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Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel

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Abstract

Fabrication of 316L steel with the desired surface integrity and thin modified recast layer for manufacturing various devices is very challenging using the traditional techniques and requires post-processing. Nevertheless, electro-discharge machining (EDM) is an evolving candidate among the non-traditional processes offering concurrent machining and surface alteration for the instrument manufacturing industry. To explore its full potential, this research work provides a thorough investigation of process variables on the machining performances and surface features primarily required for processing 316L steel in the industry. In this study, nano multi-walled carbon nanotubes (MWCNT) are utilized to improve the machining and surface responses. In addition to this, the parametric optimization is conducted through a Taguchi-based design which assists to obtain the highest material removal rate (MRR) of 42.25 mg/min corresponding to a 10-A peak current, 16-μs pulse-on time, 1-g/l MWCNT amount, and 45% duty cycle while the lowest surface roughness (SR) and recast layer thickness (RLT) of 1.58 μm and 5.243 μm respectively are attained at a 5-A peak current, 8-μs pulse-on time, 0.7-g/l MWCNT amount, and 45% duty cycle. Analysis of variance (ANOVA) reports peak current being the most momentous parameter complied by the MWCNT amount, pulse-on time, and duty cycle for MRR, SR, and RLT. The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. Accuracy levels of the predicted solution sets are verified by the confirmatory experiments showing the estimated errors of less than 10%. SEM analyses confirm excellent surface integrity with a comparatively thin recast layer formation.

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Data availability

The authors have ensured 100% data transparency. All data generated or analyzed during this study are available in this research paper.

Code availability

The authors customized the NSGA-II algorithms for optimization. Code is available as per request.

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Acknowledgements

Authors reveal their cordial gratefulness and admiration to Universiti Teknologi Petronas (UTP) for providing components and a working place.

Funding

The present research was fully assisted by the Ministry of Higher Education, Malaysia, through the FRGS fund-2020 (FRGS/1/2020/TK0/UTP/02/39; Cost center: 015MA0-132).

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak, Malaysia

    Md Al-Amin & Ahmad Majdi Abdul-Rani

  2. Department of Chemical Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia

    Rasel Ahmed

  3. Department of Materials Science and Engineering, Institute of Space Technology, Islamabad, Pakistan

    Muhammad Umair Shahid

  4. Department of Agroforestry and Environment Science, Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka, Bangladesh

    Fatema Tuj Zohura

  5. Lab Management Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia

    Muhammad Danial Bin Abd Rani

Authors
  1. Md Al-Amin
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  3. Rasel Ahmed
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Contributions

Idea and design of the study: Md Al-Amin, Ahmad Majdi Abdul-Rani; Conducting of experiments: Md Al-Amin; Data acquisition: Md Al-Amin, Rasel Ahmed, Muhammad Danial Bin Abd Rani, and Muhammad Umair Shahid; Analysis of data: Md Al-Amin, Rasel Ahmed, and Fatema Tuj Zohura.

Drafting the manuscript: Md Al-Amin, Fatema Tuj Zohura; Revision of the manuscript thoroughly for important intellectual content: Md Al-Amin, Ahmad Majdi Abdul-Rani, Muhammad Umair Shahid.

Approval of the manuscript for publication: Ahmad Majdi Abdul-Rani, Md Al-Amin, Fatema Tuj Zohura, Rasel Ahmed, Muhammad Umair Shahid, and Muhammad Danial Bin Abd Rani.

Corresponding author

Correspondence to Md Al-Amin.

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Al-Amin, M., Abdul-Rani, A.M., Ahmed, R. et al. Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel. Int J Adv Manuf Technol 115, 179–198 (2021). https://doi.org/10.1007/s00170-021-07169-1

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  • DOI: https://doi.org/10.1007/s00170-021-07169-1

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