2009
DOI: 10.1002/mame.200900227
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Predicting the Long‐Term Mechanical Performance of Polycarbonate from Thermal History during Injection Molding

Abstract: The influence of the thermal history experienced during injection molding on the mechanical properties of polycarbonate is investigated. Distributions of the yield stress as they result from inhomogeneous cooling during processing, predicted by a previously developed modeling approach, are validated and are in good agreement with experiment. Predictions of the mechanical performance for different mold temperatures during processing are also validated over a range of applied strain and deformation rates and app… Show more

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Cited by 34 publications
(47 citation statements)
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“…In this study, the prediction of yield stress of PC from processing thermal history is based on a phenomenological approach developed by Govaert et al [19][20][21]. They assumed the physical processes of the evolution of yield stress during processing are identical to those governing the evolution of yield stress during annealing.…”
Section: Predictive Model Of the Processing-induced Yield Stress Of Pcmentioning
confidence: 99%
See 3 more Smart Citations
“…In this study, the prediction of yield stress of PC from processing thermal history is based on a phenomenological approach developed by Govaert et al [19][20][21]. They assumed the physical processes of the evolution of yield stress during processing are identical to those governing the evolution of yield stress during annealing.…”
Section: Predictive Model Of the Processing-induced Yield Stress Of Pcmentioning
confidence: 99%
“…R is the universal gas constant, T is the annealing temperature and T ref is the reference temperature. Govaert et al [19][20][21] proposed a logarithmic evolution equation of the yield stress during annealing process by using the master curve: …”
Section: Predictive Model Of the Processing-induced Yield Stress Of Pcmentioning
confidence: 99%
See 2 more Smart Citations
“…[5] Computed yield stresses of injection-molded plates proved in excellent agreement with the experimental values, including their dependence on mold temperature and plate thickness. [2,3] Moreover, it was demonstrated that the method can be extended to quantitatively predict both short-and long-term failure of polymer products, [6] thus making it a powerful tool for true product optimization. A point of possible concern regarding this approach is that it completely neglects the influence of molecular orientation.…”
Section: Introductionmentioning
confidence: 99%