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Process parameter optimization of viscoelastic properties of FDM manufactured parts using response surface methodology
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 4 (2017) 8250–8259
www.materialstoday.com/proceedings
ICAAMM-2016
Process parameter optimization of viscoelastic properties of FDM manufactured parts using response surface methodology Omar Ahmed Mohameda, *,Syed Hasan Masooda, Jahar Lal Bhowmikb 0F
a
Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia. b Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.
Abstract The main aim of this study is to optimize the FDM process parameters for better dynamic mechanical properties and part structure. The FDM process parameters considered in this study includes layer thickness, air gap, raster angle, build orientation, road width and number of contours. The measured response includes complex modulus and dynamic viscosity. The IV-optimal design has been used as the experimental strategy whereas the process optimization has been done through desirability function. The structure of the manufactured parts was studied and characterized using Scanning electron microscope (SEM). The results show that layer thickness, air gap and number of contours are the important parameters that influence the complex modulus and dynamic viscosity. The optimal condition was found to be layer thickness of 0.3302 mm, air gap of 0.0 mm, raster angle of 0˚, build orientation of 90˚, road width of 0.4572 mm and 10 contours. © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility ofthe Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016). Keywords:FDM, IV-Optimal design, ANOVA, process parameters, viscoelastic properties, optimization
1. Introduction Additive manufacturing (AM) also referred to as layered manufacturing (LM) or solid freeform fabrication (SFF),is a modern fabrication technology that produces 3D objects by adding layer-upon-layer[1].The term AM comprises many technologies including stereo-lithographyapparatus (SLA), laminated object manufacturing (LOM), fused *Omar Ahmed Mohamed. Tel. +61 470761892 E-mail address:[email protected]
2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility ofthe Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016).
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deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), and electronic beam melting (EBM)[2]. In contrast with other additive manufacturing processes, fused deposition modeling (FDM) has become an increasingly important process among the available additive manufacturing technologies in various applications due to its capability of creating complex parts at the lowest cost without requiring additional tooling. FDM is the additive manufacturing process that builds parts with production-grade thermoplastics[3].FDM builds 3D objects in layer by layer fashion from three dimensional computer aided design (CAD) data by heating and extruding thermoplastic filament through a nozzle onto a platform and translates the parts cross sectional geometry into X, Y and Z coordinates to create 3D object[4]. The layer of plastic cools down and hardens. Once the layer is completed, the base is lowered to start printing of the next layer. Despite obvious advantages of FDM process, a limiting factor of its industrial adoption is the lack of superior mechanical properties and dense structure of the manufactured part. This is because the properties of the fabricated part by FDM are strongly dependent upon the selection of FDM process parameters for that part. Many researchers have investigated the influence of process variables on the static mechanical properties of the manufactured part and proposed various solutions. For example, Rayegani and Onwubolu[5]investigated the influence of process parameters on the tensile properties of FDM manufactured parts using design of experiment. The study concluded that negative air gap, small road width, raster angle of 50˚ and zero build orientation are the best parameter settings to improve the tensile strength. Wang et al. [6]also studied the impact of some FDM process conditions on tensile strength. It was found that the tensile strength is highlyinfluenced by part orientation. Sood et al. [7]analyzed the effect of various process parameters on compressive strength. Results have shown that thick layers and zero air gap enhanced the part strength. The most recent study was conducted by Lanzotti et al.[8] to establish the relationship between process parameters and the static mechanical properties of PLA processed part by FDM. The study concluded that the mechanical properties decreased with the increase in infill orientation, layer thickness and perimeters. On the basis of past literature, it can be concluded that some attempts have been made on improving the mechanical properties for FDM manufactured parts. However, the previous studies only focused on the static mechanical properties, and none of them investigated the effect of process parameters on the dynamic mechanical properties under wide range of temperatures for long-term prediction. Unlike other previous approaches, this paper investigates the effect of process parameters on viscoelastic properties namely complex modulus and dynamic viscosity of FDM manufactured Polycarbonate-Acrylonitrile Butadiene Styrene (PC-ABS) parts. In this study, a response surface methodology based on IV-optimal design was used to establish a rigorous relationship between process parameters and viscoelastic properties. The study also highlights the development of comprehensive regression models to establish functional relationship between process parameters and viscoelastic properties. Scanning electron microscope (SEM) was used to examine and characterize the morphology of the structures for the specimens. Optimal parameter settings for achieving highest mechanical properties were obtained. The outcome of this study is useful for industries in the selection of process parameters and it also provides practical suggestions to set the optimal process conditions to get better microstructure, properties and performance of the manufactured products under dynamic and cyclic loading conditions. 2. Experimental details 2.1. Material And Specimen Fabrication A total of 60 specimens having dimension of 35 (length) mm × 12.5 mm (width) × 3.5 mm (thickness) were manufactured according to ASTM D5418 [9]and TA instrument manufacturer recommendations[10]. The specimens are made by PC-ABS material developed by Stratasys, Inc. The PC-ABS is a blend of polycarbonate and ABS plastic and it has amorphous structures. The specimens were modeled in Pro Engineer software and exported as an STL (StereoLithography) file. The STL was then imported to the FDM Insight software to create the tool path and to set all process parameters for all specimens as per experimental design..
