Investigation of post processing techniques to reduce the surface

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME

531

INVESTIGATION OF POST PROCESSING TECHNIQUES TO

REDUCE THE SURFACE ROUGHNESS OF FUSED DEPOSITION

MODELED PARTS

Addanki Sambasiva Rao1*

, Medha A Dharap2, J V L Venkatesh

3, Deepesh Ojha

4

1 Assistant Professor, Department of Mechanical Engineering, Veermata Jijabai

Technological Institute, Mumbai, India. Email: [email protected] 2 Professor, Department of Mechanical Engineering, Veermata Jijabai Technological

Institute, Mumbai, India. Email: [email protected] 3Associate Professor, Production Engineering Department, SGGSIE&T, Nanded,

Maharashtra, India. Email: [email protected] 4PG Student, Department of Mechanical Engineering, Veermata Jijabai Technological

Institute, Mumbai, India. Email: [email protected]

ABSTRACT

Fused Deposition Modeling is most popular rapid prototyping process because of its

faster, economical and clean technology, however it suffers from low surface finish quality.

To improve its surface finish quality, various attempts had been made by several researchers

by controlling various process parameters. The main objective of this research is to apply

chemical treatment processes through Design of Experiments using different chemicals with

variant conditions like different levels of concentration, time of exposure, temperatures and

initial roughness, interaction effects of the process parameters have also been analyzed.

ANOVA technique is used to find out the significant factors affecting the surface finish.

Results show satisfactory improvement in surface finish of FDM parts (ABS) with simple

inexpensive and harmless chemical treatment processes.

Keywords : Acrylonitrile Butadiene Styrene (ABS), ANOVA, Chemical Treatment, Design

of Experiments, Fused Deposition Modeling, Post-Processing, Surface Roughness.

I. INTRODUCTION

Rapid prototyping (RP) technologies provide the ability to fabricate initial prototypes

from various model materials. Stratasys’s Fused Deposition Modeling (FDM) is a typical RP

process that can fabricate prototypes out of ABS plastic [1]. FDM rapid prototyping process

is quite popular in industry for various reasons such as: different raw materials (thermo

plastics) can be used as long as the appropriate hot head is available; FDM parts are very

strong and hence can work as functional parts; it does not employ lasers, hence is less

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 3, Issue 3, September - December (2012), pp. 531-544

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532

expensive and there are no safety related issues; It does not use liquid/powder raw materials

and hence is a clean process; It can be kept in an office environment as a 3D printer; very

easy to remove the support material; this is probably the easiest of all RP processes; this is

the cheapest technology; etc.

Parts produced by FDM are, however, less accurate than those produced by other

rapid prototyping processes such as Stereo lithography Apparatus (SLA), Solid Ground

Curing (SGC). Besides, FDM process is very slow as every point of the volume is addressed

by a mechanical device. The key issue with FDM process is surface roughness because of its

staircase effect (the angle between the vertical axis and surface tangents) [2]. The poor

surface finish affects the functioning of RP parts, depending on the geometry of the enclosing

surface, the building strategy, layer thickness and orientation of the part; this drawback may

outweigh the advantages of RP parts [3].

In literature, several researchers have proposed various methods to reduce the surface

roughness of the FDM parts of ABS material. One of the prominent methods is to control the

process parameters like layer thickness, build orientation, raster width, raster angle, air gap

etc.. In this method process parameters were optimized using statistical techniques like design

of experiments and gray relational analysis have been integrated for obtaining the optimum

process parameter values [3]. The process parameters influence the responses in a highly non-

linear manner; therefore, prediction of overall dimensional accuracy is made based on

artificial neural network (ANN) [4]. Several algorithms were also developed to obtain

optimum part deposition orientation for fused deposition modeling process for enhancing part

surface finish and reducing build time [5, 6].

Another method is adaptive slicing scheme in which slices of different thicknesses in

different zones are produced while building the part [7-10]. Daekeon Ahn et al investigated

the relation between surface roughness and overlap interval [11], they also analyzed and

discussed the effects of surface angle and filament section shape to the surface roughness.

