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Investigation of post processing techniques to reduce the surface roughness of fused deposition modeled parts

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  • 1. INTERNATIONAL Mechanical Engineering and Technology (IJMET), ISSN 0976 – International Journal of JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 –AND TECHNOLOGYSep- Dec (2012) © IAEME 6359(Online) Volume 3, Issue 3, (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online) IJMETVolume 3, Issue 3, September - December (2012), pp. 531-544© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2012): 3.8071 (Calculated by GISI) ©IAEMEwww.jifactor.com INVESTIGATION OF POST PROCESSING TECHNIQUES TO REDUCE THE SURFACE ROUGHNESS OF FUSED DEPOSITION MODELED PARTS Addanki Sambasiva Rao1*, Medha A Dharap2, J V L Venkatesh3, Deepesh Ojha4 1 Assistant Professor, Department of Mechanical Engineering, Veermata Jijabai Technological Institute, Mumbai, India. Email: asrao@vjti.org.in 2 Professor, Department of Mechanical Engineering, Veermata Jijabai Technological Institute, Mumbai, India. Email: madharap@vjti.org.in 3 Associate Professor, Production Engineering Department, SGGSIE&T, Nanded, Maharashtra, India. Email: meghavenkatesh@gmail.com 4 PG Student, Department of Mechanical Engineering, Veermata Jijabai Technological Institute, Mumbai, India. Email: deepeshojha@gmail.com 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 531
  • 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEexpensive and there are no safety related issues; It does not use liquid/powder raw materialsand hence is a clean process; It can be kept in an office environment as a 3D printer; veryeasy to remove the support material; this is probably the easiest of all RP processes; this isthe cheapest technology; etc. Parts produced by FDM are, however, less accurate than those produced by otherrapid prototyping processes such as Stereo lithography Apparatus (SLA), Solid GroundCuring (SGC). Besides, FDM process is very slow as every point of the volume is addressedby a mechanical device. The key issue with FDM process is surface roughness because of itsstaircase effect (the angle between the vertical axis and surface tangents) [2]. The poorsurface finish affects the functioning of RP parts, depending on the geometry of the enclosingsurface, the building strategy, layer thickness and orientation of the part; this drawback mayoutweigh the advantages of RP parts [3]. In literature, several researchers have proposed various methods to reduce the surfaceroughness of the FDM parts of ABS material. One of the prominent methods is to control theprocess parameters like layer thickness, build orientation, raster width, raster angle, air gapetc.. In this method process parameters were optimized using statistical techniques like designof experiments and gray relational analysis have been integrated for obtaining the optimumprocess parameter values [3]. The process parameters influence the responses in a highly non-linear manner; therefore, prediction of overall dimensional accuracy is made based onartificial neural network (ANN) [4]. Several algorithms were also developed to obtainoptimum part deposition orientation for fused deposition modeling process for enhancing partsurface finish and reducing build time [5, 6]. Another method is adaptive slicing scheme in which slices of different thicknesses indifferent zones are produced while building the part [7-10]. Daekeon Ahn et al investigatedthe relation between surface roughness and overlap interval [11], they also analyzed anddiscussed 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 layerFDM or CLFDM which offers solution to the issues of surface roughness and strength forthin curved shell-type parts, this process proposes an entirely new building paradigm forFDM, the filaments would be deposited along curved (essentially non horizontal) pathsinstead of planar (horizontal) paths [12]. A mathematical technique has been developed byW. 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 amethod to fabricate the highest quality of multi-material parts. In this method, a virtualsimulation system and experimental real time video microscopy have been developed. In thisvirtual simulation, one can check or test a variety of the layered manufacturing processparameters, and make the best selection of tool path and other parameters to obtain highquality parts [14] One more method for improving surface finish is chemical treatment method which hasbeen proposed by L.M. Galantucci et al [2]. In this chemical treatment method,‘Dimethylketone (Acetone)’ with 90% concentrated solution and 10% water was used andparts were immersed in diluted solution for 5 minutes and also suggested that further studiesneed to be conducted on freeform products, also using other dimethylketone solvents such asethylene and using designed experiments to optimize the process in terms of the solutionconcentration and process time. To the best of the author’s knowledge, no investigations ofchemical treatment method have been reported since the work of L.M. Galantucci et al. andhence the present study has been undertaken by the authors to investigate the optimumconditions for obtaining best surface finish from the chemical treatment process. 532