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2.2. Dynamic Mechanical Measurement Dynamic Mechanical Analysis (DMA) was performed using a DMA 2980, TA Instrument. The measurements of dynamic properties namely complex modulus and dynamic viscosity were performed at a frequency of 1 Hz by single cantilever clamp with a heating rate of 3◦C/min, and the temperature range of35-170◦C with an isothermal soak time of 5 min and oscillation amplitude of 15 µm. The maximum complex modulus and dynamic viscosity were measured for each experimental run and then recorded as the response based on design matrix plan. The average value for complex modulus and dynamic viscosity weremeasured from a set of values of tested samples. The complex modulus represents the elastic response of the specimen material under an applied stress and strain. It uses as an indicator of viscoelasticity. The dynamic viscosity represents the rate of deformation energy dissipation of the specimen material through the flow. 2.3. Development Of IV-Optimal Design Matrix The IV-optimal design is one form of design provided by a computer algorithm. This type of computer-aided designis particularly useful when the process is subject to many constraints and different number of levels between factors[11]. The IV-optimal design is a novel design recommended to build response surface designs when the main goal is to determine the optimum process settings[12]. Unlike standard classical designs such as central composite design, factorials and fractional factorials designs used extensively in all previous studies, the IV-optimal design matrices require minimum number of experimental runs and can fit a high polynomial regression models.This optimality criterion results in minimizing average variance of prediction over the experimental region making it more appropriate for response surface designs[13].The IV-optimal design algorithm suggested 38 experimental runs for the six process parameters at different levels. The generated design matrix was then augmented with additional model points (22 runs) to the 38 runs (total 60 experimental runs) in order to minimize the standard error, to estimate lack of fit, to improve the prediction performance and to provide the appropriate degrees of freedom (DOF) required in developing adequate regression models. The FDM process parameters at which the experiments were conducted are given in Table 1. The levels of these process parameters are chosen based on the literature review, their significance, experience and the permissible low and high levels recommended by the equipment manufacturer.In this study, the layer thickness has only four levels due to constraintsby the nozzle diameter (tip size) as there are only four types of Tip sizes for PC-ABS material. Other factors have six levels because they are flexible. More than four levels for each factor are recommendedbecause it helps to generate the true behaviour of responses. Table 2shows the experimental design matrix developed for this study with the measured responses. Table 1.Factors and their levels Factor Layer thickness Air gap Raster angle Build orientation Raster width Number of contours
Symbol A B C D E F
Unit mm mm Deg Deg mm -
Level 1 0.1270 0 0 0 0.4572 1
Level 2 0.1778 0.1 15 30 0.4814 3
Level 3 0.2540 0.2 30 45 0.5056 5
Level 4 0.3302 0.3 45 60 0.5298 7
Level 5 0.4 60 75 0.5540 8
Level 6 0.5 90 90 0.5782 10
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Table 2. IV-optimal design matrix and responses Coded factors S. No
A
B C
D E
F
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
3 3 3 4 1 3 1 4 4 1 4 4 4 2 1 1 1 4 1 1 4 3 1 4 2 3 2 2 3 1
6 4 4 5 3 3 1 6 1 1 6 3 4 6 1 6 6 1 3 6 1 6 1 1 1 6 6 6 4 4
6 3 3 2 6 6 3 6 6 1 1 6 1 1 3 5 1 4 3 5 1 6 6 3 1 5 1 6 3 1
1 3 3 1 1 3 3 1 5 4 1 6 4 1 6 3 6 1 6 5 1 6 2 6 1 1 6 1 3 4
6 4 4 6 4 4 1 1 6 4 6 1 6 1 6 6 6 6 4 3 3 6 6 3 6 6 5 1 4 1
1 4 4 2 1 1 1 2 1 3 6 1 1 1 1 3 6 2 6 5 1 1 6 4 4 5 1 3 4 4
Responses Complex Dynamic modulus viscosity (MPa) (MPa. sec) 448.904 2.512 935.276 5.590 957.901 5.771 538.746 2.688 728.446 4.442 969.294 5.924 1293.451 8.188 429.051 2.513 1398.740 9.227 1343.448 8.547 504.312 2.917 1221.670 7.590 980.632 5.714 468.565 2.721 1326.190 7.824 769.360 4.672 1263.012 7.402 1301.156 9.286 1175.663 7.091 1014.562 6.018 1311.528 9.311 1190.354 6.987 1096.516 6.471 1420.340 8.689 1251.390 8.344 561.606 3.270 1171.898 6.976 1249.700 8.315 972.177 5.770 1057.240 6.339
Coded factors S. No
A
B C
D
E F
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
4 4 4 4 3 4 4 3 1 3 3 1 2 1 1 3 1 3 2 2 3 3 4 4 3 1 4 1 1 3
6 6 6 3 6 3 1 4 6 4 1 1 6 3 1 1 4 4 1 4 2 4 3 1 4 4 6 5 6 4
3 6 1 6 6 1 4 3 6 3 6 1 2 3 1 1 1 2 6 6 2 3 6 1 3 3 1 4 6 3
1 6 6 6 6 6 6 4 6 4 3 3 6 6 6 1 1 3 6 4 6 4 6 6 1 3 3 3 1 4
4 6 1 5 1 1 1 4 1 4 4 4 4 4 1 1 6 6 1 6 6 4 5 6 1 1 4 2 2 4
6 6 6 1 4 1 3 3 1 3 4 4 1 6 1 6 1 6 6 6 3 3 1 6 3 6 3 2 6 3
Responses Complex Dynamic modulus viscosity (MPa) (MPa. sec) 1191.061 6.703 1201.060 7.096 1186.248 6.620 738.803 4.630 1044.789 6.191 858.490 5.638 1305.912 8.399 961.586 5.517 516.938 3.368 922.169 5.463 1374.503 8.504 1268.935 7.893 533.068 3.123 1144.909 6.561 1096.921 6.989 1460.294 8.554 716.868 3.975 1235.360 7.074 1289.578 7.073 1158.690 6.732 1052.130 6.612 941.852 5.503 737.073 4.619 1367.836 8.032 887.336 5.243 1180.655 6.927 829.426 4.619 677.935 3.849 1171.615 6.877 911.669 5.469
3. Results and discussion Analysis of the experimental data obtained from IV-optimal design matrix is done usingMinitab 17 software using second order polynomial response surface model as given by Eq. (1).The final response surface models for complex modulus (CM) and dynamic viscosity (DV) are given from Eqs. (2) - (3), respectively, in terms of actual factor. =
+
+
+
+ (1)
where denotes the predicted response, and are the coded variables, is total number of variables, β is the constant term of the regression equation, is the linear regression coefficient, is the regression coefficient of the is the coefficient of the interactive terms, and is the random error. quadratic term of each variable, CM (MPa)= −1324.94 + 2090.24 ∗ A − 3713.6 ∗ B − 0.687233 ∗ D + 9764.38 ∗ E + 15.5796 ∗ F − 1169.94 ∗ AB + 2825.05 ∗ BE + 125.765 ∗ BF + 0.0489839 ∗ DF − 3529.84 ∗ A + 1874.97 ∗ B − 10236 ∗ E . [2] DV (MPa. sec)= −10.8756 + 11.2836 ∗ A − 29.329 ∗ B − 0.0106798 ∗ D + 77.0584 ∗ E − 0.0156105 ∗ F − 15.0577 ∗ AB + 0.0252161 ∗ AD + 26.9048 ∗ BE + 0.952604 ∗ BF + 0.000510783 ∗ DF − 15.1467 ∗ A + 14.2857 ∗ B − 82.508 ∗ E . [3]
