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Experimental investigation and multi-objective optimization of eco-friendly near-dry electrical discharge machining of shape memory alloy using Cu/SiC/Gr composite electrode

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Abstract

The near-dry electrical discharge machining processes have been conducted using air-mist or gas mist as a dielectric fluid to minimize the environmental impacts. In this article, near-dry electrical discharge machining (NDEDM) experiments have been performed to improve machining performance using an oxygen-mist dielectric fluid, a copper composite electrode, and Cu-Al-Be polycrystalline shape memory alloy (SMA) work materials. The copper composite electrode is made up of 12 wt% silicon carbide and 9 wt% graphite particles. The oxygen-mist pressure (Op), pulse on time (Ton), spark current (Ip), gap voltage (Gv), and flow rate of mixed water (Fr) were used as process parameters, and the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) were used as performance characteristics. The global optimal alternative solution has been predicted by the PROMETHEE-II (Preference Ranking Organization METhod for Enrichment Evaluations-II) optimization technique. The best combinations of process parameters have been used to examine the microstructure of composite tools and SMA-machined surfaces by scanning electron microscopy (SEM) analysis. The best global optimum settings (oP: 9 bar, Ip: 60 µs, Ip: 12 A, Gv: 40 V, and Fr: 12 ml/min) are predicted to attain optimum machining performance (MRR: 39.049 g/min, TWR: 1.586 g/min, and SR: 1.78 µm). The tool wear rate of the NDEDM process has been significantly reduced by the copper composite electrode due to increasing microhardness, wear resistance, and melting point. When compared to the pure copper electrode tool, the MRR of NDEDM is improved to 21.91%, while the TWR and SR are reduced to 46.66% and 35.02%, respectively.

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Abbreviations

\(\left[{D}_{j}\right]\) :

Evaluate difference matrix

\(\left[{N}_{ij}\right]\) :

Normalized matrix

\(\left[{P}_{j}\right]\) :

Preference matrix

\(\left[{Q}_{j}\right]\) :

Aggregate preference matrix

\(\left[{Y}_{ij}\right]\) :

Decision matrix

\({\nabla }^{-}\) :

Entering outranking flow value

\({\nabla }^{+}\) :

Leaving outranking flow value

\({\nabla }^{net}\) :

Calculate net outranking flow

\({w}_{j}\) :

Weight of each criterion

µm:

Micro-meter

µs:

Microseconds

Adj. MS:

Adjusted mean sequential sum of square

Adj. SS:

Adjusted sequential sum of square

C:

Number of parameters

Cu:

Copper

dB:

Decibel

D:

Degree of freedom

EDM:

Electrical discharge machining

F-value:

Statistical F-table value

g/Min:

Gram per minute

Gr:

Graphite

Gv:

Gap voltage

h:

Parameter’s level

Ip:

Spark current

L27:

Taguchi’s L27 orthogonal array

MSQ:

Mean sum of square

MRR:

Material removal rate

MRRn:

Normalized material removal rate

NDEDM:

Near-dry electrical discharge machining

Op:

Oxygen-mist pressure

Fr:

Mixing water flow rate

p :

Number of alternative solutions

Pa:

Total mean value

PC:

Percentage of contribution

Pi:

Mean value of ith trial

PROMETHEE-II:

Preference Ranking Organization METhod for Enrichment Evaluations-II

q :

Number of criteria

S/N:

Signal-to-noise ratio

SMA:

Shape memory alloy

SEM:

Scanning electron microscopy

SQ:

Sequential sum of square

Sj:

The sum of ith factor value

SiC:

Silicon carbide

SRn:

Normalized surface roughness

SR:

Surface roughness

Ton:

Pulse on time

TWRn:

Normalized tool wear rate

TWR:

Tool wear rate

wt%:

Weight percentage

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, 500075, India

    Nagarajan Vasantha Gowri & Kondreddi Krishnaveni

  2. Department of Electronics and Communication Engineering, Lingaya’s Vidyapeeth, Faridabad, Haryana, 121002, India

    Jaiprakash Narain Dwivedi

  3. Department of Mechanical Engineering, Muthayammal Engineering College, Namakkal, Tamil Nadu, 637 408, India

    Sampath Boopathi

  4. Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh, 11432, Saudi Arabia

    Murugesan Palaniappan

  5. Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522302, India

    Nageswara Rao Medikondu

Authors
  1. Nagarajan Vasantha Gowri
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  2. Jaiprakash Narain Dwivedi
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  3. Kondreddi Krishnaveni
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  4. Sampath Boopathi
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  5. Murugesan Palaniappan
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  6. Nageswara Rao Medikondu
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Contributions

Nagarajan Vasantha Gowri contributed to conducting experiments and literature survey.

Jaiprakash Narain Dwivedi contributed to Taguchi methods and analysis.

Kondreddi Krishnaveni contributed to conducting experiments.

Sampath Boopathi contributed results analyzing and interpreting the PROMETHEE-II method.

Murugesan Palaniappan contributed to fund for the research, proof writing, and analysis.

Nageswara Rao Medikondu contributed to review and interpretation of results.

Corresponding author

Correspondence to Sampath Boopathi.

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The authors declare no competing interests.

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Responsible Editor: Philippe Garrigues

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Gowri, N.V., Dwivedi, J.N., Krishnaveni, K. et al. Experimental investigation and multi-objective optimization of eco-friendly near-dry electrical discharge machining of shape memory alloy using Cu/SiC/Gr composite electrode. Environ Sci Pollut Res 30, 107498–107516 (2023). https://doi.org/10.1007/s11356-023-26983-6

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  • DOI: https://doi.org/10.1007/s11356-023-26983-6

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