The influences of multiple factors for flexural performance of polypropylene: crystallization, crystal evolution, nanoparticles

Zhang, Wei; Liang, Bo

doi:10.1007/s10853-021-06243-z

Cited by 2 publications

(2 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysing the complex relationships between the structure and properties, as well as the interplay between process conditions and the structure in polypropylene alloys, presents additional challenges. 9,10 Traditional analytical methods struggle to provide quantitative insights into these multidimensional problems. In response to these challenges, machine learning methods have been employed to predict the performance of polymer alloys and composites.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys

Zheng,

Huang,

et al. 2024

React. Chem. Eng.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys

Zheng,

Huang,

et al. 2024

React. Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…Polyolefins, even if widely adopted to produce commodities because of their low price, generally do not show sufficient strength and rigidity for such applications. In recent years, the strategies to increase the strength and rigidity of injection-molded polyolefins, especially polypropylene, are attracting relevant research interest [ 1 ]. Indeed, the optimization of the crystallization process through the control of process parameters, and the introduction of fillers and nucleating agents, can lead to an increase in polyolefin strength and rigidity up to the level required for the final applications [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Morphology of Injection Molding Polypropylene Parts Induced by In-Mold Annealing

et al. 2022

View full text Add to dashboard Cite

It is generally recognized that high-temperature treatments, namely annealing, influence the microstructure and the morphology, which, in turn, determine the mechanical properties of polymeric parts. Therefore, annealing can be adopted to control the mechanical performance of the molded parts. This work aims to assess the effect of annealing on the morphology developed in isotactic polypropylene (iPP) injection-molded parts. In particular, a two-step annealing is adopted: the polymer is injected in a mold at a high temperature (413 or 433 K), which is kept for 5 min (first annealing step); afterward, the mold temperature is cooled down at 403 K and held at that temperature for a time compatible with the crystallization half-time at that temperature (second annealing step). The characterization of morphology is carried out by optical and electronic scanning microscopy. The temperature of the first annealing step does not influence the thickness of the fibrillar skin layer; however, such a layer is thinner than that found in the molded parts obtained without any annealing steps. The second annealing step does not influence the thickness of the fibrillar skin layer. The dimension of spherulites found in the core is strongly influenced by both annealing steps: the spherulite dimensions enlarge by the effect of annealing steps. A model that considers spherulite and fibril evolutions is adopted to describe the effect of molding conditions on the final morphology distribution along the part thickness. The model, which adopts as input the thermo-mechanical histories calculated by commercial software for injection molding simulation, consistently predicts the main effects of the molding conditions on the morphology distributions.

show abstract

The influences of multiple factors for flexural performance of polypropylene: crystallization, crystal evolution, nanoparticles

Cited by 2 publications

References 48 publications

Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys

Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys

Modeling and Analysis of Morphology of Injection Molding Polypropylene Parts Induced by In-Mold Annealing

Contact Info

Product

Resources

About