Debapriya Chakraborty et al introduced a new kind of deposition method called Curved layer

FDM or CLFDM which offers solution to the issues of surface roughness and strength for

thin curved shell-type parts, this process proposes an entirely new building paradigm for

FDM, the filaments would be deposited along curved (essentially non horizontal) paths

instead of planar (horizontal) paths [12]. A mathematical technique has been developed by

W. Rattanawong et al to determine best part orientation based on minimum volume error

(VE) in the part due to staircase effect [13]. Noshir A. Langrana et al have developed a

method to fabricate the highest quality of multi-material parts. In this method, a virtual

simulation system and experimental real time video microscopy have been developed. In this

virtual simulation, one can check or test a variety of the layered manufacturing process

parameters, and make the best selection of tool path and other parameters to obtain high

quality parts [14]

One more method for improving surface finish is chemical treatment method which has

been proposed by L.M. Galantucci et al [2]. In this chemical treatment method

,‘Dimethylketone (Acetone)’ with 90% concentrated solution and 10% water was used and

parts were immersed in diluted solution for 5 minutes and also suggested that further studies

need to be conducted on freeform products, also using other dimethylketone solvents such as

ethylene and using designed experiments to optimize the process in terms of the solution

concentration and process time. To the best of the author’s knowledge, no investigations of

chemical treatment method have been reported since the work of L.M. Galantucci et al. and

hence the present study has been undertaken by the authors to investigate the optimum

conditions for obtaining best surface finish from the chemical treatment process.



533

II. PROBLEM DEFINITION

L.M. Galantucci et al [2] had proposed a method of chemical treatment of ABS

(Acrylonitrile Butadiene Styrene) parts which yields a significant improvement of the surface

finish of the treated specimens. The chemical treatment method is economical, fast and easy

to use. However chemical treatment method has not been analyzed considering different

types of chemicals, different concentration levels of chemicals, effect of elevated

temperatures, different initial roughness of the parts, time of exposure along with the

underlying interaction effects for obtaining optimum surface finish. This paper reports design

of experiments for analyzing chemical post processing treatment method to identify main

controlling factors, side effects of the process parameter settings and disturbances to the

process for ABS plastics.

III.METHODOLOGY

In this paper we will be optimizing the chemical treatment process using Design of

Experiments (DOE). The factors affecting the chemical treatment process were identified by

performing numerous trials, based on these trials concentration, temperature, time of

exposure and initial roughness were identified as possible main factors. These are analyzed

using Design of Experiments (DOE). DOE is done for two different chemicals i.e.

Dimethylketone (Acetone) and Methylethylketone (MEK), test specimen selected are shown

in Fig.1(a) to Fig.1(e)... The optimization method is based on Design of Experiments (DOE)

and Analysis of Variance (ANOVA). It identifies significant parameters affecting the surface

finish, to which more attention must be paid in order to attain best possible results.

3.1 Statistical Design Of Experiments Statistical DOE refers to the process of planning the experiment so that appropriate data

that can be analyzed by statistical methods will be collected, resulting in valid and objective

conclusions [15,16]. A statistical tool is always preferred for drawing the meaningful

conclusion from a experimental design data. There are two aspects to any experimental

problem; the design of the experiment and the statistical analysis of the data. When many

factors control the performance of any system then it is essential to find out significant

factors which need special attention either to control or optimize the system performance.

Taguchi’s concept of Orthogonal Array (OA) as a part of Statistical DOE is used in such

situations to plan the set of experiments and ANOVA technique is used to find out the

significant factors.

These techniques have been used in the current study to investigate significant factors

affecting the surface roughness of FDM parts (ABS P400) out of concentration of solution C,

temperature of the chemical bath Tp, initial roughness of parts Ri and Time for which the

parts are treated Tm. The first step in constructing an orthogonal array to fit a specific case

study is to count the total degrees of freedom that tell the minimum number of experiments

that must be performed to study all the chosen control factors. The number of degrees of

freedom associated with a factor is equal to one less than the number of levels for that factor.