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The analysis of variance (ANOVA) of response surface quadratic model for complex modulus and dynamic viscosity were shown in Table 3and Table 4respectively. ANOVAwas employed totest the significance of the developed regression models.The significance of all process parameters was tested by a Fisher's –test (F-test) and respective Probability (P-value). If the P-value is smaller than 0.05 (P<0.05), then the factor, interaction effect or quadratic term, has a significant effect on the response. Insignificant model terms with the highest probability values of more than 0.1 were removed using the backward elimination technique, as it improves the quality of fit by reducing noise. Lack of fit must be insignificant (P>0.05). Insignificant lack of fit is recommended because it indicates that the developed model fits the experimental data well.ANOVA results presented in Table 3and Table 4 show that the developed regression models have P-values< 0.0001, which implies that the selected process parameters are considered to be statistically significant for the responses along with insignificant lack of fit. ANOVA results also show other criteria to check and validate the adequacy of the developed models such as R2, adjusted R2, and predicted R2. The multiple regression coefficients (R2) for complex modulus and dynamic viscosity models were found to be 99.01% and 98.07% respectively. The R2 values arevery high, close to one, and indicatethat the developed regression models for complex modulus and dynamic viscosity are adequate to represent the fabrication parameters, which proves the ability of the developed models to be used for practical engineering applications. Furthermore, the adequate precision ratios for the developed regression models are greater than 4, which indicate an adequate signal for the models. The normal probability plots (see Fig.1 (a) and (b)) show that the residuals follow a normal distribution, which implies that the models are well fitted with the experimental data. Table 3.ANOVA results for reduced quadratic model for complex modulus Source Sum of squares DOF Mean square F-value 6 5 Model 4.689×10 12 3.907×10 391.08 A-Layer thickness 13221.97 1 13.221.97 13.23 B-Air gap 1.787×106 1 1.787×106 1788.20 D-Build orientation 12586.60 1 12586.60 12.60 E-Road width 2208.81 1 2208.81 2.21 F-Number of contours 1.820×106 1 1.820×106 1821.41 AB 22625.54 1 22625.54 22.65 BE 45.671.57 1 45.671.57 45.71 BF 5.064×105 1 5.064×105 506.87 DF 2619.33 1 2619.33 2.62 A2 13.197.80 1 13.197.80 13.21 B2 1.609×105 1 1.609×105 161.04 2 E 15026.80 1 15026.80 15.04 Residual 46959.39 47 999.14 Lack of Fit 40813.97 38 1074.05 1.57 Pure Error 6145.42 9 682.82 Cor. Total 4.736×106 59 R2 = 99.01%, Adjusted R2 = 98.76%, Predicted R2 = 98.41%, Adequate precision = 70.009
Fig.1. Normal probability plot of residuals for: (a) complex modulus, and (b) dynamic viscosity
P-value < 0.0001 0.0007 < 0.0001 0.0009 0.1437 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.1121 0.0007 < 0.0001 0.0003 0.2407 -
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Table 4.ANOVA results for reduced quadratic model for dynamic viscosity. Source Sum of squares DOF Mean square Model 200.29 13 15.41 A-Layer thickness 1.17 1 1.17 B-Air gap 93.32 1 93.32 D-Build orientation 0.32 1 0.32 E-Road width 0.36 1 0.36 F-Number of contours 45.27 1 45.27 AB 3.73 1 3.73 AD 0.34 1 0.34 BE 4.14 1 4.14 BF 29.03 1 29.03 DF 0.28 1 0.28 A2 0.24 1 0.24 B2 9.33 1 9.33 E2 0.98 1 0.98 Residual 3.95 46 0.086 Lack of Fit 3.49 37 0.094 Pure Error 0.46 9 0.051 2 2 2 R = 98.07%, Adjusted R = 97.52%, Predicted R = 96.64%, Adequate precision = 49.092
F-value 179.39 13.59 1086.57 3.72 4.25 527.13 43.39 3.96 48.23 338.05 3.32 2.83 108.58 11.37 1.83 -
P-value < 0.0001 0.0006 < 0.0001 0.0600 0.0450 < 0.0001 < 0.0001 0.0525 < 0.0001 < 0.0001 0.0751 0.0995 < 0.0001 0.0015 0.1690 -
Main effect plots for mean for each factor level (Fig 2 (a) and (b)) were generated using Minitab 17 software. These graphs show how the complex modulus and dynamic viscosity change as each factor moves from low level to higher level by varying one factor while other factors are held constant at their center levels. Fig. 2 (a) and (b) showthat with the increase in layer thickness, there is an increase in complex modulus and dynamic viscosity up to a certain level (0.2794 mm), and then with the further increase in layer thickness, the complex modulus and dynamic viscosity starts marginally decreasing. This is due to the fact that a thicker layer produces less number of layers and thermal cycles required to build the part, which is therefore, subjectedto minimum distortion and thus the complex modulus and dynamic viscosity are increased. However, if the part is processed with lower value of layer thickness, then the thin layers are easy to be distorted causing pinholes and air voids in the part structure as shown in Fig 3 (a). Thus the manufactured parts with thin layers are expected to be weaker and have a brittle structure.
Fig.2. Effect of different FDM process parameters on (a) complex modulus, and (b) dynamic viscosity.
Air gap is a highly significant factor affecting the complex modulus and dynamic viscosity. As the air gap decreases, the printedraster’s are closer to each other, resulting in stronger structures and interlayer bonds.Fig. 2 (a) and (b)
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show that the raster angle has a marginal influence on the responses, but lower raster angle (0˚) helps in obtaining better structure and properties. Lower value of raster angle leads to less number of raster’s, and thus results in minimum distortion and minimum accumulated residual stresses, which are the mainly responsible for weak interlayer bonding. The SEM image (Fig.4 (b)) shows that there are areas of incomplete filling and air voids in case of using raster angle of 90˚. Thus the manufactured part with the higher value of raster angle is expected to be weaker than the part manufactured with lower value of raster angle. The influence of raster angle on the functionality of the manufactured part is further illustrated in Fig.4 (a) and (b). It can be seen from Fig.3 (a)that if the part is processed with raster angle of 0˚ and is subjected to a tension load, the load is required to separate and fracture several number of layers, raster’s, fibers and polymer chain molecules. Hence, the part exhibits higher properties and resistance to deformation. However, if the part is processed with raster angle of 90˚ (see Fig.3 (b)), the part reaches the ultimate failure quickly and breaks. This is due to the fact that the separation and fracture occur at the interface of raster’s, to which it is bonded.
Fig.3.Failure on the partmanufactured with (a) raster angle of 0˚, and (b) raster angle of 90˚.
