage PO11 Project Management & Finance
PO6 Engineer & Society PO12 Life-long Learning
PSO1 Professional skills PSO2 Problem-solving skills
PSO3 Successful Career and Entrepreneurship

ABSTRACT
This project seeks to optimize printing parameters in Fused Deposition Modeling (FDM) to
enhance part quality and improve the mechanical strength of the printed part. FDM is a
widely adopted 3D printing technology, and improving its efficiency is crucial for
advancing additive manufacturing. The project aims to systematically investigate various
printing parameters and their interactions to find an optimal configuration that balances part
quality, production time, and mechanical strength. Through experimental research and
analysis, the project aims to provide valuable insights and guidelines for users aiming to
maximize the benefits of FDM technology, with a focus on fast and quality prototype
production.
We are aiming to build a low cost 3d printer by purchasing various parts and assembling
them to perform our experimentation. By using CAD design and Slicing softwares, we are
going to vary the various process parameters like layer height, extrusion temperature etc.,
and perform Design of Experiments (Taguchi Approach) to find the optimal parameters
suitable for our 3d printer built in order to obtain good surface finish and improved
mechanical strength of the printed parts.

TABLE OF CONTENTS
CHAPTERS NO’S TITLE PAGE
NO’S
CHAPTER 1 INTRODUCTION............................................................... 1
1.1 Introduction to Additive Manufacturing................................. 1
1.2 Additive vs Subtractive Manufacturing................................. 2
1.3 Additive Manufacturing......................................................... 4
1.4 Subtractive Manufacturing..................................................... 4
1.5 Comparison b/w Additive and Subtractive Manufacturing.... 5
1.6 Fused Deposition Modelling.................................................. 7
1.7 Filament Types....................................................................... 8
1.7.1 PLA (Poly Lactic Acid) .................................................... 9
1.7.2 ABS (Acrylonitrile Butadiene Styrene) ........................... 9
1.7.3 PP (Poly Propylene) ........................................................ 10
1.8 Printing Process on FDM Machine...................................... 11
1.9 Applications of 3D Printing................................................. 11
CHAPTER 2 LITERATURE REVIEW ................................................. 15
CHAPTER 3 PROJECT OVER VIEW .................................................. 17
3.1 Parameter Optimization in FDM...........................................17
3.1.1 Process Parameters affecting the Mechanical Properties.17
3.2 Optimization techniques....................................................... 20
3.2.1 Taguchi Design................................................................ 20
CHAPTER 4 INTRODUCTION TO CAD.............................................. 22
4.1 COMPUTER AIDED DESIGN (CAD) .............................. 22
4.2 CAD Overview .................................................................... 23
4.3 ONSHAPE............................................................................ 24
4.4 Features of ONSHAPE CAD software..................................24
CHAPTER 5 INTRODUCTION TO SLICING...................................... 27
5.1 Slicing in 3D Printing........................................................... 27
5.2 Features of Slicers................................................................ 27
5.3 Ulti-Maker CURA................................................................ 28
5.4 Features of cura.................................................................... 28
CHAPTER 6 ANYCUBIC KOBRA 2 NEO 3D PRINTER…………… 30
6.1 Features and Specifications of Any Cubic Kobra 2.............. 30
CHAPTER 7 DESIGNOF EXPERIMENTS............................................ 33
7.1 Introduction to Design of Experiments................................. 33

CHAPTER 8 TAGUCHI DESIGN APPROACH USING MINITAB
SOFTWARE......................................................................... 35
8.1 TAGUCHI DESIGN.............................................................. 35
8.2 Output tables for a Taguchi design........................................ 36
8.3 Control factors and Noise factors.......................................... 38
8.3.1 Control Factors................................................................ 38
8.3.2 Noise Factors................................................................... 38
8.3.3 Using Noise Factors to identify Optimal control factor
Settings............................................................................ 39
8.4 Signal factor............................................................................ 40
8.5 Steps for conducting a Taguchi designed experiment............ 40
8.6 Catalogue of Taguchi Designs................................................ 42
8.7 How Minitab adds a signal factor to an existing design......... 44
8.8 Two-step optimization for Taguchi designs............................ 48
8.9 Signal-to-Noise ratio in a Taguchi design............................... 52
CHAPTER 9 SAMPLE PREPARATION & EXPERIMENTATION...... 54
9.1 Sample Preparation.................................................................. 54
9.1.1 Process Parameters and their levels......................................... 54
9.2 Experimentation....................................................................... 56
9.2.1 Printing of Samples with varying levels of process
Parameters on 3D Printer...................................................... 56
9.2.2 Evaluation of Strength using Universal Testing
Machine (UTM).................................................................... 57
9.2.3 Evaluation of Average Surface Roughness (Ra) .................. 60
CHAPTER 10 RESULTS.............................................................................. 63
10.1 Taguchi Analysis: Ultimate Tensile Strength (MPa)
Versus Layer Height (mm), Infill Percent (%),
Extrusion Temperature(0
C) ................................................... 63
10.2 Taguchi Analysis: Surface Roughness Ra (microns) versus
Layer Height (mm), Infill Percent (%), Extrusion
Temperature(0
C) .................................................................... 65
10.3 Taguchi Analysis: Ultimate Tensile Strength (MPa), Surface
Roughness Ra (microns) versus Layer Height (mm), Infill
Percent (%), Extrusion Temperature(0
C) .............................. 67
CHAPTER 11 CONCLUSION....................................................................... 69
REFERENCES....................................................................... 70

FIGURE NO. LIST OF FIGURES PAGE NO.
FIGURE 1 PRINCIPLE OF ADDITVE MANUFACTURING 1
FIGURE 2 ADDITIVE MANUFACTURING 3
FIGURE 3 SUBTRACTIVE MANUFACTURING 3
FIGURE 4 ADDITIVE VS SUBTRACTIVE MANUFACTURING 4
FIGURE 5 USE OF 3D PRINTING IN MEDICAL IMPLANTS 12
FIGURE 6 APPLICATIONS OF 3D PRINTING 14
FIGURE 7 ILLUSTRATION OF LAYER HEIGHT 19
FIGURE 8 ILLUSTRATION OF INFILL DENSITY 19
FIGURE 9 EXTRUSION TEMPERATURE IN 3D PRINTING 20
FIGURE 10 COMPONENTS OF COMPUTER AIDED DESIGN 22
FIGURE 11 DESIGN OF PIPE FITTING IN ONSHAPE 26
FIGURE 12 DESIGNING IN ONSHAPE CAD SOFTWARE 26
FIGURE 13 SLICING OF TEST SPECIMEN ASTM D638 TYPE-I 29
FIGURE 14 ANYCUBIC KOBRA 2 NEO 3D PRINTER 30
FIGURE 15 OUR ASSEMBLED 3D PRINTER 32
FIGURE 16 PRINTING ON ANYCUBIC KOBRA 2 NEO 3D PRINTER 32
FIGURE 17 DESIGNED SPECIMEN DIMENSIONS 54
FIGURE 18 PRINTING OF TEST SPECIMENS 56
FIGURE 19 9 PRINTED SAMPLES WITH VARYING LEVELS 56
FIGURE 20 UNIVERSAL TESTING MACHINE UTE-40 57
FIGURE 21 TENSILE TESTING OF ASTM D638 TYPE-I SPECIMEN 58
FIGURE 22 BROKEN TEST SPECIMEN 59
FIGURE 23 SURFACE ROUGHNESS(Ra) 60
FIGURE 24 SURFACE ROUGHNESS(Rz) 61
FIGURE 25 MEXTECH SRT-6200 SURFACE ROUGHNESS TESTER 62
FIGURE 26 TESTING OF AVERAGE SURFACE ROUGHNESS(Ra) 62
FIGURE 27 TAGUCHI ANALYSIS: UTS VS PROCESS PARAMETERS
RESPONSE TABLE FOR SN RATIOS
63
MAIN EFFECTS PLOT FOR SN RATIOS
63
RESPONSE TABLE FOR MEANS
64
MAIN EFFECTS PLOT FOR MEANS
64
FIGURE 31 TAGUCHI ANALYSIS: Ra VS PROCESS PARAMETERS
65
65
66
66
FIGURE 35 TAGUCHI ANALYSIS: UTS, Ra VS PARAMETERS
67
67
68
68

TABLE N0. LIST OF TABLES PAGE NO.
TABLE 1 COMPARISION BETWEEN ADDITIVE AND
SUBTRACTIVE MANUFACTURING
6
TABLE 2 TAGUCHI’S L9 ORTHOGONAL ARRAY FOR 3 FACTORS
WITH 3 LEVELS
20
TABLE 3 EXPERIMENTAL FACTORS WITH THEIR RESPECTIVE
LEVELS
21
TABLE 4 L9 ORTHOGONAL ARRAY DESIGN TABLE 21
TABLE 5 SIGNAL TO NOISE RATIOS 53
TABLE 6 DESIGN MATRIX FOR EXPERIMENTATION 55
TABLE 7 TAGUCHI’S L9 (3 FACTOR,3 LEVEL) DESIGN 55
TABLE 8 EXPERIMENTAL RESULTS FOR TENSILE TEST 59
TABLE 9 EXPERIMENTAL RESULTS FOR SURFACE ROUGHNESS
TEST
62

