Evidence map›Paper›PMID 39870639›Full record

ArticleScientific reports2025

Sustainability metrics targeted optimization and electric discharge process modelling by neural networks.

Muhammad Sana, Muhammad Asad, Muhammad Umar Farooq, Mehdi Tlija, Rodolfo Haber

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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

5 authors.

Muhammad SanaDepartment of Industrial and Manufacturing Engineering, Faculty of Mechanical Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.ORCID https://orcid.org/0000-0003-1613-4188
Muhammad AsadDepartment of Industrial and Manufacturing Engineering, Faculty of Mechanical Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan.
Muhammad Umar FarooqThe Sargent Centre for Process Systems Engineering, University College London, London, SW7 2AZ, UK. Umarmuf0@gmail.com.ORCID https://orcid.org/0000-0003-4139-2082
Mehdi TlijaDepartment of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia.
Rodolfo HaberCenter for Automation and Robotics, CSIC-Universidad Politécnica de Madrid, Arganda del Rey, Madrid, 28500, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aluminium and its alloys, especially Al6061, have gathered significant interest among researchers due to its less density, great durability, and high strength. Due to their lightweight properties, the precise machining of these alloys can become expensive through conventional machining operations for intricate products. Therefore, non-traditional machining such as electric discharge machining (EDM) can potentially be opted for the cutting of Al6061. EDM is often criticized due to its low machining rates, therefore, in the current work,  cryogenic treatment (CT) has been performed on the brass electrode to evaluate the improvement in the machining rates. In addition, kerosene oil (KO) has been engaged in traditional EDM which is replaced with the deionized water (DI) based dielectric as a sustainable alternative. The machining variables such as spark voltage (S

Indexed as

Aluminium 6061Artificial neural networkCryogenic treatmentDeionized waterElectric discharge machiningMaterial removal rate

Identifiers

PMID39870639
PMCPMC11772881

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.