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Experimental investigations on hybrid manufacturing: WEDM of WAAM-fabricated stainless-steel components using ANFIS modelling
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  • Published: 26 March 2026

Experimental investigations on hybrid manufacturing: WEDM of WAAM-fabricated stainless-steel components using ANFIS modelling

  • P. Thejasree1,
  • N. Manikandan1,
  • Siva Marimuthu2,
  • Rajadurai Murugesan3,
  • D. Palanisamy4,
  • Mukesh Kumar5,
  • Arun Kumar6 &
  • …
  • Regasa Yadeta Sembeta7 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Materials science
  • Mathematics and computing

Abstract

Wire Arc Additive Manufacturing (WAAM) enables the fabrication of large, near-net-shape stainless steel components, but the resulting surfaces require precision post-processing to meet industrial standards. In this study, Wire Electrical Discharge Machining (WEDM) was applied as a finishing process for WAAM-fabricated SS316L components, and a hybrid optimization–prediction framework was developed using Taguchi design, Grey Relational Analysis (GRA), and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. In total, there were 27 experimental runs conducted at different pulse-on, pulse-off, and current conditions. The results showed that pulse-on time (Ton) was the dominant influencing factor in the case of material removal rate (MRR), dimensional deviation (DD), and GD&T errors, while pulse-off time (Toff) was significantly regulated to surface roughness (SR) and geometric stability. The experimental analysis revealed that pulse-on time (Ton) was the most influential parameter governing material removal, dimensional accuracy, and geometric errors, whereas pulse-off time (Toff) played a key role in controlling surface finish and geometric stability. This emphasizes the critical importance of discharge control for achieving high-quality post-processing of WAAM components. For multi-response optimization, GRA provided a composite performance index that was used to train the ANFIS model. The predictive outcomes exhibited excellent agreement with experiments, confirmed by very low error metrics (MAPE = 2.19%, RMSE = 0.027, MAE = 0.022) and a strong correlation (R² = 0.9985). Overall, the WAAM–WEDM hybrid framework not only improves surface quality and dimensional consistency but also establishes a scalable, intelligent manufacturing pathway with strong potential for aerospace, biomedical, and energy applications.

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The necessary data used in the manuscript are already present.

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Acknowledgements

The authors gratefully thank the authors’ respective institutions for their strong support of this study.

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, School of Engineering, Mohan Babu University, Tirupati, Andhra Pradesh, 517102, India

    P. Thejasree & N. Manikandan

  2. Department of Engineering, Stoke on Trent, University of Staffordshire, Stoke-on-Trent, UK

    Siva Marimuthu

  3. Department of Aeronautical Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, India

    Rajadurai Murugesan

  4. Department of Mechanical Engineering, Adhi College of Engineering and Technology, Kancheepuram, Tamil Nadu, India

    D. Palanisamy

  5. Department of Mechanical Engineering, Mewar University, Chittorgarh, Rajasthan, India

    Mukesh Kumar

  6. Center for Promotion of Research, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, 248001, India

    Arun Kumar

  7. Department of Civil Engineering, College of Engineering and Technology, Mattu University, Metu, 318, Ethiopia

    Regasa Yadeta Sembeta

Authors
  1. P. Thejasree
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  2. N. Manikandan
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Contributions

P. Thejasree: Conceptualization, Data curation, Formal analysis, Investigation, Writing - original draft. N. Manikandan: Writing – original draft, Writing – review & editing. Siva Marimuthu: Supervision, Validation, Visualization, Investigation, Review & editing. Rajadurai Murugesan: Writing – original draft, Writing – review & editing. D. Palanisamy: Writing – original draft, Writing – review & editing. Mukesh Kumar: Writing – original draft, Writing – review & editing. Arun Kumar: Writing – review & editing. Regasa Yadeta Sembeta: Project administration, Writing – original draft, Writing – review and editing.

Corresponding author

Correspondence to Regasa Yadeta Sembeta.

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The authors declare no competing interests.

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This study did not involve human participants or animals; no ethical approval was required. All research procedures adhered to relevant ethical guidelines and best practices for non-human and non-animal research.

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The authors declare that this manuscript is original, has not been published before and is not currently being considered for publication elsewhere. The authors confirm that the manuscript has been read and approved by all named authors and that no other persons have satisfied the criteria for authorship but are not listed. The authors further confirm that all have approved the order of authors listed in the manuscript of us. The authors understand that the corresponding author is the sole contact for the Editorial process. The corresponding author is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. No additional information is available for this paper.

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Thejasree, P., Manikandan, N., Marimuthu, S. et al. Experimental investigations on hybrid manufacturing: WEDM of WAAM-fabricated stainless-steel components using ANFIS modelling. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45952-w

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  • Received: 14 August 2025

  • Accepted: 23 March 2026

  • Published: 26 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-45952-w

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Keywords

  • Hybrid manufacturing
  • SS316L
  • Wire arc additive manufacturing (WAAM)
  • Wire electrical discharge machining (WEDM)
  • Adaptive neuro fuzzy inference system (ANFIS)
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