Evidence map›Paper›PMID 37631476›Full record

ArticlePolymers2023

Optimization of 3D Printing Parameters for Enhanced Surface Quality and Wear Resistance.

Alexandra Ileana Portoacă, Razvan George Ripeanu, Alin Diniță, Maria Tănase

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 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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.

Alexandra Ileana PortoacăMechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiești, Romania.ORCID 0000-0003-1986-8419
Razvan George RipeanuMechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiești, Romania.ORCID 0000-0001-8255-8144
Alin DinițăMechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiești, Romania.ORCID 0000-0002-9242-9431
Maria TănaseMechanical Engineering Department, Petroleum-Gas University of Ploiești, 100680 Ploiești, Romania.ORCID 0000-0002-5563-6554

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, there has been a growing interest in the field of 3D printing technology. Among the various technologies available, fused deposition modeling (FDM) has emerged as the most popular and widely used method. However, achieving optimal results with FDM presents a significant challenge due to the selection of appropriate process parameters. Therefore, the objective of this research was to investigate the impact of process parameters on the tribological and frictional behavior of acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA) 3D-printed parts. The design of experiments (DOE) technique was used considering the input design parameters (infill percentage and layer thickness) as variables. The friction coefficient values and the wear were determined by experimental testing of the polymers on a universal tribometer employing plane friction coupling. Multi-response optimization methodology and analysis of variance (ANOVA) were used to highlight the dependency between the coefficient of friction, surface roughness parameters, and wear on the process parameters. The optimization analysis revealed that the optimal 3D printing input parameters for achieving the minimum coefficient of friction and linear wear were found to be an infill percentage of 50% and layer thickness of 0.1 mm (for ABS material), and an infill percentage of 50%, layer thickness of 0.15 mm (for PLA material). The suggested optimization methodology (which involves minimizing the coefficient of friction and cumulative linear wear) through the optimized parameter obtained provides the opportunity to select the most favorable design conditions contributing to a more sustainable approach to manufacturing by reducing overall material consumption.

Indexed as

3D printingABSdesign of experimentsfriction coefficientinfill percentagelayer thicknessparametric optimizationPLAroughnesswear

Identifiers

PMID37631476
PMCPMC10459717

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.