  • 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEII. 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 surfacefinish of the treated specimens. The chemical treatment method is economical, fast and easyto use. However chemical treatment method has not been analyzed considering differenttypes of chemicals, different concentration levels of chemicals, effect of elevatedtemperatures, different initial roughness of the parts, time of exposure along with theunderlying interaction effects for obtaining optimum surface finish. This paper reports designof experiments for analyzing chemical post processing treatment method to identify maincontrolling factors, side effects of the process parameter settings and disturbances to theprocess for ABS plastics.III.METHODOLOGY In this paper we will be optimizing the chemical treatment process using Design ofExperiments (DOE). The factors affecting the chemical treatment process were identified byperforming numerous trials, based on these trials concentration, temperature, time ofexposure and initial roughness were identified as possible main factors. These are analyzedusing Design of Experiments (DOE). DOE is done for two different chemicals i.e.Dimethylketone (Acetone) and Methylethylketone (MEK), test specimen selected are shownin 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 surfacefinish, 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 datathat can be analyzed by statistical methods will be collected, resulting in valid and objectiveconclusions [15,16]. A statistical tool is always preferred for drawing the meaningfulconclusion from a experimental design data. There are two aspects to any experimentalproblem; the design of the experiment and the statistical analysis of the data. When manyfactors control the performance of any system then it is essential to find out significantfactors 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 suchsituations to plan the set of experiments and ANOVA technique is used to find out thesignificant factors. These techniques have been used in the current study to investigate significant factorsaffecting 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 theparts are treated Tm. The first step in constructing an orthogonal array to fit a specific casestudy is to count the total degrees of freedom that tell the minimum number of experimentsthat must be performed to study all the chosen control factors. The number of degrees offreedom 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 ofchemicals. For Acetone concentration levels of 90%, 85%, 80%, 70% were taken and time ofbath of 5 min and 10 min were found to be suitable on the other hand for Methyl ethyl ketoneconcentration of 20%, 25%, 30%, 35% were taken, also 3 min and 6 min were found to besuitable exposure time. These chemicals have higher diffusion rate at elevated temperaturesso two different temperatures i.e. 25°C and 55°C were chosen. 533
  • 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME 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 and0.3302 layer thickness. Time of exposure was also identified as factor affecting the results of chemicaltreatment 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 thefactors and their interactions there are 13 degrees of freedom. Hence this experiment is carried outusing L16, orthogonal array for 4 factors one at 4 level and 3 at 2 levels to design the experiments forfinding out the surface roughness of given parts under the simultaneous variation of 4 differentparameters at different levels as shown in Table 1.Figure1(a) Test Specimen with 0.2540 mm Figure1(b) Test specimen with 0.3302mm 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 forerror. 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 expectedthat factors C, Tm, Tp to interact, no factors are assigned to columns 5, 6 and 7. This is done to avoidconfounding. The results of surface roughness value of various FDM samples with combinations ofparameters for chemical 1 are shown in Table 2. Similar results for chemical 2 is shown in Table 3 534
  • 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 2 Orthogonal Array L16 with results of trials for chemical 1C(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 0.496 535
