optimization of process parameters of fdm printed carbon fiber … · 2020. 5. 18. · optimization...

Optimization of Process Parameters of FDM Printed

Carbon Fiber Reinforced PLA & PET-G

N S G Saranya1, G Raju2, G Ganesh3

[email protected], [email protected], [email protected]

1,2,3AssistantProfessor,Department of Mechanical Engineering

BVC ENGINEERING COLLEGE, Odalarevu, Andhra Pradesh, India.

Abstract: Micro Air Vehicles are the logical successors to modern aircraft and advancements in automated technology. In recent years, the use of Micro Air Vehicles (MAV) and Unmanned Aerial Vehicles (UAV) are playing important roles in different applications. It is used in many aspects of military and civil broadly, such as aerial photogrammetry, warzone, reconnaissance, attack missions, surveillance of pipelines, and interplanetary exploration and so on. The current trend in aircraft structural design is to use composite materials as primary structural elements. The main objective of this work is to analysis of Mechanical behavior of 3D printed composite materials Carbon Fiber PLA and Carbon Fiber Reinforced PET-G parts by varying various 3D- printing parameters like Printing Speed (mm/sec), Infill Density (%), Layer Height(microns). Various tests such as Tensile Test, Compression test and Flexural test and are performed to determine failure characteristics of Carbon Fiber PLA and Carbon Fiber Reinforced PET-G materials. Optimization techniques like Taguchi and Taguchi Grey Relational Analysis are also applied to know the printing parameters influence on the mechanical properties of the printed part in order to obtain how parts can be manufactured (printed) to achieve improved mechanical properties. The ANOVA also carried out to know the percentage contribution of printingparameters.

Keywords: Rapid Prototyping, FDM, 3D Printing, PLA & PET-G.

1. INTRODUCTION In recent years, the biodegradable composites draw many attentions in aerospace applications

(MAV), as the increasingly serious environmental pollution problems caused by thermosetting composites.

Mohanty et al. (2000) reviewed the application of biopolymers and considered that the biopolymers offer

environmental benefits including biodegradability, renewability and less greenhouse gas emissions.

Polylactide(PLA) & Polyethylene Terephthalate (PET-G) is a kind of biodegradable materials derived from

renewable resource and possesses good mechanical properties, which makes it promising an ecologically

friendly material for composite applications. At least one species of bacteria in the genus Nocardia can

degrade these materials with an esterase enzyme. Japanese scientists have isolated a bacterium

Ideonellasakaiensis that possesses two enzymes which can break down these into smaller pieces that the

bacterium can digest. A colony of I. sakaiensis can disintegrate a plastic film in about six weeks.

To develop the 3D printing of continues PLA & carbon fiber reinforced PET-G composites with

curved structures and higher mechanical strength for practical applications, we proposed a prototyping

approach for the rapid and continuous printing. Different Specimens were printed with different setting and

range of parameters. Then mechanical properties like Tensile Strength, Flexural Strength & Compressive

Strength were tested to make the comparison between printed samples to know which sample will exhibit

better mechanical property, so that we can produce the components in real life applications and how we can

increase the mechanical properties of the printed components.

2. RAPIDPROTOTYPING

The term rapid prototyping (RP) refers to a class of technologies that can automatically construct

physical models from Computer-Aided Design (CAD) data. These "three dimensional printers" allow

designers to quickly create tangible prototypes of their designs, rather than just two-dimensional pictures.

Such models have numerous uses. “Rapid Prototyping (RP) can be defined as a group of techniques used to

quickly fabricate a scale model of a part or assembly using three-dimensional computer aided design (CAD)

data”. Rapid Prototyping has also been referred to as solid free-form manufacturing; computer automated

manufacturing, and layered manufacturing. RP has obvious use as a vehicle for visualization. In addition, RP

models can be used for testing, such as when an airfoil shape is put into a wind tunnel. RP models can be

used to create molds for tooling, such as silicone rubber molds and investment casts. In some cases, the RP

part can be the final part, but typically the RP material is not strong or accurate enough. When the RP

material is suitable, highly convoluted shapes (including parts nested within parts) can be produced because

of the nature of RP.

Mukt Shabd Journal

Volume IX Issue IV, APRIL/2020

Issn No : 2347-3150

Page No : 4152

Selection of printing material (PLA & Carbon Reinforced PET-G)

Selecting the 3D Printing Technique (Fused Deposition Modeling)

