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.
Data availability
The necessary data used in the manuscript are already present.
References
Wu, B. et al. A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement. J. Manuf. Process. 35, 127–139. https://doi.org/10.1016/j.jmapro.2018.08.001 (2018).
Dávila, J. L., Neto, P. I., Noritomi, P. Y., Coelho, R. T. & da Silva, J. V. L. Hybrid manufacturing: a review of the synergy between directed energy deposition and subtractive processes. Int. J. Adv. Manuf. Technol. 110, 3377–3390. https://doi.org/10.1007/s00170-020-06062-7 (2020).
Wang, Z., Zhang, Y., Orquera, M., Millet, D. & Bernard, A. A new hybrid generative design method for functional & lightweight structure generation in additive manufacturing. Procedia CIRP. 119, 66–71. https://doi.org/10.1016/j.procir.2023.02.127 (2023).
Sasikumar, C. & Oyyaravelu, R. Mechanical properties and microstructure of SS316L created by WAAM based on GMAW. Mater. Today Commun. 38, 107807. https://doi.org/10.1016/j.mtcomm.2023.107807 (2024).
Manikandan, N., Binoj, J. S., Thejasree, P., Sasikala, P. & Anusha, P. Application of Taguchi method on wire electrical discharge machining of Inconel 625. Mater. Today: Proc. 39, 121–125 (2021).
Manikandan, N., Binoj, J. S., Krishnamachary, P. C. & Thejasree, P. & Arul Kirubakaran, D. Predictive models for wire spark erosion machining of AA 7075 alloy using multiple regression analysis. In Advances in Industrial Automation and Smart Manufacturing: Select Proceedings of ICAIASM 2019, 429–438Springer, Singapore, (2020).
Kruth, J. P., Leu, M. C. & Nakagawa, T. Progress in additive manufacturing and rapid prototyping. CIRP Ann. Manuf. Technol. 47, 525–540. https://doi.org/10.1016/s0007-8506(07)63240-5 (1998).
Edwards, K. L. Rapid manufacturing — the technologies and applications of rapid prototyping and rapid tooling. Mater. Eng. 23, 347–348. https://doi.org/10.1016/s0261-3069(01)00077-2 (2002).
Gogineni, A., Kale, R. V., Roy, S., Modi, P. & Kumar, P. Spatial Assessment of Snow Cover Patterns in the Sutlej River Basin Using Machine Learning Approaches and Remote Sensing Data. Physics Chem. Earth Parts A/B/C, 103996. (2025).
Achillas, C., Tzetzis, D. & Raimondo, M. O. Alternative production strategies based on the comparison of additive and traditional manufacturing technologies. Int. J. Prod. Res. 55, 3497–3509. https://doi.org/10.1080/00207543.2017.1282645 (2017).
Steenhuis, H. J. & Pretorius, L. Consumer additive manufacturing or 3D printing adoption: an exploratory study. J. Manuf. Technol. Manag. 27, 990–1012. https://doi.org/10.1108/jmtm-01-2016-0002 (2016).
Mani, M., Lane, B. M., Donmez, M. A., Feng, S. C. & Moylan, S. P. A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes. Int. J. Prod. Res. 55, 1400–1418. https://doi.org/10.1080/00207543.2016.1223378 (2017).
Srivastava, M. & Rathee, S. Additive manufacturing: recent trends, applications and future outlooks. Prog Addit. Manuf. 7, 261–287. https://doi.org/10.1007/s40964-021-00229-8 (2022).
Khoo, Z. X. et al. Y. 3D printing of smart materials: a review on recent progresses in 4D printing. Virtual Phys. Prototyp. 10, 103–122. https://doi.org/10.1080/17452759.2015.1097054 (2015).
Pei, E. 4D printing: dawn of an emerging technology cycle. Assem Autom. 34, 310–314. https://doi.org/10.1108/aa-07-2014-062 (2014).
