Evidence map›Paper›PMID 40428633›Full record

ArticleMicromachines2025

Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN).

Beytullah Erdoğan, Abdulsamed Güneş, İrfan Kılıç, Orhan Yaman

Abstract read
In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Beytullah ErdoğanDepartment of Mechanical Engineering, Zonguldak Bülent Ecevit University, Zonguldak 67100, Turkey.ORCID 0000-0002-6120-9196
Abdulsamed GüneşDepartment of Electric and Energy, Firat University, Elazığ 23119, Turkey.ORCID 0000-0002-8652-3720
İrfan KılıçDepartment of Information Technology, Firat University, Elazığ 23119, Turkey.ORCID 0000-0001-5079-2825
Orhan YamanDepartment of Forensic Engineering, Firat University, Elazığ 23119, Turkey.ORCID 0000-0001-9623-2284

Funding

FUBAP (Firat University Scientific Research Projects Unit ADEP 25.55
6 · The paper itself

Abstract

Machining processes often face challenges such as elevated temperatures and wear, which traditional cutting fluids are insufficient to address. As a result, solutions involving nanoparticle additives are being explored to enhance cooling and lubrication performance. This study investigates the effect of thermal conductivity, an important property influenced by the densities of mono and hybrid nanofluids. To this end, various nanofluids were prepared by incorporating hexagonal boron nitride (hBN), zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), titanium dioxide (TiO

Indexed as

cutting fluidsfeedforward artificial neural network (FFANN)nanoparticlethermal conductivity

Identifiers

PMID40428633
PMCPMC12113555

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.