Design of Experimentation(DOE) using MiniTab-17

Selection of process parameters & their levels for printing

Optimization of process parameters

Single Variable Optimization-Taguchi

Multi variable Optimization- Taguchi Grey RelationalAnalysis

Results & Discussions

2.1 RAPID PROTOTYPINGTECHNOLOGIES

Rapid Prototyping Technologies are classified as:

a) Liquid Based Rapid PrototypingSystems

Stereo lithography

Solid Ground Curing

b) Solid Based Rapid Prototyping Systems

Laminated ObjectManufacturing

Fused DepositionModeling

c) Powder Based Rapid PrototypingSystems

• Selective LaserSintering

• Three-DimensionalPrinting

3. EXPERIMENTATION:

Fig. 4.1 Process of Experimentation

Preparing the CAD model using Designing Software following ASTM

Standards

Printing the Objects(Specimens) using FDM technique

Performing the Tensile, Compressive & Flexural tests

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4. PRINCIPLE OF FDM: This process constructs three dimensional objects directly from 3D CAD data. Temperature

controlled head extrudes thermoplastic material layer by layer. This process starts with importing an STL file of a model into preprocessing software. this model is oriented and mathematically sliced into horizontal layers varying from 0.127 -0.254 mm thickness. A support structure is created were needed, based on the part position andgeometry.

The system operates in X, Y, Z Axes, drawing the model one layer at a time. This process is similar to how a hot glue gun extrudes melted beads of glue. The temperature controlled extrusion head is fed with thermoplastic modeling material that is heated to a semi-liquid state. The head extrudes and directs the material with precision in ultrathin layers onto a fixtureless base. The result of the solidified material laminating to the preceding layer is a plastic 3D MODEL built upon stand at time. Once the part is completed the support columns are removed and the surface isfinished.

5. MATERIAL SELECTION

The material selection is the most important thing for printing, because we have different materials. The

material has different working materials based on their properties.There are different technologies that are used in 3D printing and so there are various material that are used in this process. Some printers support around 170 different types of material for printing. this can broadly be categorized into four important heads.

plastic powder resins othermaterial

6. PREPARING CAD MODEL FOR THE SPECIMENS TO BE PRINTED:

In the project we are going to analyze the strength of the objects printed with PLA & Carbon

reinforced PET-G material, by varying various process parameters of 3D-printing machine. To analyze their mechanical strengths, the objects has to undergo Tensile, Compressive & Flexural tests. To perform these tests, the objects has to be printed as per the ASTM standards design required for these tests. So first the objects called as specimens, CAD model has to be designed using any of the designing software and they are to be printed.

7. ASTMINTERNATIONAL

ASTM International, formerly known as the American Society for Testing and Materials (ASTM),

is a globally recognized leader in the development and delivery of international voluntary consensus standards. Today, some 13,000 ASTM standards are used around the world to improve product quality, enhance safety, facilitate market access and trade, and build consumerconfidence.

As these are polymers which comes under the plastics ASTM standards that has to be follow for the design of Tensile test is ASTM D638, Compressive Test ASTM D695 & Flexural Test ASTM D790.

8. DESIGN OF CADMODEL Using the design package CATIA V5 the specimens were designed as per the ASTM Standards. CAD model for Tensile test: Tensile Test Specimens were designed in CATIA V5, a rectangular block with 12.7mmX 12.7mm X 25.4mm following ASTM D695standards for plastics. CAD model for Compressive test: Compression Test Specimens were designed in CATIA V5, a rectangular block with 12.7mmX 12.7mm X 25.4mm following ASTM D695standards for plastics. CAD model for Flexural test: Flexural Test Specimens were designed in CATIA V5 with 127mm X 12.7 mm X 6.4 mm following ASTM D790 standards forplastics.

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Figure: 8.1 Tensile Specimen Figure: 8.2 Compression Specimen

Figure: 8.3 Flexural Specimen

9. INPUT PROCESSPARAMETERS

Input Parameter Level 1 Level 2 Level 3

Print Speed(mm/sec) 60 80 100

Infill Density(%) 40 60 80

Layer Height(microns) 100 200 300

Table 9.1 Process Parameters and their levels

10. DESIGN OFEXPERIMENT A 9 run experiment is selected based on Taguchi’s technique by the information from the table

Process Parameters and their levels, the L9 orthogonal array was created by using the MINITAB 17 software.

Steps involved in creating L9 Orthogonal Array using MINITAB 17: 1. Open the MINITAB 17 window, then an empty worksheet will bedisplayed.

2. Go to STAT > DOE > Taguchi > Create TaguchiDesign

3. Then select 3 -level design, number of factors 3 and enter factors (Print Speed(mm/sec), Infill Density (%), Layer Height(microns)) and their levels. Then the required orthogonal array was displayed as in the Table:10.1

S.No Print Speed

(mm/sec) Infill Density

(%) Layer Height

(microns)

1 60 40 100

2 60 60 200

3 60 80 300

4 80 40 200

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5 80 60 300

6 80 80 100

7 100 40 300

8 100 60 100

9 100 80 200

Table10.1 Experimental Design created by MINITAB 17 for PLA & Carbon reinforced PET-G

11. PROCEDURE FOR3D-PRINTING

The following steps were carried out for fabrication of the components

1. A suitable model was chosen for fabrication. For this purpose, many models were modeled using CATIA

V5 or Solid Works software. One of the models was then chosen, after considering followingfactors.

a) The component should have a flat surface, a concave surface and a convexsurface.

b) The component should be measurable insize.

c) The component should be small, since the time taken for fabrication should beless.