Tibbits 4D printing: multi-material shape change. Archit. Des. 84, 116–121. https://doi.org/10.1002/ad.1710 (2014).
Gogineni, A., Sharma, S., Roy, S. & Kumar, P. Long-Term drought analysis and forecasting using hybrid wavelet Denoise random forest models with SPI, Z-Score, and China Z-Index. Arabian J. Sci. Engineering, 1–29. (2025).
Somsole, L. N. et al. Experimental investigation and optimization of epoxy composites reinforced with jute fiber and alumina using the Jaya ANFIS approach. Sci. Rep. 15 (1), 30462 (2025).
Li, F., Zhang, W., Kooi, B. J. & Pei, Y. Eutectic aluminum alloys fabricated by additive manufacturing: a comprehensive review. J. Mater. Sci. Technol. 250, 123–164. https://doi.org/10.1016/j.jmst.2025.06.016 (2026).
Natarajan, M. et al. Investigational analysis on wire electrical discharge machining of aluminium based composites by Taguchi’s method. SAE Tech. Pap. -28-0075 (2023). (2023).
Natarajan, M. et al. Evolution of regression and neural network models on wire electrical discharge machining of nickel-based superalloy. SAE Tech. Pap. -28-0078 (2023). (2023).
Natarajan, M., Pasupuleti, T., Katta, L. N., Somsole, L. N. & Kiruthika, J. Application of Taguchi approach on wire electrical discharge machining of SS304 for automotive applications. SAE Tech. Pap. -28-0151 (2023). (2023).
Gebhardt, A. Materials, design, and quality aspects for additive manufacturing. In Understanding Additive Manufacturing, 129–149 (Carl Hanser Verlag, München, (2011).
Manikandan, N., Binoj, J. S., Varaprasad, K. C., Thejasree, P. & Raju, R. Investigations on wire electrical discharge machining of nickel-based superalloy using Taguchi’s approach. In Advances in Industrial Automation and Smart Manufacturing: Select Proceedings of ICAIASM 2019, 267–274Springer, Singapore, (2020).
ASTM F42 Committee. Terminology for Additive Manufacturing – General Principles – Terminology (ASTM International, 2017).
Ahn, D. G. Directed energy deposition (DED) process: state of the art. Int. J. Precis Eng. Manuf. -Green Technol. 8, 703–742. https://doi.org/10.1007/s40684-020-00302-7 (2021).
Binoj, J. S. et al. Machinability studies on wire electrical discharge machining of nickel alloys using multiple regression analysis. Mater. Today: Proc. 39, 155–159 (2021).
Natarajan, M., Pasupuleti, T., Silambarasan, R. & Katta, L. N. Development of prediction models for spark erosion machining of SS304 using regression analysis. SAE Tech. Pap. -28-0339 (2022). (2022).
Singh, A., Kapil, S. & Das, M. A comprehensive review of the methods and mechanisms for powder feedstock handling in directed energy deposition. Addit. Manuf. 35, 101388. https://doi.org/10.1016/j.addma.2020.101388 (2020).
Manikandan, N., Thejasree, P., Reddy, D. R. & Kumar, P. P. Development of regression model and optimization of process parameters for wire electrical discharge of SAE 1010 steel using Taguchi grey approach. J. Phys. : Conf. Ser. 2837, 012084 (2024).
Zhang, J. Additive manufacturing: materials, processes, quantifications and applications (Butterworth-Heinemann, 2018).
Tang, Z. J. et al. H.-C. A review on in situ monitoring technology for directed energy deposition of metals. Int. J. Adv. Manuf. Technol. 108, 3437–3463. https://doi.org/10.1007/s00170-020-05569-3 (2020).
Zhang, D., Lim, W. Y. S., Duran, S. S. F., Loh, X. J. & Suwardi, A. Additive manufacturing of thermoelectrics: emerging trends and outlook. ACS Energy Lett. 7, 720–735. https://doi.org/10.1021/acsenergylett.1c02553 (2022).