ANOVA results show that the build orientation has a significant effect on complex modulus (p = 0.0009), while it has marginal effect (p = 0.06) on dynamic viscosity, but it helps in improving the part performance. In Fig. 2 (a) and (b), it can be observed that the complex modulus and dynamic viscosity decreased with an increasein build orientation from low level (0˚) to higher level (90˚). A possible explanation is that the build orientation at 0˚ reduces the stair-stepping imperfection effect, which is responsible for creating porosity and voids in the part structure. Road width has an insignificant effect on complex modules, while it has a significant effect on the dynamic viscosity. This can be noticed by their corresponding P-values. It can be seen from Fig. 2 (a) and (b) that with the increase in the road width from 0.4572 mm to the center level of 0.5177 mm, the complex modulus and dynamic viscosity are improved. With further increase in the road width beyond 0.5177 mm to the higher level (0.5782 mm), the complex modulus and dynamic viscosity are decreased. This is due to the fact that it is easy to deform thin raster’swhile thick raster’swidth tends to be distorted because the excess heat input.Number of contours (F) is the most influential factor on the complex modulus and dynamic viscosity. The default machine setting for this factor is a single contour. However, it can be seen from Fig. 2 (a) and (b) that the complex modulus and dynamic viscosity increase significantly with increasing in number of contours. This may be due to the fact that the higher number of contours reduces the number of raster’s and raster’s length, and therefore reducesthe air voids and porosity between the adjacent raster’s. Thus complex modulus and dynamic viscosity increased. Furthermore, as the number of contours increases, more heating time is needed, which effectively melts the layers and raster’s, and hence, complex modulusand dynamic viscosity are improved. TheSEM image of specimen (Fig. 4 (c)) fabricated with ten contours clearly shows that the best microstructure and performance for the specimen are obtained.
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Fig.4. SEM images showing large voids for part fabricated with (a) 0.127 mm of layer thickness, (b) 90˚ of raster angle, and (c) with 10 contours.
Regression models developed in this study show an interesting phenomenon through 3D responses surface plots for the interaction effects between air gap and number of contours on the complex modulus and dynamic viscosity. As discussed earlier the main effect plots that with the increase in air gap, there were significant reductions in complex modulus and dynamic viscosity. However, from 3D response surface plots (see Fig. 5), it is seen that if the part is processed with the highest air gap with ten contours then the part is still in a solid structure and it has high mechanical properties in terms of complex modulus and dynamic viscosity. This optimum combination of two process parameters is useful as it helps the designers to manufacture parts having high mechanical properties with minimum built time. Thus the manufacturing cost of the part is reduced without compromising the functionality of the part.
Fig.5. 3D response surface plots showing the interaction effects of air gap and number of contours on (a) complex modulus, and (b) dynamic viscosity.
4. Optimization Once the regression modelshave been developed and checked for adequacy, the optimization study is required to find the optimal process settings for the complex modulus and dynamic viscosity simultaneously. The STATISTICA (version 10) software is then employed to construct the responses desirability profile in order to identify the best
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combination of input parameter settings that jointly optimize a set of responses (complex modulus and dynamic viscosity).This studyaims to maximize the complex modulus and dynamic viscosity. Based on the profile, the desirability plots show the desirability value caused by different factor settings.The highest overall desirability value is obtained, determined by the geometric mean of the individual response desirability and is found to be 0.99558. Therefore, the predicted complex modulus was 1469.0 MPa and dynamic viscosity was 9.2512 MPa.sec. The optimum process parameters obtained in actual units are layer thickness = 0.3302 mm, air gap = 0.0 mm, raster angle = 0˚, build orientation = 90˚, road width = 0.4572 mm and number of contours = 10,which give the highest composite desirability (1.00) as shown in Fig.6.Moreover, to ensure that the current study is reliable, conclusionsarevalid and the optimalsolution is feasible, confirmation experiment was conducted, which was compared to the predicted value. The results (see Table 5) revealed that the percentage error between the confirmation experiment and the predicted values is -0.19% for the complex modulus and 0.297% for the dynamic viscosity. It can be concluded that the optimum process settings agrees well with the predicted values.
Fig.6. Desirability function plot showing the optimum process parameter settings
Table 5. The comparison between experimental and predicted values for optimum parameter settings.
Response Complex modulus (MPa) Dynamic viscosity (MPa.sec)
A 0.3302
Optimum parameter settings B C D E 0.00 0.00 90 0.4572
F 10
Predicted values
Actual values
Error (%)
1469.0 9.2512
1466.17 9.2787
-0.19 0.297
5. Conclusion In this study, an experimental design was used to determine the influence of FDM process parameters (layer thickness, air gap, raster angle, build orientation, road width and number of contours) on the complex modulus and dynamic viscosity. The present study proposed a methodology based on IV-optimal design to determine the optimal process settings to achieve high dynamic mechanical properties of manufacturedparts. Quadratic modelsfor the
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complex modulus and dynamic viscosity as a function of the significant variables were developed. The developed models were found to be reliable and accurate since the values of R2 for the complex modulus (99.01%) and dynamic modulus (98.07%) are high and closed to 1.The optimum process parameters for maximizingthe complex modulus and dynamic viscosity were determined to be as follows: layer thickness = 0.3302 mm, air gap = 0.0 mm, raster angle = 0˚, build orientation = 90˚, road width = 0.4572 mm and number of contours = 10. The experimental results obtained through confirmation experiment using optimized process parameters agreed well with the predicted values. References [1] A. Gebhardt, Understanding additive manufacturing: rapid prototyping-rapid tooling-rapid manufacturing, Carl Hanser Verlag GmbH Co KG2012. [2] C.C. Kai, L.K. Fai, L. Chu-Sing, Rapid prototyping: principles and applications in manufacturing, World Scientific Publishing Co., Inc.2003. [3] S.H. Masood, Introduction to advances in additive manufacturing and tooling, in: S. Hashmi (Ed.) Comprehensive Materials Processing, Science Direct Elsevier2014, pp. 1-2. [4] O.A. Mohamed, S.H. Masood, J.L. Bhowmik, Optimization of fused deposition modeling process parameters: a review of current research and future prospects, Advances in Manufacturing, (2015) 1-12. [5] G.C. Onwubolu, F. Rayegani, Characterization and Optimization of Mechanical Properties of ABS Parts Manufactured by the Fused Deposition Modelling Process, International Journal of Manufacturing Engineering, 2014 (2014). [6] C.C. Wang, T.-W. Lin, S.-S. Hu, Optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysis, Rapid Prototyping Journal, 13 (2007) 304-315. [7] A.K. Sood, R.K. Ohdar, S.S. Mahapatra, Experimental investigation and empirical modelling of FDM process for compressive strength improvement, Journal of Advanced Research, 3 (2012) 81-90. [8] A. Lanzotti, M. Grasso, G. Staiano, M. Martorelli, E. Pei, R.I. Campbell, The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer, Rapid Prototyping Journal, 21 (2015). [9] ASTMD5418-07, Standard Test Method for Plastics: Dynamic Mechanical Properties: In Flexure (Dual Cantilever Beam), ASTM International, West Conshohocken, 2007. [10] DMA2980, Dynamic Mechanical Analysis. Operator’s Manual, TA Instrument, New Castle, 2002, pp. 4-8. [11] D.C. Montgomery, Design and analysis of experiments, John Wiley & Sons2008. [12] R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons2009. [13] G. Vining, Statistical Process Monitoring and Optimization, CRC Press1999.