2. After choosing the model, the CAD format of the model was converted to STL format using CATIA V5or

Solid Works software. STL format is the standard format, which many modeling software provide for

exporting the CAD models to a neutral format, so that other software could use the models.

3. STL is the de-facto standard of RP process. STL format model was then fed to Quick Slice software.

Quick Slice software was developed by Stratasys Corporation, developers ofmachine.

4. The Quick Slice software takes input as slice thickness, road width, air gap, nozzle diameter, material type

etc. The software calculates various machine specific parameters like time, filament usageetc

5. The file was then converted to SML format. SML format is a machine specific parameter. This is the

format, which FDM machines recognize. This file was then fed to the PRAMAAN200machine.

6. The PRAMAAN200 machine, which is interfaced to a computer, then checks the given input file and

takes the necessary details from thefile.

7. PRAMAAN200 then starts building the component layer bylayer.

8. The above procedure was repeated with fixed parameters Traveling speed-60mm/sec, Infill pattern-

honeycomb, Layer type-rectilinear, Layer thickness-200microns, Bed temperature-220°C, and with varying the

parameters Build Orientation (00, 300, and 60°) and Infill density (60%, 80%) to print Tensile, Compression and

Flexural Test Specimens (Total 27Specimens).

Fig 11.1TensileSpecimens Fig. 11.2FlexuralSpecimens Fig.11.3 Compression Specimens

12. SINGLE VARIABLE OPTIMIZATION (TAGUCHIMETHOD)

Taguchi’s method is systematic and experimentally designed to find the main process parameters

and will locate a good combination of process parameters to improve the output quality by using the experiments of Orthogonal Array. In this method each experimental value is converted to Signal to Noise ratio and is defined as the deviation between the experimental value and idealvalue.

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12.1 Process parameters optimization by Taguchi Design ofExperimentation

Process parameters are optimized using Taguchi Design by Using the MINITAB 17. In this Means

and S/N ratios for all response parameters were calculated. Then response table for each response parameter was created to find out the optimum level of experiment for each parameter i.e., Tensile Strength, Flexural Strength, Compressive Strength.

12.2 Procedure to calculate Mean and S/N ratio in MNITAB17 After creating Orthogonal Array L9 for the selected levels of process parameters, the next step is to

enter the values of response variables i.e. Tensile Strength, Flexural Strength, Compressive Strength which were calculated after the experimentation.

• Then go to STAT-DOE-TAGUCHI-DEFINE CUSTOM TAGUCHI DESIGN. In this enter the factors (process parameters that effect outputresponses).

• Then again go to STAT-DOE-TAGUCHI-ANALYZE TAGUCHI DESIGN. In this enter response data (response variable for which we want S/N ratios) and in storage select S/N ratios. In options give the S/N criteria whether Larger is better, Smaller is better or Nominal isbetter.

• Then we can see the generation of S/N ratios for all 9 experimental runs as in Table No:12.1

S. No

Print Speed (mm/sec)

Infill Density (%)

Layer Height (microns)

Ultimate Tensile Strength (N/mm2)

S/N Ratio of UTS

Flexural Strength (N/mm2)

S/N Ratio of FS

Compressive Strength (N/mm2)

S/N Ratio of CS

1 60 40 100 17.118 24.66906 141.41 43.009602 13.15 22.3778545 2 60 60 200 15.26 23.671091 152.77 43.680762 16.93 24.5715999 3 60 80 300 20.26 26.132789 110.7 40.882952 17.16 24.691864 4 80 40 200 14.916 23.473047 141.74 43.029849 13.87 22.8434077 5 80 60 300 26.304 28.400436 135.33 42.627882 33.05 30.3834293 6 80 80 100 21.961 26.833042 62.67 35.941194 23.64 27.4729494 7 100 40 300 18.068 25.138202 91.43 39.221774 15.04 23.5472665 8 100 60 100 19.465 25.785088 57.85 35.246067 20.6 26.2790308 9 100 80 200 16.659 24.432979 131.28 42.363971 23.25 27.3273383

Table 12.1 Signal to Noise Ratios for Tensile Strength, Flexural Strength, Compressive Strength for PLA material

13. Taguchi Analysis: Tensile Strength, Flexural Strength and Compressive Strength VS Print Speed(PS), Infill Density(ID), Layer Height(LH) for PLAmaterial

S/N Ratios Means

Level Print

Speed

(mm/sec)

Infill

Density

(%)

Layer

Height

(microns)

Print

Speed

(mm/sec)

Infill

Density

(%)

Layer

Height

(microns)

