Evidence map›Paper›PMID 39379423›Full record

ArticleScientific reports2024

Enhanced machining of Al 10%SiC

Umair Arif, Imtiaz Ali Khan, Faisal Hasan

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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

3 authors.

Umair ArifDepartment of Mechanical Engineering, Aligarh Muslim University, Aligarh, India. umairarif@zhcet.ac.in.
Imtiaz Ali KhanDepartment of Mechanical Engineering, Aligarh Muslim University, Aligarh, India.
Faisal HasanDepartment of Mechanical Engineering, Aligarh Muslim University, Aligarh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A sustainable approach was proposed to address environmental pollution, carbon footprint and economic efficiency challenges in Electrical Discharge Machining (EDM). This approach involved the use of Bio-dielectric such as biodiesel and Bio fuel (distilled water with 10% ethanol). The EDM process performance was further optimized by experimenting with both electrodes' rotation (i.e., in same direction, opposite direction, no rotation) and the use of treated tools (no treatment, heat treatment, cryogenic treatment). Biodiesel as a bio-dielectric showed promise by delivering the highest Material Removal Rate (MRR) and the lowest Tool Wear Rate (TWR). Bio-fuel (distilled water with 10% ethanol) resulted in the lowest Surface Roughness (SR) and cleaner machined surface with least carbon deposition. Additionally, electrode rotation improved flushing and enhanced performance parameters, with opposite direction rotation yielding the highest MRR and the lowest SR. However, no rotation of electrodes resulted in the lowest TWR. The use of treated tools, specifically heat-treated and cryogenically treated tools, also improved performance and reduced energy consumption, with cryogenic treatment providing the highest MRR, heat treatment giving least SR, and no treatment providing least TWR. Certain interactions between factors significantly impacted performance parameters. Grey relational analysis revealed that using distilled water with 10% ethanol as a dielectric, employing cryogenically treated copper tools, and having no rotation of both electrodes yielded the best performance parameters.

Indexed as

Annealed cu toolBiodieselCarbon footprintCryogenically treated toolDistilled water with 10% ethanolRotary tool rotary workpiece EDM

Identifiers

PMID39379423
PMCPMC11461653

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.