ADITYA COLLEGE OF ENGINEERING & TECHNOLOGY 1
CHAPTER 1
INTRODUCTION
1.1 Introduction to Additive Manufacturing:
AM is the generic term for the collective advanced manufacturing technologies that build
parts layer by layer. The layers are produced by adding material instead of removing it as
opposed to subtractive manufacturing such as machining. The material addition or fusion is
controlled by G-codes generated directly from 3D CAD models. FDM, One of the AM
technologies, builds parts layer by layer by heating a thermoplastic filament to a semi-liquid
state and extruding it through a small nozzle per 3D CAD models usually in STL format. The
filament is usually of circular cross section with specific diameters for each FDM system. The
most widely used diameters are either 1.75 mm or 3.0 mm. Due to the nature of FDM process,
many advantages arise, such as the design freedom to produce complex shapes without the need
to invest in dies and moulds, the ability to produce internal features, which is impossible
using traditional manufacturing techniques. FDM enables the reduction of the number of
assemblies by producing consolidated complex parts. More advantage of FDM can be reaped
through the supply chain by reducing the lead time and the need for storage and transportation,
especially in applications where high customization is necessary. On the other hand, FDM
technology has challenges; such as producing parts with anisotropic mechanical properties,
staircase effect at curves, coarse surface finish, the need for supports for overhanging regions
and more. To overcome these challenges, many researchers focus on refining the quality of
FDM parts. Techniques to improve the quality of AM or FDM parts, in particular, vary between
chemical treatment, machining, heat treatment, and optimization of processing parameters.
FIG:1 PRINCIPLE OF ADDITIVE MANUFACTURING

Over the past years, additive manufacturing (AM) processes have evolved from just being
employed in rapid prototyping techniques to assist in manufacturing methods. The latter aims
to produce finished parts that are economically feasible, robust, with high strength, and with
long-term stability. Moreover, these processes do not require special or costly tooling for
manufacturing the parts, which allows the AM machine to handle a variety of polymers.
Material extrusion is an additive manufacturing process preferred for building components due
to its low cost, ease of creating complex shapes, and reduced waste. This process is also known
as fused filament fabrication (FFF) or fused deposition modelling (FDM), which is a trademark
name. The FDM method starts by selectively dispensing material through a nozzle. The polymer
is then melted and forced out of the outlet by applying pressure. The polymer, when extruded,
is in a semisolid state, and it solidifies and bonds with the already extruded material. The nozzle
is capable of moving in the XY plane, while the build platform moves along the z axis. In this
way, FDM technology allows for complex shapes and internal structures.
For many polymers, building material and support material are used during the FDM process.
Both of them are heated and extruded using different nozzles. The support material holds the
structure while printing the layers of the piece. Since this material does not adhere to the build
polymer, it can be removed by submerging the part in a bath.
Despite being a technology that provides several benefits, material extrusion is a manufacturing
process that requires some attention regarding its energy consumption. Because such electricity
is obtained from fossil fuel sources, it generates an environmental impact. As a consequence, it
is vital to optimizing the energy consumption of the FDM process, along with the typical
operation measures (productivity, quality, and structural performance of the part).
1.2 Additive Vs Subtractive Manufacturing:
Additive manufacturing is a process that builds parts from the base up by adding successive
layers to manufacture a product. 3D printing is the technology most associated with additive
manufacturing. Subtractive manufacturing removes material to manufacture a part. This
process traditionally uses Computer Numerical Control (CNC) machining.
Both technologies can utilize computer-aided design (CAD) software models to produce
products. These manufacturing technologies have tremendously impacted prototypes and
production and continue to make advancements.

AdditiveManufacturingvs.SubtractiveManufacturing:WhatareTheirDifferences?
The differences between additive manufacturing and subtractive manufacturing are
significant. Additive manufacturing, often referred to as 3D printing, adds successive layers of
material to create an object. Subtractive manufacturing removes material to create an object.
FIG:2 ADDITIVE MANUFACTURING
FIG: 3 SUBTRACTIVE MANUFACTURING

1.3 Additive Manufacturing:
Both technologies utilize CAD drawings to create parts; additive manufacturing melts or fuses
powder or cures liquid polymer materials to form parts based on the CAD drawings. Additive
processes are slower to manufacture, and several technologies require post-manufacturing
methods to cure, clean, or finish the product. The surface finish is not as smooth as subtractive
manufacturing, and the tolerances aren’t as precise. These processes are ideal for lighter parts,
material efficiencies, rapid prototyping, and small to medium-batch manufacturing.
Complex geometries, including the printing of articulating joints with additive manufacturing, are
available. The geometries are more complicated, and set-up is quick and easy, with no operator
required during the printing process. The most common materials used in additive manufacturing
are plastics and metals. The equipment cost is less than subtractive manufacturing, and various
material colors are available for most 3D printing operations.
1.4 Subtractive Manufacturing:
Subtractive manufacturing involves material removal with turning, milling, drilling, grinding,
cutting, and boring. The material is typically metals or plastics, and the end product has a smooth
finish with tight dimensional tolerances. A wide variety of materials are available. Change-overs
are longer, but automatic tool changers help reduce time-consuming delays. The processes can be
fully automated, although an attendant may oversee two or more machines.
The equipment costs are higher and usually require additional jigs, fixtures, and tooling. It is best
suited for large production with reasonably fast manufacturing time but lengthy changeovers.
Material handling equipment helps both processes with material loading and removal. Geometries
are not as complex as additive manufacturing processes.
FIG: 4 ADDITVE VS SUBTRACTIVE MANUFACTURING

1.5 Comparison Between Additive Manufacturing vs. Subtractive Manufacturing:
Additive Manufacturing Subtractive Manufacturing
Process: Builds an object by adding
layers of material.
Removes material from a
larger piece or material to
create an object.
Equipment: This process includes digital
manufacturing, 3D printing,
and additive fabrication, such
as binder jetting, powder bed
fusion, sheet laminate, directed
energy deposit, material
extrusion, and material jetting.
This process includes
traditional machining, CNC
machining, laser cutting, EDM,
abrading, plasma cutting, and
waterjet cutting. These
processes include turning,
milling, drilling, grinding,
cutting, and boring.
Production: Suitable for prototypes and
small batch production.
Best suited for mass production
Equipment
Costs:
Professional Desktop Printers:
$3,500+
Industrial Printers: $100,000—
$400,000+
Hobby Grade Mills and Lathes:
$2,000+
Entry Level CNC Machining:
$60,000+
Industrial 5 Axis Machining:
$500,000+
Accuracy: Tolerance as small as 0.004″ Tolerance as small as 0.001″
Area
Requirements:
Desktop printers can operate in
most offices or workshops.
Industrial printers often require
a large footprint on the
manufacturing floor. A
controlled environment is
often required.
Small machines often operate
in garages and workshops.
Industrial machines require a
large footprint on the
manufacturing floor.
Additional
Equipment:
Post-processing systems for
curing, finishing, and cleaning
are required with certain
printers. Industrial applications
may have material handling
systems.
Various tooling, fixtures,
robotics, and material handling
systems may be required.
Coolant systems, tool
changers, and waste removal
are also necessary.
Complexity: Extremely complex designs
are achievable, including
articulating parts.
Ideal for intermediate
geometry parts.

Cost: Typically, more expensive than
subtractive manufacturing,
although plastic prototyping is
much quicker.
Less expensive for metal
manufacturing.
Materials: Primarily plastic materials.
Other materials include metals,
ceramics, plasters, graphite,
carbon fiber, nitinol, polymers,
and paper.
Materials include hard metals,
soft metals, thermoset plastics,
acrylic, wood, plastics, foam,
composites, glass, and stone.
Properties: Thermoset plastics have a
potential structural weakness
at the layers.
Metals are structurally sound
with excellent heat resistance.
Set-Up: Little/no set-up required. Set-up is often substantial but
can increase the speed with
automatic tool changers.
Speed: Printing processes are slower
than machining but operate
faster with thermosets than
with metal materials. It is
preferred for prototyping and
small-batch production.
A relatively fast process. Best
for large production due to
extensive set-up time.
Surface Finish: The surface can be slightly
rough depending on the
material and printing speed.
The finish is smooth. A variety
of finishes are available for
machining.
Training: Desktop printers are quick to
set up, but programming takes
some time. Industrial printers
require training and an
attendant.
Hobby-grade machines require
some training. Industrial
equipment requires extensive
training.
Table:1Comparisonbetween AdditiveManufacturingandSubtractiveManufacturing
Subtractive vs. Additive Manufacturing Cost: Which Is More Expensive?
Additive and subtractive technologies have a variety of different processes. The costs and
capabilities range from desktop machines to large industrial equipment. Prices have dropped
significantly in recent years, especially for additive technologies. Compact, easy-to-use desktop
additive and subtractive manufacturing tools are available today for professional workspaces,
machine shops, and workshops.

Entry-level 3D printers start at several hundred dollars, and the cost of suitable desktop printers
for enthusiasts begins at approximately $3,500 up to $20,000. Industrial printers start at $10,000
and cost over $400,000.
1.6 FUSED DEPOSITION MODELLING:
Fused deposition modelling (FDM) is one of the methods used in 3D printing. This technique
is one of the manufacturing methods under the additive manufacturing engineering class,
gaining popularity among researchers and industry to study and develop. Additive
manufacturing techniques can create various complex shapes and structures while properly
managing materials, resulting in less waste and various other advantages over conventional
manufacturing, making it increasingly popular. Technically, the FDM technique has the same
role as injection molding in the manufacturing aspect. For example, mass customization. It
means producing a series of personalized items, so that each product can be different while
maintaining low prices due to mass production. It does not need the additional costs of
making molds and tools for customized products.
The basic concept of the FDM manufacturing process is simply melting the raw material and
forming it to build new shapes. The material is a filament placed in a roll, pulled by a drive
wheel, and then put into a temperature-controlled nozzle head and heated to semiliquid. The
nozzle precisely extrudes and guides materials in an ultrathin layer after layer to produce
layer-by-layer structural elements. This follows the contours of the layer specified by the
program, usually CAD, which has been inserted into the FDM work system.
Since the shapes in FDM are built from layers of the thin filament, the filament thermo-
plasticity plays a vital role in this process, which determines the filament’s ability to create
bonding between layers during the printing process and then solidify at room temperature
after printing. The thickness of the layers, the width, and the filament orientation are the few
processing parameters that affect the mechanical properties of the printed part. The complex
requirements of FDM have made the material development for the filament a quite
challenging task.