1 24.82 24.43 25.76 17.55 16.7 19.51

2 26.24 25.95 23.86 21.06 20.34 15.61

3 25.12 25.8 26.56 18.06 19.63 21.54

Delta 1.41 1.53 2.7 3.51 3.64 5.93

Rank 3 2 1 3 2 1

Table 13.1 Response Table for Ultimate Tensile Strength (N/mm2)(Mean and S/N ratios) (Larger is better criteria) (PLA Material)

13.1 Prediction of Optimal Value for TensileStrength

The optimum value of Tensile Strength is predicted at the selected levels of significant parameters. The

predicted optimal mean of the response characteristics Tensile Strength can be computed as

�̅predicted=�̅exp+(�̅PS-�̅exp)+(�̅ID-�̅exp)+(�̅LH-�̅exp)

(or)

�̅predicted=�̅PS+�̅ID+�̅LH–2*�̅exp

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Where, �̅predicted=PredictedTensileStrength

FromTable:4.1

�̅exp=Totalaverageresponseoftheexperimentsinthearray=ΣY i.e,

ΣY=�=170.014/9 = 18.89N/mm2

�

Where T = sum of all experiments Tensile Strength mean values

N = number of experiments=9

�̅PS=averageTensileStrengthatsecondlevelofPrintingSpeed,80m/sec i.eΣ

PSL2/3= (21.961+14.916+26.304)/3 = 21.06 N/mm2

Similarly, for Infill Density & Layer Height

Therefore, the predicted optimal values of Tensile strength are

�̅predicted=�̅PS+�̅ID+�̅LH-2*�̅exp

= 21.06+20.34+21.54– 2 x 18.89

= 25.16N/mm2

13.2 Confirmation Test for TensileStrength

The final step in verifying the improvement in Tensile strength was done by conducting experiments using optimal conditions. The confirmation experiment was conducted at the optimum setting of process parameters namely Printing speed at level 2(80m/s), Infill Density level 2 (60%), Layer Height level 3(300microns) and the Tensile strength observed to be 24.89 N/mm2, which was around the confidence interval of the predicted Tensile Strength 25.16 N/mm2.

S/N Ratios Means

Level Print Speed

(mm/sec)

Infill

Density

(%)

Layer Height

(microns)

Print Speed

(mm/sec)

Infill

Density

(%)

Layer Height

(microns)

1 42.28 41.13 35.59 131.74 116.59 60.26 2 40.53 40.52 43.02 113.25 115.32 141.93 3 38.94 39.73 40.91 93.52 101.55 112.49

Delta 3.34 1.4 7.43 38.22 15.04 81.67

Rank 2 3 1 2 3 1

Table 13.2 Response Table for Flexural Strength (N/mm2)(Mean and S/N ratios) (Larger is better criteria) (PLA Material)

13.3 Prediction of Optimal Value for FlexuralStrength:

The optimum value of Flexural Strength is predicted at the selected levels of significant parameters.

The predicted optimal mean of the response characteristics Flexural Strength can be computed as

�̅predicted=�̅exp+(�̅PS-�̅exp)+(�̅ID-�̅exp)+(�̅LH-�̅exp) (or)


Where, �̅predicted=PredictedFlexuralStrength


ΣY=�=1024.78/9 = 113.91N/mm2

�

Where T = sum of all experiments Flexural Strength mean values


�̅PS=averageFlexuralStrengthatFirstlevelofPrintingSpeed,60m/sec i.e.

PSL1/3= (141.41+152.77+110.70)/3 = 134.96 N/mm^2 Similarly, for Infill Density & Layer Height

Therefore, the predicted optimal values of Flexural Strength are


= 134.96+124.86+141.93– 2 x 113.91

= 173.93N/mm2

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13.4 Confirmation Test for FlexuralStrength

The final step in verifying the improvement in Flexural strength was done by conducting experiments using optimal conditions. The confirmation experiment was conducted at the optimum setting of process parameters namely Printing speed at level 1(60m/s), Infill Density level 1 (40%), Layer Height level 2(200microns) and the Flexural strength observed to be 162.91N/mm2, which was around the confidence interval of the predicted optimal Flexural strength 173.93N/mm2.

S/N Ratios Means

Level Print

Speed

(mm/sec)

Infill

Density

(%)

Layer

Height

(microns)

Print

Speed

(mm/sec)

Infill

Densit

y (%)

Layer

Height

(microns)

1 24.63 23.2 26.88 17.05 14.46 22.12

2 26.9 27.08 24.91 23.52 23.53 18.02

3 25.72 26.5 26.21 19.63 21.35 21.75

Delta 2.27 3.88 1.96 6.48 9.07 4.11

Rank 2 1 3 2 1 3

Table 13.4Response Table for Compression Strength (N/mm2)(Mean and S/N ratios) (Larger is better criteria) (PLAMaterial)

13.5 Prediction of Optimal Value for CompressiveStrength

The optimum value of Compressive Strength is predicted at the selected levels of significant parameters. The

predicted optimal mean of the response characteristics Compressive Strength can be computed as



Where,�̅predicted=PredictedCompressiveStrength


ΣY=�=176.697/9 = 19.633N/mm2

�

Where T = sum of all experiments Compressive Strength mean values


�̅PS=averageCompressiveStrengthatSecondlevelofPrintingSpeed,80m/sec i.e.