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ICAAMM-2016
Process parameter optimization of viscoelastic properties of FDM manufactured parts using response surface methodology Omar Ahmed Mohameda, *,Syed Hasan Masooda, Jahar Lal Bhowmikb 0F
a
Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia. b Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia.
Abstract The main aim of this study is to optimize the FDM process parameters for better dynamic mechanical properties and part structure. The FDM process parameters considered in this study includes layer thickness, air gap, raster angle, build orientation, road width and number of contours. The measured response includes complex modulus and dynamic viscosity. The IV-optimal design has been used as the experimental strategy whereas the process optimization has been done through desirability function. The structure of the manufactured parts was studied and characterized using Scanning electron microscope (SEM). The results show that layer thickness, air gap and number of contours are the important parameters that influence the complex modulus and dynamic viscosity. The optimal condition was found to be layer thickness of 0.3302 mm, air gap of 0.0 mm, raster angle of 0˚, build orientation of 90˚, road width of 0.4572 mm and 10 contours. © 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility ofthe Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016). Keywords:FDM, IV-Optimal design, ANOVA, process parameters, viscoelastic properties, optimization
1. Introduction Additive manufacturing (AM) also referred to as layered manufacturing (LM) or solid freeform fabrication (SFF),is a modern fabrication technology that produces 3D objects by adding layer-upon-layer[1].The term AM comprises many technologies including stereo-lithographyapparatus (SLA), laminated object manufacturing (LOM), fused *Omar Ahmed Mohamed. Tel. +61 470761892 E-mail address:[email protected]
2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility ofthe Committee Members of International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM-2016).
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deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), and electronic beam melting (EBM)[2]. In contrast with other additive manufacturing processes, fused deposition modeling (FDM) has become an increasingly important process among the available additive manufacturing technologies in various applications due to its capability of creating complex parts at the lowest cost without requiring additional tooling. FDM is the additive manufacturing process that builds parts with production-grade thermoplastics[3].FDM builds 3D objects in layer by layer fashion from three dimensional computer aided design (CAD) data by heating and extruding thermoplastic filament through a nozzle onto a platform and translates the parts cross sectional geometry into X, Y and Z coordinates to create 3D object[4]. The layer of plastic cools down and hardens. Once the layer is completed, the base is lowered to start printing of the next layer. Despite obvious advantages of FDM process, a limiting factor of its industrial adoption is the lack of superior mechanical properties and dense structure of the manufactured part. This is because the properties of the fabricated part by FDM are strongly dependent upon the selection of FDM process parameters for that part. Many researchers have investigated the influence of process variables on the static mechanical properties of the manufactured part and proposed various solutions. For example, Rayegani and Onwubolu[5]investigated the influence of process parameters on the tensile properties of FDM manufactured parts using design of experiment. The study concluded that negative air gap, small road width, raster angle of 50˚ and zero build orientation are the best parameter settings to improve the tensile strength. Wang et al. [6]also studied the impact of some FDM process conditions on tensile strength. It was found that the tensile strength is highlyinfluenced by part orientation. Sood et al. [7]analyzed the effect of various process parameters on compressive strength. Results have shown that thick layers and zero air gap enhanced the part strength. The most recent study was conducted by Lanzotti et al.[8] to establish the relationship between process parameters and the static mechanical properties of PLA processed part by FDM. The study concluded that the mechanical properties decreased with the increase in infill orientation, layer thickness and perimeters. On the basis of past literature, it can be concluded that some attempts have been made on improving the mechanical properties for FDM manufactured parts. However, the previous studies only focused on the static mechanical properties, and none of them investigated the effect of process parameters on the dynamic mechanical properties under wide range of temperatures for long-term prediction. Unlike other previous approaches, this paper investigates the effect of process parameters on viscoelastic properties namely complex modulus and dynamic viscosity of FDM manufactured Polycarbonate-Acrylonitrile Butadiene Styrene (PC-ABS) parts. In this study, a response surface methodology based on IV-optimal design was used to establish a rigorous relationship between process parameters and viscoelastic properties. The study also highlights the development of comprehensive regression models to establish functional relationship between process parameters and viscoelastic properties. Scanning electron microscope (SEM) was used to examine and characterize the morphology of the structures for the specimens. Optimal parameter settings for achieving highest mechanical properties were obtained. The outcome of this study is useful for industries in the selection of process parameters and it also provides practical suggestions to set the optimal process conditions to get better microstructure, properties and performance of the manufactured products under dynamic and cyclic loading conditions. 2. Experimental details 2.1. Material And Specimen Fabrication A total of 60 specimens having dimension of 35 (length) mm × 12.5 mm (width) × 3.5 mm (thickness) were manufactured according to ASTM D5418 [9]and TA instrument manufacturer recommendations[10]. The specimens are made by PC-ABS material developed by Stratasys, Inc. The PC-ABS is a blend of polycarbonate and ABS plastic and it has amorphous structures. The specimens were modeled in Pro Engineer software and exported as an STL (StereoLithography) file. The STL was then imported to the FDM Insight software to create the tool path and to set all process parameters for all specimens as per experimental design..
