Evidence map›Paper›PMID 39339072›Full record

ArticlePolymers2024

Optimization of 3D Printing Parameters of High Viscosity PEEK/30GF Composites.

Dmitry Yu Stepanov, Yuri V Dontsov, Sergey V Panin, Dmitry G Buslovich, Vladislav O Alexenko, Svetlana A Bochkareva, Andrey V Batranin, Pavel V Kosmachev

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Polymers, 2024. 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

8 authors.

Dmitry Yu StepanovMicroelectronics of Multispectral Quantum Introscopy Laboratory of the R&D Center "Advanced Electronic Technologies", National Research Tomsk State University, 634050 Tomsk, Russia.ORCID 0000-0003-2558-7613
Yuri V DontsovDepartment of Materials Science, Engineering School of Advanced Manufacturing Technologies, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia.
Sergey V PaninDepartment of Materials Science, Engineering School of Advanced Manufacturing Technologies, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia.ORCID 0000-0001-7623-7360
Dmitry G BuslovichLaboratory of Nanobioengineering, Institute of Strength Physics and Materials Science of Siberian Branch of Russian Academy of Sciences, 634055 Tomsk, Russia.ORCID 0000-0001-7464-9628
Vladislav O AlexenkoLaboratory of Mechanics of Polymer Composite Materials, Institute of Strength Physics and Materials Science of Siberian Branch of Russian Academy of Sciences, 634055 Tomsk, Russia.
Svetlana A BochkarevaLaboratory of Mechanics of Polymer Composite Materials, Institute of Strength Physics and Materials Science of Siberian Branch of Russian Academy of Sciences, 634055 Tomsk, Russia.ORCID 0000-0003-4889-6128
Andrey V BatraninEngineering School of Non Destructive Testing, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia.ORCID 0000-0001-9678-2905
Pavel V KosmachevMicroelectronics of Multispectral Quantum Introscopy Laboratory of the R&D Center "Advanced Electronic Technologies", National Research Tomsk State University, 634050 Tomsk, Russia.ORCID 0000-0002-2776-7928

Funding

The Ministry of Education and Science of the Russian Federation FSWM-2022-0018
6 · The paper itself

Abstract

The aim of this study was to optimize a set of technological parameters (travel speed, extruder temperature, and extrusion rate) for 3D printing with a PEEK-based composite reinforced with 30 wt.% glass fibers (GFs). For this purpose, both Taguchi and finite element methods (FEM) were utilized. The artificial neural networks (ANNs) were implemented for computer simulation of full-scale experiments. Computed tomography of the additively manufactured (AM) samples showed that the optimal 3D printing parameters were the extruder temperature of 460 °C, the travel speed of 20 mm/min, and the extrusion rate of 4 rpm (the microextruder screw rotation speed). These values correlated well with those obtained by computer simulation using the ANNs. In such cases, the homogeneous micro- and macro-structures were formed with minimal sample distortions and porosity levels within 10 vol.% of both structures. The most likely reason for porosity was the expansion of the molten polymer when it had been squeezed out from the microextruder nozzle. It was concluded that the mechanical properties of such samples can be improved both by changing the 3D printing strategy to ensure the preferential orientation of GFs along the building direction and by reducing porosity via post-printing treatment or ultrasonic compaction.

Indexed as

additive manufacturing (AM)ANNcomputed micro-tomography (micro-CT)FDMglass fiberoptimizationPEEKpolymer matrix composite (PMC)Taguchi method

Identifiers

PMID39339072
PMCPMC11435271

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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.