EffectofProcessParametersonShortFiberOrientationalongthe...

Research ArticleEffect of Process Parameters on Short Fiber Orientation along theMelt Flow Direction in Water-Assisted Injection Molded Part

Haiying Zhou 12 Hesheng Liu 12 Qingsong Jiang2 Tangqing Kuang3 Zhixin Chen2

and Weiping Li2

1School of Mechanical and Electrical Engineering Nanchang University Nanchang 330031 China2Jiangxi Province Key Laboratory of Polymer MicroNano Manufacturing and Devices East China University of TechnologyNanchang 330013 China3School of Mechanical and Electrical Engineering East China Jiaotong University Nanchang 330013 China

Correspondence should be addressed to Hesheng Liu hsliuvip163com

Received 21 February 2019 Revised 8 July 2019 Accepted 22 July 2019 Published 19 August 2019

Academic Editor Francisco Chinesta

Copyright copy 2019 Haiying Zhou et al)is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

)e short fiber orientation (SFO) distribution in the water-assisted injection molding (WAIM) is more complicated than that intraditional injection molding due to the new process parameters In this work an improved fiber orientation tensor method wasused to simulate the SFO in WAIM )e result was compared with the scanning electron micrograph which was consistent withthe experiments )e effect of six process parameters including filling time melt temperature mold temperature delay timewater pressure and water temperature on the SFO along the melt flow direction were studied through orthogonal experimentaldesign range analysis and variance analysis An artificial neural network was used to establish the nonlinear agent model betweenthe process parameters and A11 representing the fiber orientation in melt flow direction Results show that water pressure melttemperature and water temperature have significant effects on SFO )e three-dimensional (3D) response surfaces and contourplots show that the values of A11 decrease with the increase in water pressure and melt temperature and increase as the watertemperature rises

1 Introduction

With the development of advanced economical and envi-ronmentally friendly society higher requirements are placed onthe performance of plastic products)e short-fiber-reinforcedpolymer composite (SFRPC) is a material with a polymer as amatrix and short fibers as a dispersed phase Its characteristicsare lightweight high specific strength and specific modulusstable chemical properties heat resistance and good wearresistance [1] In recent years SFRPCs have gradually replacedmetal materials in some fields making them widely used inaviation automotive shipping and medicine [2 3]

Fluid-assisted injection molding is an emerging processthat includes gas-assisted injection molding (GAIM) andwater-assisted injection molding (WAIM) [4 5] )efluids used in GAIM and WAIM are nitrogen and waterrespectively Due to incompressibility high heat capacity

and good thermal conductivity of water the advantages ofWAIM over GAIM are high product efficiency more uni-form and thinner residual wall thickness (RWT) [6] Basedon whether or not the cavity is completely filled with meltWAIM can be categorized into two types short-shot WAIMand overflow WAIM In short-shot WAIM the mold cavityis partially filled with melt followed by the injection of waterinto the core of melt In overflow WAIM the mold cavity iscompletely filled with melt followed by the injection ofwater that pushes the melt into the overflow cavity to form apart with a hollow cross section Compared with the stan-dard injection molding WAIM offers significant advantagesin the preparation of shaped hollow plastic parts withuniform RWT However due to the difficulty in controllingthe water injection pressure and the turbulence character-istics of the injection water the quality of the products is notstable [7 8] Present researches on WAIM focus on the

HindawiAdvances in Materials Science and EngineeringVolume 2019 Article ID 7201215 10 pageshttpsdoiorg10115520197201215

distribution of RWT [9] the length of water column pen-etration [10 11] and the defects of molded parts [12 13]

)e RWT of water-assisted injection molded parts isthin and the mechanical properties of the parts can begreatly improved by using SFRPC as a raw material Manystudies reported that the distribution of short fiber orien-tation (SFO) determined the mechanical and physicalproperties of the plastic parts while fiber orientation wasaffected by mold structure molding process parametersflow field distribution initial state of fibers fiber propertiesand matrix properties [14ndash16] )e molding process pa-rameters influence temperature velocity distribution meltviscosity gradient and flow field which ultimately de-termine the fiber orientation Liu et al [7 12] found that theshort fibers mostly aligned along the melt flow direction inWAIM Huang et al [17] suggested that high-pressure waterpenetration significantly affected fiber orientation inWAIMand increasing melt temperature decreased fiber orientationSystematic studies on the influence of process parameters onthe SFO help understand the fiber orientation mechanismoptimize the SFO distribution and improve the overallperformance of the parts in WAIM

WAIM including the melt and high-pressure waterfilling stages is more complicated than the standard in-jection molding process Due to the difficultly in accuratelycontrolling all the process parameters simultaneously theresearch of SFO in laboratory is performed for qualitativeanalysis With the development of computer technology thethree-dimensional numerical simulation technology de-veloped rapidly enabling simulating complex injectionmolding )e process parameters can be accurately con-trolled in simulation but the reliability of SFO simulationdepends on the accuracy of the mathematical model )efiber orientation distribution in injection molding is verycomplicated microscopically In the past three decadestheoretical studies on fibers suspension rheology haveachieved a great success Based on the classic fiber orien-tation models including Jeffery hydrodynamics modelFolgarndashTucker model [18] and ARD-RSC model [19] Tsenget al recently proposed an improved iARD-RPR model[20 21] which can provide good simulation results of SFOin standard injection molding

In this work based on the iARD-RPR model the SFO inWAIM was simulated and the results were compared withthe scanning electron micrographs (SEMs) to verify theapplicability of this model for WAIM )e influences ofprocess parameters on the fiber orientation along the meltflow direction were studied through the methods of or-thogonal experimental design range analysis and varianceanalysis )e nonlinear proxy model between process pa-rameters and fiber orientation along the melt flow directionwas constructed by artificial neural network (ANN) and theinteraction effect of significant process parameters was in-vestigated using 3D response surfaces and contour plots