  • 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Trials were carried out according to the various combinations of parameters displayedin the Orthogonal Array and the results for surfaces roughness values were recorded. Threereadings were taken on sample1, A11 correspondence to the top surface, A12 correspondenceto the reading along the build direction, A13 correspondence to the reading perpendicular tothe build direction. These were then analyzed to obtain the optimum condition usingMINITAB software. The Data Means plot for main effects and interaction plots for Chemical1 is shown in Fig.2.The S-N ratio plot for main effects and interaction plots for Chemical 1 isshown in Fig 3. The experimental data was solved using both Data Means and S-N Ratio. Thecondition was S-N Ratio taken was Smaller is Better hence we will be accepting the highervalue as preferred value from the graph where as in means graph lower value will be taken aspreferred value. Results from both Data Means and S-N Ratio give the same optimizedcondition. Data Means plot for main effects and interaction plots for Chemical 2 is shown inFig.4 and S-N ratio plot for main effects and interactions is shown in Fig.5. Main Effects Plot (data means) for Means Interaction Plot (data means) for Means concent rat ion temp 25 55 3 concentration 2.5 70 2.0 2 80 concentr ation 85 1.5 90 1 1.0 Mean of Means 0.5 3 temp 25 70 80 85 90 25 55 2 55 roughness t ime temp 1 2.5 2.0 3 time 5 1.5 2 10 1.0 time 1 0.5 0.2540 0.3302 5 10 70 80 85 90 5 10Figure-2 (a) Figure-2 (b) Figure 2-Main effects and Interaction Plots for data means Main Effects Plot (data means) for SN ratios Interaction Plot (data means) for SN ratios 25 55 concent ration temp 10 10 concentration 70 5 80 0 concentr ation 85 0 90 Mean of SN ratios -5 -10 10 temp -10 25 70 80 85 90 25 55 55 0 temp roughness time 10 -10 5 10 time 5 0 10 0 time -5 -10 -10 0.2540 0.3302 5 10 70 80 85 90 5 10Signal-to-noise: Smaller is better Signal-to-noise: Smaller is betterFigure-3 (a) Figure-3 (b) Figure 3-Main effects and Interaction Plots for S-N Ratio 536
  • 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME 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 Main Effects Plot (data means) for Means Interaction Plot (data means) for Means 25 55 concentration temp 2.5 4 concentration 20 2.0 25 2 30 1.5 concentr ation 35 1.0 Mean of Means 0 4 0.5 temp 25 20 25 30 35 25 55 55 2 temp roughness time 2.5 2.0 4 0 time 1.5 3 6 1.0 2 time 0.5 0 0.2540 0.3302 3 6 20 25 30 35 3 6Figure-4 (a) Figure-4 (b) Figure 4-Main effects and Interaction Plots for Means (MEK) Main Effects Plot (data means) for SN ratios Interaction Plot (data means) for SN ratios concent ration temp 25 55 10 concentration 10 20 5 25 0 concentr ation 30 0 35 Mean of SN ratios -10 -5 temp -10 10 25 20 25 30 35 25 55 55 roughness time temp 0 10 -10 5 time 10 3 0 6 0 time -5 -10 -10 0.2540 0.3302 3 6 20 25 30 35 3 6Signal-to-noise: Smaller is better Signal-to-noise: Smaller is betterFigure-5 (a) Figure-5 (b) Figure 5-Main effects and Interaction Plots for S-N Ratio (MEK) 537
  • 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME3.2 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 theindividual factors and their interactions on the surface roughness of parts. The analysis byusing ANOVA technique is done analytically. An equation for total variation may be writtenasSS = SS C + SS + SS + SS + SS + SS + SS (1) T Tp Ri Tm TmxC TpxC TmxTpwhere SST is total sum of squares, SSC, SSTp, SSRi, SSTm, are sum of squares for ConcentrationC, Temperature Tp, Initial roughness Ri, Time Tm. SSTmxc, SSTpxc, , SSTmXTp are sum ofsquares of Concentration-Temperature, Concentration -Time and Time-Temperatureinteractions 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  T 2 (2) SS T =   ∑ Fi 2  −  N i = 1   Sum of squares of Concentration(C) factor is given as  N Ci 2  T 2SS C = ∑  − (3)  i=1 N Ci  Nwhere, N is the number of levels of Concentration factor, Ci and NCi are the sum and numberof observations respectively under ith level. Similarly, sum of squares of other five factors canalso be calculated. Sum of squares of interaction of C and Tm is given by  n  ( CXTm )   T 2 2 = ∑   − iSS − SS − SS (4)  N  C Tm  i =1 CXTm N    ( CXTm )iwhere (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 numberof possible combinations of the interacting factors C and Tm. Similarly, the sum of squaresfor other two interactions can also be found out. The results obtained from ANOVA forchemical 1 and chemical 2 are given in Table 4 and Table 5 respectively. 538
  • 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME 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 2039.9 100.00 539