In this experiment we decided to analyze surface finish for four different concentrations of

chemicals. For Acetone concentration levels of 90%, 85%, 80%, 70% were taken and time of

bath of 5 min and 10 min were found to be suitable on the other hand for Methyl ethyl ketone

concentration of 20%, 25%, 30%, 35% were taken, also 3 min and 6 min were found to be

suitable exposure time. These chemicals have higher diffusion rate at elevated temperatures

so two different temperatures i.e. 25°C and 55°C were chosen.



534

Table 1 Factors and their levels for experiment

Levels

Sr. No. Control Factors 1 2 3 4

1 C (% of concentration) Chemical 1 70 80 85 90

Chemical 2 20 25 30 35

2 Tp (◦C) 25-30 50-70 - -

3 Ri (Initial Roughness, µm) 0.254 0.3302 - -

4 Tm(Time of exposure in Min.) Chemical 1 5 10 - -

Chemical 2 3 6

Initial roughness of the parts was taken as roughness corresponding to 0.2540 layer thickness and

0.3302 layer thickness. Time of exposure was also identified as factor affecting the results of chemical

treatment process. Therefore degrees of freedom (DOF) of factors are (C(3), Tm(1), Ri(1), Tm(1).

Degrees of freedom of their interactions are (C&Tp (3), C&Tm(3), Tm&Tp(1). Considering all the

factors and their interactions there are 13 degrees of freedom. Hence this experiment is carried out

using L16, orthogonal array for 4 factors one at 4 level and 3 at 2 levels to design the experiments for

finding out the surface roughness of given parts under the simultaneous variation of 4 different

parameters at different levels as shown in Table 1.

Figure1(a) Test Specimen with 0.2540 mm Figure1(b) Test specimen with 0.3302

mm layer thickness layer thickness of Sample1/A1

Figure 1(c) Sample 2/A2 Figure 1(d) Sample 3 Figure 1(e) Sample 4

In total, L16 has 15 degrees of freedom. The remaining (15-13) two degrees of freedom are used for

error. The design of experiments based on the L16 array for the present case is shown in Table 2.

Inability to distinguish effect of factors and interactions is called confounding [16]. As it is expected

that factors C, Tm, Tp to interact, no factors are assigned to columns 5, 6 and 7. This is done to avoid

confounding. The results of surface roughness value of various FDM samples with combinations of

parameters for chemical 1 are shown in Table 2. Similar results for chemical 2 is shown in Table 3



535

Table 2 Orthogonal Array L16 with results of trials for chemical 1

C(1) Tp(2) Ri(3) Tm(4) CxTp(5) CxTm(6) TpxTm(7) A11 A12 A13 A2

1 1 1 1 1.182 5.99 1.934 1.63

1 1 2 2 0.676 5.25 2.237 5.73

1 2 1 2 0.302 3.139 1.288 1.12

1 2 2 1 0.242 4.968 2.674 3.95

2 1 2 1 0.263 4.187 0.837 4.53

2 1 1 2 0.988 4.935 0.942 1.78

2 2 2 2 0.147 0.64 0.54 4.08

2 2 1 1 0.196 0.274 0.171 1.28

3 1 2 1 0.141 0.67 0.622 0.22

3 1 1 2 0.115 0.156 0.134 0.295

3 2 2 2 0.102 0.165 0.22 1.9

3 2 1 1 0.15 0.424 0.407 0.151

4 1 1 1 0.281 0.855 0.12 0.264

4 1 2 2 0.277 0.583 0.132 0.382

4 2 1 2 0.371 0.463 0.142 0.367

4 2 2 1 0.101 0.141 0.171 0.496



536

Trials were carried out according to the various combinations of parameters displayed

in the Orthogonal Array and the results for surfaces roughness values were recorded. Three

readings were taken on sample1, A11 correspondence to the top surface, A12 correspondence

to the reading along the build direction, A13 correspondence to the reading perpendicular to

the build direction. These were then analyzed to obtain the optimum condition using