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2.2. Dynamic Mechanical Measurement Dynamic Mechanical Analysis (DMA) was performed using a DMA 2980, TA Instrument. The measurements of dynamic properties namely complex modulus and dynamic viscosity were performed at a frequency of 1 Hz by single cantilever clamp with a heating rate of 3◦C/min, and the temperature range of35-170◦C with an isothermal soak time of 5 min and oscillation amplitude of 15 µm. The maximum complex modulus and dynamic viscosity were measured for each experimental run and then recorded as the response based on design matrix plan. The average value for complex modulus and dynamic viscosity weremeasured from a set of values of tested samples. The complex modulus represents the elastic response of the specimen material under an applied stress and strain. It uses as an indicator of viscoelasticity. The dynamic viscosity represents the rate of deformation energy dissipation of the specimen material through the flow. 2.3. Development Of IV-Optimal Design Matrix The IV-optimal design is one form of design provided by a computer algorithm. This type of computer-aided designis particularly useful when the process is subject to many constraints and different number of levels between factors[11]. The IV-optimal design is a novel design recommended to build response surface designs when the main goal is to determine the optimum process settings[12]. Unlike standard classical designs such as central composite design, factorials and fractional factorials designs used extensively in all previous studies, the IV-optimal design matrices require minimum number of experimental runs and can fit a high polynomial regression models.This optimality criterion results in minimizing average variance of prediction over the experimental region making it more appropriate for response surface designs[13].The IV-optimal design algorithm suggested 38 experimental runs for the six process parameters at different levels. The generated design matrix was then augmented with additional model points (22 runs) to the 38 runs (total 60 experimental runs) in order to minimize the standard error, to estimate lack of fit, to improve the prediction performance and to provide the appropriate degrees of freedom (DOF) required in developing adequate regression models. The FDM process parameters at which the experiments were conducted are given in Table 1. The levels of these process parameters are chosen based on the literature review, their significance, experience and the permissible low and high levels recommended by the equipment manufacturer.In this study, the layer thickness has only four levels due to constraintsby the nozzle diameter (tip size) as there are only four types of Tip sizes for PC-ABS material. Other factors have six levels because they are flexible. More than four levels for each factor are recommendedbecause it helps to generate the true behaviour of responses. Table 2shows the experimental design matrix developed for this study with the measured responses. Table 1.Factors and their levels Factor Layer thickness Air gap Raster angle Build orientation Raster width Number of contours
Symbol A B C D E F
Unit mm mm Deg Deg mm -
Level 1 0.1270 0 0 0 0.4572 1
Level 2 0.1778 0.1 15 30 0.4814 3
Level 3 0.2540 0.2 30 45 0.5056 5
Level 4 0.3302 0.3 45 60 0.5298 7
Level 5 0.4 60 75 0.5540 8
Level 6 0.5 90 90 0.5782 10
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Table 2. IV-optimal design matrix and responses Coded factors S. No
A
B C
D E
F
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
3 3 3 4 1 3 1 4 4 1 4 4 4 2 1 1 1 4 1 1 4 3 1 4 2 3 2 2 3 1
6 4 4 5 3 3 1 6 1 1 6 3 4 6 1 6 6 1 3 6 1 6 1 1 1 6 6 6 4 4
6 3 3 2 6 6 3 6 6 1 1 6 1 1 3 5 1 4 3 5 1 6 6 3 1 5 1 6 3 1
1 3 3 1 1 3 3 1 5 4 1 6 4 1 6 3 6 1 6 5 1 6 2 6 1 1 6 1 3 4
6 4 4 6 4 4 1 1 6 4 6 1 6 1 6 6 6 6 4 3 3 6 6 3 6 6 5 1 4 1
1 4 4 2 1 1 1 2 1 3 6 1 1 1 1 3 6 2 6 5 1 1 6 4 4 5 1 3 4 4
Responses Complex Dynamic modulus viscosity (MPa) (MPa. sec) 448.904 2.512 935.276 5.590 957.901 5.771 538.746 2.688 728.446 4.442 969.294 5.924 1293.451 8.188 429.051 2.513 1398.740 9.227 1343.448 8.547 504.312 2.917 1221.670 7.590 980.632 5.714 468.565 2.721 1326.190 7.824 769.360 4.672 1263.012 7.402 1301.156 9.286 1175.663 7.091 1014.562 6.018 1311.528 9.311 1190.354 6.987 1096.516 6.471 1420.340 8.689 1251.390 8.344 561.606 3.270 1171.898 6.976 1249.700 8.315 972.177 5.770 1057.240 6.339
Coded factors S. No
A
B C
D
E F
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
4 4 4 4 3 4 4 3 1 3 3 1 2 1 1 3 1 3 2 2 3 3 4 4 3 1 4 1 1 3
6 6 6 3 6 3 1 4 6 4 1 1 6 3 1 1 4 4 1 4 2 4 3 1 4 4 6 5 6 4
3 6 1 6 6 1 4 3 6 3 6 1 2 3 1 1 1 2 6 6 2 3 6 1 3 3 1 4 6 3
1 6 6 6 6 6 6 4 6 4 3 3 6 6 6 1 1 3 6 4 6 4 6 6 1 3 3 3 1 4
4 6 1 5 1 1 1 4 1 4 4 4 4 4 1 1 6 6 1 6 6 4 5 6 1 1 4 2 2 4
6 6 6 1 4 1 3 3 1 3 4 4 1 6 1 6 1 6 6 6 3 3 1 6 3 6 3 2 6 3
Responses Complex Dynamic modulus viscosity (MPa) (MPa. sec) 1191.061 6.703 1201.060 7.096 1186.248 6.620 738.803 4.630 1044.789 6.191 858.490 5.638 1305.912 8.399 961.586 5.517 516.938 3.368 922.169 5.463 1374.503 8.504 1268.935 7.893 533.068 3.123 1144.909 6.561 1096.921 6.989 1460.294 8.554 716.868 3.975 1235.360 7.074 1289.578 7.073 1158.690 6.732 1052.130 6.612 941.852 5.503 737.073 4.619 1367.836 8.032 887.336 5.243 1180.655 6.927 829.426 4.619 677.935 3.849 1171.615 6.877 911.669 5.469
3. Results and discussion Analysis of the experimental data obtained from IV-optimal design matrix is done usingMinitab 17 software using second order polynomial response surface model as given by Eq. (1).The final response surface models for complex modulus (CM) and dynamic viscosity (DV) are given from Eqs. (2) - (3), respectively, in terms of actual factor. =
+
+
+
+ (1)
where denotes the predicted response, and are the coded variables, is total number of variables, β is the constant term of the regression equation, is the linear regression coefficient, is the regression coefficient of the is the coefficient of the interactive terms, and is the random error. quadratic term of each variable, CM (MPa)= −1324.94 + 2090.24 ∗ A − 3713.6 ∗ B − 0.687233 ∗ D + 9764.38 ∗ E + 15.5796 ∗ F − 1169.94 ∗ AB + 2825.05 ∗ BE + 125.765 ∗ BF + 0.0489839 ∗ DF − 3529.84 ∗ A + 1874.97 ∗ B − 10236 ∗ E . [2] DV (MPa. sec)= −10.8756 + 11.2836 ∗ A − 29.329 ∗ B − 0.0106798 ∗ D + 77.0584 ∗ E − 0.0156105 ∗ F − 15.0577 ∗ AB + 0.0252161 ∗ AD + 26.9048 ∗ BE + 0.952604 ∗ BF + 0.000510783 ∗ DF − 15.1467 ∗ A + 14.2857 ∗ B − 82.508 ∗ E . [3]