  • 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME SepIV. RESULTS All the test sample from group 1 to 16 having average minimum and maximumroughness 5.56 micron and 6.67 micron respectively had experienced reduced roughnessvalue after the chemical treatment. The minimum average roughness observed is equal to treatment0.175 micron and maximum average roughness equal to 3.47 micron for Chemical 1 andaverage minimum 0.134 micron and maximum 3.58 micron for chemical 2. Fig.6 shows maximumeffect 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 thechemicals. Following are the detailed explanation of results:-4.1 CHEMICAL 1 From the ANOVA table (Table 4) we find that for chemical 1 the most importantfactor is concentration contributing 65.9%, concentration-temperature interaction is the concentration temperaturesecond most important factor contributing 11.16% followed by initial roughness 7.3% time ofexposure is the least significant factor. Fig.2 and Fig.3 also display the similar results. 7 6 5 4 3 2 1 0 15 16 12 13 14 7 8 9 10 11 Ra(initial) 5 6 Ra(acetone) 4 Ra(MEK) 1 2 3 Ra(initial) Ra(acetone) Ra(MEK) Figure 6 Effect of chemical treatment on average roughness value of Group1 Group1-16It is observed from Fig.2(a) and Fig.3(a) that the optimum condition is C4-Tp2- Ri1-Tm1 i.e. (a) Flevel 4 for concentration (90%) , level 2 for temperature (55°C) , level 1 for initial roughness (55°C)(corresponding to layer thickness 0.2540 mm) and level 1 for exposure time (5 min. is the min.)optimum condition without taking interaction into account. Since it is clear from the results tion clearfrom ANOVA that concentration temperature interaction is the second most important factor concentration-temperaturecontributing 11.16% so when considering interactions CxTp and Tp xTm we can conclude Tmfrom Fig.2(b) and Fig.3(b) that the optimum condition is C3-Tp2- Ri1-Tm1. 540
  • 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME4.2 CHEMICAL 2 From Table 5 we find that for chemical 2, concentration, concentration- temperatureinteraction, temperature and concentration-time interaction are the most significant factorcontributing 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 correspondingto 0.2540 mm layer thickness and 3 minutes exposure time. It is obvious from ANOVAanalysis that concentration - temperature interaction is the most dominant factor afterconcentration factor in this chemical treatment process contributing 31.59% followed byconcentration-time interaction. Considering both C xTm and CxTp interactions we find fromFig.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 interactionfor both the chemicals 1 & chemical 2. Table 6 shows the tabulated results and roughnessvalues at optimum levels. It is clear from Table 6 that optimum condition with interactiongives better results for both chemical 1 and chemical 2. Condition 1 refers to the optimumcondition without taking account for interaction while condition 2 refers to optimumcondition when taking account for interactions. Table 6 Results for optimum condition and roughness values at optimum levels.Factors Acetone Methyl Ethyl Ketone Condition 1( Condition Condition 1 Condition 2 C4-Tp2- Ri1- 2(C3-Tp2- Ri1- (C3-Tp2-Ri1- (C3-Tp2-Ri1- Tm1) Tm1) Tm1) Tm2)C(% of 90 85 30 30concentration)Tp(◦C) 55 25 55 55Ri (Initial 0.254 0.254 0.254 0.254Roughness, µm)Tm (Time of 5 10 3 6exposure inMin.)Average 0.367 0.175 0.314 0.143roughness value,Ra ( µm)Further a comparison is made between results obtained with chemical 1 and chemical 2 onfour different samples which were manufactured on Stratasys’s ‘Dimension SST 1200’ FDMmachine. 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 withcomparisons for chemical 1 and chemical 2 is shown in Table 7. Fig.7 shows the originalpart where as Fig.8 and Fig.9 show the parts treated with Chemical 1 and Chemical 2respectively at their optimum conditions. 541
  • 12. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 7 Comparison between Chemical 1 and Chemical 2 Chemical 1( Acetone) Chemical 2 ( Methylethylketone)Samples Roughn Aestheti Curi % Roughn Aestheti Curi % ess at c ng change ess at c ng change optimu appeara Time in optimu appeara Time in m cond. nce hrs dimensi m cond. nce hrs dimensi µm ons µm onsSampl 0.175 Very 1 Less 0.143 Glossy 2 Lesse1 smooth than than 1% 0.5%Sampl 1.13 Very 1 Less 0.98 Glossy 2 Lesse2 smooth than than 1% 0.5%Sampl 0.847 Very 1 Less 0.495 Glossy 2 Lesse3 smooth than than 1% 0.5%Sampl 3.3 Very 1.5 Less 2.12 Glossy 3 Lesse4 smooth than than 1% 0.5% 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 thechemically treated parts as shown in Fig.8 and Fig.9 are made of ABS material in whitecolor.The size of the specimen was measured before and after the chemical treatment process inorder to account for the variation in dimensions due to chemical treatment process. Baselengths were taken as l1 and l2 , height of the specimen was taken as H. Readings were takenby ACCURATE SPECTRA co-ordinate measuring machine. The results show less than 1%deviation. Detailed results are shown in Table 8. 542
