Evidence map›Paper›PMID 41469456›Full record

ArticleScientific reports2025

Predictive modelling and optimization of WEDM of nickel aluminium bronze alloy using optimised support vector regression and evolutionary algorithm.

Subhankar Saha, Sri Srinivasa Raju Modampuri, Hrishikesh Dutta, Rammohan Mallipeddi, Dhanaraj Savary Nasan, Mridusmita Roy Choudhury

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Subhankar SahaDepartment of Mechanical Engineering, Brainware University, Kolkata, India.
Sri Srinivasa Raju ModampuriCollege of Computer Science, Sichuan University, Chengdu, 610065, China.
Hrishikesh DuttaCentre for Additive Manufacturing, Chennai Institute of Technology, Chennai, Tamil Nadu, 600069, India. hrishikeshdutta@citchennai.net.
Rammohan MallipeddiDepartment of Artificial Intelligence, School of Electronics, Kyungpook National University, Daegu, Republic of Korea.
Dhanaraj Savary NasanThe Institution of Engineers (India), Kolkata, India.
Mridusmita Roy ChoudhuryDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. mridusmita.choudhury@manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The primary objective of this study was to develop an accurate predictive framework and an efficient multi-objective optimisation strategy for wire electric discharge machining (WEDM) of NAB alloy, focusing on Cutting Speed (CS) and Surface Roughness (SR). An optimized Support Vector Regression (OSVR) model was constructed to capture the complex and stochastic input-output relationships inherent to the spark erosion process. The model exhibited excellent predictive accuracy, with MSE = 0.0027 and R

Indexed as

IBEA-AOGMachine learningNAB alloyOptimizationOSVRWEDM

Identifiers

PMID41469456
PMCPMC12855905

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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.