2 Methods

21 Fluid Mechanics Governing Equations )e movementof short fibers in injection molding is a transient non-

Newtonian and nonisothermal process In numericalsimulations the melt is considered to be incompressibleand laminar and the inertial term is ignored )e gov-erning equations for transient and nonisothermal fluidmotion in WAIM processes are as follows

zρzt

+ nabla middot (ρu) 0

z

zt(ρu) + nabla middot (ρuu minus σ) ρg

σ minus pI + η nablau + nablauT1113872 1113873

ρCpzT

zt+ u middot nablaT1113888 1113889 nabla middot (knablaT) + η _c

2

(1)

where u is the velocity vector η is the shear viscosity p is thepressure ρ is the melt density σ is the total stress tensor g isthe gravity Cp is the specific heat capacity T is the tem-perature k is the thermal conductivity t is the time and _c isthe shear rate

22 Rheological Model )e Cross-WLF rheological modelwith seven parameters was used in the simulation which candescribe complex viscosity behaviors including the viscosityvarying with shear rate and the zero-shear-rate viscositydepending on temperature and pressure [20]

η( _c T P) η0(T P)

1 + η0 _cτlowast( 11138571minus n

η0(T P) D1 expminus A1 T minus Tc( 1113857

A2 + T minus Tc( 11138571113888 1113889

Tc D2 + D3P

A2 1113957A2 + D3 middot P

(2)

where η is the viscosity of the melt η0 is the zero shearviscosity _c is the shear rate τlowast is the material constant n isthe power law index in the shear rate T is the melt tem-perature Tc is the glass transition temperature and D1 D2D3 A1 and 1113957A2 are the constants associated with the selectedmaterial

23 Fiber Orientation Model )e fiber orientation is oftendescribed by two methods including orientation probabilitydensity distribution function and orientation tensor )eorientation probability density distribution function is usedto calculate the ratio of short fibers to the total number offibers in a certain orientation direction It is intuitive andeasy to understand but its wide application is limited due tothe large amount of calculation In this work the numericalsimulation of SFO inWAIM is based on the fiber orientationtensor evolution equation To succinctly represent the ori-entation of a large number of fibers a second-order ori-entation tensor is defined as follows [18]

2 Advances in Materials Science and Engineering

A 1113929ψ(P)PP dP

A11 A12 A13

A12 A22 A23

A13 A23 A33

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

A11 + A22 + A33 1

(3)

where ψ(P) is the probability density distribution functionof the whole orientation space P is the fiberrsquos unit vector Ais a symmetric matrix and when A I3 it represents theorientation state isotropic where I represents an identitymatrix )e three components A11 A22 and A33 on thediagonal represent the fiber orientation in the melt flowdirection the cross-flow direction and the thickness di-rection respectively

Tseng recently proposed a new fiber orientation pre-diction model (iARD-RPR) [21] as below

_A _AHD+ _AiARD

CI CM( 1113857 + _ARPR(α) (4)

where _AHD is a Jeffery hydrodynamic model _AiARD is an

improved anisotropic rotational diffusion model withtwo effective parameters fiber-fiber interaction factorCI(0ltCI lt 01) and fiber-matrix interaction influence factorCM(0ltCM lt 1) and _A

RPR is the retarding principal ratemodel that contains the parameter α (0ltαlt 1) which is meanto slow the response rate of fiber orientation )e orientationtensor will be influenced by the parameters and the defaultparameters (CI 0005 CM 01 and α 07) of iARD-RPRmodel for short fibers were used in the simulations

_AHD (W middot A) minus (A middot W) + ξ D middot A + A middot D minus 2A4 D( 1113857

(5)where W is the vortex tensor D is the deformation ratetensor ξ is the particle shape factor and the fourth-orderorientation tensor A4 can be estimated from A by using theeigenvalue-based optimal fitting (EBOF) closure [22] or theinvariant-based optimal fitting (IBOF) closure [23] EBOFwas chosen in this paper

_AiARD _c 2Dr minus 2tr Dr( 1113857A minus 5Dr middot A minus 5A middot Dr + 10A4 Dr1113858 1113859

Dr CI I minus CMD2

D21113888 1113889

_ARPR minus R middot _ΛIOK middot RT

_ΛIOKii α _λi i j k 1 2 3

(6)where _ΛIOK is a material derivative of a diagonal tensor andits superscript is the intrinsic orientation kinetic (IOK)assumption _ΛIOKii is the i-th diagonal component of _ΛIOK Ris a rotation matrix λi is the eigenvalue of matrix A andλ1 gt λ2 gt λ3

In the simulation the melt flow problem was solvedfirstly and then the resulting velocity field was applied tocompute the short fiber orientation

24 Geometric Model )e geometric model is shown inFigure 1 )e length of the functional hollow duct is245mm and the diameter is 20mm )e overflow cavitywas used to accommodate the melt pushed out by thehigh-pressure water )e connecting pipe between theduct and the overflow cavity had a diameter of 10mm)e3D model was created using ProE software Moldex3Ddeveloped by CoreTech System Co Ltd was used tosimulate the melt filling and high-pressure water pene-tration process of WAIM )e combined model importedinto the Moldex3D R150 was meshed with BoundaryLayer Mesh format In the simulation a short-fiber-reinforced PP (Fiberfil J-6830E with a short grass fibermass fraction of 30 and an aspect ratio of 20) was se-lected as material and its properties were available in thedata bank of Moldex3D )e simulation of overflowWAIM process was carried out First the melt was in-jected into the mold cavity of the functional plastic partSecond after a short delay time the high-pressure waterwas injected into the core of the melt and penetrated alongthe core position with the least flow resistance and pushedthe core melt into the overflow cavity to form a plastic partwith a hollow cross section

