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<p>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 (I<sub>p</sub>), pulse-off time (T<sub>off</sub>), and pulse-on time (T<sub>on</sub>) 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&#xa0;g/min) and lowest SR (2.12&#xa0;μm) due to superior spark stability and thermal conductivity. The simultaneous optimization for MWCNT-based WEDM process has given the optimal parametric settings of I<sub>p</sub> of 4&#xa0;A, T<sub>off</sub> of 20 µs, and T<sub>on</sub> of 42 µs with the response values of MRR and SR as 2.8144&#xa0;g/min and 3.06&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月18日

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

摘要

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.