PSL2/3= (23.64+13.873+33.050)/3 = 23.521 N/mm2


Therefore, the predicted optimal values of Compressive Strength are


= 23.521+23.527+19.131– 2 x 19.633

= 26.913 N/mm2

13.6 Confirmation Test for CompressiveStrength

The final step in verifying the improvement in compressive strength was done by conducting experiments using optimal conditions. The confirmation experiment was conducted at the optimum setting of process parameters namely printing speed at level 2 (80m/s), infill density level 2 (60%), layer height level 1(100microns) and the compressive strength observed to be 28.633n/mm2, which was around the confidence interval of the predicted optimal compressive strength 26.913 n/mm2.The experimental results were analyzed with the Analysis of Variance (ANOVA), which is used to know the design parameters percentage contribution towards the Tensile Strength, Flexural Strength & Compressive Strength.

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Table 13.5 Signal to Noise Ratios for Tensile Strength, Flexural Strength, Compressive Strength for PETG

material

14. Taguchi Analysis: Tensile Strength, Flexural Strength and Compressive Strength VS Print Speed (PS),

Infill Density(ID), Layer Height(LH) for PETGmaterial:

S/N Ratios Means

Level Print Speed

(mm/sec)

Infill

Density

(%)

Layer Height

(microns)

Print Speed

(mm/sec)

Infill

Density

(%)

Layer Height

(microns)

1 28.28 21.02 28.99 26.63 18.57 28.25

2 29.04 28.07 28.33 28.34 25.7 26.55

3 21.79 30.02 21.79 21.06 31.76 21.22

Delta 7.25 8.99 7.2 7.27 13.19 7.02

Rank 2 1 3 2 1 3

Table 14.1 Response Table for Ultimate Tensile Strength (N/mm2)(Mean and S/N ratios) (Larger is better criteria) (PETG Material)

14.1 Prediction of Optimal Value for TensileStrength:

The optimum value of TensileStrength is predicted at the selected levels of significant parameters. The predicted optimal mean of the response characteristics Tensile Strength can be computed as



Where,�̅predicted=PredictedTensileStrength

�̅exp=Totalaverageresponseoftheexperimentsinthearray=ΣY

i.e, ΣY=�=228.068/9 = 25.34N/mm2

�

Where T = sum of all experiments Tensile Strength mean values


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�̅PS=averageTensileStrengthatsecondlevelofprintingSpeed,80m/sec

i.e. PSL2/3= (29.466+28.481+27.059)/3 = 28.33 N/mm^2


Therefore, the predicted optimal values of Tensile strength are


= 28.33+31.75+28.24– 2 x 25.34

= 37.64N/mm^2

14.2 Confirmation Test for TensileStrength:

The final step in verifying the improvement in Tensile strength was done by conducting experiments using optimal conditions. The confirmation experiment was conducted at the optimum setting of process parameters namely Printing speed at level 2(80m/s), Infill Density level 3 (80%), Layer Height level 1(100microns) and the Tensile strength observed to be 36.82 N/mm2, which was around the confidence interval of the predicted optimal Tensile Strength37.64N/mm2.

S/N Ratios Means

Level Print Speed

(mm/sec)

Infill

Density (%)

Layer Height

(microns)

Print Speed

(mm/sec)

Infill

Density (%)

Layer Height

(microns)

1 30.55 29.44 30 33.79 29.87 31.77

2 29.56 29.79 30.38 30.12 31.08 33.24

3 29.35 30.23 29.07 29.57 32.52 28.47

Delta 1.2 0.79 1.31 4.22 2.65 4.77

Rank 2 3 1 2 3 1

Table 14.2 Response Table for Flexural Strength (N/mm2) (Mean and S/N ratios) (Larger is better criteria) (PETGMaterial)

14.3 Prediction of Optimal Value for FlexuralStrength:

The optimum value of Flexural Strength is predicted at the selected levels of significant parameters. The predicted optimal mean of the response characteristics Flexural Strength can be computed as


�̅predicted=� ̅PS+�̅ID+�̅LH–2*�̅exp

Where,�̅predicted=PredictedFlexuralStrength


i.e, ΣY=�=280.44/9 = 31.16 N/mm2

�

Where T = sum of all experiments Flexural Strength mean values


�̅PS=averageFlexuralStrengthatFirstlevelofPrintingSpeed,60m/sec

i.e. PSL1/3= (25.244+20.005+34.629)/3 = 33.79 N/mm2


Therefore, the predicted optimal values of Flexural Strength are

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�̅predicted=� ̅PS+�̅ID+�̅LH-2*�̅exp