25 Orthogonal Experimental Design During the injectionmolding process due to the differences in viscosity gradientand velocity field distribution the melt was sheared andstretched by the surrounding fluid the shearing actioncauses the short fibers to align along the melt flow directionand the stretching action causes the short fibers to orientalong the stretch direction Many factors affect the melt flowfield in WAIM )e process parameters considered in thisstudy include filling time (A) melt temperature (B) moldtemperature (C) delay time (D) water pressure (E) andwater temperature (F) )ese factors and their corre-sponding levels are listed in Table 1

)e full factorial experimental design method demands56 experiments for 6 factors (A B C D E and F) with 5levels To reduce the number of experiments and com-prehensively examine the influence of process parameterson SFO the orthogonal experimental design (L25 (56)) wasused to arrange the test plan In the final stage the shortfibers were mainly oriented along the melt flow direction)e values of the component of orientation tensor repre-senting the melt flow direction were got at twenty differentpoints along the thickness direction at the position I ofRWT and objective A11 is the average value of these points(Table 2)

26 RangeAnalysis Range analysis a statistical method wasused to analyze the experimental results obtained in theorthogonal experimental design Range is defined as thedifference between the maximum value and the minimumvalue )e factor with the maximum range is regarded as themost sensitive process parameter

Advances in Materials Science and Engineering 3

Kij 1113944

p1

i1yijk

Rj max K1j K2j K5j1113872 1113873 minus min K1j K2j K5j1113872 1113873

(7)

where i (i 1 2 3 4 5) is the level of a factor j (jA B CDE and F) is a certain factor Kij is the sum value of A11 of alllevel in each factor yijk is the value of the k-th experimentalresult p1 is the test times with the j-th factor and the i-thlevel and range Rj represents the influence degree of thefactor )e larger the range Rj is the stronger the influenceof the factor is

27Analysis ofVariance Analysis of variance was employedto determine which factors were significant In this methodthe F value of each factor is used to indicate the degree ofinfluence

T 1113944n

i11113944

p1

k1yijk

Ssum 1113944

n

i11113944

p

k1yijk1113872 1113873

2minus

T2

p0

Sj 1113936

ni1 Kij1113872 1113873

2

p1minus

T2

p0

Se Ssum minus 1113944m

j1Sj

F Sjfj

Sefe

(8)

where T is the sum of test indexes n is the number of levelsm is the number of factors Ssum is the total differences Sj isthe differences of test results caused by the change in everylevel of factor j Se is the differences of the test caused by theerror p0 is the total test times fj and fe are the degrees of

Water entrance

Overflow cavity

Melt entrance

ф20

245

Overflow port

Plastic part

I

Figure 1 Geometric model for the numerical simulation experiment

Table 1 Process parameters and levels of orthogonal experiment design

Process parametersLevels

1 2 3 4 5Filling time (A) (s) 1 14 18 22 26Melt temperature (B) (degC) 220 230 240 250 260Mold temperature (C) (degC) 40 50 60 70 80Delay time (D) (s) 1 15 2 25 3Water pressure (E) (MPa) 6 65 7 75 8Water temperature (F) (degC) 20 25 30 35 40

Table 2 Orthogonal experimental arrangement and results ofsimulation

No A B C D E F A11

1 1 1 1 1 1 1 06082 1 2 2 2 2 2 05943 1 3 3 3 3 3 05534 1 4 4 4 4 4 05535 1 5 5 5 5 5 05596 2 1 2 3 4 5 05887 2 2 3 4 5 1 05578 2 3 4 5 1 2 05859 2 4 5 1 2 3 055610 2 5 1 2 3 4 056211 3 1 3 5 2 4 06412 3 2 4 1 3 5 058413 3 3 5 2 4 1 055614 3 4 1 3 5 2 05415 3 5 2 4 1 3 057616 4 1 4 2 5 3 054917 4 2 5 3 1 4 063918 4 3 1 4 2 5 06319 4 4 2 5 3 1 054920 4 5 3 1 4 2 053721 5 1 5 4 3 2 058522 5 2 1 5 4 3 056923 5 3 2 1 5 4 055524 5 4 3 2 1 5 057325 5 5 4 3 2 1 0545


freedom of each factor and test error respectively and othersymbols are the same as above

Given a significant level α the criterion Fα(fj fe) canbe found in the F distribution table )e influence of a factorwith F value greater than Fα value is significant

28 Artificial Neural Network (ANN) ANN inspired by thebiologic neural system is a computing model used to maplinear or nonlinear relationships between factors and re-sponses ANN can work as a human brain to establish asample model from the perspective of information pro-cessing without prior information or heuristic assumptionsAn ANN model comprises three parts one input layer oneor more hidden layers and one output layer)e numbers ofneurons in the input and output layer are determined by thenumbers of factors and responses respectively In generalthe number of neurons in hidden layer is determined by trialand error

According to Kolmogorovrsquos theorem [24] an ANNmodel with a single hidden layer has the ability to map anycomplex nonlinear relationship between input and outputIn this study an ANNmodel with one hidden layer was usedfor modeling Gradient descent algorithm was used fortraining model )e transfer functions of Tansig and Purelinwere employed in the hidden and output layer respectivelyBy changing the number of neurons in the hidden layer from5 to 15 the ANN topology of 6-13-1 with the minimummean square error between the targets and the outputs wasdetermined indicating that there were six neurons in theinput layer thirteen neurons in the hidden layer and oneneuron in the output layer (Figure 2)