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The analysis of variance (ANOVA) of response surface quadratic model for complex modulus and dynamic viscosity were shown in Table 3and Table 4respectively. ANOVAwas employed totest the significance of the developed regression models.The significance of all process parameters was tested by a Fisher's –test (F-test) and respective Probability (P-value). If the P-value is smaller than 0.05 (P<0.05), then the factor, interaction effect or quadratic term, has a significant effect on the response. Insignificant model terms with the highest probability values of more than 0.1 were removed using the backward elimination technique, as it improves the quality of fit by reducing noise. Lack of fit must be insignificant (P>0.05). Insignificant lack of fit is recommended because it indicates that the developed model fits the experimental data well.ANOVA results presented in Table 3and Table 4 show that the developed regression models have P-values< 0.0001, which implies that the selected process parameters are considered to be statistically significant for the responses along with insignificant lack of fit. ANOVA results also show other criteria to check and validate the adequacy of the developed models such as R2, adjusted R2, and predicted R2. The multiple regression coefficients (R2) for complex modulus and dynamic viscosity models were found to be 99.01% and 98.07% respectively. The R2 values arevery high, close to one, and indicatethat the developed regression models for complex modulus and dynamic viscosity are adequate to represent the fabrication parameters, which proves the ability of the developed models to be used for practical engineering applications. Furthermore, the adequate precision ratios for the developed regression models are greater than 4, which indicate an adequate signal for the models. The normal probability plots (see Fig.1 (a) and (b)) show that the residuals follow a normal distribution, which implies that the models are well fitted with the experimental data. Table 3.ANOVA results for reduced quadratic model for complex modulus Source Sum of squares DOF Mean square F-value 6 5 Model 4.689×10 12 3.907×10 391.08 A-Layer thickness 13221.97 1 13.221.97 13.23 B-Air gap 1.787×106 1 1.787×106 1788.20 D-Build orientation 12586.60 1 12586.60 12.60 E-Road width 2208.81 1 2208.81 2.21 F-Number of contours 1.820×106 1 1.820×106 1821.41 AB 22625.54 1 22625.54 22.65 BE 45.671.57 1 45.671.57 45.71 BF 5.064×105 1 5.064×105 506.87 DF 2619.33 1 2619.33 2.62 A2 13.197.80 1 13.197.80 13.21 B2 1.609×105 1 1.609×105 161.04 2 E 15026.80 1 15026.80 15.04 Residual 46959.39 47 999.14 Lack of Fit 40813.97 38 1074.05 1.57 Pure Error 6145.42 9 682.82 Cor. Total 4.736×106 59 R2 = 99.01%, Adjusted R2 = 98.76%, Predicted R2 = 98.41%, Adequate precision = 70.009
Fig.1. Normal probability plot of residuals for: (a) complex modulus, and (b) dynamic viscosity
P-value < 0.0001 0.0007 < 0.0001 0.0009 0.1437 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.1121 0.0007 < 0.0001 0.0003 0.2407 -
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Table 4.ANOVA results for reduced quadratic model for dynamic viscosity. Source Sum of squares DOF Mean square Model 200.29 13 15.41 A-Layer thickness 1.17 1 1.17 B-Air gap 93.32 1 93.32 D-Build orientation 0.32 1 0.32 E-Road width 0.36 1 0.36 F-Number of contours 45.27 1 45.27 AB 3.73 1 3.73 AD 0.34 1 0.34 BE 4.14 1 4.14 BF 29.03 1 29.03 DF 0.28 1 0.28 A2 0.24 1 0.24 B2 9.33 1 9.33 E2 0.98 1 0.98 Residual 3.95 46 0.086 Lack of Fit 3.49 37 0.094 Pure Error 0.46 9 0.051 2 2 2 R = 98.07%, Adjusted R = 97.52%, Predicted R = 96.64%, Adequate precision = 49.092
F-value 179.39 13.59 1086.57 3.72 4.25 527.13 43.39 3.96 48.23 338.05 3.32 2.83 108.58 11.37 1.83 -
P-value < 0.0001 0.0006 < 0.0001 0.0600 0.0450 < 0.0001 < 0.0001 0.0525 < 0.0001 < 0.0001 0.0751 0.0995 < 0.0001 0.0015 0.1690 -
Main effect plots for mean for each factor level (Fig 2 (a) and (b)) were generated using Minitab 17 software. These graphs show how the complex modulus and dynamic viscosity change as each factor moves from low level to higher level by varying one factor while other factors are held constant at their center levels. Fig. 2 (a) and (b) showthat with the increase in layer thickness, there is an increase in complex modulus and dynamic viscosity up to a certain level (0.2794 mm), and then with the further increase in layer thickness, the complex modulus and dynamic viscosity starts marginally decreasing. This is due to the fact that a thicker layer produces less number of layers and thermal cycles required to build the part, which is therefore, subjectedto minimum distortion and thus the complex modulus and dynamic viscosity are increased. However, if the part is processed with lower value of layer thickness, then the thin layers are easy to be distorted causing pinholes and air voids in the part structure as shown in Fig 3 (a). Thus the manufactured parts with thin layers are expected to be weaker and have a brittle structure.
Fig.2. Effect of different FDM process parameters on (a) complex modulus, and (b) dynamic viscosity.
Air gap is a highly significant factor affecting the complex modulus and dynamic viscosity. As the air gap decreases, the printedraster’s are closer to each other, resulting in stronger structures and interlayer bonds.Fig. 2 (a) and (b)
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show that the raster angle has a marginal influence on the responses, but lower raster angle (0˚) helps in obtaining better structure and properties. Lower value of raster angle leads to less number of raster’s, and thus results in minimum distortion and minimum accumulated residual stresses, which are the mainly responsible for weak interlayer bonding. The SEM image (Fig.4 (b)) shows that there are areas of incomplete filling and air voids in case of using raster angle of 90˚. Thus the manufactured part with the higher value of raster angle is expected to be weaker than the part manufactured with lower value of raster angle. The influence of raster angle on the functionality of the manufactured part is further illustrated in Fig.4 (a) and (b). It can be seen from Fig.3 (a)that if the part is processed with raster angle of 0˚ and is subjected to a tension load, the load is required to separate and fracture several number of layers, raster’s, fibers and polymer chain molecules. Hence, the part exhibits higher properties and resistance to deformation. However, if the part is processed with raster angle of 90˚ (see Fig.3 (b)), the part reaches the ultimate failure quickly and breaks. This is due to the fact that the separation and fracture occur at the interface of raster’s, to which it is bonded.
Fig.3.Failure on the partmanufactured with (a) raster angle of 0˚, and (b) raster angle of 90˚.
