INTERNATIONALMechanical Engineering and Technology (IJMET), ISSN 0976 – International Journal of JOURNAL OF MECHANICAL ENG...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) V...
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Process parameters optimization in sls process using design of experiments 2

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Process parameters optimization in sls process using design of experiments 2

  1. 1. INTERNATIONALMechanical Engineering and Technology (IJMET), ISSN 0976 – International Journal of JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME AND TECHNOLOGY (IJMET)ISSN 0976 – 6340 (Print)ISSN 0976 – 6359 (Online) IJMETVolume 4, Issue 2, March - April (2013), pp. 162-171© IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2013): 5.7731 (Calculated by GISI) ©IAEMEwww.jifactor.com PROCESS PARAMETERS OPTIMIZATION IN SLS PROCESS USING DESIGN OF EXPERIMENTS Dr. T. Nancharaiah Professor and Head, Department of Mechanical Engineering DMSSVH College of Engineering, Machilipatnam-521002 A.P., INDIA. M. Nagabhushanam Associate Professor, Department of Mechanical Engineering DMSSVH College of Engineering, Machilipatnam-521002 A.P., INDIA. B. Amar Nagendram Associate Professor, Department of Mechanical Engineering DMSSVH College of Engineering, Machilipatnam-521002 A.P., INDIA. ABSTRACT Rapid prototyping (RP) is an additive manufacturing process which builds the parts directly from CAD data sources. RP Techniques are increasingly being used to manufacture complex precision parts for the automotive, aerospace and medical industries. One of the popular RP processes is the Selective Laser Sintering (SLS) process which manufactures parts by sintering metallic, polyoneric and ceramic powder under the effect of laser power. This paper presents the effect of slice thickness and part orientation on total area of sintering (TAS) and the laser energy. Using design of experiments (DOE) L9 orthogonal array was selected and experiments were conducted. The experimental results were statistically analyzed using ANOVA analysis, S/N ratio to find the contribution of each parameter and to optimize the process parameters. The significance of each process parameter is further strengthened by the correlation analysis. Finally confirmation tests were conducted for optimum process parameters in SLS process. 162
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMEKeywords: Rapid Prototyping, additive manufacturing, selective laser sintering, laserenergy, ANOVA.1. INTRODUCTION Rapid prototyping (RP) is the most common name given to host of relationtechnologies that are used to fabricate physical objects directly from three dimensionsCAD model. These methods are unique in that they add and bond materials in layers toform objects. Such systems are also known by the general names free from fabrication(FFF), solid free from fabrication (SFF) and layered manufacturing. The materials usedin rapid prototyping are numerous plastics, ceramics, metals ranging from stainless steelto titanium and wood like paper. There are different types of RP processes and are classified based on their layergeneration methods. Among these, most commonly used processes are Stereolithography (SLA), fused deposition modeling (FDM), Selective laser sintering (SLS),laminated object manufacturing (LOM) and 3 D printings (3DP). Among the different RP processes the SLS process has gained traction in themanufacturing industry due to its capability to produce complex parts of any geometrywithout the need for special tooling and support structures. SLS also able to manufactureparts from materials such as metal and nylon which are difficult to fabricate usingtraditional methods. RP processes offer several advantages but have limitations like low productivity(large build time), low part quality (dimensional accuracy) and low yield. A need thusexist to carry out research and development on RP process to enable it to producefunctional parts of good quality with reduced production time and cost. The present workprimarily focuses on determining optimum slice thickness and part orientation forminimal process energy consumption in selective layer sintering (SLS) process. Selective laser sintering (SLS) is an additive manufacturing technique that uses ahigh power laser (for example, a carbon dioxide laser) to fuse small particles of plastic,metal (direct metal laser sintering), ceramic, or glass powders into a mass that has adesired three-dimensional shape. The laser selectively fuses powdered material byscanning cross-sections generated from a 3-D digital description of the part on the surfaceof a powder bed. After each cross-section is scanned, the powder bed is lowered by onelayer thickness, a new layer of material is applied on top, and the process is repeated untilthe part is completed. Figure (1) shows the basic process of SLS. Compared with other methods of additive manufacturing, SLS can produce partsfrom a relatively wide range of commercially available powder materials. These includepolymers such as nylon (glass-filled, or with other fillers) or polystyrene, metalsincluding steel, titanium, alloy mixtures, and composites and green sand. The physicalprocess can be full melting, partial melting, or liquid-phase sintering. Depending on thematerial, up to 100% density can be achieved with material properties comparable tothose from conventional manufacturing methods. In many cases large number of parts canbe packed within the powder bed, allowing very high productivity. 163