3 Results and Discussion

31 Comparison between Simulation Result and ExperimentalObservation Figure 3 shows the component of orientationtensor along the melt flow direction in a section taken fromthe position I (Figure 1) of the model used in the simulationwith each process parameter set to level 3 )e values of A11first increase and then decrease from the region near themold cavity to the region near the water channel and thelarger values of A11 indicate more short fibers orient in theflow direction in the corresponding region Based on theSFO in the flow direction the RWTcan be divided into tworegions such as the ordered region with large A11 anddisordered region with small A11 To verify the simulationresults water-assisted injection molded tubes with short-fiber-reinforced composite were observed by scanningelectron microscopy (Nova NanoSEM 450) with an accel-erating voltage of 5 kV )e tubes were produced by alaboratory-developed WAIM device improved from a 110 -ton traditional injection molding machine )e material wasPP containing 30 short grass fibers )e process param-eters were set to the same as the simulation A small samplewas taken from the tube and immersed in liquid nitrogen fortwo hours and then cryofractured along the melt flow di-rection )e surface of the sample was subjected to gold-sputtered treatment before observation Figure 4 shows that

the short fibers in the RWTmostly orient along the melt flowdirection Figures 4(a) and 4(b) the magnified micrographstaken from the ordered region and disordered region in-dicate that more short fibers orient along the melt flowdirection in the region near the mold cavity than that nearthe water channel )e experimental observations areconsistent with the simulation results indicating that theiARD-RPR model can well predict the SFO distribution inoverflow WAIM

32 Analysis of Short Fiber OrientationMechanism inWAIMFigure 5 shows the values of A11 obtained from 100 pointsalong the diameter in the simulation)emelt filling processwas similar to the conventional injectionmolding process inwhich the short fibers oriented with a typical shell-corestructure )e short fibers in the shell layer mainly orientedalong the melt flow direction due to the shear actionwhereas the short fibers in the core layer weakly oriented dueto stretch action )e values of A11 increased from about 07to 088 within the thickness of 0 to 2mm )is could be

Filling time

Delay time

Melt temperature

Mold temperature

Water pressure

Water temperature

1

2

3

13

Input layer Hidden layer Output layer

A11

Figure 2 Topology of error back-propagation ANN

0898

0786

0673

0560

0448

0335

0222

0110

Mold cavity

Water channel

Figure 3 Contour of A11 in the RWT cross section


explained that the surface melt temperature decreasesrapidly due to the cold mold cavity and the short fibers aresolidified without being sufficiently oriented as the thick-ness increases the cooling effect decreases and A11 increases

In the water filling stage the values of A11 in the regionnear the mold cavity are similar to that in the melt fillingstage which are caused by high shear action the values ofA11 decrease rapidly in the region near the water channelindicating that the high-pressure water filling has an im-portant influence on the SFO in this region To prevent theoccurrence of water vaporization the water filling processlasts very short and the water column in a turbulent stateexerts strong squeezing and friction at the interface between

the melt and water Furthermore as the melt moves into theoverflow cavity the moving melt stretches the adjacent melt)e short fibers originally aligned along the flow directionreadjust the posture due to the multiple disturbancesmaking short fibers in a disordered state in the region nearwater channel In addition the components of orientationtensors in all directions were assigned the values of 033 inthe water channel with no fiber as shown in Figure 5

33 Sensitivity Analysis )e results of range analysis areshown in Table 3 )e rank of sensitivity of A11 to the sixselected process parameters was determined and shown inthe following based on the magnitude of the range R (waterpressure melt temperature water temperature mold tem-perature filling rate and delay time) As shown in Table 4the results of the variance analysis indicate that the melttemperature and water pressure are highly significant factorsfor A11 with an F value of 7346 and 8612 respectively andthe water temperature with an F value of 4102 is a significantfactor for A11 )e filling rate mold temperature and delaytime are not significant for A11 with the F values of 09241163 and 0913 respectively

34 Artificial Neural Network Modeling Experimental data(Table 2) obtained from orthogonal experimental designwere divided randomly into three data sets 19 of overall 25points were used to train the ANN model and the others(3 + 3) were used to validate and test the model respectively)e trained neural network was used to predict A11 in theorthogonal experimental scheme As shown in Figure 6 thetraining sets and validation sets were almost on the diagonalline and the test sets were very close to this line indicatingthat the neural network has established the nonlinear

Wat

er ch

anne

l

Mol

d ca

vity

1mm

300μm 300μm

Observation area

Flow

dire

ctio

n

(a) (b)

Figure 4 SEM micrographs of RWT (a) ordered region and (b) disordered region

RWT

Shell ShellCore

Water channel RWT

00

02

04

06

08

10

A11

Melt filling stageWater filling stage

5 10 15 200Thickness (mm)

Figure 5 Distribution of SFO along the melt direction


relationship between the process parameters and A11 andcould provide acceptable predictions

35 Interaction Effect of Significant Process ParameterRange and variance analysis results show that melt tem-perature water pressure and water temperature are sig-nificant for A11 of RWT in WAIM )e values of A11 werepredicted by the trained neural network by changing twoprocess parameters while keeping the others constant )e3D response surfaces and contour plots were used to de-scribe the interaction effect of process parameters on A11

As shown in Figures 7 and 8 the value of A11 decreases asthemelt temperature rises)e reasons come from two aspectsFirstly the increasing melt temperature results in a decrease inviscosity At the same shear rate the shear stress decreases andthis is not conducive to the fiber orientation Secondly duringthe melt filling stage the polymer molecular chains are alsoaligned and the high melt temperature facilitates the deor-ientation behavior of the molecular chains after the completionof water injection process which changes the orientationdistribution of the short fibers and decreases A11

)e values of A11 decrease with the increase in the waterpressure (Figures 7 and 9) After melt filling stage the short

Table 3 Results of range analysis

Factors A B C D E FK1j 28667 297 29105 28399 29812 28139K2j 28475 2944 28624 28345 29657 28405K3j 28967 28784 28583 28649 28322 28042K4j 29043 27707 28163 29005 28025 2949K5j 28272 27794 28949 29026 27609 29347R 00772 01993 00942 00681 02203 01448Rank EgtBgt FgtCgtAgtD

y = x

052

056

060

064

Pred

icte

d A

11

052 056Experimental A11

060 064

Training setsValidation setsTest sets

Figure 6 Predicted A11 values versus experimental A11 values

Table 4 Results of variance analysis

Factors Sum of squares Degree of freedom Mean square F value Fa SignificantA 0845E minus 3 4 0211E minus 3 0924 F005(4 12) 326B 0672E minus 2 4 0168E minus 2 7346 lowastlowastlowast

