Numerical simulation of flow and thermal behaviour of polymer under microinjection moulding process: role of the thermal joint model at the mould–melt interface

Ayad, Anass Ben; Otmani, Rabie El; Hakimi, Abdelhadi El; Touache, Abdelhamid; Boutaous, Mhamed; Musa, Kamal R.; Derdouri, Salim

doi:10.1007/s00170-022-08682-7

Cited by 3 publications

(6 citation statements)

References 41 publications

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“…28 We considered the thermal joint resistance approach to simulate the intricate heat transfer along the melt-mold interface. 29 Furthermore, the rheological behavior of polymer is modeled through the Cross-Arrhenius model, while physical and thermal properties of homopolymer POM were integrated within the numerical framework to perform our simulations. Shear rates and temperatures were then predicted to assist the experimental analysis.…”

Section: Simulation Of the Microinjection Molding Processmentioning

confidence: 99%

“…The thermal crystallization kinetics of polyoxymethylene (POM) is achieved through Schneider equations, coupled and solved in tandem with the dynamic flow equations using the finite element method (FEM) 28 . We considered the thermal joint resistance approach to simulate the intricate heat transfer along the melt‐mold interface 29 . Furthermore, the rheological behavior of polymer is modeled through the Cross‐Arrhenius model, while physical and thermal properties of homopolymer POM were integrated within the numerical framework to perform our simulations.…”

Section: Simulation Of the Microinjection Molding Processmentioning

confidence: 99%

See 1 more Smart Citation

Microinjection molding of polyoxymethylene stepped‐parts: Morphology, crystallinity, shrinkage, and thermal characterization

Ayad

Hakimi

Otmani

et al. 2022

J of Applied Polymer Sci

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This paper addresses an experimental investigation of the microinjection molding process through three increasing sections of polyoxymethylene (POM) stepped parts. High and low levels of mold temperature and injection velocity were defined to evaluate the relationship between machine settings, melting and crystallization properties, morphology formation, and shrinkage. The internal morphology of parts showed a mix of structures, from the mold surface towards the core region of each section. Differential scanning calorimetry (DSC) tests highlighted the difficulty of concluding the relationship between the melting temperatures, the processing conditions, and the position of the samples within the mold. The skin‐layer crystallinity declines with increasing part‐step thickness, while the crystallinity degrees of intermediate and core layers are sensitive to the thermal gradients. It was also observed that crystallinity rises along with increasing thickness, benefiting from further polymer chain relaxation and perfection. The unsymmetrical mold cavity results in substantial differences in shrinkage. High shrinkage occurs across the thickness direction due to fast material solidification. The experimental results were supported by the calculated shear rates and temperature fields at the end of the mold‐filling phase. This work yields new insights into comprehending the intricate flow and thermal properties of stepped micropart geometries, which are widely encountered in the plastics manufacturing industry.

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Section: Simulation Of the Microinjection Molding Processmentioning

confidence: 99%

Section: Simulation Of the Microinjection Molding Processmentioning

confidence: 99%

Microinjection molding of polyoxymethylene stepped‐parts: Morphology, crystallinity, shrinkage, and thermal characterization

Ayad

Hakimi

Otmani

et al. 2022

J of Applied Polymer Sci

Self Cite

View full text Add to dashboard Cite

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“…On the contrary, the crystallization behavior of immiscible blends of POM and high density polyethylene was not altered by the shearing. Two studies 19,20 were recently conducted. In both cases, the numerical simulations of the microinjection molding process are carried out by considering that the POM crystallization kinetics is described by adopting the Kolmogoroff approach.…”

Section: Crystallization Of Polyoxymethylene (Pom) Has Been Investigatedmentioning

confidence: 99%

“…The model correctly describes the phenomenon of crystallization in a wide range of cooling rates that also covers the cooling rate characteristics of the polymer processing. In the literature, 19,20,22 the models for POM crystallization that use the Kolmogoroff approach were defined considering isothermal or quasiisothermal conditions (assuming cooling rates up to a few K/s). Therefore, they adopted only homogeneous nucleation and a growth mechanism.…”

Section: Crystallization Kinetics Modelmentioning

confidence: 99%

“…Such an approach is characterized as a process of nucleation and growth of activated crystalline nuclei. However, in Reference 20 parameters of the POM Kolmogoroff model are taken according to Krevelen and Nijenhuis 21 which adopt standard calorimetric tests. In Reference 19 parameters of nucleation and growth of crystalline entities are taken from a different paper, 22 and also in this paper, parameters are determined by adopting standard calorimetric test at low cooling rates.…”

Section: Introductionmentioning

confidence: 99%

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An investigation of crystallization kinetics of polyoxymethylene in processing conditions

Volpe

Speranza

Schrank

et al. 2022

Polymers for Advanced Techs

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In this work, a very wide set of quiescent isothermal and non‐isothermal calorimetric experiments are carried out in order to analyze POM crystallization kinetics also under cooling rates comparable to those experienced by the polymer during processing. To investigate the effect of flow on the POM crystallization behavior, also some Linkam shearing tests are conducted. An enhancement of the POM crystallization process is observed under flow. A Kolmogoroff–Avrami–Evans (KAE) model for quiescent crystallization is proposed. It is based on nucleation (considering both a homogeneous and a heterogeneous process) and growth mechanisms. Model parameters of nucleation and growth are determined, and the overall model is able to describe the POM crystallization process in the whole cooling rate range adopted for the experiments. This allows to obtain a reliable description of the POM crystallization evolution during processing and provides important knowledge for managing the POM injection molding process and the final physical properties of POM products.

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Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

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One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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Numerical simulation of flow and thermal behaviour of polymer under microinjection moulding process: role of the thermal joint model at the mould–melt interface

Cited by 3 publications

References 41 publications

Microinjection molding of polyoxymethylene stepped‐parts: Morphology, crystallinity, shrinkage, and thermal characterization

Microinjection molding of polyoxymethylene stepped‐parts: Morphology, crystallinity, shrinkage, and thermal characterization

An investigation of crystallization kinetics of polyoxymethylene in processing conditions

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

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