= 33.79+32.52+33.23– 2 x31.16

= 37.22N/mm2

14.4 Confirmation Test for FlexuralStrength:

The final step in verifying the improvement in Flexural strength was done by conducting experiments using optimal conditions. The confirmation experiment was conducted at the optimum setting of process parameters namely Printing speed at level 1(60m/s), Infill Density level 3 (80%), Layer Height level 2(200microns) and the Flexural strength observed to be 35.28 N/mm2, which was around the confidence interval of the predicted optimal Flexural strength 37.22 N/mm2

S/N Ratios Means

Level

Print Speed

(mm/sec)

Infill

Density

(%)

Layer Height

(microns)

Print Speed

(mm/sec)

Infill

Density

(%)

Layer Height

(microns)

1 24.25 20.70 24.58 16.63 10.95 17.34

2 23.65 24.17 24.09 16.02 16.17 17.27

3 24.19 27.22 23.41 17.51 23.05 15.55

Delta 0.61 6.51 1.17 1.48 12.10 1.80

Rank 3 1 2 3 1 2

Table 14.4Response Table for Compressive Strength (N/mm2)(Mean and S/N ratios) (Larger is better criteria) (PETG Material)

14.5 Prediction of Optimal Value for CompressiveStrength:

The optimum value of Compressive Strength is predicted at the selected levels of significant parameters. The predicted optimal mean of the response characteristics Compressive Strength can be computed as



Where,�̅predicted=PredictedCompressiveStrength


i.e., ΣY= �=150.482/9 = 16.72N/mm2

�

Where T = sum of all experiments Compressive Strength mean values


�̅PS=averageCompressiveStrengthatFirstlevelofPrintingSpeed,60m/sec

i.e., Σ PSL1/3= (13.118+15.792+20.979)/3 = 16.62 N/mm^2


Therefore, the predicted optimal values of Compressive Strength are


= 16.62+23.04+17.34– 2 x 16.72

= 23.56 N/mm^2

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14.6 Confirmation Test for Compressive Strength:

The final step in verifying the improvement in compressive strength was done by conducting experiments using optimal conditions. The confirmation experiment was conducted at the optimum setting of process parameters namely printing speed at level 1 (60m/s), infill density level 3 (80%), layer height level 1(100microns) and the compressive strength observed to be 24.35 n/mm2, which was around the confidence interval of the predicted optimal compressive strength 23.56 n/mm2.

The experimental results were analyzed with the Analysis of Variance (ANOVA), which is used to know the design parameters percentage contribution towards the Tensile Strength, Flexural Strength & Compressive Strength.

15. ANOVA for TensileStrength(PLA):

Table 15 ANOVA forTensileStrength Fig: 15 .1 % of process parameters for TensileStrength

15.1 ANOVA for Flexural Strength(PLA):

Table 15.1 ANOVA forFlexuralStrength Fig: 15.1% of process parameters for FlexuralStrength

15.2 ANOVA for Compressive Strength(PLA):

Table 15.2ANOVA forCompressiveStrength Fig. 15.2 % of process parameters for CompressiveStrength

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16. ANOVA for Tensile Strength(PETG):

Table 16. ANOVA for Tensile StrengthFig.16% of process parameters for Tensile Strength

16.1 ANOVA for Flexural Strength(PETG):

Table 16.1 ANOVA for Flexural Strength Fig.16.1 % of process parameters for Flexural Strength

16.2 ANOVA for Compressive Strength(PETG):

Table16.2 ANOVA for Compressive Strength Fig. 16.2 % of process parameters for Compressive Strength

17. MULTI VARIABLE OPTIMIZATION (

Since optimizing multiple output qualities of a pronot optimize the multiple output qualities simultaneously by using the Taguchi’s method. Therefore, a Grey Relational Analysis(GRA) is recommended and used to integrate and optimize the multiple qprocess.

for Tensile Strength(PETG):

for Tensile StrengthFig.16% of process parameters for Tensile Strength

ANOVA for Flexural Strength(PETG):


for Compressive Strength(PETG):


OPTIMIZATION (GREY RELATIONALANALYSIS)

Since optimizing multiple output qualities of a process requires the calculation of overall S/N ratios and may not optimize the multiple output qualities simultaneously by using the Taguchi’s method. Therefore, a Grey Relational Analysis(GRA) is recommended and used to integrate and optimize the multiple q



cess requires the calculation of overall S/N ratios and may not optimize the multiple output qualities simultaneously by using the Taguchi’s method. Therefore, a Grey Relational Analysis(GRA) is recommended and used to integrate and optimize the multiple qualities of a

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∗ � �

17.1 Process steps for Grey Relational Analysis optimization for PLA&PETG Material

The basic process steps for multi-response optimization are given below.

(a) Normalization of experimental results for all performancecharacteristics.

(b) Calculation of grey relational coefficient(GRC).

(c) Calculation of grey relational grade (GRG) using weighing factor forperformancecharacteristics.