C 0106E minus 2 4 0266E minus 3 1163

F001(4 12) 541

D 0834E minus 3 4 0208E minus 3 0913E 0787E minus 2 4 0197E minus 2 8612 lowastlowastlowast

F 0375E minus 2 4 0938E minus 3 4102 lowastlowast

Err 0274E minus 2 12 0228E minus 3Sum 00211 24lowastlowastSignificant lowastlowastlowastHighly significant


fibers in the shell layer highly orient along the melt flowdirection During the high-pressure water filling stage theinner layers of RWT bear the pressing and friction of watercolumn as well as the stretching of the moving melt )e

short fibers originally oriented along the melt flow directionare posture-adjusted and arranged in a disordered state )egreater the water pressure is the stronger the disturbance tothe RWT is and the smaller A11 in the melt flow direction is

052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)

Water pressure (MPa)

230 240 250 260220Melt temperature (degC)

Melt temperature (degC)

06200

06000

05800

05600

05400

A11

Figure 7 3D response surface and contour plot of interaction effect of melt temperature and water pressure on A11

220230

240250

260 2025

3035

40

Water temperature (degC)


A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)

250 260230 240220Melt temperature (degC)

06075

05950

05825

05700

05575

05450

Figure 8 3D response surface and contour plot of interaction effect of melt temperature and water temperature on A11

6065

7075

80 2025

3035

40

Water temperature (degC)Melt pressure (MPa)

A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)

65 70 75 8060Melt pressure (MPa)

Figure 9 3D response surface and contour plot of interaction effect of water pressure and water temperature on A11


While the water injection temperature rises the values ofA11 of RWT in WAIM increase (Figures 8 and 9) )epolymer melt has a viscoelastic property and can return tothe original state after the external force is removed )emultiple disturbances cause the short fibers to be disorderedin the inner layers of RWTduring the water filling stage andthe higher water temperature favors the elastic recovery ofthe short fiber posture thereby resulting in an increase ofA11

4 Conclusions

)e iARD-RPR model was used to simulate the orientationof short fibers in WAIM Compared with the SEM micro-graphs the simulation results indicated that this model wassuitable for the SFO prediction in overflow WAIM )eshort fibers in the region near the mold cavity mostly ori-ented in the melt flow direction while those in the regionnear the water channel were disordered )e melt temper-ature water pressure and water temperature have signifi-cant effects on the SFO along the melt flow direction It wasfound that the values of A11 decreased with increasing waterinjection pressure and melt temperature and increased withincreasing water temperature Finally this study is beneficialfor the subsequent optimization of SFO in WAIM

Data Availability

)e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

)e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

)is work was funded by the National Natural ScienceFoundation of China (Grant nos 21664002 and 51563010)and the Jiangxi Provincial Key Technology RampD Program(Grant no 20161BBE50073)

References

[1] S Mortazavian and A Fatemi ldquoFatigue behavior and modelingof short fiber reinforced polymer composites a literature reviewrdquoInternational Journal of Fatigue vol 70 pp 297ndash321 2015

[2] C Rohrig T Scheffer and S Diebels ldquoMechanical charac-terization of a short fiber-reinforced polymer at room tem-perature experimental setups evaluated by an opticalmeasurement systemrdquo Continuum Mechanics and Fermo-dynamics vol 29 no 5 pp 1093ndash1111 2017

[3] B M Rudresh and B N Ravi Kumar ldquoInfluence of experi-mental parameters on friction and wear mechanisms of PA66PTFE blend reinforced with glass fiberrdquo Transactions of theIndian Institute of Metals vol 71 no 2 pp 339ndash349 2018

[4] S-J Liu and Y-C Wu ldquoDynamic visualization of cavity-filling process in fluid-assisted injection molding-gas versuswaterrdquo Polymer Testing vol 26 no 2 pp 232ndash242 2007

[5] L Li Y Peng andW Wei ldquoRecent advances on fluid assistedinjection molding techniquerdquo Recent Patents on MechanicalEngineering vol 7 no 1 pp 82ndash91 2014

[6] H P Park B-S Cha S-B Park et al ldquoA study on the voidformation in residual wall thickness of fluid-assisted injectionmolding partsrdquo Advances in Materials Science and Engi-neering vol 2014 Article ID 238251 6 pages 2014

[7] S Liu M Lin and Y Wu ldquoAn experimental study of thewater-assisted injection molding of glass fiber filled poly-butylene-terephthalate (PBT) compositesrdquo Composites Sci-ence and Technology vol 67 no 7-8 pp 1415ndash1424 2007

[8] T Q Kuang B P Xu G F Zhou and L S Turng ldquoNumericalsimulation on residual thickness of pipes with curved sectionsin water-assisted co-injection moldingrdquo Journal of AppliedPolymer Science vol 132 no 34 pp 1ndash10 2015

[9] H Park B S Cha and B Rhee ldquoExperimental and numericalinvestigation of the effect of process conditions on residualwall thickness and cooling and surface characteristics ofwater-assisted injection molded hollow productsrdquo Advancesin Materials Science and Engineering vol 2015 Article ID161938 11 pages 2015

[10] J G Yang X H Zhou and Q Niu ldquoModel and simulation ofwater penetration in water-assisted injection moldingrdquo FeInternational Journal of Advanced Manufacturing Technologyvol 67 no 1ndash4 pp 367ndash375 2013

[11] T Q Kuang K Zhou L X Wu G F Zhou and L S TurngldquoExperimental study on the penetration interfaces of pipeswith different cross-sections in overflow water-assistedcoinjection moldingrdquo Journal of Applied Polymer Sciencevol 133 no 1 pp 1ndash8 2016