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Fig: (1) Selective Laser Sintering Process2. LITERATURE REVIEW RP in general and SLS in particular have recently gained popularity as a main streamof manufacturing process for producing functional parts in bulk quantities. However, therehas been very little reported research on understanding the relation between the part shape,process parameters and energy consumption in SLS process. Traditionally, researches haveconcentrated on the physical, chemical and mechanical changes involved in the creation ofslices in different RP process. Phatak and pande (1) designed a modular system andimplemented to find the optimum orientation of the CAD part quality model in RP processusing generic algorithm technique for improvements in manufacture. Canellidis et al (2)proposed methodology using genetic algorithm, to get optimum part orientation using a multicriteria objective function comprising of the estimated build time, post processing time andthe average surface roughness of the part. Nelson et al (3) analyzed the SLS process and developed a one-dimensional thermalmodel to predict the laser energy required for the complete sintering of bisphenol – Apolycarbonate powder. They studied the effect of laser scan speed, laser power, and powdersize and powder bed temperature among various other parameters on the development of thesintered layers. Eho et al(4) used a genetic algorithm (GA) method to optimize the SLAprocess by analyzing the dimensional errors in SLA parts and correlating them to the laserpower used for creating the slices. LW et al (5) investigated the environmental effects ofthree RP process: SLA, SLS and FDM and calculated the life cycle energy utilization inthese processes. No systematic work has however, been directed towards findings as optimalprocess parameters for minimum laser energy consumption in SLS process. The workreported in this paper is an attempt in this direction.3. METHODOLOGY This section presents a methodology to analyze the energy utilization in the SLSprocess by modeling the virtual manufactures of a part and correlating the energy to the slicethickness and part orientation. The overall methodology for calculations the laser energy formanufactures a part in SLS is shoes in fig (2). The part is first modeled in a CAD system andthe CAD model is exported to the STL file format. The part orientation and slice thicknessare selected and the STL file is sliced. For each slice, the sintering area is calculated and the 164
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEMEtotal area of sintering (TAS) is calculated by adding up the sintering areas for all the slices.The total laser energy is than calculated from the TAS and correlated to the orientation andslice thickness. STL file as the part Fix build orientation and slice thickness For each slice, calculate sintering area using connect hull approach Sum sintering areas for all slices to calculate total area of sintering (TAS) Calculate laser energy from TAS Repeat for different values of build orientation and slice thickness Fig (2): Over all Methodology to calculate laser energy4. DESIGN OF EXPERIMENTS Design of experiments and analysis of results are engaging the attention of theResearch Scholars and also practicing engineers. Many statistical tools are being used in therecent past. Present day competition in the industry is pushing for more and more emphasison quality. Improved quality and enhancement in the market share can be achieved throughpreventive action rather than inspection and process control techniques. Design ofexperiments is one such quality improvement process which builds quality into products andprocess as that eliminates expensive controls and inspection. It is a valuable tool to optimizeproduct and process design, to accelerate development cycle and to reduce development cost.This will also improve easy transition of products from R & D stage to manufacture.4.1 Selection of Process Parameters in SLS Process When preparing to build SLS parts, many fabrication parameters are needed in thesoftware. To achieve optimum quality, these parameters are set differently according torequirements of applications. Therefore, the first step in the experiment was to identify theprocess control parameters that are likely to affect the laser energy in SLS process. The twoprocess parameters are selected at three different levels as shown in table (1). 165
