Evidence map›Paper›PMID 36143740›Full record

ArticleMaterials (Basel, Switzerland)2022

Parametric Optimisation of Friction-Stir-Spot-Welded Al 6061-T6 Incorporated with Silicon Carbide Using a Hybrid WASPAS-Taguchi Technique.

Neeru Chaudhary, Sarbjit Singh, Mohinder Pal Garg, Harish Kumar Garg, Shubham Sharma, Changhe Li, Elsayed Mohamed Tag Eldin, Samah El-Khatib

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Neeru ChaudharyDepartment of Mechanical Engineering, Punjab Engineering College (Deemed to be University), Chandigarh 160012, India.ORCID 0000-0001-5627-0315
Sarbjit SinghDepartment of Mechanical Engineering, Punjab Engineering College (Deemed to be University), Chandigarh 160012, India.
Mohinder Pal GargDepartment of Mechanical Engineering, DAV University, Jalandhar 144001, India.ORCID 0000-0003-2276-707X
Harish Kumar GargDepartment of Mechanical Engineering, DAV University, Jalandhar 144001, India.
Shubham SharmaMechanical Engineering Department, University Center for Research & Development, Chandigarh University, Mohali 140413, India.ORCID 0000-0001-9446-8074
Changhe LiSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.ORCID 0000-0003-4409-956X
Elsayed Mohamed Tag EldinFaculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt.ORCID 0000-0003-3151-9967
Samah El-KhatibFaculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt.ORCID 0000-0001-5968-2796

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Friction stir spot welding (FSSW) is one of the most popular fusion joining processes. The process is a solid-state welding process that allows welding of weldable as well as non-weldable materials. As a part of this investigation, weld samples of Al6061-T6 were reinforced with silicon carbide (SiC) powder with an average particle size of 45 µm. Initially, a Taguchi L9 orthogonal array was developed with three factors, i.e., rotational speed of the tool, pre-dwelling time, and diameter of the hole that was filled with SiC before welding. The effects of the SiC particles and process parameters were investigated as tensile-shear load and micro-hardness. The optimisation of parameters in order to maximise the output responses-i.e., strength and hardness of the welded joints-was performed using a hybrid WASPAS-Taguchi method. The optimised process parameters obtained were a 3.5 mm guiding hole diameter, 1700 rpm tool rotation speed, and 14 s of pre-dwelling time.

Indexed as

friction stir spot weldingMCDMSiC microparticlesTaguchiWASPAS

Identifiers

PMID36143740
PMCPMC9500981

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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.