[12] S-J Liu and Y-S Chen ldquo)emanufacturing of thermoplasticcomposite parts by water-assisted injection-molding tech-nologyrdquo Composites Part A Applied Science andManufacturing vol 35 no 2 pp 171ndash180 2004

[13] S-J Liu and S-P Lin ldquoStudy of ldquofingeringrdquo in water assistedinjection molded compositesrdquo Composites Part A AppliedScience andManufacturing vol 36 no 11 pp 1507ndash1517 2005

[14] I Fiebig and V Schoeppner ldquoFactors influencing the fiberorientation in welding of fiber-reinforced thermoplasticsrdquoWelding in the World vol 62 no 5 pp 997ndash1012 2018

[15] Q-S Jiang H-S Liu Q-W Xiao S-F Chou A-H Xiongand H-R Nie ldquo)ree-dimensional numerical simulation oftotal warpage deformation for short-glass-fiber-reinforcedpolypropylene composite injection-molded parts using cou-pled FEMrdquo Journal of Polymer Engineering vol 38 no 5pp 493ndash502 2018

[16] S C Lee D Y Yang J Ko and J R Youn ldquoEffect ofcompressibility on flow field and fiber orientation during thefilling stage of injection moldingrdquo Journal of Materials Pro-cessing Technology vol 70 no 1-3 pp 83ndash92 1997

[17] H-X Huang R-H Zhou and C Yang ldquoFiber orientationpropelled by high-pressure water penetration in water-assisted injection molded fiber-reinforced thermoplasticspartrdquo Journal of Composite Materials vol 47 no 2pp 183ndash190 2013

[18] S G Advani and C L Tucker ldquo)e use of tensors to describeand predict fiber orientation in short fiber compositesrdquoJournal of Rheology vol 31 no 8 pp 751ndash784 1987

[19] B O Agboola D A Jack and S Montgomery-Smith ldquoEf-fectiveness of recent fiber-interaction diffusion models fororientation and the part stiffness predictions in injectionmolded short-fiber reinforced compositesrdquo Composites PartA Applied Science and Manufacturing vol 43 no 11pp 1959ndash1970 2012


[20] H-C Tseng R-Y Chang and C-H Hsu ldquoNumerical pre-diction of fiber orientation and mechanical performance forshortlong glass and carbon fiber-reinforced compositesrdquoComposites Science and Technology vol 144 pp 51ndash56 2017

[21] H-C Tseng R-Y Chang and C-H Hsu ldquo)e use of shear-rate-dependent parameters to improve fiber orientationpredictions for injection molded fiber compositesrdquo Com-posites Part A Applied Science and Manufacturing vol 104pp 81ndash88 2018

[22] J S Cintra Jr and C L Tucker III ldquoOrthotropic closureapproximations for flow-induced fiber orientationrdquo Journal ofRheology vol 39 no 6 pp 1095ndash1122 1995

[23] D H Chung and T H Kwon ldquoInvariant-based optimal fittingclosure approximation for the numerical prediction of flow-induced fiber orientationrdquo Journal of Rheology vol 46 no 1pp 169ndash194 2002

[24] V Kurkova ldquoKolmogorovrsquos theorem and multilayer neuralnetworksrdquo Neural Networks vol 5 no 3 pp 501ndash506 1992


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BioMed Research InternationalMaterials

Journal of


Na

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ls


Journal ofNanomaterials

Submit your manuscripts atwwwhindawicom

distribution of RWT [9] the length of water column pen-etration [10 11] and the defects of molded parts [12 13]

)e RWT of water-assisted injection molded parts isthin and the mechanical properties of the parts can begreatly improved by using SFRPC as a raw material Manystudies reported that the distribution of short fiber orien-tation (SFO) determined the mechanical and physicalproperties of the plastic parts while fiber orientation wasaffected by mold structure molding process parametersflow field distribution initial state of fibers fiber propertiesand matrix properties [14ndash16] )e molding process pa-rameters influence temperature velocity distribution meltviscosity gradient and flow field which ultimately de-termine the fiber orientation Liu et al [7 12] found that theshort fibers mostly aligned along the melt flow direction inWAIM Huang et al [17] suggested that high-pressure waterpenetration significantly affected fiber orientation inWAIMand increasing melt temperature decreased fiber orientationSystematic studies on the influence of process parameters onthe SFO help understand the fiber orientation mechanismoptimize the SFO distribution and improve the overallperformance of the parts in WAIM

WAIM including the melt and high-pressure waterfilling stages is more complicated than the standard in-jection molding process Due to the difficultly in accuratelycontrolling all the process parameters simultaneously theresearch of SFO in laboratory is performed for qualitativeanalysis With the development of computer technology thethree-dimensional numerical simulation technology de-veloped rapidly enabling simulating complex injectionmolding )e process parameters can be accurately con-trolled in simulation but the reliability of SFO simulationdepends on the accuracy of the mathematical model )efiber orientation distribution in injection molding is verycomplicated microscopically In the past three decadestheoretical studies on fibers suspension rheology haveachieved a great success Based on the classic fiber orien-tation models including Jeffery hydrodynamics modelFolgarndashTucker model [18] and ARD-RSC model [19] Tsenget al recently proposed an improved iARD-RPR model[20 21] which can provide good simulation results of SFOin standard injection molding

In this work based on the iARD-RPR model the SFO inWAIM was simulated and the results were compared withthe scanning electron micrographs (SEMs) to verify theapplicability of this model for WAIM )e influences ofprocess parameters on the fiber orientation along the meltflow direction were studied through the methods of or-thogonal experimental design range analysis and varianceanalysis )e nonlinear proxy model between process pa-rameters and fiber orientation along the melt flow directionwas constructed by artificial neural network (ANN) and theinteraction effect of significant process parameters was in-vestigated using 3D response surfaces and contour plots