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Table (1) Selection of Process parameters and their levels Process parameters Level 1 Level 2 Level 3 Slice thickness in mm (A) 0.03 0.05 0.10 Part orientation in degrees (B) 0 30 454.2 Orthogonal Arrays (OA) An experiment in which all possible combination of factor levels are used is called‘full factorial experiments’. If an experiment consists of ‘n’ number of factors and each factorat ‘X’ levels. Number of trials possible (treatment combinations) = Xn. As the number offactors considered at multi levels increases, it becomes increasingly difficult to conduct theexperiment with all treatment combinations. In this situation, orthogonal arrays are at ourrescue (which are highly fractionalized factorial layouts), becomes useful in reducing thenumber of trials.4.3 Selection of Orthogonal Array The first step in selecting the correct standard OA involves counting the total degreesof freedom (dof) in the study. This count fixes the minimum number of experiments that mustbe run to study the factors involved. In counting the total dof, the investigator commits 1 dofto the overall mean of the response under study. This begins the dof count as 1.The number of dof associated with each factor under study equals one less than the number oftreatment levels available for that factor. One determines the total dof in the study as follows:if nA and nB represent the number of treatments available for two factors A and Brespectively, Then1 = dof to be used by the overall meannA – 1 = dof for AnB – 1 = dof for BAn example will illustrate this procedure. If a design study involves three 3-level factors (Aand B), then the dof would be as follows:Source of dof required dofOverall mean 1A,B 2(3 – 1) = 4Hence,Total dof = 1 + 4 = 5Therefore, in this example, one must conduct at least 5 experiments to be able to estimate thedesired main effects. The corresponding OA must therefore have at least 5 rows. ThereforeL9 orthogonal array is selected for experimentation and shown in table (2). 166
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Table (2) L9 orthogonal array Expt. Columns No. 1 2 3 4 1 1 1 1 1 2 1 2 2 2 3 1 3 3 3 4 2 1 2 3 5 2 2 3 1 6 2 3 1 2 7 3 1 3 2 8 3 2 1 3 9 3 3 2 15 EXPERIMENTAL RESULTS A trial run was performed in which a series of samples were built on the SLSmachine. Totally 9 samples were produced by SLS according to the L9 array. The dimensionsof the sample specimen shown in figure (3) Fig (3): CAD model of the Part5.1 Results The study involved 9 sample components produced by SLS machine. Experimentalresults for laser energy were shown in the table (3) and in figure (4). From graphs it wasfound that the slice thickness increases as laser energy required sintering the part decreasesand part orientation effects moderately. 167
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Table (3) Experimental results for Laser Energy Expt. TAS in Laser Energy in No. mm2 Kilo Joules 1 8958.8 21.67 2 8977.4 21.72 3 8964.9 21.69 4 5386.0 13.03 5 5399.7 13.06 6 5389.5 13.04 7 2707.6 6.54 8 2707.6 6.55 9 2709.1 6.55 Slice thickness Vs Laser energy Part orientation Vs Laser energy 25 13.775 Las er energy Laser energy 13.77 20 13.765 15 13.76 10 13.755 5 13.75 0 13.745 0 1 2 3 4 0 1 2 3 4 Slice thickness in mm Part orientation Fig (4): Variation of Laser Energy with respect to Slice thickness and Part orientation5.2 AnalysisA. Signal to noise (S/N) ratio The signal to noise ratio measures the sensitivity of the quality characteristic beinginvestigated to those uncontrollable external factors. To minimize the problem, the governingrelationships for the S/N ratio in terms of the experimentally measured values of laser energy, i.e., S/N ratio = -10 log 10 MSDWhere MSD = ∑(yi - ) / y)2 /n, y the target value that is to be achieved, the number ofsamples. The S/N ratio values obtained for the trials are listed in Table (4). From the resultsoptimum laser energy value obtained at level 3 of slice thickness and level 1 of partorientation. 168
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME Table (4) S/N ratio for optimization of Laser energy Factor Level 1 Level 2 Level 3 Max. – Min. Slice thickness -26.728 -22.30 -16.317 10.41 Part build orientation -21.77 -21.79 -21.78 0.02B. ANOVA analysis ANOVA analysis provides significance rating of the various factors analyzed in thisstudy. Based on the above rating, factors, which influence the objective functionsignificantly, could be identified and proper control measures adopted. In a similar way, thosefactors with minimum influence could be suitably modified to suit economic considerations.The ANOVA computations are carried out based on procedure out lined in ref (10) and listedtable in (5). A variable possessing the maximum value of variance is said to have the mostsignificant effect on the process under consideration. When the contribution of any factor issmall, then the sum of squares, (SS) for that factor is combined with the error (SSe). Thisprocess of disregarding the contribution of a selected factor and subsequently adjusting thecontributions of the other factors is known as pooling. Table (5) ANOVA analysis Factor Sum of Degree of Variance Percentage F – test f-table squares freedom of value (SS) (dof) contributionSlice thickness 352.028 2 176.014 97.79 260.76 99.3Part orientation 5.539 2 2.7695 1.538 4.103 18.33Error 2.699 4 0.675 0.749 -- --Total 359.966 8 -- -- -- --C. Correlation analysis In process control, the aim is to control the characteristics of the output of the processby controlling a process parameter. One succeeds if the parameters are chosen correctly. Thechoice is usually based on judgment and knowledge of the concerned technology. Acorrelation is assumed between a variable product characteristic and a variable processparameter. 169
