Evidence map›Paper›PMID 42356960›Full record

ArticlePolymers2026

Introduction of Micro-Scale CFD Model of Foam Injection Moulding Process.

Daniel C Fritsche, Malte Schön, Christian Hopmann

Abstract read
In one paragraph

Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Daniel C FritscheChair of Plastics Processing, Rheinisch-Westfälische Technische Hochschule Aachen University, Seffenter Weg 201, 52074 Aachen, Germany.ORCID 0000-0001-9225-1285
Malte Schön3M Deutschland GmbH, Carl-Schurz-Straße 1, 41453 Neuss, Germany.ORCID 0000-0003-4756-0382
Christian HopmannChair of Plastics Processing, Rheinisch-Westfälische Technische Hochschule Aachen University, Seffenter Weg 201, 52074 Aachen, Germany.ORCID 0000-0003-4343-1508

Funding

Deutsche Forschungsgemeinschaft 493892533
6 · The paper itself

Abstract

Foam injection moulding (FIM) enables lightweight thermoplastic parts, but current process simulations do not resolve microstructure formation. This work presents a micro-scale CFD framework for FIM that captures gas-melt interaction and bubble morphology. A two-phase, compressible volume-of-fluid solver (OpenFOAM) with surface tension and viscoelastic Phan-Thien-Tanner rheology is coupled to a nucleation pre-processor based on classical nucleation theory, which places bubbles stochastically using macro-scale pressure and temperature histories. The approach was demonstrated on a plate geometry using a 2D through-thickness section to investigate bubble nucleation, deformation, coalescence, and interaction under realistic process conditions. The simulations reproduced characteristic morphology trends across the thickness. In particular, the predicted aspect ratio and orientation show the expected skin-core behaviour and agree qualitatively with experimental observations. These results demonstrate that the framework can describe morphology development beyond simplified spherical-cell assumptions and provides a proof of concept for multiscale coupling between macro-scale process conditions and micro-scale foam structure evolution. A simplified surrogate growth representation was used to enable bubble expansion; however, a physically based mass-transfer model is required for quantitatively accurate growth kinetics.

Indexed as

bubble deformationcell growth kineticsclassical nucleation theory (CNT)coalescencefoam injection moulding (FIM)microcellular foamingmultiscale simulationOpenFOAMvolume of fluid (VoF)

Identifiers

PMID42356960
PMCPMC13306941

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.