MINITAB software. The Data Means plot for main effects and interaction plots for Chemical

1 is shown in Fig.2.The S-N ratio plot for main effects and interaction plots for Chemical 1 is

shown in Fig 3. The experimental data was solved using both Data Means and S-N Ratio. The

condition was S-N Ratio taken was Smaller is Better hence we will be accepting the higher

value as preferred value from the graph where as in means graph lower value will be taken as

preferred value. Results from both Data Means and S-N Ratio give the same optimized

condition. Data Means plot for main effects and interaction plots for Chemical 2 is shown in

Fig.4 and S-N ratio plot for main effects and interactions is shown in Fig.5.

Mean of Means

90858070

2.5

2.0

1.5

1.0

0.5

5525

0.33020.2540

2.5

2.0

1.5

1.0

0.5

105

concentrat ion temp

roughness t ime

Main Effects Plot (data means) for Means

concentration

3

2

1

temp

time

105

5525

3

2

1

90858070

3

2

1

concentration

85

90

70

80

temp

25

55

time

5

10

Interaction Plot (data means) for Means

Figure-2 (a) Figure-2 (b)

Figure 2-Main effects and Interaction Plots for data means

Mean of SN ratios

90858070

10

5

0

-5

-10

5525

0.33020.2540

10

5

0

-5

-10

105

concentration temp

roughness time

Main Effects Plot (data means) for SN ratios

Signal-to-noise: Smaller is better

concentration

10

0

-10

temp

time

105

5525

10

0

-10

90858070

10

0

-10

concentration

85

90

70

80

temp

25

55

time

5

10

Interaction Plot (data means) for SN ratios



Figure 3-Main effects and Interaction Plots for S-N Ratio



537

Table 3 Orthogonal Array L16 with results of trials for chemical 2

C(1) Tp(2) Ri(3) Tm(4) CxTp(5) CxTm(6) TpxTm(7) M11 M12 M13

1 1 1 1 2.048 0.594 8.081

1 1 2 2 0.106 0.215 0.082

1 2 1 2 0.147 0.14 0.123

1 2 2 1 2.277 0.526 6.045

2 1 2 1 1.606 1.972 4.247

2 1 1 2 1.312 0.42 3.283

2 2 2 2 1.367 0.291 3.775

2 2 1 1 1.129 0.217 0.33

3 1 2 1 0.097 0.143 0.178

3 1 1 2 0.308 0.253 0.258

3 2 2 2 0.236 0.387 0.32

3 2 1 1 0.16 0.244 0.216

4 1 1 1 0.612 1.279 1.66

4 1 2 2 0.136 1.832 1.294

4 2 1 2 0.137 0.268 0.241

4 2 2 1 2.048 0.594 8.081

Mean of Means

35302520

2.5

2.0

1.5

1.0

0.5

5525

0.33020.2540

2.5

2.0

1.5

1.0

0.5

63

concentration temp

roughness time

Main Effects Plot (data means) for Means

concentration

4

2

0

temp

time

63

5525

4

2

0

35302520

4

2

0

concentration

30

35

20

25

temp

25

55

time

3

6

Interaction Plot (data means) for Means


Figure 4-Main effects and Interaction Plots for Means (MEK)

Mean of SN ratios

35302520

10

5

0

-5

-10

5525

0.33020.2540

10

5

0

-5

-10

63

concentration temp

roughness time

Main Effects Plot (data means) for SN ratios


concentration

10

0

-10

temp

time

63

5525

10

0

-10

35302520

10

0

-10

concentration

30

35

20

25

temp

25

55

time

3

6

Interaction Plot (data means) for SN ratios

Signal-to-noise: Smaller is better Figure-5 (a) Figure-5 (b)

Figure 5-Main effects and Interaction Plots for S-N Ratio (MEK)