Research on this material stigmatizes the limitations of the material for this technique.
Currently, 51% of the products produced by the additive manufacturing system are polymer–
plastic filament types. It is because these materials not only have sufficient criteria to be used
and developed but also help to make FDM processes for manufacturing products more
manageable and more optimal. The most well-known polymers used in this technique are
polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS). Moreover, other materials
such as polypropylene (PP) also began to be noticed for development because it is one of the
plastics that is commonly found in everyday life. In Japan, filaments made of PP are being
used and offer superior resistance to heat, fatigue, chemicals, and better mechanical properties
such as stiffness, hinges, and high tensile strength with a smooth surface finish. Also, several
other types of filaments are currently being developed and introduced as commercial
filaments.
Some previous studies showed that although the filament composition is the same, the test may
obtain different results. In other studies, some researchers optimized the performance of FDM
machine by changing some of the parameters and concluded that each combination of
parameters would be showing different results. These studies have shown that many factors
critically determine the results of the FDM process.
This study aims to provide a comprehensive picture of the various factors that influence the
mechanical characteristics of FDM products. The review is carried out by critical mapping
parameters and critical parameters determining FDM factors and analysing each parameter’s
main effects and their interactions in the FDM process. The review starts with producing the
filaments, the impact of different filament materials, and the critical printing parameters of the
FDM techniques. Understanding these factors will be useful to get a combination of each
influential factor, which can later be optimized to obtain printing results with mechanical
properties that can be adjusted to the target application.
1.7 Filament types
According to its composition, polymer filament is divided into two categories, namely, pure
polymer filament and composite filament. The pure polymer filament is entirely made from a
polymer compound without adding additive solutions. Each type of pure polymer filament has
its inherent characteristics and mechanical properties. Still, sometimes the intrinsic properties

of pure polymers cannot accommodate the need for mechanical properties for certain products.
This problem requires researchers and industries to continuously develop polymer filaments
suitable for commercial needs. One of the steps that can be taken to improve the mechanical
properties of a filament is adding additives to the filament composition. This process finally led
to the composite filament. The following is using some pure polymer filaments that are often
used in 3D printing and development processes.
1.7.1 PLA (Poly Lactic Acid)
PLA is one of the most innovative materials developed in various fields of application. This
type of polymer is thermoplastic and biodegradable. PLA can be developed in medical
applications because of its biocompatibility which is not metabolically harmful. This process
can be achieved by turning it into a filament and then processing it through the FDM method.
The filament can then be converted into various forms commonly used as implants. The 3D
printing scaffolding technique of FDM made a recent development of a PLA/graphene oxide
(GO) nanocomposite material with a customized structure. This study was carried out to analyze
many scaffolding parameters such as morphology, chemistry, structural and mechanical
properties, and biocompatibility to show their potential uses in biological applications. The
study concluded that the use of PLA/GO nanocomposite in 3D printing is a platform with
promising mechanical properties and cytocompatibility, which has the potential in bone
formation application.
The development of PLA-based filaments to improve their mechanical properties has been
carried out comprehensively, starting from testing pure PLA, thermoplastic elastomeric
thermoplastic (TPU) blends, and E-glass fibre reinforced composites (GF). From these studies,
it is concluded that GF as fibre reinforced is generally very beneficial because it can increase
the tensile modulus and flexural modulus. On the other hand, the addition of TPU provides
increased toughness to PLA blends.
1.7.2 ABS (Acrylonitrile Butadiene Styrene)
ABS is a general term used to describe various acrylonitrile blends and copolymers, butadiene-
containing polymers, and styrene. ABS was introduced in the 1950s as a stricter alternative to
styrene–acrylonitrile (SAN) copolymers. ABS was a mixture of SAN or better known as nitrile

rubber at that time. Nitrile is rubbery, and SAN is glassy and the room temperature makes this
structure an amorphous, glassy, tough, and impact-resistant material. ABS has complex
morphology with various compositions and effects of additives, therefore making it quite bad
in some aspects. However, ABS is a prevalent material used in the 3D printing process of the
FDM method. Still, the choice of other ingredients also has their respective weaknesses.
Researchers carried out various developments to correct the deficiencies in the mechanical
properties of ABS, one of which was to develop an ABS composite filament reinforced with
GO with the addition of 2 wt% GO, made from a solvent mixing method. This method
succeeded in printing the filament ABS into a 3D model. The tensile strength and Young’s
modulus of ABS can be increased by adding GO.
1.7.3 PP (Poly Propylene)
PP is a homopolymer member of polyolefins and one of the most widely used low-density and
low-cost thermoplastic semi crystals. PP applications are generally used in different industries
such as the military, household appliances, cars, and construction because of their physical and
chemical properties. However, PP has low thermal, electrical, and mechanical properties
compared to other engineering plastics (PC, PA, etc.) and has a high coefficient of friction in
dry shear conditions. The mechanical properties of PP are improved by combining with
inorganic fillers in the form of nanoparticles. Yetgin (2019) examined the effect of GO addition
to PP, with and without maleic-anhydride-grafted-polypropylene (PP-g-MA) as a compatibilizer
agent using extrusion and injection processes. The results show that the friction and wear rate
of PP nanocomposites increase with the applied load and sheer speed. The coefficient of friction
is reduced to 74.7% below the shear speed.Another research tried to compare the printing ability
of PP filled with 30% glass fiber to unfilled PP in terms of mechanical properties. The addition
of glass fibers increases Young’s modulus and ultimate tensile strength of about 40% for the
same printing conditions. Similar enhancements in modules were also observed for 3D-printed
PPs filled with cellulose nanofibrils as well as studies of optimizing PP compounds that contain
spherical microspheres for FDM application by maximizing matrix–filler compatibility that
affects printability, properties’ pull, and toughness. In a concluding impact test on printed
composites, the optimized system exhibited impact energies 80% higher than pure PP.

1.8 Printing process on the FDM machine
The FDM machine’s working principle is to heat the filament on the nozzle to reach a semiliquid state
and then extruding it on a plate or layer that was previously printed. Thermo-plasticity of polymer
filaments allows the filaments to fuse during printing and then solidify at room temperature after
printing. Although a simple 3D printing using the FDM method has complex processes with various
parameters that affect product quality and material properties, each of these parameters is linked to one
another, making this combination of parameters often challenging to understand. In contrast, every
product that results from the 3D printing process has different quality requirements and material
properties. The print parameter combination on the FDM machine is determined by the type of filament
and the size of the filament used in the FDM process.
Therefore, it is crucial to examine the effect of a combination of mechanical performance parameters.
The parameters that affect the printing process are divided into two categories, namely, the parameters
of the FDM machine and the working parameters. Machine parameters include bed temperature, nozzle
temperature, and nozzle diameter. In contrast, the working parameters include raster angle, raster width,
build orientations, etc., and these parameters are usually inputted in the slicing process using the software
before the design and work parameters are entered into the FDM machine.
1.9 APPLICATIONS OF 3D PRINTING:
The use of 3D printing has exploded since the turn of the 21st century and has changed the
traditional ways of manufacturing products. With 3D printers, machines that build complex,
intricate parts layer-by-layer, limited only by the designer's imagination and the capabilities of
the printed materials, seemingly anything can be manufactured. 3D printing, compared to
traditional manufacturing methods such as CNC machining or injection molding, requires less
skill and expertise and less upfront preparation to make parts. From advanced aerospace
components and medical implants to tools and equipment to home decor, the applications of
3D printing are evidently endless. This article will review 10 applications of 3D printing, and
briefly discuss different types of 3D printing, the benefits of 3D printing, and related topics.
1. Prosthetics
3D printing has revolutionized how prosthetics are created. As 3D printing processes and
techniques are refined, the creation of custom, tailored prosthetics becomes more
straightforward and more efficient. Prosthetics can quickly be modelled in CAD (computer-
aided design) software and fabricated by 3D printing. If any errors or defects are found in a 3D-

printed prosthetic, it can easily be modified in CAD, and reprinted. Consequently, 3D printing
of prosthetics can lead to better patient outcomes, comfort, and satisfaction.
2. Replacement Parts
Another application of 3D printing is the ability to fabricate replacement parts easily. This can
be enormously beneficial to consumers since it reduces both the need to travel to pick up parts
and the long lead times to obtain them. 3D printing enables consumers and businesses to
maximize the value of their purchases and spend more time on more important matters.
3. Implants
The 3D printing of implants allows the construction of more specialized products for patients.
Patient outcomes are improved when parts with complex geometries can be fabricated quickly.
Items like tooth implants, heart valves, knee replacements, and maxillofacial implants are all
examples of implants that can be 3D printed. Soon, entire organs could be 3D printed which
could dramatically improve outcomes for patients awaiting transplants. Figure 1 below shows
a 3D-printed dental implant:
FIG: 5 USE OF 3D PRINTING IN MEDICAL IMPLANTS

