Evidence map›Paper›PMID 38116220›Full record

Article3D printing and additive manufacturing2023

Conductive Additive Manufactured Acrylonitrile Butadiene Styrene Filaments: Statistical Approach to Mechanical and Electrical Behaviors.

Osman Ulkir

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

1 author.

Osman UlkirDepartment of Electric and Energy, Technical Sciences Vocational School, Mus Alparslan University, Mus, Turkey.ORCID https://orcid.org/0000-0002-1095-0160

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Additive manufacturing is a process in which digital three-dimensional (3D) design data are used to build a component in layers by accumulating materials. There are many materials used in additive manufacturing technology. The most basic features that distinguish these materials are their strength and electrical behavior. They can be strong or flexible, resistant to abrasion, depending on the application used. Recently, 3D printing filament and polymeric composite materials combined with carbon nanostructures with electrical conductivity have been used. In this study, acrylonitrile butadiene styrene (ABS), a carbon black-filled conductive material with high strength and hardness, was preferred. The aim in this study is to focus on the mechanical and electrical behavior of the material processed in filament form. Fabrication of samples was done using a fused deposition modeling-based printer that controls filament orientation. Different experimental studies were conducted: (1) mechanical tests to determine the maximum tensile strength values of the samples; and (2) electrical tests to analyze the electrical resistances of the samples. In the design of the first experiment, infill volume, layer height, infill type, and printing direction were determined as factors affecting strength. In the design of the second experiment, the length, nozzle temperature, and measurement temperature were determined as the factors affecting the electrical resistance. Statistical analysis of the measured data was performed to evaluate the overall result of the experiments. Finally, a prediction model of real-time tensile strength and resistance values was created using machine learning algorithms. These algorithms are Gaussian Process Regression and Support Vector Machine. The results confirmed the known linear dependence of electrical resistance on the length of the 3D-printed conductive ABS samples and showed how changing the fabrication settings affected the strength values.

Indexed as

acrylonitrile butadiene styreneadditive manufacturingconductive filamentelectrical conductivityfused deposition modelingmachine learningstrength

Identifiers

PMID38116220
PMCPMC10726190

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.