538

3.2 Analysis of variance

The results obtained for surface roughness data from the white light interferometer (WLI)

data are analyzed by using the statistical tool ANOVA. It determines the relative effect of the

individual factors and their interactions on the surface roughness of parts. The analysis by

using ANOVA technique is done analytically. An equation for total variation may be written

as

TmxTpSSSSSSSSSSSSSS

TSS

TpxCTmxCTmRiTpC +++++= +

where SST is total sum of squares, SSC, SSTp, SSRi, SSTm, are sum of squares for Concentration

C, Temperature Tp, Initial roughness Ri, Time Tm. SSTmxc, SSTpxc, , SSTmXTp are sum of

squares of Concentration-Temperature, Concentration -Time and Time-Temperature

interactions respectively and SSE is sum of square of the error. If T is the sum of all (N)

Surface roughness values, the total sum of squares is given by

N

TN

i

FiSS T

2

1

2−

=

= ∑

Sum of squares of Concentration(C) factor is given as

N

T

N

CiSS

N

i

C

Ci

2

1

2

−

= ∑

=

where, N is the number of levels of Concentration factor, Ci and NCi are the sum and number

of observations respectively under ith

level. Similarly, sum of squares of other five factors can

also be calculated. Sum of squares of interaction of C and Tm is given by

TmC

iCXTm

i

CXTmSSSS

N

T

N

CXTmSS

n

i

−−−

∑

=

=

2

1 )(

2)(

where (C xTm)i and N(CXTm)i are the sum and number of observations (surface roughness)

respectively under ith

condition of the combinations of factors C and Tm and n is the number

of possible combinations of the interacting factors C and Tm. Similarly, the sum of squares

for other two interactions can also be found out. The results obtained from ANOVA for

chemical 1 and chemical 2 are given in Table 4 and Table 5 respectively.

(4)

(1)

(2)

(3)



539

Table-4 ANOVA table for Chemical 1

Source DF Seq SS Percent contribution

Concentration 3 955.65 65.90

Temp 1 42.45 2.93

Initial roughness 1 105.92 7.30

Time 1 18.84 1.30

Concentration*Temp 3 161.79 11.16

Concentration*Time 3 68.9 4.75

Temp*Time 1 81.09 5.59

Residual Error 2 15.55 1.07

Total 15 1450.2 100.00

Table-5 ANOVA table for Chemical 2

Source DF Seq SS Percent

contribution

Concentration 3 770.72 37.78

Temp 1 288.32 14.13

Initial roughness 1 12.13 0.59

Time 1 11.69 0.57

Concentration*Temp 3 644.31 31.59

Concentration*Time 3 286.54 14.05

Temp*Time 1 10.95 0.54

Residual Error 2 15.23 0.75

Total 15 2039.9 100.00

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep

IV. RESULTS

All the test sample from group 1 to 16 having average minimum and maximum

roughness 5.56 micron and 6.67 micron

value after the chemical treatment

0.175 micron and maximum average roughness equal to 3.47 micron for Chemical 1 and

average minimum 0.134 micron and maximu

effect of chemical treatment on average roughness value for chemical 1 and chemical 2.

The roughness values are analyzed on the basis on DOE and ANOVA for both the

chemicals. Following are the detailed explanation

4.1 CHEMICAL 1

From the ANOVA table (Table 4) we find that for chemical 1 the most important

factor is concentration contributing 65.9%, concentration

second most important factor contributing 11.16% followed by

exposure is the least significant factor. Fig.2 and Fig.3 also display the similar results.

Figure 6 Effect of chemical treatment on average roughness value of Group1

It is observed from Fig.2(a) and F

level 4 for concentration (90%) , level 2 for temperature (55°C

(corresponding to layer thickness 0.2540

optimum condition without taking interaction into account. Since it is c

from ANOVA that concentration

contributing 11.16% so when considering interactions

from Fig.2(b) and Fig.3(b) that the optimum condition is

Ra

(in

itia

l)

Ra

(ace

ton

e)

