Evidence map›Paper›PMID 32752064›Full record

ReviewMicromachines2020

Recent Advances and Perceptive Insights into Powder-Mixed Dielectric Fluid of EDM.

Asarudheen Abdudeen, Jaber E Abu Qudeiri, Ansar Kareem, Thanveer Ahammed, Aiman Ziout

Abstract readReview
In one paragraph

Review in Micromachines, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Multi-Response Optimization of AlMaterials (Basel, Switzerland) · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Article
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.

Asarudheen AbdudeenMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, UAE.ORCID 0000-0003-2422-3906
Jaber E Abu QudeiriMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, UAE.ORCID 0000-0003-4740-2253
Ansar KareemMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, UAE.
Thanveer AhammedMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, UAE.
Aiman ZioutMechanical Engineering Department, College of Engineering, United Arab Emirates University, Al Ain 15551, UAE.ORCID 0000-0002-2355-3084

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrical discharge machining (EDM) is an advanced machining method which removes metal by a series of recurring electrical discharges between an electrode and a conductive workpiece, submerged in a dielectric fluid. Even though EDM techniques are widely used to cut hard materials, low efficiency and high tool wear remain remarkable challenges in this process. Various studies, such as mixing different powders to dielectric fluids, are progressing to improve their efficiency. This paper reviews advances in the powder-mixed EDM process. Furthermore, studies about various powders used for the process and its comparison are carried out. This review looks at the objectives of achieving a more efficient metal removal rate, reduction in tool wear, and improved surface quality of the powder-mixed EDM process. Moreover, this paper helps researchers select suitable powders which are exhibiting better results and identifying different aspects of powder-mixed dielectric fluid of EDM.

Indexed as

EDMMRRPMEDMSRTWR

Identifiers

PMID32752064
PMCPMC7464327

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.