Evidence map›Paper›PMID 37112021›Full record

ArticlePolymers2023

Methods to Reduce Energy and Polymer Consumption for Fused Filament Fabrication 3D Printing.

Owen James Harding, Christian Andrew Griffiths, Andrew Rees, Dimitrios Pletsas

Abstract read
In one paragraph

Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Let's Print an Ecology in 3D (and 4D).Materials (Basel, Switzerland) · 2024
    Review
  2. 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

4 authors.

Owen James HardingCollege of Engineering, Swansea University, Swansea SA2 8PP, UK.
Christian Andrew GriffithsCollege of Engineering, Swansea University, Swansea SA2 8PP, UK.ORCID 0000-0002-7054-6135
Andrew ReesCollege of Engineering, Swansea University, Swansea SA2 8PP, UK.ORCID 0000-0003-0455-5444
Dimitrios PletsasCollege of Engineering, Swansea University, Swansea SA2 8PP, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fused Filament Fabrication (FFF) 3D printing is an additive technology used to manufacture parts. Used in the engineering industry for prototyping polymetric parts, this disruptive technology has been adopted commercially and there are affordable printers on the market that allow for at-home printing. This paper examines six methods of reducing the energy and material consumption of 3D printing. Using different commercial printers, each approach was investigated experimentally, and the potential savings were quantified. The modification most effective at reducing energy consumption was the hot-end insulation, with savings of 33.8-30.63%, followed by the sealed enclosure, yielding an average power reduction of 18%. For material, the most influential change was noted using 'lightning infill', reducing material consumption by 51%. The methodology includes a combined energy- and material-saving approach in the production of a referenceable 'Utah Teapot' sample object. Using combined techniques on the Utah Teapot print, the material consumption was reduced by values between 55.8% and 56.4%, and power consumption was reduced by 29% to 38%. The implementation of a data-logging system allowed us to identify significant thermal management and material usage opportunities to minimise power consumption, providing solutions for a more positive impact on the sustainable manufacturing of 3D printed parts.

Indexed as

3D printingfused deposition modellingfused filament fabricationmanufacturing optimisationpolylactic acid (PLA)power efficiency

Identifiers

PMID37112021
PMCPMC10144130

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.