FE-simulation and validation of liquid-bi-orientation-

Zimmer, Johannes; Chauvin, G.; Stommel, Markus

doi:10.1063/1.4873741

Cited by 1 publication

(5 citation statements)

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“…In previous analyses [14], the evaluation of the temperature field in combination with straininduced crystallization shows an overestimation of the temperature increase. On the one hand, this is based on the fact that not 100% of the heat of crystallization directly contribute to a temperature increase, which would require an infinite crystallization velocity [10].…”

Section: Numerical Simulationmentioning

confidence: 82%

“…W characterizes a heat source which corresponds to the exothermic strain-induced crystallization of PET during the forming process. The thermal parameters and the heat source are calibrated in [14] for a one-dimensional analogous model of the LF-process. Thereby, W is dependent on the current preform thickness based on DSC-measurements.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…The resulting value of the Enthalpy is corrected by the initial value of a non-deformed preform sample. The procedure is explained in detail in [14]. The resulting formulation of W is displayed in Fig.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…As a consequence, the total increase in temperature during the process is lower than predicted by the simulation. To prevent an overestimation, the heat flux is corrected by a factor of 0.7 to match the experimental data in [14]. The partial differential Eq.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…In a previous paper [13], a three-dimensional FSI-simulation of the LF process has been introduced considering the mechanical interaction between liquid and preform as well as the initial preform temperature distribution but neglecting the cooling during forming. Furthermore in [14], a one-dimensional analogous model is introduced and calibrated, which predicts the temperature field along the preform thickness at one specific point in the axial direction. However, to investigate the temperature influence on failure cases, the time-dependent temperature field has to be predicted for every preform point.…”

Section: Introductionmentioning

confidence: 99%

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Experimental and Numerical Analysis of Liquid-Forming

Zimmer

Klein

Stommel

2015

KEM

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The packaging of liquid products is conventionally realized by using two production stages, which are the stretch blow molding and the filling. In the stretch blow molding process, hot polyethylene terephthalate (PET) preforms are inflated by pressurized air into a cavity to form plastic bottles. In a follow-up process, these packages are filled by a separate machine with the desired liquid product. In contrast to that, liquid-forming combines the blowing and filling stages by directly using the liquid product to form a plastic bottle. Through this substitution, two main challenges arise. Firstly, there are significant inertia effects through the liquid mass, leading to additional reaction forces and a spatially inhomogeneous pressure distribution inside the preform. Secondly, the heat transfer between preform and fluid is drastically increased. Because of this cooling effect, a specific combination of forming speed as well as initial preform and liquid temperatures is necessary to avoid thermally induced preform rupture. This is based on the fact that the formability of PET rapidly declines below its glass transition temperature (Tg). Consequently, a process control requires the knowledge of how the process parameters influence the preform cooling. In this paper, a numerical simulation of the liquid-forming process (LF) is introduced including the preform cooling during forming. In addition, the strain-dependent self-heating effect of PET is implemented. Process experiments under different parameter combinations are conducted using simplified bottle geometry. Through a comparison of the results from experiments and from simulation, the influence of process parameters on the temperature drop and thus on thermally induced failure is determined. In this way, process understanding and control are increased.

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Section: Numerical Simulationmentioning

confidence: 82%

Section: Numerical Simulationmentioning

confidence: 99%

Section: Numerical Simulationmentioning

confidence: 99%

Section: Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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