4. Pharmaceuticals
3D printing can create drugs of different shapes and sizes and can be used to spatially distribute
active and inactive ingredients in the body. This enables 3D-printed drugs to have special
delivery profiles that can be tailored to patients’ specific needs. While only one drug, Spritam®,
a levetiracetam produced by Aprecia Pharmaceuticals has been 3D printed, 3D printing may
enable on-demand, local fabrication of additional drugs in the future.
5. Emergency Structures
Natural disasters such as hurricanes, wildfires, and tornados can leave many people homeless
for an extended time. 3D printing can help alleviate the hardships of affected families by
building houses, hospitals, and other structures much faster than the time it takes to build these
structures by traditional means.
6. Aeronautics and Space Travel
As humanity looks to expand its presence in space, 3D printing can be used for the on-demand
fabrication of tools, equipment, and entire structures in space and extraterrestrial environments.
Meanwhile on Earth, 3D printing can be used to produce advanced aerospace components such
as airframes, avionics housings, and more. Overall, 3D printing can help make space travel
more cost-effective and consequently aid in creating a sustainable human presence.
7. Custom Clothing
The fashion industry is notorious for the amount of waste generated by discarded apparel. 3D
printing can help alleviate some of this waste by enabling the fabrication of custom clothing.
By allowing consumers the ability to print clothing specific to their measurements and fashion
tastes on demand, consumers can obtain more of what they want with less waste.
8. Custom-Fitted Personal Products
Many of the objects that people encounter every day are designed with the average body type
or size in mind. Items like doors, chairs, clothing, keyboards, and desks are designed to be used
by a person with an average build within a particular region. This is difficult for many people
who fall outside of these “average build” bounds and can lead to discomfort and disability. 3D

printing allows the creation of custom-fitted personal products which improve ergonomics,
comfort, and safety for everyone.
9. Educational Materials
3D printing can be used to provide students with tangible objects that can be used for learning.
Items like topographical maps or biological replicas can be 3D printed to enhance learning. As
a result, 3D printing can be used to catalyze creativity, better learning, and foster collaboration.
10. Food
3D printing can also be used to print food. Today, stem cells are already used to make lab-
grown meat and vegetables. In the future, 3D printing could be used to produce large amounts
of fruits, vegetables, and meat, which can help to feed the world while reducing the amount of
land dedicated to livestock and farming.
FIG:6 APPLICATIONS OF 3D PRINTING

CHAPTER 2
LITERATURE REVIEW
In the past several years, researchers have examined the outcomes of 3D printing parameters on
key metrics of FDM to improve the condition of the part, decrease the building cycle, and
guarantee reliable structural performance by maximizing yield strength and ultimate tensile
strength, among other mechanical properties.
1. Hassanifard and Hashemi [1] studied the effect of part’s build orientation and raster angle
on the strain-life fatigue of specimens made of Ultem 9085, polycarbonate (PC), and
polylactic acid (PLA). Parts were created based on ASTM D638-14 and ASTM D790-17
standards. The authors concluded that infill density affected the mechanical properties of
the printed part.
2. The aim of the work reported by Verbeeten et al. [2] was to investigate the strain-rate
dependence of the yield stress for tensile samples made of PLA, based on ISO 527-2
standard. Printing speed, infill orientation angle, and bed temperature were modified. One
of the conclusions of the study was that a change of infill orientation angle from 0 to 90°
provided anisotropic effects to the pieces.
3. Zhao et al. [3] explored the effect of printing angle and layer thickness on the mechanical
properties of specimens made of PLA. The standard used to fabricate the units was ISO
527-2-2012. Tensile strength increased with higher values of printing angle and reduced
ones of layer thickness.
4. Tanoto et al. [4] evaluated dimensional accuracy, processing time, and tensile strength of
3D printed components. The components were made of ABS, using FDM technology. The
printing plane and the orientation angle were selected as the response variables to be
analyzed. The specimens employed in the experimental trials belong to type IV, according
to the ASTM D638-02 standard. Printing time diminished when the part was oriented in
the XZ plane at 90°. This orientation also provided a specimen’s length value closer to the
one of the ASTM standard.
5. The work of Alafaghani et al. [5] presented an experiment to determine the values of infill
rate, infill pattern, the orientation of the part, and layer thickness that enhanced
dimensional accuracy and mechanical properties of specimens made of PLA. The part
design followed type IV specifications according to the ASTM D638-15 standard. Lower

values of fill density and shell thickness and higher values of layer thickness and feed rate
reduced the measured values.
6. Huynh et al. [6] considered the effect of infill rate, infill pattern, and layer thickness on the
dimensional precision of parts made of PLA using FDM. The piece was a CAD model
created by the authors, and an orthogonal array L27 was applied, along with a fuzzy
approach to optimize printing parameters.
7. The work of Padhi et al. [7] shows a comparison between the dimensional deviation of
printed specimens from the dimensions of a CAD model.An L27 orthogonal array allowed
to modify the infill angle, raster width, air gap, orientation of the part, and layer thickness.
The material of the specimens is ABS P400. A medium value for layer thickness and raster
width, the greatest one for the air gap and the least for orientation and raster angle, granted
the highest dimensional precision.
8. Mohamed et al. [8] investigated the dimensional accuracy of specimens made of a PC-
ABS blend, employing FDM. The process parameters that were modified are raster angle,
raster width, air gap, part orientation, layer thickness, and the number of contours. The
geometry of the specimens is according to the standards ASTM D5418-07 and ASTM
D7028-07e1. The layer thickness was the factor that affected all the responses.
9. Raut et al. [9] analysed the tensile and flexural behaviour, as well as the processing time
of specimens made of ABS P400, following the standards ASTM D638 and ASTMD790.
The process parameter that was varied was the orientation of the piece on the build
platform.
10. Peng et al. [10] investigated the relationship among dimensional accuracy, processing time,
and layer thickness during 3D printing of specimens made of ABS. The authors developed
the geometry employed in the experiment, and they used the response surface method
(RSM) along with a fuzzy interference system to improve process metrics. Warp
deformation diminished with an increase of layer thickness, and a reduction of filling
velocity.
11. Basavaraj C.K et al [11] in uses the Taguchi L9 orthogonal array to study the influence of
layer thickness, orientation angle and shell thickness to ultimate tensile strength,
dimensional accuracy and manufacturing time in Nylon Fused Deposition Modelling 3D
printed parts.4
Ref.5,10
also implemented their research on the impact of infill percentage,
infill pattern, layer thickness and temperature extrusion on the mechanical property of
FDM parts. These results indicate that process parameters of layer thickness, infill pattern,
infill percentage are the most interested factors for effects of mechanical properties.

CHAPTER 3
PROJECT OVERVIEW
3.1 PARAMETER OPTIMIZATION IN FUSED DEPOSITION
MODELLING:
In order to find the optimal levels of parameters for our assembled Any Cubic Kobra 2 Neo 3D
Printer for obtaining high mechanical strength and good surface finish, we have considered 3
factors which are majorly influencing the mechanical properties i.e., strength and surface
roughness of the 3d printed parts. The 3 factors are layer height, infill percent and extrusion
temperature. From the previous studies done by different researchers, layer height is the main
influencing factor behind the surface roughness of the 3d printed parts and the factors layer
height and infill percent and extrusion temperature influence the strength of the printed parts.
3.1.1 Process Parameters affecting the Mechanical Properties of the printed parts
1. Layer Height:
The thickness of each layer of deposited material is called the ‘layer height’.
For Fused Deposition Modelling, one variable that affects the final quality of a 3D print is the
layer height. The surface quality of the finished part is proportional to how small the layer
height is; smaller layer heights result in smother surface finishes.
Different layer heights affect the time it takes a 3D print to finish.
For FDM printers, the number of layers is one indicator of how much time a 3D print will take.
Choosing a smaller layer height will divide a 3D model into more layers, increasing the print
time. For example, an object printed at 0.4mm layer height would take half as much time as an
object printed at 0.2mm layer height, because there are half as many layers. For Any Cubic
Kobra 2 Neo 3D Printer, the recommended layer height in Cura is 0.20mm. And our nozzle
diameter is 0.40mm. So, we have taken 3 levels of layer height from 0.20-0.30 mm.
Layer height: Level-1: 0.20mm
Level-2: 0.25mm
Level-3: 0.30mm

This object was printed at four different layer heights, indicated by the text on the object.
You can see how the quality differs between each section.
FIG: 7 ILLUSTRATION OF LAYER HEIGHT
2.Infill Density:
FIG: 8 ILLUSTRATION OF INFILL DENSITY

Infill density can significantly affect material consumption
Infill density is the “fullness” of the inside of a part. In slicers, this is usually defined as a
percentage between 0 and 100, with 0% making a part hollow and 100%, completely solid. As
you can imagine, this greatly impacts a part’s weight: The fuller the interior of a part, the heavier
it is.
Besides weight, print time, material consumption, and buoyancy are also impacted by infill
density. So, too, is strength, also in combination with many other elements such
as material and layer height.
Infill Density: Level-1: 15%
Level-2: 60%
Level-3: 100%
3. Extrusion Temperature:
Extrusion temperature is the temperature the extruder heats to during your print. It depends
on a few other variables, mainly the properties of the plastic filament and your print speed.
Different plastics melt at different temperatures and in different ways. PLA makes a solid
to liquid transition, like that of ice to water, and melts at extrusion temperatures from about
180°C up. It also gets shinier and, with translucent colours, clearer when it's extruded at
higher temperatures.
The suggested Extrusion Temperature for PLA material is 200°C in Cura Slicing Software
and the Maximum temperature the printer can attain is 260 °C
Extrusion Temperature: Level-1: 200 °C
Level-2: 230 °C
Level-3: 260 °C

FIG: 9 EXTRUSION TEMPERATURE IN 3D PRINTING
3.2. Optimization Techniques:
To find the optimal levels of the printing parameters i.e., layer height, infill percent and
extrusion temperature, we are considering 3 levels for each factor following low, medium and
high levels of the respective printing parameters as per the specifications of the 3d printer.
We are using Design of Experiments (D.O.E) for finding the optimal parameter levels. In
D.O.E, we are using L9 Orthogonal Array from Taguchi Design Approach and with the help of
Mini Tab Statistical Software, we are finding the optimal levels of the considered three factors.
3.2.1 Taguchi Design
L9 Orthogonal Array for 3 factors with 3 levels
S.
No.
A B C
1. 1 1 1
2. 1 2 2
3. 1 3 3
4. 2 1 2
5. 2 2 3
6. 2 3 1
7. 3 1 3
8. 3 2 1
9. 3 3 2
TABLE:2 Taguchi’s L9 Orthogonal Array for 3 factors with 3 levels