(d) Analysis of experimental results usingGRG.

(e) Selection of optimal levels of processparameters.

(f) Conducting confirmation experiment to verify optimal process parametersettings.

Model Calculations for Normalization Sequences for PLA Material: For

Tensile Strength ��(�)−��(�)

�=��(�)−��(�)

� �

�∗=17.118−14.916

� 26.304−14.916

=0.1933Similarly, for FS and CS

17.2 Model Calculations for Deviation Sequences for TensileStrength ∆��=‖�−�.��‖

= 0.8067 Similarly, FS and CS.

17.3 Model Calculations for Grey relational coefficient for TensileStrength

∆��+�∆��

��(�)=∆

��(�)+�∆

��

0+(0.5)(1)

��(�)=0.8067+(0.5)(1)

=0.3826

Table 17.4 Results for Comparability and Deviation Sequences

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Table 17.5Results for Grey Relation Coefficient and Grey Relational Grades

18. Taguchi Analysis for GreyRelationalGrade:

Table 18 Response Table for Mean and Signal to Noise Ratios (Larger is better) of Grey Relational Grade

The optimum value ofGrey Relational Grade is predicted at the selected levels of significant parameters. The predicted optimal mean of the response characteristics Ultimate Tensile Strength(UTS), Flexural Strength(FS) & Compressive Strength(CS) can be computed as



Where, �̅predicted=PredictedGreyRelationalGrade


i.e, ΣY= ��

Where T = sum of all experiments


18.1 For Ultimate Tensile Strength(UTS):

�̅exp=ΣY=�=170.011/9=18.89N/mm2

�

�̅PS=averageUTSatsecondlevelofPrintingSpeed,80m/sec i.e., Σ

PSL2/3= (14.916+26.304+21.961)/3 = 21.06 N/mm2

�̅ID=averageUTSatsecondlevelofInfillDensity,60%

i.e., Σ IDL2/3= (15.26+26.304+19.465)/3 = 20.343N/mm2

�̅LH=averageUTSatThirdlevelofLayerHeight,300microns

i.e., Σ LHL3/3= (20.26+26.304+18.068)/3 = 21.544N/mm2

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Therefore, the predicted optimal value of UTS is


= 21.06 + 20.343 + 21.544– 2 x 18.89

= 25.167 N/mm2

Similarly, we calculate predicted optimal values for Flexural Strength(FS) & Compressive Strength(CS) at

levels of Printing Speed(PS)-80m/sec, Infill Density(ID)-60%, LayerHeight(LH)-300microns

18.2 For Flexural Strength(FS):

The predicted optimal value of FSis

�̅exp=ΣY=�=1025.18/9=113.908N/mm2

�

Therefore, �̅predicted=�̅PS+�̅ID+�̅LH-2*�̅exp

= 113.24 + 115.31 + 112.48– 2 x 113.908

= 113.21 N/mm2

18.3 For Compressive Strength(CS):

The predicted optimal value of CS is

�̅exp=ΣY=�=176.9/9=19.632N/mm2

�

Therefore, �̅predicted=�P̅S+�̅ID+�̅LH-2*�̅exp

= 23.52 + 23.52 + 21.75– 2 x 19.632

= 29.526N/mm2

19. Main Effect Plots for Printing Process Parameters VS Ultimate Tensile, Flexural and Compressive Strengths:

Fig. 19.1 Graph of Printing Parameters v/s Tensile Strength

Fig. 19.2 Graph of Printing Parameters v/s Flexural Strength

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Fig 19.3 Graph of Printing Parameters v/s Compressive Strength

Fig. 19.4 Graph of Printing Parameters v/s Grey Relational Grade(GRG)

20. PREDICTED RESULTS AND CONFIRMATION TEST RESULTS OBTAINED FROM TAGUCHI AND GREY RELATIONALANALYSIS:

Table20.1 Results obtained from Taguchi AnalysisandTaguchi Grey Relational Analysis for PLA

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Optimization Optimum Level Predicted

Confirmation Test

Taguchi Analysis of Design

Tensile Strength Printing Speed=80mm/sec Infill Density= 80% Layer Height= 100microns

37.64

N/mm2

36.82 N/mm2

Flexural Strength

Printing Speed=60mm/sec Infill Density= 80% Layer Height= 200microns

37.22

N/mm2

35.28 N/mm2

Compressive Strength

Printing Speed=60mm/sec Infill Density= 80% Layer Height= 100microns

23.56

N/mm2

24.35 N/mm2

Optimization

Predicted Grey Relational Grade

Optimal Level Of Parameters

Confirmation Test For

Tensile Strength Flexural Strength

Compressive Strength

Grey Relational Analysis

Printing 0.8130

Speed=60mm/sec Infill Density= 80% Layer Height=

32.5

N/mm2

35.23

N/mm2

26.23

N/mm2

200microns

Table 20.2 Results obtained from Taguchi AnalysisandTaguchi Grey Relational Analysis for Carbon Reinforced PET-G

21. Conclusion:

Materials with high strength to weight ratio like Carbon Fiber composite materials are used to manufacture

light weight parts which can be used to produce MAV’s and also to replace some parts in other beneficiary

industries.