2 Methods

21 Fluid Mechanics Governing Equations )e movementof short fibers in injection molding is a transient non-

Newtonian and nonisothermal process In numericalsimulations the melt is considered to be incompressibleand laminar and the inertial term is ignored )e gov-erning equations for transient and nonisothermal fluidmotion in WAIM processes are as follows

zρzt

+ nabla middot (ρu) 0

z

zt(ρu) + nabla middot (ρuu minus σ) ρg

σ minus pI + η nablau + nablauT1113872 1113873

ρCpzT

zt+ u middot nablaT1113888 1113889 nabla middot (knablaT) + η _c

2

(1)

where u is the velocity vector η is the shear viscosity p is thepressure ρ is the melt density σ is the total stress tensor g isthe gravity Cp is the specific heat capacity T is the tem-perature k is the thermal conductivity t is the time and _c isthe shear rate

22 Rheological Model )e Cross-WLF rheological modelwith seven parameters was used in the simulation which candescribe complex viscosity behaviors including the viscosityvarying with shear rate and the zero-shear-rate viscositydepending on temperature and pressure [20]

η( _c T P) η0(T P)

1 + η0 _cτlowast( 11138571minus n

η0(T P) D1 expminus A1 T minus Tc( 1113857

A2 + T minus Tc( 11138571113888 1113889

Tc D2 + D3P

A2 1113957A2 + D3 middot P

(2)

where η is the viscosity of the melt η0 is the zero shearviscosity _c is the shear rate τlowast is the material constant n isthe power law index in the shear rate T is the melt tem-perature Tc is the glass transition temperature and D1 D2D3 A1 and 1113957A2 are the constants associated with the selectedmaterial

23 Fiber Orientation Model )e fiber orientation is oftendescribed by two methods including orientation probabilitydensity distribution function and orientation tensor )eorientation probability density distribution function is usedto calculate the ratio of short fibers to the total number offibers in a certain orientation direction It is intuitive andeasy to understand but its wide application is limited due tothe large amount of calculation In this work the numericalsimulation of SFO inWAIM is based on the fiber orientationtensor evolution equation To succinctly represent the ori-entation of a large number of fibers a second-order ori-entation tensor is defined as follows [18]


A 1113929ψ(P)PP dP

A11 A12 A13

A12 A22 A23

A13 A23 A33

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

A11 + A22 + A33 1

(3)



_A _AHD+ _AiARD

CI CM( 1113857 + _ARPR(α) (4)







Dr CI I minus CMD2

D21113888 1113889










Kij 1113944

p1

i1yijk


(7)



T 1113944n

i11113944

p1

k1yijk

Ssum 1113944

n

i11113944

p

k1yijk1113872 1113873

2minus

T2

p0

Sj 1113936

ni1 Kij1113872 1113873

2

p1minus

T2

p0


j1Sj

F Sjfj

Sefe

(8)


Water entrance

Overflow cavity

Melt entrance

ф20

245

Overflow port

Plastic part

I






No A B C D E F A11

1 1 1 1 1 1 1 06082 1 2 2 2 2 2 05943 1 3 3 3 3 3 05534 1 4 4 4 4 4 05535 1 5 5 5 5 5 05596 2 1 2 3 4 5 05887 2 2 3 4 5 1 05578 2 3 4 5 1 2 05859 2 4 5 1 2 3 055610 2 5 1 2 3 4 056211 3 1 3 5 2 4 06412 3 2 4 1 3 5 058413 3 3 5 2 4 1 055614 3 4 1 3 5 2 05415 3 5 2 4 1 3 057616 4 1 4 2 5 3 054917 4 2 5 3 1 4 063918 4 3 1 4 2 5 06319 4 4 2 5 3 1 054920 4 5 3 1 4 2 053721 5 1 5 4 3 2 058522 5 2 1 5 4 3 056923 5 3 2 1 5 4 055524 5 4 3 2 1 5 057325 5 5 4 3 2 1 0545










Filling time

Delay time

Melt temperature

Mold temperature

Water pressure

Water temperature

1

2

3

13


A11


0898

0786

0673

0560

0448

0335

0222

0110

Mold cavity

Water channel








Wat

er ch

anne

l

Mol

d ca

vity

1mm

300μm 300μm

Observation area

Flow

dire

ctio

n

(a) (b)


RWT

Shell ShellCore

Water channel RWT

00

02

04

06

08

10

A11











y = x

052

056

060

064

Pred

icte

d A

11


060 064





C 0106E minus 2 4 0266E minus 3 1163

F001(4 12) 541







052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)




06200

06000

05800

05600

05400

A11


220230

240250

260 2025

3035

40



A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)


06075

05950

05825

05700

05575

05450


6065

7075

80 2025

3035

40


A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls




A 1113929ψ(P)PP dP

A11 A12 A13

A12 A22 A23

A13 A23 A33

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

A11 + A22 + A33 1

(3)



_A _AHD+ _AiARD

CI CM( 1113857 + _ARPR(α) (4)







Dr CI I minus CMD2

D21113888 1113889










Kij 1113944

p1

i1yijk


(7)



T 1113944n

i11113944

p1

k1yijk

Ssum 1113944

n

i11113944

p

k1yijk1113872 1113873

2minus

T2

p0

Sj 1113936

ni1 Kij1113872 1113873

2

p1minus

T2

p0


j1Sj

F Sjfj

Sefe

(8)


Water entrance

Overflow cavity

Melt entrance

ф20

245

Overflow port

Plastic part

I






No A B C D E F A11

1 1 1 1 1 1 1 06082 1 2 2 2 2 2 05943 1 3 3 3 3 3 05534 1 4 4 4 4 4 05535 1 5 5 5 5 5 05596 2 1 2 3 4 5 05887 2 2 3 4 5 1 05578 2 3 4 5 1 2 05859 2 4 5 1 2 3 055610 2 5 1 2 3 4 056211 3 1 3 5 2 4 06412 3 2 4 1 3 5 058413 3 3 5 2 4 1 055614 3 4 1 3 5 2 05415 3 5 2 4 1 3 057616 4 1 4 2 5 3 054917 4 2 5 3 1 4 063918 4 3 1 4 2 5 06319 4 4 2 5 3 1 054920 4 5 3 1 4 2 053721 5 1 5 4 3 2 058522 5 2 1 5 4 3 056923 5 3 2 1 5 4 055524 5 4 3 2 1 5 057325 5 5 4 3 2 1 0545