In this design, Factor A is Layer Height with 3 different levels 1,2,3
Factor B is Infill Percent with 3 different levels 1,2,3
Factor C is Extrusion Temperature with three different levels 1,2,3
The respective levels 1,2,3 for the Factors A, B, C are as follows:
S.No. Factors Level-1 Level-2 Level-3
1 Factor-A: Layer Height 0.20 0.25 0.30
2 Factor-B: Infill Density 15 60 100
3 Factor-C: Extrusion
Temperature
200 230 260
TABLE:3 Experimental Factors with their respective Levels
The Experimental Runs to be conducted are as follows:
Runs Layer Height (mm) Infill Density (%) Extrusion Temperature
(0
C)
1 0.20 15 200
2 0.20 60 230
3 0.20 100 260
4 0.25 15 230
5 0.25 60 260
6 0.25 100 200
7 0.30 15 260
8 0.30 60 200
9 0.30 100 230
TABLE:4 L9 Orthogonal Array Design Table
The respective 3 levels of the 3 factors are adjusted in Slicing Software and 9 experimental runs
are conducted and the Strength and Surface quality of the 9 specimens are tested through
experimentation.
Ultimate Tensile Strength is calculated on Universal Testing Machine (UTM) and Average
Surface Roughness (Ra) is calculated by a Surface Roughness Tester. A standard specimen of
required dimensions is to be designed for experimentation process.
After finding out the experimental values required, Optimal Levels of the 3 Process Parameters
are found out by Taguchi Analysis with the aid of Mini Tab Software.

CHAPTER- 4
INTRODUCTION TO COMPUTER AIDED DESIGN(CAD)
4.1 Computer Aided Design (CAD):
In general, a Computer Aided Design (CAD) package has three components: a)
Design, b) Analysis, and c) Visualization, as shown in figure 10. A brief description of
these components are as follows:
a) Design: Design refers to geometric modeling, i.e., 2-D and 3-D
modeling, including, drafting, part creation, creation of drawings with various
views of the part, assemblies of the parts, etc.
b) Analysis: Analysis refers to finite element analysis, optimization, and
other number crunching engineering analyses. In general, a geometric model is
first created and then the model is analyzed for loads, stresses, moment of inertia,
and volume, etc.
c) Visualization: Visualization refers to computer graphics, which
includes: rendering
a model, creation of pie charts, contour plots, shading a model, sizing, animation,
etc.
FIG: 10 COMPONENTS OF COMPUTER AIDED DESIGN

Each of these three areas has been extensively developed in the last 30 years. Several books
are written on each of these subjects and courses are available through the academic institutions
and the industry. Most commercial CAD packages (software) consist of only a single
component: design or analysis or visualization. However, a few of the vendors have developed
an integrated package that includes not only these three areas, but also includes the
manufacturing software (CAM). Due to the large storage requirement, integrated packages use
either an UNIX workstation or a mainframe platform, and not the popular PC platform. With
the improvement in PC computing speed, it’s only a matter of time before we see an integrated
package run on a PC. CAD has revolutionized the modern engineering practice; small and large
companies use it alike, spending several billion dollars for the initial purchase or lease alone.
CAD related jobs are high in demand and the new graduates have advantage over their senior
colleagues, as they are more up to date and more productive. In this course, we will limit our
coverage to the design only. Those of you interested in analysis area, look into the course ME
160 – Introduction to Finite Element Analysis.
4.2 CAD Overview:
Computer-aided design is one of the many tools used by engineers and designers and is used in
many ways depending on the profession of the user and the type of software in question.
CAD is one part of the whole digital product development (DPD) activity within the product
lifecycle management (PLM) processes, and as such is used together with other tools, which
are either integrated modules or stand-alone products, such as:
• Computer-aided engineering (CAE) and finite element analysis (FEA, FEM)
• Computer-aided manufacturing (CAM) including instructions to computer
numerical control (CNC) machines
• Photorealistic rendering and motion simulation
• Document management and revision control using product data
management (PDM)
CAD is also used for the accurate creation of photo simulations that are often required in the
preparation of environmental impact reports, in which computer-aided designs of intended
buildings are superimposed into photographs of existing environments to represent what that
locale will be like, where the proposed facilities are allowed to be built. Potential blockage of
view corridors and shadow studies are also frequently analyzed through the use of CAD.

4.3 ONSHAPE CAD SOFTWARE:
Onshape is a computer-aided design (CAD) software system, delivered over the Internet via
a software as a service (SAAS) model. It makes extensive use of cloud computing, with
compute-intensive processing and rendering performed on Internet-based servers, and users are
able to interact with the system via a web browser or the iOS and Android apps. As a SAAS
system, Onshape upgrades are released directly to the web interface, and the software does not
require maintenance work from the user.
Onshape allows teams to collaborate on a single shared design, the same way multiple writers
can work together editing a shared document via cloud services.It is primarily focused on
mechanical CAD (MCAD) and is used for product and machinery design across many
industries, including consumer electronics, mechanical machinery, medical devices, 3D
printing, machine parts, and industrial equipment.
4.4 Features of ONSHAPE CAD Software:
Cloud Native CAD
Unlike file-based CAD, which relies on fragile file references and slow check-in/check-out
procedures, Onshape uses the full power of cloud architecture to transcend these limitations.
Cloud-native CAD provides a centralized repository of design data, eliminating the problems
of lost data and the need for users to manage their own files. Onshape is accessible on any web
browser, allowing users to access, manage and share their design data securely from anywhere
in the world on any web-connected device.
Robust Collaboration
Onshape is a multi-user environment, one that allows designers, internal teams, customers, and
external partners to access, collaborate, and work concurrently from conception to production.
Onshape’s built-in collaboration tools allow teams to streamline their communication by
enabling quicker responses to feedback, customer needs and market demands. As a result,
product development teams can explore more alternative ideas and launch more innovative
products.
Lower Cost
Onshape’s flexible pricing plans offer out-of-the-box solutions for product development in the
cloud for teams of all sizes. With a zero-IT footprint, Onshape delivers a 24/7 stable design

platform in a web browser and doesn't require an investment in high-performance workstations,
dedicated servers or administration. Time is money. While efficiency costs can be less tangible
than software and hardware costs, Onshape is saving teams 50%+ of their design time with
modern advancements like built-in PDM, collaboration tools and business analytics.
Built-in PDM
Onshape's built-in Product Data Management (PDM) includes a set of powerful tools for
managing and controlling design data within a 3D CAD environment. Onshape eliminates files
entirely, removing the most frustrating bottlenecks associated with traditional CAD and PDM
systems. Teams can edit the same design concurrently with real-time updates – there is no need
to check-in, check-out or lock files. With Onshape, it is easy to store and track metadata
associated with parts and assemblies, enabling users to find and reuse existing designs more
efficiently, saving valuable time and money.
Agile Product Development
The fastest moving industries trust Onshape for agile design, adopting the best practices from
software development for creating hardware products. Cloud-native Onshape enables teams to
collaborate on designs in real time or asynchronously, and provides automatic version control,
resulting in faster iterations. Onshape helps streamline communication between the core
engineering team and non-CAD users across the organization – or with outside clients or
partners.
Automation and Customization
Onshape offers powerful automation and customization tools, such as FeatureScript, an open-
source programming language enabling companies to create their own custom CAD features.
Feature Script helps engineering teams eliminate repetitive tasks and create custom workflows
tailored to their specific industry needs. Feature Script is the same programming language used
to create all of Onshape’s standard features.

FIG: 11 DESIGN OF PIPE FITTING IN ONSHAPE CAD SOFTWARE
FIG: 12 DESIGNING IN ONSHAPE CAD SOFTWARE

CHAPTER 5
INTRODUCTION TO SLICING
5.1 SLICING IN 3D PRINTING:
A slicer is a toolpath generation software used in 3D printing. It facilitates the conversion of a
3D object model to specific instructions for the printer. The slicer converts a model
in STL (stereolithography) format into printer commands in G-code format. This is particularly
usable in fused filament fabrication and other related 3D printing processes.
5.2 Features of Slicers
A slicer initially segments the object as a stack of flat layers. It then describes these layers
through linear movements of the 3D printer's extruder, the fixation laser, or an equivalent
component. All these movements, together with some specific printer commands like the ones
to control the extruder temperature or bed temperature, are ultimately compiled in the G-code
file. This file can then be transferred to the printer for execution.
Additional features of slicer are listed below:
• Infill: Printing solid objects requires a significant amount of material (such as
filament) and time. To mitigate this, slicers can automatically convert solid volumes
to hollow ones, thereby saving costs and reducing print time. These hollow objects can
be reinforced with internal structures, like internal walls, to enhance robustness. The
proportion of these structures, known as 'infill density', is a key parameter that can be
adjusted in the slicer.
• Supports: Since most 3D printing processes build objects layer by layer, from the
bottom up, each new layer is deposited directly on top of the previous one.
Consequently, every part of the object must, to some extent, rest on another part. For
layers that are 'floating'—for example, the flat roof of a house or a horizontally extended
arm in a figure—the slicer can automatically add supports. These supports are designed
to touch the object in a manner that allows for easy detachment upon the completion of
the object's production.
Rafts, skirts and brims: The printing of the first object layer, which contacts the printer bed,
presents unique challenges, such as adherence issues, surface rugosity, and the smooth