The experiments were conducted to analyze the mechanical properties of 3D-printed specimens

with Carbon Fiber PLA and Carbon Fiber Reinforced PET-G and optimization was done to optimize the

different machining parameters like Printing Speed, Infill Density and Layer Height of FDMmachine.

From Taguchi and Grey Relational Analysis it can be concluded that:

• For the Ultimate Tensile Strength Applications Carbon fiber reinforced PET-G has betterresults.

• For the Flexural and Compressive Strength Carbon Fiber PLA has betterresults.

• Overall Carbon Fiber PLA can be concluded as the suitable material to prepare MAVvehicles.

• The optimal set of printing parameters for Carbon Fiber PLAis

Taguchi Analysis: Tensile Strength-Printing Speed=80mm/sec, Infill Density= 60%, Layer Height= 300microns Flexural Strength-Printing Speed=60mm/sec, Infill Density= 40%, Layer Height= 200microns Compressive Strength-Printing Speed=100mm/sec, Infill Density= 60%, Layer Height= 200microns Taguchi Grey Relational Analysis: Printing Speed=80mm/sec, Infill Density= 60%, Layer Height= 300microns

• The optimal set of printing parameters for Carbon Fiber reinforced PET-Gis Taguchi Analysis: Tensile Strength-Printing Speed=80mm/sec, Infill Density= 80%, Layer Height= 100microns Flexural Strength-Printing Speed=60mm/sec, Infill Density= 80%, Layer Height= 200microns Compressive Strength-Printing Speed=60mm/sec, Infill Density= 10%, Layer Height= 200microns Taguchi Grey Relational Analysis: Printing Speed=60mm/sec, Infill Density= 80%, Layer Height= 200microns

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From ANOVAanalysis:

From ANOVA it can be concluded that Layer Height is the most influential parameter on Tensile Strength,

Printing Speed and Layer Height are the influential parameters for Flexural strength, Infill Density is the

influential parameter for Compressive Strength.

By considering all these observations and conclusions one can develop the MAV vehicles according to the

usage.

22. REFERENCES:

[1] Hopkinson, N & Dickens, P 2006, 'Emerging Rapid Manufacturing Processes', in Rapid

Manufacturing; An industrial revolution for the digital age, Wiley & Sons Ltd, Chichester, W.

Sussex.

[2] Paul Marks., 2011, 3D printing has been extensively developed the World’s First Printed Plane”.

New Scientist, August2011.

[3] Jamieson, the solid models from various resources are converted into STL format files or other

format files, which mostly come along with the FDM machines. Slicing procedures are

implemented before the deposition.

[4] Karalekas D and Antonioua K, 2004, “Composite rapid prototyping: overcoming the drawback of

poor mechanical properties” Journal of Materials Processing Technology, Vol 153-154,pp.526-530.

[5] Hague R, Mansour S, and Saleh N, 2004, “Material and design considerations for rapid

manufacturing”, International Journal of Production Research.

[6] Wu H., Sun, D., Zhou, Z., 2004, “Micro Air Vehicle: Configuration, Analysis, Fabrication and

Test”, IEEE/ASME Transactions on Mechatronics, vol.9.

[7] John K Borchardt Unmanned aerial vehicles spur composites use Reinforced Plastics, Volume 48,

Issue 4, April 2004.

[8] Bitzer, T., “Honeycomb Technology: Materials, Design, Manufacturing, Applications and Testing,”

Springer,1997.

[9] "Minitab Statistical Software Features – Minitab." Software for Statistics, Process Improvement,

Six Sigma, Quality – Minitab. N.p., n.d. Web. 11 Apr.2011.

[10] Groebner, David F., Mark L. Berenson, David M. Levine, Timothy C. Krehbiel, and Hang Lau.

Applied management statistics. Custom ed. Boston, MA: Pearson Custom

[11] Publishing/Pearson/Prentice Hall, 2008. Print “Thermoplastic composites gain leading edge on the

A380". Composites World. 3 January 2006. Archived from the original on 17 July 2009. Retrieved 6

March2012.

[12] Guzman, Enrique; Gmür, Thomas (dir.) (2014). "A Novel Structural Health Monitoring Method

for Full-Scale CFRPStructures".

[13] Michelson, R.C., “Test and Evaluation for Fully Autonomous Micro Air Vehicles,” The ITEA

Journal, December 2008, Volume 29, Number 4, ISSN 1054-0229 International Test and

EvaluationAssociation.

[13] Cocke, B. W., “Wind-tunnel Investigation of the Aerodynamic and Structural Deflection

Characteristics of the Goodyear inflatoplane,” National Advisory Committee for Aeronautics, 1958.

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