Evidence map›Paper›PMID 38138417›Full record

ArticleMicromachines2023

Experimental Investigations of Using Aluminum Oxide (Al

Rakesh Chaudhari, Sakshum Khanna, Vivek K Patel, Jay Vora, Soraya Plaza, Luis Norberto López de Lacalle

Abstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Rakesh ChaudhariDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India.ORCID 0000-0001-6904-2362
Sakshum KhannaSchool of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India.ORCID 0000-0001-7918-3206
Vivek K PatelDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India.ORCID 0000-0001-7508-186X
Jay VoraDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India.ORCID 0000-0002-7543-903X
Soraya PlazaDepartment of Mechanical Engineering, University of the Basque Country, Escuela Superior de Ingenieros Alameda de Urquijo s/n, 48013 Bilbao, Spain.
Luis Norberto López de LacalleDepartment of Mechanical Engineering, University of the Basque Country, Escuela Superior de Ingenieros Alameda de Urquijo s/n, 48013 Bilbao, Spain.ORCID 0000-0002-1573-2787

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present study, a comprehensive parametric analysis was carried out using the electrical discharge machining of Ti6Al4V, using pulse-on time, current, and pulse-off time as input factors with output measures of surface roughness and material removal rate. The present study also used two different nanopowders, namely alumina and nano-graphene, to analyze their effect on output measures and surface defects. All the experimental runs were performed using Taguchi's array at three levels. Analysis of variance was employed to study the statistical significance. Empirical relations were generated through Minitab. The regression model term was observed to be significant for both the output responses, which suggested that the generated regressions were adequate. Among the input factors, pulse-off time and current were found to have a vital role in the change in material removal rate, while pulse-on time was observed as a vital input parameter. For surface quality, pulse-on time and pulse-off time were recognized to be influential parameters, while current was observed to be an insignificant factor. Teaching-learning-based optimization was used for the optimization of output responses. The influence of alumina and nano-graphene powder was investigated at optimal process parameters. The machining performance was significantly improved by using both powder-mixed electrical discharge machining as compared to the conventional method. Due to the higher conductivity of nano-graphene powder, it showed a larger improvement as compared to alumina powder. Lastly, scanning electron microscopy was operated to investigate the impact of alumina and graphene powder on surface morphology. The machined surface obtained for the conventional process depicted more surface defects than the powder-mixed process, which is key in aeronautical applications.

Indexed as

aluminum oxide (Al2O3) nanopowderEDMnano-grapheneTi6Al4VTLBO algorithm

Identifiers

PMID38138417
PMCPMC10745897

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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.