deposition of the initial filament.To mitigate these problems, the slicer can automatically add
detachable structures. Common types of these base structures include:
• A skirt: A single band encircling the object's base, without touching it.
• A brim: Multiple lines of filament around the base of the object, touching but not
underneath it, and extending outward.
• A rafts: Several layers of material forming a detachable base on which the object is
printed.
5.3 ULTIMAKER CURA:
Cura is an open source slicing application for 3D printers. It was created by David Braam who
was later employed by UltiMaker, a 3D printer manufacturing company, to maintain the
software. Cura is available under LGPLv3 license. Cura was initially released under the open
source Affero General Public License version 3, but on 28 September 2017 the license was
changed to LGPLv3.This change allowed for more integration with third-party CAD
applications. Development is hosted on GitHub. UltiMaker Cura is used by over one million
users worldwide and handles 1.4 million print jobs per week. It is the preferred 3D printing
software for UltiMaker 3D printers, but it can be used with other printers as well.
UltiMaker Cura works by slicing the user’s model file into layers and generating a printer-
specific g-code. Once finished, the g-code can be sent to the printer for the manufacture of the
physical object.
The open source software, compatible with most desktop 3D printers, can work with files in the
most common 3D formats such as STL, OBJ, X3D, 3MF as well as image file formats such
as BMP, GIF, JPG, and PNG.
5.4 Features of Cura:
Cura is simple but powerful 3D slicing software produced by UltiMaker. The print profiles are
optimised for UltiMaker 3D printers, but the software will slice 3D files for any 3D printer
brand/model. The software supports STL, 3MF and OBJ 3D file formats and also has a function
that will import and convert 2D images (.JPG .PNG .BMP and .GIF) to 3D extruded models.
The software will allow you to open and place multiple models on the print bed (each with
different slicing settings if required). This allows you to print multiple models at a time, making
classroom management of the printing process simpler.

Cura is desktop software that can be downloaded free of charge from the UltiMaker website
and is available for Windows, Mac and Linux.
UltiMaker Cura prepares your model for 3D printing. For novices, it makes it easy to get great
results. For experts, there are over 200 settings to adjust to your needs. And integration with
major software platforms makes 3D printing even simpler.
UltiMaker Cura creates a seamless integration between your 3D printer, software and materials
to achieve the perfect print.
• Novices can start printing right away and experts are able to customize 200 settings to
achieve the best results for their models
• Optimized profiles for UltiMaker materials
• Print multiple objects at once with different settings for each object
• Download plugins to create seamless integration with leading design and engineering
software
• Supports STL, 3MF and OBJ file formats
• It's open source and completely free
• Combine with the UltiMaker 3 for optimized dual extrusion printing and multiple printer
management with Cura Connect
FIG: 13 SLICING OF TENSILE TEST SPECIMEN ASTM D638 TYPE-I IN ULTIMAKER CURA

CHAPTER 6
ANYCUBIC KOBRA 2 NEO 3D PRINTER
6.1 Features and Specifications of Any Cubic Kobra 2:
FIG: 14 ANYCUBIC KOBRA 2
DETAILS
Net weight: 7.3kg
Gross weight: 9.5kg
Machine Dimensions: 485*440*440mm (HWD)
Hotbed Temperature: ≤230℉/110°C
Maximum nozzle temperature: ≤500℉/260°C
Build volume: 250x220x220mm (HWD)
Printing Platform: PEl Magnetic Spring Steel
Control board: 32-bit motherboard
Flament sensor: Optional

FEATURES:
1. 5 x High: Speedboating a remarkable maximum print speed of 250mms. And recommended
print speed of 150mms ensures a perfect blend of precision and performance, empowering
you to bring your ideas to life with unparalleled speed and accuracy.
2. New Integrated Extruder: The newly upgraded extrusion system and cooling system melt
filaments quickly through the 60W hot end. At the same time, it is equipped with a 7000rpm
cooling fan to ensure rapid cooling and molding of the model.
3. Details Even Better: Apply the linear propulsion and input shaping functions in Marlin
firmware to reduce spillage and print resonance, improve print quality and stability, and
create smoother and clearer model details. Novices can also produce high-quality works
for their first print.
4. Levi Q 2.0 Automatic Levelling: With the Levi Q 2.0, the Kobra 2 Neo is equipped with
another intelligent tool. Print surface irregularities are recorded during auto-levelling and
compensated for by Z-offset adjustments on the fly. This happens either automatically or
through user-defined values - depending on your requirements, all options are open to you.
5. A Completely Renewed UI Design: With the use of 2.4-inch LCD knob screen and the
introduction of new design elements, provide a more intuitive, simple, easy-to-navigate
interface. Easier to control and use, more agile to interact and respond.
6. Large Build Volume: With a generous build volume, you can create larger 3D prints or
multiple smaller prints in a single batch.
7. Filament Sensor: An integrated filament sensor detects when your filament is running low,
pausing the print and allowing you to reload filament, preventing wasted prints.
8. Silent Printing: The Kobra Neo 2 boasts a quiet printing operation, thanks to its silent
stepper motor drivers. This makes it suitable for home or office environments where noise
can be a concern.
9. Resume Print Function: In case of power outages or unexpected interruptions, the printer
can resume printing from where it left off, saving both time and filament.

FIG: 15 OUR ASSEMBLED 3D PRINTER
FIG: 16 PRINTING ON ANYCUBIC KOBRA 2 NEO 3D PRINTER

CHAPTER 7
DESIN OF EXPERIMENTS (DOE)
7.1 Introduction to D.O.E
The design of experiments (DOE), also known as experiment design or experimental design,
is the design of any task that aims to describe and explain the variation of information under
conditions that are hypothesized to reflect the variation. The term is generally associated
with experiments in which the design introduces conditions that directly affect the variation,
but may also refer to the design of quasi-experiments, in which natural conditions that influence
the variation are selected for observation.
In its simplest form, an experiment aims at predicting the outcome by introducing a change of
the preconditions, which is represented by one or more independent variables, also referred to
as "input variables" or "predictor variables." The change in one or more independent variables
is generally hypothesized to result in a change in one or more dependent variables, also referred
to as "output variables" or "response variables." The experimental design may also
identify control variables that must be held constant to prevent external factors from affecting
the results. Experimental design involves not only the selection of suitable independent,
dependent, and control variables, but planning the delivery of the experiment under statistically
optimal conditions given the constraints of available resources. There are multiple approaches
for determining the set of design points (unique combinations of the settings of the independent
variables) to be used in the experiment.
Main concerns in experimental design include the establishment of validity, reliability,
and replicability. For example, these concerns can be partially addressed by carefully choosing
the independent variable, reducing the risk of measurement error, and ensuring that the
documentation of the method is sufficiently detailed. Related concerns include achieving
appropriate levels of statistical power and sensitivity.
Correctly designed experiments advance knowledge in the natural and social sciences and
engineering, with design of experiments methodology recognised as a key tool in the successful
implementation of a Quality by Design framework. Other applications include marketing and
policy making.
The study of the design of experiments is an important topic in metascience.

Some Design Approaches in D.O.E:
• Full factorial designs
• Fractional factorial designs (Screening designs)
• Response surface designs
• Mixture designs
• Taguchi array designs
• Split plot designs
Design of Experiments represents a family of techniques.
Experimental designs provide the ability to:
• Investigate multiple process variables at the same time.
• Identify which variables have significant effects on the process output.
• Study the relationships between variables to identify interactions.
The DOE approach is selected depending on the objective of the experimentation, the type of
process, and the number of variables that will be studied. Experimental strategies often start
with screening experiments.
DOE techniques include:
• Screening Experiments: A special extreme type of Fractional Factorial. Often used at
the start of an experimental sequence; few experimental runs but yields important
information about key variables.
• Fractional Factorials: Less runs (than Full Factorials) but less information, too.
Studies a predetermined fraction of a Full Factorial.
• Full Factorials: Generates lots of information but requires many runs. Usually used to
study variables at 2 or 3 levels (settings).
• Response Surface Analysis (RSA): An optimizing design in which the main
independent variables are already known. Limited runs, highly selective information.
• EVOP: An iterative optimizing design; experiments are run within the existing range
of process parameters. Relatively high number of runs, selective information.
• Taguchi Methods: Taguchi methods are statistical methods, sometimes called robust
design methods, developed by Genichi Taguchi to improve the quality of
manufactured goods, and more recently also applied to engineering, biotechnology,
marketing and advertising. Professional statisticians have welcomed the goals and
improvements brought about by Taguchi methods, particularly by Taguchi's
development of designs for studying variation, but have criticized the inefficiency of
some of Taguchi's proposals.

CHAPTER 8
TAGUCHI DESIGN APPROACH FOR
OPTIMIZATION
8.1 TAGUCHI DESIGN:
A Taguchi design is a designed experiment that lets you choose a product or process that
functions more consistently in the operating environment. Taguchi designs recognize that not
all factors that cause variability can be controlled. These uncontrollable factors are called noise
factors. Taguchi designs try to identify controllable factors (control factors) that minimize the
effect of the noise factors. During experimentation, you manipulate noise factors to force
variability to occur and then determine optimal control factor settings that make the process or
product robust, or resistant to variation from the noise factors. A process designed with this
goal will produce more consistent output. A product designed with this goal will deliver more
consistent performance regardless of the environment in which it is used.
A well-known example of Taguchi designs is from the Ina Tile Company of Japan in the 1950s.
The company was manufacturing too many tiles outside specified dimensions. A quality team
discovered that the temperature in the kiln used to bake the tiles varied, causing nonuniform tile
dimension. They could not eliminate the temperature variation because building a new kiln was
too costly. Thus, temperature was a noise factor. Using Taguchi designed experiments, the team
found that by increasing the clay's lime content, a control factor, the tiles became more resistant,
or robust, to the temperature variation in the kiln, letting them manufacture more uniform tiles.
Taguchi designs use orthogonal arrays, which estimate the effects of factors on the response
mean and variation. An orthogonal array means the design is balanced so that factor levels are
weighted equally. Because of this, each factor can be assessed independently of all the other
factors, so the effect of one factor does not affect the estimation of a different factor. This can
reduce the time and cost associated with the experiment when fractionated designs are used.
Orthogonal array designs concentrate primarily on main effects. Some of the arrays offered in
Minitab's catalog let a few selected interactions to be studied.