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME In the present study, a relationship is assumed between the slice thickness (processparameter) and laser energy, part orientation and laser energy. Slice thickness is the propertywhich significantly affects the quality of the prototypes in SLS process. This is proved by thecontribution at 99% level of significance. The correlation coefficient (r) obtained from theresults is - 0.9477 for slice thickness. The range of values for (r) lies between 1 and -1. Theexperimental value indicates a reasonably strong negative (indirect) relation. Therefore, asslice thickness increases, the laser energy decreases. The correlation coefficient (r) obtainedis 0.242 for part orientation. The experimental value indicates moderate positive (direct)relation. Therefore, as part orientation increases, the laser energy value increases moderately.6 CONFORMATION TESTS Once the optimal level of design parameters has been selected, the confirmation testswere conducted. The experimental results confirm the prior design and analyses foroptimizing he process parameters. Table (6) Results of the confirmation experiments for laser energy Optimal process parameters Prediction Experimental Level A3B1 A3 B 1 Laser energy in KJ 6.54 6.4997 CONCLUSIONS This paper presents the effect of slice thickness and part orientation on laser energy ofa part manufactured in the SLS process. Nine sample parts were virtually manufactured andtheir laser energies were calculated for different sets of slice thickness and part orientationsand the results are presented. From the results, it can be concluded that the slice thickness ininversely proportional to the total laser energy building the part, and the effect of partorientation on laser ene4rgy is dependant upon the geometry of the part. In this study thedesign of experiments and S/N ratio provides a systematic and efficient methodology for thedesign optimization of the process parameters. From the ANOVA analysis it was found thatthe effect of slice thickness on laser energy is 97.79% and part orientation is 1.538%. Thissignificance is further strengthened by correlation analysis. The confirmation experimentswere conducted to verify the optimal process parameters.7.1 Scope of future work In this paper, only the SLS laser energy has been analyzed while other energycomponents such as platform energy, energy for heating the bed etc., have been neglected.This work can be extended considering other energies. This work can also be extending toother process parameter incorporating in addition to these two process parameter foroptimization. 170
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 2, March - April (2013) © IAEME8 REFERENCES1. Amar M.Phatak & S.S.Pande, “Optimum part orientation M Rapid phototyping using genetic algorithm”, Journal of manufacturing systems 31 (2012) 395 – 402.2. Canellidis V. Giannalisis J, Dedoussis V., “Genetic – algorithm – based Multi Objective optimization of he build orientation in stereolithograthy”, international journal of Advanced Manufacturing Technology 2009, 45: 714-30.3. Nelson J C, Xue S.Barlow JW, Beaman J J, Marcus H L, Boureil DL. “Model of he selective laser sintering of bisphenol – a polycarbonate”, industrial and Engineering Chemistry Research 1993; 32 (10) 2305 – 17.4. Cho H S, park WS, Choi B W, Leu Mc, “Determining optimal parameters for stereolithography processes Via genetic algorithm”, Journal of Manufacturing systems 2000; 19(1) 18-27.5. Luo YC, Ji ZM, Leu MC, Candill R., “ Environmental performance analysis of solid free form fabrication processes. In proceedings of the 1999 EEE international symposium on electronics and the Environment, ISEE – 1999, P.1-6.6. Diane A. Schaub, Kou-Rey Chu, Douglas C. Montgomery, “Optimizing Stereolithography Throughput”, Journal of Manufacturing Systems, Vol. 16/No.4, (1997).7. Diane A. Schaub, Douglas C. Montgomery, “Using Experimental Design to Optimize The Stereolithography Process”, Quality Engineering, 9(4), pp575-585, (1997).8. A. Armillotta, G.F. Biggioggero, M. Carnevale, M. Monno, “Optimization of Rapid Prototypes with Surface Finish Constraints: A Study on The FDM Technique”, Proceedings 3rd International Conference on Management of Innovative Technologies, Piran, Slovenija, 28-29, (October 1999).9. D.Karalekas and D.Rapti, “Investigation of the processing dependence of SL solidification residual stresses”, Rapid prototyping journal, vol. 8, number 4, pp. 243 – 24, (2002).10. Douglas C. Montgomery, “Design and Analysis of Experiments”, 3rd edition John Wiley & Sons, (1991).11. Tapan .P. Bagchi, “Tagichi Methods Explained Practical Steps to Robust design” Prentices – Hall of India Pvt Ltd , New Delhi, (1993).12. U. D. Gulhane, A. B. Dixit, P. V. Bane and G. S. Salvi, “Optimization of Process Parameters for 316L Stainless Steel Using Taguchi Method And Anova” International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 67 - 72, ISSN Print: 0976 – 6340, , ISSN Online: 0976 – 635913. Raju B S, Chandra Sekhar U and Drakshayani D N, “Web Based E- Manufacturing of Prototypes By Using Rapid Prototyping Technology”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 2, 2013, pp. 32 - 38, ISSN Print: 0976 – 6340, , ISSN Online: 0976 – 6359. 171

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