Evidence map›Paper›PMID 41593350›Full record

ArticleScientific reports2026

Multi-response optimization and machine learning-based prediction of straight-groove warm incremental sheet forming of AZ31 magnesium alloy.

Amar A Khot, Rohit A Magdum, Anjali R Magdum, Alemu Workie Kebede, Pandivelan Chinnaiyan

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In one paragraph

Article in Scientific reports, 2026. 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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.

Amar A KhotDepartment of Mechanical Engineering, Sharad Institute of Technology College of Engineering, Yadrav, Maharashtra, 416115, India.
Rohit A MagdumDepartment of Mechanical Engineering, Kasegaon Education Society's Rajarambapu Institute of Technology, Affiliated to Shivaji University, Sakharale, Maharashtra, 415414, India. Rmagdum73@gmail.com.
Anjali R MagdumDepartment of Electronics and Computer Engineering, Sharad Institute of Technology College of Engineering, Yadrav, Maharashtra, 416115, India.
Alemu Workie KebedeDepartment of Mechanical Engineering, Institute of Technology, Debre Markos University, P.O. Box 269, Debre Markos, Ethiopia. alemu_workie@dmu.edu.et.
Pandivelan ChinnaiyanSchool of Mechanical Engineering, Vellore Institure of Technology, Vellore, 632014, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the warm straight-groove incremental sheet forming (ISF) behavior of AZ31 magnesium alloy using an integrated experimental, statistical, and machine learning approach. To test the effect of forming temperature, step-down, spindle speed and feed rate, a Taguchi L27 design was used to study the effect of above variables on forming time and forming force. TOPSIS multi-response optimization was used to find the most balanced parameter combination to result in low force and high process efficiency. The statistical result showed that temperature and step-down were the most prevailing factors that controlled the deformation behaviour at warm forming conditions. A Random Forest regression model was constructed in order to increase the predictive ability, and it was able to successfully recreate the trends in the forming time, forming force, and performance index. The fractographic analysis of the fractured wall of the groove proved the presence of a ductile failure mechanism in which voids and localisation of shear dominate. The combined DOE-TOPSIS-ML-SEM analysis offers a very powerful procedure of comprehending and optimizing the warm incremental sheet forming of lightweight AZ31 magnesium alloy.

Indexed as

AZ31 magnesium alloyForming forceIncremental sheet formingMachine learningMulti-response optimizationRandom forestTaguchi methodTOPSISWarm forming

Identifiers

PMID41593350
PMCPMC12909978

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LicenceCC BY-NC-ND
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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.