Evidence map›Paper›PMID 41708749›Full record

ArticleScientific reports2026

Performance optimization of wire EDM of Nitinol shape memory alloy using BBD RSM and TLBO with alumina nano graphene and MWCNT Powder mixed dielectric.

Inam Ur Rehman, Rakesh Chaudhari, Jay Vora, Vivek Patel, Sakshum Khanna, Subraya Krishna Bhat

Abstract read
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.

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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

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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

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5 · Who and what money

Authors and funding

6 authors.

Inam Ur RehmanDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India.
Rakesh ChaudhariDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India. rakesh.chaudhari@sot.pdpu.ac.in.
Jay VoraDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India.
Vivek PatelDepartment of Mechanical Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India.
Sakshum KhannaSchool of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India.
Subraya Krishna BhatManipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. sk.bhat@manipal.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study investigated the performance optimization of Wire Electrical Discharge Machining (WEDM) of Nitinol Shape Memory Alloy (SMA) using a hybrid design approach combining Box-Behnken design and Teaching–Learning based optimization (TLBO). A comparative study of three nano-powders, namely, alumina, nano-graphene, and multi-walled carbon nanotubes (MWCNTs), was conducted to investigate their effect on material removal rate (MRR), surface roughness (SR), and surface morphology. The influence of key process parameters, discharge current (Ip), pulse-off time (Toff), and pulse-on time (Ton) has been systematically evaluated through experimental trials. Non-linear regression models were developed for both MRR and SR responses, and their statistical adequacy was validated using ANOVA and R² values, all exceeding 96%, confirming strong model accuracy. ANOVA further identified discharge current as the most significant factor, with the highest F-values for MRR and SR. Among all powders, MWCNTs consistently outperformed, achieving the highest MRR (3.6353 g/min) and lowest SR (2.12 μm) due to superior spark stability and thermal conductivity. The simultaneous optimization for MWCNT-based WEDM process has given the optimal parametric settings of Ip of 4 A, Toff of 20 µs, and Ton of 42 µs with the response values of MRR and SR as 2.8144 g/min and 3.06 μm, respectively. Additionally, a comparative experimental at optimized variables revealed that MWCNT-assisted WEDM yielded a 60.57% increase in MRR and a 75.81% reduction in SR over conventional WEDM. SEM analysis further shown that MWCNT-based machining produced the smoothest surfaces with minimal defects, while conventional EDM exhibited extensive pitting and re-solidified debris. This integrated experimental and optimization study provides a robust framework for improving the machinability and surface integrity of Nitinol SMA using advanced nano-powder-assisted WEDM.

Indexed as

Alumina, Nano-graphene, MWCNTManufacturing OptimizationNitinolShape Memory AlloyWire Electrical Discharge Machining (WEDM)

Identifiers

PMID41708749
PMCPMC13004933

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