Filling time

Delay time

Melt temperature

Mold temperature

Water pressure

Water temperature

1

2

3

13


A11


0898

0786

0673

0560

0448

0335

0222

0110

Mold cavity

Water channel








Wat

er ch

anne

l

Mol

d ca

vity

1mm

300μm 300μm

Observation area

Flow

dire

ctio

n

(a) (b)


RWT

Shell ShellCore

Water channel RWT

00

02

04

06

08

10

A11











y = x

052

056

060

064

Pred

icte

d A

11


060 064





C 0106E minus 2 4 0266E minus 3 1163

F001(4 12) 541







052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)




06200

06000

05800

05600

05400

A11


220230

240250

260 2025

3035

40



A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)


06075

05950

05825

05700

05575

05450


6065

7075

80 2025

3035

40


A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls




Kij 1113944

p1

i1yijk


(7)



T 1113944n

i11113944

p1

k1yijk

Ssum 1113944

n

i11113944

p

k1yijk1113872 1113873

2minus

T2

p0

Sj 1113936

ni1 Kij1113872 1113873

2

p1minus

T2

p0


j1Sj

F Sjfj

Sefe

(8)


Water entrance

Overflow cavity

Melt entrance

ф20

245

Overflow port

Plastic part

I






No A B C D E F A11

1 1 1 1 1 1 1 06082 1 2 2 2 2 2 05943 1 3 3 3 3 3 05534 1 4 4 4 4 4 05535 1 5 5 5 5 5 05596 2 1 2 3 4 5 05887 2 2 3 4 5 1 05578 2 3 4 5 1 2 05859 2 4 5 1 2 3 055610 2 5 1 2 3 4 056211 3 1 3 5 2 4 06412 3 2 4 1 3 5 058413 3 3 5 2 4 1 055614 3 4 1 3 5 2 05415 3 5 2 4 1 3 057616 4 1 4 2 5 3 054917 4 2 5 3 1 4 063918 4 3 1 4 2 5 06319 4 4 2 5 3 1 054920 4 5 3 1 4 2 053721 5 1 5 4 3 2 058522 5 2 1 5 4 3 056923 5 3 2 1 5 4 055524 5 4 3 2 1 5 057325 5 5 4 3 2 1 0545










Filling time

Delay time

Melt temperature

Mold temperature

Water pressure

Water temperature

1

2

3

13


A11


0898

0786

0673

0560

0448

0335

0222

0110

Mold cavity

Water channel








Wat

er ch

anne

l

Mol

d ca

vity

1mm

300μm 300μm

Observation area

Flow

dire

ctio

n

(a) (b)


RWT

Shell ShellCore

Water channel RWT

00

02

04

06

08

10

A11











y = x

052

056

060

064

Pred

icte

d A

11


060 064





C 0106E minus 2 4 0266E minus 3 1163

F001(4 12) 541







052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)




06200

06000

05800

05600

05400

A11


220230

240250

260 2025

3035

40



A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)


06075

05950

05825

05700

05575

05450


6065

7075

80 2025

3035

40


A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls












Filling time

Delay time

Melt temperature

Mold temperature

Water pressure

Water temperature

1

2

3

13


A11


0898

0786

0673

0560

0448

0335

0222

0110

Mold cavity

Water channel








Wat

er ch

anne

l

Mol

d ca

vity

1mm

300μm 300μm

Observation area

Flow

dire

ctio

n

(a) (b)


RWT

Shell ShellCore

Water channel RWT

00

02

04

06

08

10

A11











y = x

052

056

060

064

Pred

icte

d A

11


060 064





C 0106E minus 2 4 0266E minus 3 1163

F001(4 12) 541







052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)




06200

06000

05800

05600

05400

A11


220230

240250

260 2025

3035

40



A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)


06075

05950

05825

05700

05575

05450


6065

7075

80 2025

3035

40


A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls









Wat

er ch

anne

l

Mol

d ca

vity

1mm

300μm 300μm

Observation area

Flow

dire

ctio

n

(a) (b)


RWT

Shell ShellCore

Water channel RWT

00

02

04

06

08

10

A11











y = x

052

056

060

064

Pred

icte

d A

11


060 064





C 0106E minus 2 4 0266E minus 3 1163

F001(4 12) 541







052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)




06200

06000

05800

05600

05400

A11


220230

240250

260 2025

3035

40



A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)


06075

05950

05825

05700

05575

05450


6065

7075

80 2025

3035

40


A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls










y = x

052

056

060

064

Pred

icte

d A

11


060 064





C 0106E minus 2 4 0266E minus 3 1163

F001(4 12) 541







052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)




06200

06000

05800

05600

05400

A11


220230

240250

260 2025

3035

40



A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)


06075

05950

05825

05700

05575

05450


6065

7075

80 2025

3035

40


A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls






052

056

060

064

220230

240250

260 6065

7075

80

60

65

70

75

80

Wat

er p

ress

ure (

MPa

)




06200

06000

05800

05600

05400

A11


220230

240250

260 2025

3035

40



A11

064

060

056

052

20

25

30

35

40W

ater

tem

prer

atur

e (degC

)


06075

05950

05825

05700

05575

05450


6065

7075

80 2025

3035

40


A11

064

060

056

052

06150

06000

05850

0570005550 05400

20

25

30

35

40

Wat

er te

mpr

erat

ure (

degC)





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls





4 Conclusions


Data Availability




Acknowledgments


References





























Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls












Advances in



Journal of

Chemistry















Journal of





Volume 2018









Journal of


Na

nom

ate

ria

ls




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