You can also add a signal factor to the Taguchi design in order to create a dynamic response
experiment. A dynamic response experiment is used to improve the functional relationship
between a signal and an output response.
8.2 Output tables for a Taguchi design
Minitab calculates response tables, linear model results, and generates main effects and
interaction plots for:
• signal-to-noise ratios (S/N ratios, which provide a measure of robustness) vs. the control
factors
• means (static design) or slopes (Taguchi dynamic design) vs. the control factors
• standard deviations vs. the control factors
• natural log of the standard deviations vs. the control factors
Use the results and plots to determine what factors and interactions are important and assess
how they affect responses. To get a complete understanding of factor effects, you should usually
assess signal-to-noise ratios, means (static design), slopes (Taguchi dynamic design), and
standard deviations. Ensure that you choose a signal-to-noise ratio that is appropriate for the
type of data you have and your goal for optimizing the response.
NOTE
If you suspect curvature in your model, select a design - such as 3-level designs - that lets you
detect curvature in the response surface.
A comparison of Taguchi static designs and Taguchi dynamic designs
Minitab provides two types of Taguchi designs that let you choose a product or process that
functions more consistently in the operating environment. Both designs try to identify control
factors that minimize the effect of the noise factors on the product or service.
Static response
In a static response design, the quality characteristic of interest has a fixed level.

Dynamic response
In a dynamic response design, the quality characteristic operates along a range of values and
the goal is to improve the relationship between a signal factor and an output response.
For example, the amount of deceleration is a measure of brake performance. The signal factor
is the degree of depression on the brake pedal. As the driver pushes down on the brake pedal,
deceleration increases. The degree of pedal depression has a significant effect on deceleration.
Because no optimal setting for pedal depression exists, it is not logical to test it as a control
factor. Instead, engineers want to design a brake system that produces the most efficient and
least variable amount of deceleration through the range of brake pedal depression.
Example of a Taguchi design
The following table displays the L8 (27
) Taguchi design (orthogonal array). L8 means 8 runs.
27
means 7 factors with 2 levels each. If the full factorial design were used, it would have 27
=
128 runs. The L8 (27
) array requires only 8 runs - a fraction of the full factorial design. This
array is orthogonal; factor levels are weighted equally across the entire design. The table
columns represent the control factors, the table rows represent the runs (combination of factor
levels), and each table cell represents the factor level for that run.
L8 (27
) Taguchi Design
A B C D E F G
1 1 1 1 1 1 1 1
2 1 1 1 2 2 2 2
3 1 2 2 1 1 2 2
4 1 2 2 2 2 1 1
5 2 1 2 1 2 1 2
6 2 1 2 2 1 2 1
7 2 2 1 1 2 2 1
8 2 2 1 2 1 1 2

In this example, levels 1 and 2 occur 4 times in each factor in the array. If you compare the
levels in factor A with the levels in factor B, you will see that B1 and B2 each occur 2 times in
conjunction with A1 and 2 times in conjunction with A2. Each pair of factors is balanced in this
approach, letting factors to be assessed independently.
How does Minitab choose the default Taguchi design?
For 2-level designs based on L8 (3 or 4 factors), L16 (3-8 factors), and L32 (3-16 factors) arrays,
Minitab will choose a full factorial design if possible. If a full factorial design is not possible,
then Minitab will choose a Resolution IV design.
For all other designs, the default designs in Minitab are based on the catalog of designs by
Taguchi and Konishi.
Minitab takes a straightforward approach in determining the default columns that are used in
any of the various orthogonal designs. Say you are creating a Taguchi design with k factors.
Minitab takes the first k of columns of the orthogonal array.
8.3 Control factors and noise factors:
A Taguchi design has two types of factors: control factors and noise factors.
8.3.1 Control factors
Control factors are process or design parameters that you can control.
Examples of control factors are equipment settings, material used to manufacture the product,
or product design features.
8.3.2 Noise factors
Noise factors are process or design parameters that are difficult or expensive to control during
manufacturing.
Examples of noise factors are ambient temperature or humidity.
Consider a cake mixture manufacturer who wants to optimize cake flavor under various
conditions. The manufacturer wants to determine control factors that reduce the effect of noise
factors on cake flavor.
• Control factors, which are in the manufacturer's control include cake mixture
ingredients.

• Noise factors, which are out of the manufacturer's control, include the air temperature
and humidity while the consumer is making the cake.
8.3.3 Using Noise Factors to identify Optimal control factor settings
In Taguchi designs, noise factors are factors that cause variability in the performance of a
system or product, but cannot be controlled during production or product use. You can,
however, control or simulate noise factors during experimentation. You should choose noise
factor levels that represent the range of conditions under which the response should remain
robust.
Common types of noise factors are:
External
Environmental factors, customer usage, and so on.
Manufacturing variations
Part-to-part variations.
Product deterioration
Degradation that occurs through usage and environmental exposure.
During experimentation, you manipulate noise factors to force variability to occur, then from
the results, identify optimal control factor settings that make the process or product resistant, or
robust to variation from the noise factors. Control factors are those design and process
parameters that can be controlled.
For example, a printer manufacturer wants to optimize printer performance. One noise factor is
different paper types. During experimentation, the manufacturer tests several paper types to
determine control factors that reduce the effect of paper type on printer performance.
Compounding noise factors is a strategy in which you group the noise factor levels into
combinations that you anticipate will produce extreme response values. Because estimating the
effects of individual noise factors is not the primary goal, compounding is a useful way to
reduce the amount of testing. For example, if you have three noise factors, each with two levels,
you could have eight different combinations of settings to test. Instead, you could group noise
factors into two overall settings – one setting in which the noise factors levels increase the

response values and the other setting in which the noise factors levels decrease the response
values.
8.4 Signal Factor
A signal factor is a factor, with a range of settings, that is controlled by the user during use. A
signal factor is present in a dynamic Taguchi design, but is not present in a static Taguchi
design. In a dynamic response design, the quality characteristic operates along a range of values
and the goal is to improve the relationship between a signal factor and an output response. In a
static response design, the quality characteristic of interest has a fixed level.
For example, the amount of deceleration is a measure of brake performance. The signal factor
is the degree of depression on the brake pedal. As the driver pushes down on the brake pedal,
deceleration increases. The degree of pedal depression has a significant effect on deceleration.
Because no optimal setting for pedal depression exists, it is not logical to test it as a control
factor. Instead, engineers want to design a brake system that produces the most efficient and
least variable amount of deceleration through the range of brake pedal depression.
8.5 Steps for conducting a Taguchi designed experiment
Before you start using Minitab, you need to choose control factors for the inner array and noise
factors for the outer array. Control factors are factors you can control to optimize the process.
Noise factors are factors that can affect the performance of a system but are not in control during
the intended use of the product.
NOTE
While you cannot control noise factors during the process or product use, you need to be able
to control noise factors for experimentation purposes.
Engineering knowledge should guide the selection of control factors and responses. You should
also scale control factors and responses so that interactions are unlikely. When interactions
between control factors are likely or not well understood, you should choose a design that is
capable of estimating those interactions. Minitab can help you design a Taguchi experiment that
does not confound interactions of interest with each other or with main effects.
Noise factors for the outer array should also be carefully selected and might require preliminary
experimentation. The noise levels selected should represent the range of conditions under which
the response variable should remain robust.

Conducting a Taguchi designed experiment can have the following steps:
1. Choose Stat > DOE > Taguchi > Create Taguchi Design to generate a Taguchi
design (orthogonal array). Each column in the orthogonal array represents a specific
factor with two or more levels. Each row represents a run; the cell values identify the
factor settings for the run. By default, Minitab's orthogonal array designs use the
integers 1, 2, 3, to represent factor levels. If you enter factor levels, the integers 1, 2, 3,
will be the coded levels for the design. You can also
use Stat > DOE > Taguchi > Define Custom Taguchi Design to create a design from
data that you already have in the worksheet. Define Custom Taguchi Design lets you
specify which columns are your factors and signal factors. You can then easily analyse
the design and generate plots.
2. After you create the design, you can display or modify the design:
• Choose Stat > DOE > Display Design to change the units (coded or uncoded)
in which Minitab expresses the factors in the worksheet.
• Choose Stat > DOE > Modify Design to rename the factors, change the factor
levels, add a signal factor to a static design, ignore an existing signal factor (treat
the design as static), and add new levels to an existing signal factor.
3. Conduct the experiment and collect the response data. The experiment is done by
running the complete set of noise factor settings at each combination of control factor
settings (at each run). The response data from each run of the noise factors in the outer
array are usually aligned in a row, beside the factor settings for that run of the control
factors in the inner array.
4. Choose Stat > DOE > Taguchi > Analyse Taguchi Design to analyse the
experimental data.
Note
You should analyse each response variable separately with Taguchi designs. Although
Taguchi analysis accepts multiple response columns, these responses should be the same
variable measured under different noise factor conditions.
5. Choose Stat > DOE > Taguchi > Predict Taguchi Results to predict signal to noise
ratios and response characteristics for selected new factor settings.