ANOVA results show that the build orientation has a significant effect on complex modulus (p = 0.0009), while it has marginal effect (p = 0.06) on dynamic viscosity, but it helps in improving the part performance. In Fig. 2 (a) and (b), it can be observed that the complex modulus and dynamic viscosity decreased with an increasein build orientation from low level (0˚) to higher level (90˚). A possible explanation is that the build orientation at 0˚ reduces the stair-stepping imperfection effect, which is responsible for creating porosity and voids in the part structure. Road width has an insignificant effect on complex modules, while it has a significant effect on the dynamic viscosity. This can be noticed by their corresponding P-values. It can be seen from Fig. 2 (a) and (b) that with the increase in the road width from 0.4572 mm to the center level of 0.5177 mm, the complex modulus and dynamic viscosity are improved. With further increase in the road width beyond 0.5177 mm to the higher level (0.5782 mm), the complex modulus and dynamic viscosity are decreased. This is due to the fact that it is easy to deform thin raster’swhile thick raster’swidth tends to be distorted because the excess heat input.Number of contours (F) is the most influential factor on the complex modulus and dynamic viscosity. The default machine setting for this factor is a single contour. However, it can be seen from Fig. 2 (a) and (b) that the complex modulus and dynamic viscosity increase significantly with increasing in number of contours. This may be due to the fact that the higher number of contours reduces the number of raster’s and raster’s length, and therefore reducesthe air voids and porosity between the adjacent raster’s. Thus complex modulus and dynamic viscosity increased. Furthermore, as the number of contours increases, more heating time is needed, which effectively melts the layers and raster’s, and hence, complex modulusand dynamic viscosity are improved. TheSEM image of specimen (Fig. 4 (c)) fabricated with ten contours clearly shows that the best microstructure and performance for the specimen are obtained.
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Fig.4. SEM images showing large voids for part fabricated with (a) 0.127 mm of layer thickness, (b) 90˚ of raster angle, and (c) with 10 contours.
Regression models developed in this study show an interesting phenomenon through 3D responses surface plots for the interaction effects between air gap and number of contours on the complex modulus and dynamic viscosity. As discussed earlier the main effect plots that with the increase in air gap, there were significant reductions in complex modulus and dynamic viscosity. However, from 3D response surface plots (see Fig. 5), it is seen that if the part is processed with the highest air gap with ten contours then the part is still in a solid structure and it has high mechanical properties in terms of complex modulus and dynamic viscosity. This optimum combination of two process parameters is useful as it helps the designers to manufacture parts having high mechanical properties with minimum built time. Thus the manufacturing cost of the part is reduced without compromising the functionality of the part.
Fig.5. 3D response surface plots showing the interaction effects of air gap and number of contours on (a) complex modulus, and (b) dynamic viscosity.
4. Optimization Once the regression modelshave been developed and checked for adequacy, the optimization study is required to find the optimal process settings for the complex modulus and dynamic viscosity simultaneously. The STATISTICA (version 10) software is then employed to construct the responses desirability profile in order to identify the best
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combination of input parameter settings that jointly optimize a set of responses (complex modulus and dynamic viscosity).This studyaims to maximize the complex modulus and dynamic viscosity. Based on the profile, the desirability plots show the desirability value caused by different factor settings.The highest overall desirability value is obtained, determined by the geometric mean of the individual response desirability and is found to be 0.99558. Therefore, the predicted complex modulus was 1469.0 MPa and dynamic viscosity was 9.2512 MPa.sec. The optimum process parameters obtained in actual units are layer thickness = 0.3302 mm, air gap = 0.0 mm, raster angle = 0˚, build orientation = 90˚, road width = 0.4572 mm and number of contours = 10,which give the highest composite desirability (1.00) as shown in Fig.6.Moreover, to ensure that the current study is reliable, conclusionsarevalid and the optimalsolution is feasible, confirmation experiment was conducted, which was compared to the predicted value. The results (see Table 5) revealed that the percentage error between the confirmation experiment and the predicted values is -0.19% for the complex modulus and 0.297% for the dynamic viscosity. It can be concluded that the optimum process settings agrees well with the predicted values.
Fig.6. Desirability function plot showing the optimum process parameter settings
Table 5. The comparison between experimental and predicted values for optimum parameter settings.
Response Complex modulus (MPa) Dynamic viscosity (MPa.sec)
A 0.3302
Optimum parameter settings B C D E 0.00 0.00 90 0.4572
F 10
Predicted values
Actual values
Error (%)
1469.0 9.2512
1466.17 9.2787
-0.19 0.297
5. Conclusion In this study, an experimental design was used to determine the influence of FDM process parameters (layer thickness, air gap, raster angle, build orientation, road width and number of contours) on the complex modulus and dynamic viscosity. The present study proposed a methodology based on IV-optimal design to determine the optimal process settings to achieve high dynamic mechanical properties of manufacturedparts. Quadratic modelsfor the
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complex modulus and dynamic viscosity as a function of the significant variables were developed. The developed models were found to be reliable and accurate since the values of R2 for the complex modulus (99.01%) and dynamic modulus (98.07%) are high and closed to 1.The optimum process parameters for maximizingthe complex modulus and dynamic viscosity were determined to be as follows: layer thickness = 0.3302 mm, air gap = 0.0 mm, raster angle = 0˚, build orientation = 90˚, road width = 0.4572 mm and number of contours = 10. The experimental results obtained through confirmation experiment using optimized process parameters agreed well with the predicted values. References [1] A. Gebhardt, Understanding additive manufacturing: rapid prototyping-rapid tooling-rapid manufacturing, Carl Hanser Verlag GmbH Co KG2012. [2] C.C. Kai, L.K. Fai, L. Chu-Sing, Rapid prototyping: principles and applications in manufacturing, World Scientific Publishing Co., Inc.2003. [3] S.H. Masood, Introduction to advances in additive manufacturing and tooling, in: S. Hashmi (Ed.) Comprehensive Materials Processing, Science Direct Elsevier2014, pp. 1-2. [4] O.A. Mohamed, S.H. Masood, J.L. Bhowmik, Optimization of fused deposition modeling process parameters: a review of current research and future prospects, Advances in Manufacturing, (2015) 1-12. [5] G.C. Onwubolu, F. Rayegani, Characterization and Optimization of Mechanical Properties of ABS Parts Manufactured by the Fused Deposition Modelling Process, International Journal of Manufacturing Engineering, 2014 (2014). [6] C.C. Wang, T.-W. Lin, S.-S. Hu, Optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysis, Rapid Prototyping Journal, 13 (2007) 304-315. [7] A.K. Sood, R.K. Ohdar, S.S. Mahapatra, Experimental investigation and empirical modelling of FDM process for compressive strength improvement, Journal of Advanced Research, 3 (2012) 81-90. [8] A. Lanzotti, M. Grasso, G. Staiano, M. Martorelli, E. Pei, R.I. Campbell, The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer, Rapid Prototyping Journal, 21 (2015). [9] ASTMD5418-07, Standard Test Method for Plastics: Dynamic Mechanical Properties: In Flexure (Dual Cantilever Beam), ASTM International, West Conshohocken, 2007. [10] DMA2980, Dynamic Mechanical Analysis. Operator’s Manual, TA Instrument, New Castle, 2002, pp. 4-8. [11] D.C. Montgomery, Design and analysis of experiments, John Wiley & Sons2008. [12] R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response surface methodology: process and product optimization using designed experiments, John Wiley & Sons2009. [13] G. Vining, Statistical Process Monitoring and Optimization, CRC Press1999.