Evidence map›Paper›PMID 37346190›Full record

Article3D printing and additive manufacturing2023

Closed-Loop Filament Feed Control in Fused Filament Fabrication.

Michele Moretti, Arianna Rossi

Abstract read
In one paragraph

Article in 3D printing and additive manufacturing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Michele MorettiDipartimento di Ingegneria, Università di Perugia, Perugia, Italy.
Arianna RossiDipartimento di Ingegneria, Università di Perugia, Perugia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fused filament fabrication (FFF) is an additive manufacturing process where a thermoplastic polymeric material, provided in the form of a filament, is extruded to create layers. Achieving a consistent flow of the extruded material is key to ensure quality of the final part. Extrudate flow depends on many factors; among these, the rate at which the filament is fed into the extruder. In a conventional FFF machine, filament transport is achieved through the use of a drive gear. However, slippage between the gear and the filament may occur, leading to reduced transport and the consequent local decrease of extrudate flow rate, which in turn leads to a series of imperfections in the fabricated part due to underextrusion, including reduced density. In this work, we propose a closed-loop control system to ensure the correct filament transport to the extruder. The system works through the comparison between the nominal transport of the filament and the actual filament transport measured using an encoder. The measured value is used to correct the filament feed rate in real time, ensuring a material flow close to the nominal one, regardless of the other process parameters. In this work, an instrumented FFF machine prototype was used to investigate the performance of the approach. For validation, parts were realized using different process parameters, while enabling and disabling the closed-loop control system. Results showed that the relative filament transport error decreased from up to 9% to below 0.25% and a density increase up to ∼10% regardless of the process parameters, as well as the reduction of interlayer and intralayer voids, as highlighted through cross-sectional imaging of realized samples. A reduction of defects on realized parts was observed, especially at higher fabrication feed rates.

Indexed as

additive manufacturingextruderfilament transportfused deposition modelingfused filament fabricationin-process control

Identifiers

PMID37346190
PMCPMC10280219

What OpenQuestion holds

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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.