What is the notation for Taguchi designs?
The notation L(number) (number ^ exponent) informs you of the following:
• L(number) = number of runs
• (number ^ exponent)
o number = number of levels for each factor
o exponent = number of factors
For example, an L27(3^13) means that the design has 27 runs and 13 factors with 3 levels.
If your notation is L (number ^ exponent number ^ exponent) then you have a mixed-level
design. For example, an L18 (2^1 3^7) means that the design has 18 runs, 1 factor with 2 levels,
and 7 factors with 3 levels.
8.6 Catalogue of Taguchi designs
• L4 (23
)
• L8 (27
)
• L8 (24
), (41
)
• L9 (34
)
• L12 (211
)
• L16 (215
)
• L16 (212
), (41
)
• L16 (29
), (42
)
• L16 (26
), (43
)
• L16 (23
), (44
)
• L16 (45
)
• L16 (81
), (28
)
• L18 (21
), (37
)
• L18 (61
), (36
)
• L25 (56
)
• L27 (313
)
• L32 (231
)
• L32 (21
), (49
)

• L36 (211
), (312
)
• L36 (23
), (313
)
• L54 (21
), (325
)
The columns of the arrays are balanced and orthogonal. This means that in each pair of columns,
all factor combinations occur the same number of times. Orthogonal designs let you estimate
the effect of each factor on the response independently of all other factors. The notation L(runs)
(levels ^ factors) indicates the following:
• L(runs) = number of runs
• (levels ^ factors) = number of levels for each factor ^ number of factors
For example, an L8 design has 8 runs. (2^3) or (2 3
) means 3 factors at 2 levels.
If your notation is L(runs) (number ^ exponent number ^ exponent) then you have a mixed-
level design. For example, an L18 (2^1 3^7) means that the design has 18 runs, 1 factor with 2
levels, and 7 factors with 3 levels.
L4 (23)
1 2 3
1 1 1 1
2 1 2 2
3 2 1 2
4 2 2 1

L9 (34)
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
8.7 Adding a signal factor to an existing design
When you add a signal factor to an existing static design, Minitab adds a new signal factor
column after the factor columns and appends new rows (replicates) to the end of the existing
worksheet. For example, if you add a signal factor with 2 levels to an existing L4 (23
) array, 4
rows (1 replicate of 4 runs) are added to the worksheet. If you add a signal factor with 3 levels,

8 rows (2 replicates of 4 runs) are added to the worksheet. A replicate is the entire set of runs
from the static design.
A B
1 1
1 2
2 1
2 2
Static design
A B Signal factor
1 1 1
1 2 1
2 1 1
2 2 1
1 1 2
1 2 2
2 1 2
2 2 2

Dynamic design, 2-level signal
A B Signal factor
1 1 1
1 2 1
2 1 1
2 2 1
1 1 2
1 2 2
2 1 2
2 2 2
1 1 3
1 2 3
2 1 3
2 2 3
Dynamic design, 3-level signal
When you add a signal factor to an existing static design, the run order will be different from
the order that results from adding a signal factor while creating a new design. The order of the
rows does not affect the Taguchi analysis.
How to arrange Taguchi response data in the worksheet
In a usual Taguchi robust parameter design experiment, you would subject each control factor
combination to each of the noise conditions and measure the response variable. If you are doing

a dynamic experiment, the response is measured at each level of the signal factor. Record the
results for each noise condition in a separate response column in the worksheet.
Columns A–E (the control factors) and Pressure (the signal factor) are columns of the
orthogonal array design. Noise1 and Noise2 are the response data that were measured at each
noise condition.
A B C D E Pressure Noise1 Noise2
1 1 1 1 1 20 37 48
1 1 1 1 1 40 71 93
1 1 2 2 2 20 29 44
1 1 2 2 2 40 51 86
2 2 1 1 2 20 19 30
2 2 1 1 2 40 39 57
2 2 2 2 1 20 33 39
2 2 2 2 1 40 62 76
1 2 1 2 1 20 25 33
1 2 1 2 1 40 51 60

A B C D E Pressure Noise1 Noise2
1 2 2 1 2 20 44 60
1 2 2 1 2 40 79 109
2 1 1 2 2 20 31 44
2 1 1 2 2 40 66 82
2 1 2 1 1 20 22 32
2 1 2 1 1 40 47 65
8.8 Two-step optimization for Taguchi designs
The goal of a robust parameter design is usually to determine factor settings that will minimize
the variability of the response about some ideal target value (or target function in the case of a
dynamic response experiment). Taguchi methods do this by a two-step optimization process.
The first step concentrates on minimizing variability, and the second focuses on hitting the
target.
• First, set all factors that have a substantial effect on the signal-to-noise ratio at the level
where the signal-to-noise is maximized.
• Then, adjust the level of one or more factors that substantially affect the mean (or slope)
but not the signal-to-noise to put the response on target.
An alternative approach is to start by minimizing the standard deviation and then adjust a factor
that affects the mean but does not affect the standard deviation.

How to calculate the signal-to-noise ratios and the standard deviations in the
response table for Taguchi design
• Calculate the signal-to-noise ratios
• Calculate the standard deviations
The Response Table for Signal-to-Noise Ratios contains a row for the average signal-to-noise
ratio for each factor level, Delta, and Rank. The table contains a column for each factor.
The Response Table for Standard Deviations contains a row for the average signal-to-noise
ratio for each factor level, Delta, and Rank. The table contains a column for each factor.
Delta
Delta is the difference between the maximum and minimum average response (signal-
to-noise ratio or standard deviation) for the factor.
Rank
The Rank is the rank of each Delta, where Rank 1 is the largest Delta.
Calculate the signal-to-noise ratios
To get the standard deviation for each factor level, consider the following example. You
have a Taguchi design where the inner array has 2 factors (A and B), stored in C1 and C2,
respectively, and the outer array has two responses, stored in C3 and C4.
Part 1
1. Choose Calc > Row Statistics.
2. Choose Mean.
3. In Input variables, enter C3 C4.
4. In Store result in, enter C6.
5. Click OK.

Part 2
2. Choose Standard deviation.
5. Click OK.
Part 3
1. Choose Calc > Calculator.
2. In Store result in variable, enter C8.
3. In Expression, enter: 10 * LOGT(C6**2 / C7**2).
4. Click OK.
NOTE
You can also get these signal-to-noise ratios by
choosing Stat > DOE > Taguchi > Analyze Taguchi Design, clicking Storage,
checking Signal to Noise ratios, and clicking OK twice.
Part 4
1. Choose Stat > Basic Statistics > Store Descriptive Statistics > Statistics.
2. In Variables, enter C8.
3. In By variables (optional), enter C1.
4. Click Statistics.
5. Check Mean.
6. Click OK twice.
The last column in the worksheet (named Mean1 if there was not already a column with
this name before performing these steps) contains the signal-to-noise ratios that are
displayed in the Response Table for factor A.

Repeat Part 4, entering C2 in step 3 to get the signal-to-noise ratios for factor B.
Calculate the standard deviations
To get the standard deviation for each factor level, consider the following example. You
have a Taguchi design where the inner array has 2 factors (A and B), stored in C1 and C2,
respectively, and the outer array has two responses, stored in C3 and C4.
Part 1
2. Choose Standard deviation.
5. Click OK.
NOTE
You can also get these standard deviations by
choosing Stat > DOE > Taguchi > Analyze Taguchi Design, clicking Storage,
checking Standard deviations, and clicking OK twice.
Part 2
1. Choose Stat > Basic Statistics > Store Descriptive Statistics > Statistics.
2. In Variables, enter C6.
3. In By variables (optional), enter C1.
4. Click Statistics.
5. Check Mean.
6. Click OK twice.
The last column in the worksheet (named Mean1 if there was not already a column with
this name before performing these steps) contains the standard deviations that are
displayed in the Response Table for factor A.

Repeat Part 2, entering C2 in step 3 to get the standard deviations for factor B.
8.9 Signal-to-Noise ratio in a Taguchi design
In Taguchi designs, a measure of robustness used to identify control factors that reduce
variability in a product or process by minimizing the effects of uncontrollable factors (noise
factors). Control factors are those design and process parameters that can be controlled. Noise
factors cannot be controlled during production or product use, but can be controlled during
experimentation. In a Taguchi designed experiment, you manipulate noise factors to force
variability to occur and from the results, identify optimal control factor settings that make the
process or product robust, or resistant to variation from the noise factors. Higher values of the
signal-to-noise ratio (S/N) identify control factor settings that minimize the effects of the noise
factors.
Taguchi experiments often use a 2-step optimization process. In step 1 use the signal-to-noise
ratio to identify those control factors that reduce variability. In step 2, identify control factors
that move the mean to target and have a small or no effect on the signal-to-noise ratio.
The signal-to-noise ratio measures how the response varies relative to the nominal or target
value under different noise conditions. You can choose from different signal-to-noise ratios,
depending on the goal of your experiment. For static designs, Minitab offers four signal-to-
noise ratios:
Signal-
to-noise
ratio
Goal of the
experiment
Data
characteristics Signal-to-noise ratio formulas
Larger is
better
Maximize the
response
Positive S/N = −10 *log(Σ(1/Y2
)/n)
Nominal
is best
Target the response
and you want to
base the signal-to-
Positive, zero,
or negative
S/N = −10 *log(σ2
)

Signal-
to-noise
ratio
Goal of the
experiment
Data
characteristics Signal-to-noise ratio formulas
noise ratio on
standard deviations
only
Nominal
is best
(default)
Target the response
and you want to
base the signal-to-
noise ratio on means
and standard
deviations
Non-negative
with an
"absolute zero"
in which the
standard
deviation is zero
when the mean
is zero
The adjusted formula is:
Smaller
is better
Minimize the
response
Non-negative
with a target
value of zero
S/N = −10 *log(Σ(Y2
)/n))
TABLE: 4 SIGNAL TO NOISE RATIOS
For Taguchi dynamic designs, Minitab provides one signal-to-noise ratio (and an adjusted
formula), which is closely related to the nominal-is-best S/N ratio for static designs.
The Nominal is Best (default) signal-to-noise ratio is useful for analysing or identifying scaling
factors, which are factors in which the mean and standard deviation vary proportionally. Scaling
factors can be used to adjust the mean on target without affecting signal-to-noise ratios.

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