Ra

(ME

K) 654321

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME

540


roughness 5.56 micron and 6.67 micron respectively had experienced reduced roughness

the chemical treatment. The minimum average roughness observed is equal to


average minimum 0.134 micron and maximum 3.58 micron for chemical 2. Fig.6 shows



chemicals. Following are the detailed explanation of results:-


factor is concentration contributing 65.9%, concentration-temperature interaction is the

second most important factor contributing 11.16% followed by initial roughness 7.3% time of


of chemical treatment on average roughness value of Group1

(a) and Fig.3(a) that the optimum condition is C4-Tp2-

, level 2 for temperature (55°C) , level 1 for initial roughness

(corresponding to layer thickness 0.2540 mm) and level 1 for exposure time (5 min.

tion without taking interaction into account. Since it is clear from the results

from ANOVA that concentration-temperature interaction is the second most important factor

contributing 11.16% so when considering interactions CxTp and Tp xTm we can conclude

that the optimum condition is C3-Tp2- Ri1-Tm1.

0

1

2

3

4

5

6

7

16151413121110987

Ra(initial)

Ra(acetone)

Ra(MEK)


Dec (2012) © IAEME


experienced reduced roughness

The minimum average roughness observed is equal to


m 3.58 micron for chemical 2. Fig.6 shows




temperature interaction is the

initial roughness 7.3% time of


of chemical treatment on average roughness value of Group1-16

Ri1-Tm1 i.e.

) , level 1 for initial roughness

level 1 for exposure time (5 min.) is the

lear from the results

temperature interaction is the second most important factor

Tm we can conclude

Ra(initial)

Ra(acetone)

Ra(MEK)



541

4.2 CHEMICAL 2

From Table 5 we find that for chemical 2, concentration, concentration- temperature

interaction, temperature and concentration-time interaction are the most significant factor

contributing 37.78%, 31.59%, 14.13% and 14.05% respectively.

From Fig4.(a) and Fig.5(a) we find that the optimum condition for chemical 2 is C3-Tp2-

Ri1-Tm1 i.e. 30% concentration, 55 °C temperature of bath, initial roughness corresponding

to 0.2540 mm layer thickness and 3 minutes exposure time. It is obvious from ANOVA

analysis that concentration - temperature interaction is the most dominant factor after

concentration factor in this chemical treatment process contributing 31.59% followed by

concentration-time interaction. Considering both C xTm and CxTp interactions we find from

Fig.4 (b) and Fig.5 (b) that the optimum condition is C3-Tp2-Ri1-Tm2.

Samples were treated at both the conditions without interaction and with interaction

for both the chemicals 1 & chemical 2. Table 6 shows the tabulated results and roughness

values at optimum levels. It is clear from Table 6 that optimum condition with interaction

gives better results for both chemical 1 and chemical 2. Condition 1 refers to the optimum

condition without taking account for interaction while condition 2 refers to optimum

condition when taking account for interactions.

Table 6 Results for optimum condition and roughness values at optimum levels.

Factors Acetone Methyl Ethyl Ketone

Condition 1(

C4-Tp2- Ri1-

Tm1)

Condition

2(C3-Tp2- Ri1-

Tm1)

Condition 1

(C3-Tp2-Ri1-

Tm1)

Condition 2

(C3-Tp2-Ri1-

Tm2)

C(% of

concentration)

90 85 30 30

Tp(◦C) 55 25 55 55

Ri (Initial

Roughness, µm)

0.254 0.254 0.254 0.254

Tm(Time of

exposure in

Min.)

5 10 3 6

Average

roughness value,

Ra ( µm)

0.367 0.175 0.314 0.143

Further a comparison is made between results obtained with chemical 1 and chemical 2 on

four different samples which were manufactured on Stratasys’s ‘Dimension SST 1200’ FDM

machine. Sample 1 is a cube as shown in Fig.1(a) & Fig.1(b), sample 2 is shown in Fig.1(c),

sample 3 is shown in Fig.1(d) and sample 4 is shown in Fig.1(e).The results with

comparisons for chemical 1 and chemical 2 is shown in Table 7. Fig.7 shows the original

part where as Fig.8 and Fig.9 show the parts treated with Chemical 1 and Chemical 2

respectively at their optimum conditions.



