Multiphysics simulation of thermoplastic polymer crystallization

Spekowius, Marcel; Hopmann, Christian

doi:10.1016/j.matdes.2016.01.123

Cited by 43 publications

(35 citation statements)

References 29 publications

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“…Thus far, there have been many studies on the morphological simulation of polymer crystallization. Examples include: Raabe [12][13][14], Lin et al [15], and Spina et al [16,17] presented a cellular automaton method to simulate the kinetics and topology of spherulite growth for polymer crystallization; Liu et al [18,19] used a level set method to capture the growth and impingement of spherulites during the polymer cooling stage; Micheletti and Burge [20] and Ruan et al [21,22] applied a pixel coloring method to model and simulate the crystallization of polymer and short fiber reinforced polymer; and Ketdee and Anantawaraskul [23] and Ruan et al [24] presented the Monte Carlo simulation in study of crystallization kinetics and morphology development in polymer crystallization. However, we shall mention that these works were mainly concentrated on spherulite structure.…”

Section: Introductionmentioning

confidence: 99%

Kinetics and Morphology of Flow Induced Polymer Crystallization in 3D Shear Flow Investigated by Monte Carlo Simulation

Ruan

2017

Crystals

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Abstract:To explore the kinetics and morphology of flow induced crystallization of polymers, a nucleation-growth evolution model for spherulites and shish-kebabs is built based on Schneider rate model and Eder model. The model considers that the spherulites are thermally induced, growing like spheres, while the shish-kebabs are flow induced, growing like cylinders, with the first normal stress difference of crystallizing system being the driving force for the nucleation of shish-kebabs. A two-phase suspension model is introduced to describe the crystallizing system, which Finitely Extensible Non-linear Elastic-Peterlin (FENE-P) model and rigid dumbbell model are used to describe amorphous phase and semi-crystalline phase, respectively. Morphological Monte Carlo method is presented to simulate the polymer crystallization in 3D simple shear flow. Roles of shear rate, shear time and shear strain on the crystallization kinetics, morphology, and rheology are analyzed. Numerical results show that crystallization kinetics, morphology and rheology in shear flow are qualitatively in agreement with the theoretical, experimental and other numerical works which verifies the validity and effectiveness of our model and algorithm. To our knowledge, this is the first time that a model and an algorithm revealing the details of crystal morphology have been applied to the flow induced crystallization of polymers.

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Section: Introductionmentioning

confidence: 99%

Kinetics and Morphology of Flow Induced Polymer Crystallization in 3D Shear Flow Investigated by Monte Carlo Simulation

Ruan

2017

Crystals

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“…To date, a number of investigators are interested in predicting the morphological development of polymer crystallization and many research studies have been carried out on this topic [2][3][4][5][6][7][8][9][10][11][12]. In order to obtain the internal microstructure of polymer products, different approaches have been proposed for morphological modeling of polymer crystallization by researchers [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Polymer Crystallization under Isothermal and Temperature Gradient Conditions Using Particle Level Set Method

et al. 2016

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Morphological models for polymer crystallization under isothermal and temperature gradient conditions with a particle level set method are proposed. In these models, the particle level set method is used to improve the accuracy in studying crystal interaction. The predicted development of crystallinity during crystallization under quiescent isothermal condition by our model is reanalyzed with the Avrami model, and good agreement between the predicted and theoretical values is observed. In the temperature gradient, the computer simulation results with our model are consistent with the experiment results in the literature.

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“…These seemingly conflicting results can be explained by considering the two components that determine the overall crystallization rate and transcrystallization phenomenon which are analyzed in next section. It is worthy to note that besides using traditional models to fit the DSC data, researchers have developed simulation tools using finite element analysis to predict the crystallization kinetics of semicrystalline thermoplastics [10]. The simulation provided reasonable results without time-consuming analysis with experiments.…”

Section: Effective Activation Energymentioning

confidence: 99%

“…During the EAM processing, a bench-scale 3D printer (no screw in the barrel) induces extensional flow and large-scale 3D printers (screw in the barrel) cause shear flow. Flow-induced crystallization is an important factor which had been studied [9,10]. However, in this paper, differential scanning calorimentry (DSC) was applied to materials at quiescent state for studying the crystallization kinetics for simplicity.…”

Section: Introductionmentioning

confidence: 99%

“…So far, how SDCNF affects the nonisothermal crystallization kinetics and thermal expansion of iPP has not been studied. This experiment was primarily designed to analyze the effect of SDCNF content and MAPP on the nonisothermal crystallization kinetics of iPP at four cooling rates (5,10,15,20 • C/min). The cooling rates were chosen based on the previous analysis of the crystallization temperature during 3D printing as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Spray-Dried Cellulose Nanofibril-Reinforced Polypropylene Composites for Extrusion-Based Additive Manufacturing: Nonisothermal Crystallization Kinetics and Thermal Expansion

et al. 2018

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Isotactic polypropylene (iPP) is a versatile polymer. It accounts for the second-largest polymer consumption worldwide. However, iPP is difficult to 3D print via extrusion-based processing. This is attributable to its rapid crystallization rate. In this study, spray-dried cellulose nanofibrils (SDCNF) and maleic anhydride polypropylene (MAPP) were investigated to reveal their effects on the nonisothermal crystallization kinetics and thermal expansion of iPP. SDCNF at 3 wt % and 30 wt % accelerated the crystallization rate of iPP, while SDCNF at 10 wt % retarded the crystallization rate by restricting crystal growth and moderately increasing the nucleation density of iPP. Additionally, adding MAPP into iPP/SDCNF composites accelerated the crystallization rate of iPP. The effective activation energy of iPP increased when more than 10 wt % SDCNF was added. Scanning electron microscopy and polarized light microscopy results indicated that high SDCNF content led to a reduced gap size among SDCNF, which hindered the iPP crystal growth. The coefficient of thermal expansion of iPP/SDCNF10% was 11.7% lower than the neat iPP. In summary, SDCNF, at 10 wt %, can be used to reduce the warping of iPP during extrusion-based additive manufacturing.

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Multiphysics simulation of thermoplastic polymer crystallization

Cited by 43 publications

References 29 publications

Kinetics and Morphology of Flow Induced Polymer Crystallization in 3D Shear Flow Investigated by Monte Carlo Simulation

Kinetics and Morphology of Flow Induced Polymer Crystallization in 3D Shear Flow Investigated by Monte Carlo Simulation

Simulation of Polymer Crystallization under Isothermal and Temperature Gradient Conditions Using Particle Level Set Method

Spray-Dried Cellulose Nanofibril-Reinforced Polypropylene Composites for Extrusion-Based Additive Manufacturing: Nonisothermal Crystallization Kinetics and Thermal Expansion

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