  • 13. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEME Table 8 Average change in dimension after chemical treatment process. Length L1 Length L2 Height H (mm) (mm) (mm)Chemical 1 Average -0.42 -0.51 -0.43 Variance 0.01 0.01 0.01Chemical 2 Average -0.64 -0.68 -0.51 Variance 0.01 0.01 0.01The cost of the chemical treatment process is compared with the commercially availablesystems and the same is listed in the Table 9. It is observed from the Table 9 that theproposed system is economical to use and has very small setup cost as compared to thecommercial system available in the market. Table 9 Cost comparison of proposed chemical treatment process with other available commercial system.Sr. Capital Cost, Depreciation, Raw Power LabourNo INR(Approx.) INR Material Consumption Cost cost per cost per per part*, hour, INR hour, INR INR 1. Acetone 10000 2.78 per day 32 6 50 process 2. MEK 10000 2.78 per day 21 6 50 process 3. Commercial 35, 00000 959 per day 42 15 50 system* for part size 50x50x25 mmV.CONCLUSION In this paper the surface roughness of FDM prototype parts is addressed, the parameters thathave significant effect on the surface roughness (Ra) value in the chemical treatment process havebeen identified. The chemical treatment process is optimized in terms of solution concentration, timeof exposure, initial roughness and temperature of the chemical bath using Design of Experiments andANOVA. 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-temperatureinteraction and the initial roughness are the most significant factors. For Methyl ethyl ketone chemicaltreatment process, it was observed that the concentration, concentration - temperature interaction andconcentration-time interaction are the most important factors, surprisingly for MEK the initialroughness and time of exposure have negligible effect on the process. The process was applied forsimple parts to complex free form parts. The optimum levels for the parameters for chemicaltreatment process are found out which shows drastic improvement in surface finish. The appearanceof the finished parts is comparable to plastic moulded parts, the parts have glossy finish and themaximum curing time is about 2 to 4 hours. The process is very much economical compared to othercommercial systems available in the market. Further studies can be carried out to commercialize thisprocess to make it available in the market at an affordable price. 543
  • 14. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 3, Sep- Dec (2012) © IAEMEREFERENCES [1] Sung-Hoon Ahn, Caroline S. Lee, Woobyok Jeong, (2004), Development of translucent FDM parts by post-processing, Rapid Prototyping Journal, Vol. 10 Iss: 4 pp. 218 – 224. [2] L.M. Galantucci, Lavecchia M, Percoco FG, (2009), Experimental study aiming to enhance surface finish of fused deposition modeled parts, CIRP Annals – Manufacturing Technology 58(2009):189–192. [3] Che Chung Wang, Ta-Wei Lin, Shr-Shiung Hu, (2007), Optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysis, Rapid Prototyping Journal, Vol. 13 Iss: 5 pp. 304 – 315. [4] Anoop Kumar Sood , R.K. Ohdar , S.S. Mahapatra, (2009), Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method, Materials and Design 30 (2009) 4243–4252. [5] K. Thrimurthulu, Pulak M. Pandey, N. Venkata Reddy, (2004), Optimum part deposition orientation in fused deposition modeling, International Journal of Machine Tools & Manufacture 44 (2004) 585–594 [6] Hong-Seok Byun, Kwan H. Lee, (2006), Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making, Robotics and Computer-Integrated Manufacturing, 22 (2006) 69–80. [7] P.M. Pandey, N.V. Reddy, S.G. Dhande, (2003), Real time Adaptive Slicing for Fused Deposition Modeling, International Journal of Machine Tools & Manufacture 43 (2003) 61–71. [8] Sarat Singamneni, Roger Anak Joe, and Bin Huang (2012) Adaptive Slicing for Fused Deposition Modeling and Practical Implementation Schemes, Trans Tech Publications, Switzerland, Advanced Materials Research Vol. 428 (2012) pp 137-140. [9] Justin Tyberg, Jan Helge BøhnU,(1999) FDM systems and local Adaptive Slicing, Materials and Design 20(1999)77-82. [10] K.P. Karunakaran, P. Vivekananda Shanmuganathan, Sanjay Janardhan Jadhav, Prashant Bhadauria, Ashish Pandey, (2000) Rapid prototyping of metallic parts and moulds, Journal of Materials Processing Technology 105 (2000) 371-381. [11] Daekeon Ahn, Jin-Hwe Kweon, Jin-Ho Choi and Seok-Hee Lee,(2011) Relation between surface roughness and overlap interval in Fused Deposition Modeling Trans Tech Publications, Switzerland, Advanced Materials Research Vols. 264-265 (2011) pp 1625-1630. [12] Debapriya Chakraborty, B. Aneesh Reddy, A. Roy Choudhury, (2008), Extruder path generation for Curved Layer Fused Deposition Modeling”, Computer-Aided Design 40 (2008) 235–243. [13] W. Rottanawong, S. H. Masood, P. Lovenitti, (2001), A volumetric approach to part build orientations in rapid prototyping, Journal of Material Processing Technology 119(2001), 348-353. [14] Noshir A. Langrana, Dan Qiu, Evan Bossett, Stephen C. Danforth, (2000) Mohsen Jafari, Ahmad Safari, Virtual simulation and video microscopy for fused deposition Methods, Materials and Design 21(2000)75-82. [15] Montgomery DC (2006) Design and Analysis of Experiments. New Delhi: Wiley India (P) Ltd. [16] Phadke MS (1989) Quality Engineering and Robust Design. New Delhi: Tata McGraw-Hill. 544

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