542

Table 7 Comparison between Chemical 1 and Chemical 2

Sampl

es

Chemical 1( Acetone) Chemical 2 ( Methylethylketone)

Roughn

ess at

optimu

m cond.

µm

Aestheti

c

appeara

nce

Curi

ng

Time

hrs

%

change

in

dimensi

ons

Roughn

ess at

optimu

m cond.

µm

Aestheti

c

appeara

nce

Curi

ng

Time

hrs

%

change

in

dimensi

ons

Sampl

e 1

0.175 Very

smooth

1 Less

than

0.5%

0.143 Glossy 2 Less

than 1%

Sampl

e 2

1.13 Very

smooth

1 Less

than

0.5%

0.98 Glossy 2 Less

than 1%

Sampl

e 3

0.847 Very

smooth

1 Less

than

0.5%

0.495 Glossy 2 Less

than 1%

Sampl

e 4

3.3 Very

smooth

1.5 Less

than

0.5%

2.12 Glossy 3 Less

than 1%

Figure 7(original) Figure 8(Chemical1) Figure 9(Chemical2)

The original part as shown in Fig.7 is made of ABS material in white & blue color, but the

chemically treated parts as shown in Fig.8 and Fig.9 are made of ABS material in white

color.

The size of the specimen was measured before and after the chemical treatment process in

order to account for the variation in dimensions due to chemical treatment process. Base

lengths were taken as l1 and l2 , height of the specimen was taken as H. Readings were taken

by ACCURATE SPECTRA co-ordinate measuring machine. The results show less than 1%

deviation. Detailed results are shown in Table 8.



543

Table 8 Average change in dimension after chemical treatment process.

Length L1

(mm)

Length L2

(mm)

Height H (mm)

Chemical 1 Average -0.42 -0.51 -0.43

Variance 0.01 0.01 0.01

Chemical 2 Average -0.64 -0.68 -0.51

Variance 0.01 0.01 0.01

The cost of the chemical treatment process is compared with the commercially available

systems and the same is listed in the Table 9. It is observed from the Table 9 that the

proposed system is economical to use and has very small setup cost as compared to the

commercial system available in the market.

Table 9 Cost comparison of proposed chemical treatment process with other available

commercial system.

Sr.

No

Capital Cost,

INR(Approx.)

Depreciation,

INR

Raw

Material

cost per

part*,

INR

Power

Consumption

cost per

hour, INR

Labour

Cost

per

hour,

INR

1. Acetone

process 10000 2.78 per day 32 6 50

2. MEK

process 10000 2.78 per day 21 6 50

3. Commercial

system 35, 00000 959 per day 42 15 50

* for part size 50x50x25 mm

V.CONCLUSION

In this paper the surface roughness of FDM prototype parts is addressed, the parameters that

have significant effect on the surface roughness (Ra) value in the chemical treatment process have

been identified. The chemical treatment process is optimized in terms of solution concentration, time

of exposure, initial roughness and temperature of the chemical bath using Design of Experiments and

ANOVA. Two different chemical were taken, i.e. Dimethyl ketone (Acetone) and Methyl ethyl ketone

(MEK), in case of Acetone it was observed that the solution concentration, concentration-temperature

interaction and the initial roughness are the most significant factors. For Methyl ethyl ketone chemical

treatment process, it was observed that the concentration, concentration - temperature interaction and

concentration-time interaction are the most important factors, surprisingly for MEK the initial

roughness and time of exposure have negligible effect on the process. The process was applied for

simple parts to complex free form parts. The optimum levels for the parameters for chemical

treatment process are found out which shows drastic improvement in surface finish. The appearance

of the finished parts is comparable to plastic moulded parts, the parts have glossy finish and the

maximum curing time is about 2 to 4 hours. The process is very much economical compared to other

commercial systems available in the market. Further studies can be carried out to commercialize this

process to make it available in the market at an affordable price.



544

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Investigation of post processing techniques to reduce the surface

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