Pasupuleti, T., Natarajan, M., Katta, L. N. & Naidu, B. V. V. Microstructure and mechanical behaviour of dissimilar laser welded joints for automobile applications. SAE Int. J. Adv. Curr. Pract. Mobil. 5, 1592–1595 (2022).
Raju, R. et al. Optimization and performance evaluation of PLA polymer material in situ carbon particles on structural properties. Mater. Today: Proc. 39, 223–229 (2021).
Seow, C. E. et al. Wire + arc additively manufactured Inconel 718: effect of post-deposition heat treatments on microstructure and tensile properties. Mater. Des. 183, 108157. https://doi.org/10.1016/j.matdes.2019.108157 (2019).
Fu, Y. et al. Optimization of shape and performance for wire and arc additive manufacturing with in-situ rolling of Ti–6Al–4V ELI alloy. J. Mater. Res. Technol. 35, 4833–4847. https://doi.org/10.1016/j.jmrt.2025.02.068 (2025).
Kumar, L., Goyal, A. & Pathak, V. K. Prediction and optimization of WEDM parameters for machining of NiTi-shape memory alloy using ANFIS-PSO approach. Discov Appl. Sci. 7, 1–. https://doi.org/10.1007/s42452-025-06663-5 (2025).
Chauhan, P., Vaghasia, V., Chaudhari, R. & Vora, J. Experimental investigations on the fabrication of low alloy steels using wire arc additive method. Eur. Proc. Sci. Technol. Eng. Math. 28, 484–491. https://doi.org/10.55549/epstem.1523859 (2024).
Thejasree, P. & Krishnamachary, P. C. Weldability investigations on laser welding of Inconel 718 plates using Taguchi approach. In Recent Advances in Materials and Modern Manufacturing: Select Proceedings of ICAMMM 2021, 245–254Springer, Singapore, (2022).
Zaman, U. K., Rivette, M., Siadat, A. & Mousavi, S. M. Integrated product-process design: material and manufacturing process selection for additive manufacturing using multi-criteria decision making. Robot Comput. Integr. Manuf. 51, 169–180. https://doi.org/10.1016/j.rcim.2017.12.005 (2018).
Sarathchandra, D. T., Davidson, M. J. & Visvanathan, G. Parameters effect on SS304 beads deposited by wire arc additive manufacturing. Mater. Manuf. Process. 35, 852–858. https://doi.org/10.1080/10426914.2020.1743852 (2020).
Grzesik, W. Hybrid additive and subtractive manufacturing processes and systems: a review. J. Mach. Eng. 18, 5–24. https://doi.org/10.5604/01.3001.0012.7629 (2018).
Antunes, F. et al. Fatigue crack growth in maraging steel obtained by selective laser melting. Appl. Sci. 9, 4412. https://doi.org/10.3390/app9204412 (2019).
Singh, S., Sharma, S. K. & Rathod, D. W. A review on process planning strategies and challenges of WAAM. Mater. Today. 47, 6564–6575. https://doi.org/10.1016/j.matpr.2021.02.632 (2021).
Vishwanatha, Rao, R. N. et al. Effects of arc current and travel speed on the processing of stainless steel via wire arc additive manufacturing (WAAM) process. J. Adhes. Sci. Technol. 38, 2222–2239. https://doi.org/10.1080/01694243.2023.2289770 (2024).
Manikandan, N. et al. Integration of hybrid grey based ANFIS tool for enhanced laser beam welding of nickel alloy using computational modelling. Int J. Interact. Des. Manuf 1–12 (2024).
Asad, M., Sana, M., Farooq, M. U. & Tlija, M. Producing micro impressions on Al6061 under alumina-mixed deionized water as dielectric during electric discharge machining. J. Micromech Microeng. 35, 035011. https://doi.org/10.1088/1361-6439/adb044 (2025).
Sana, M., Asad, M., Farooq, M. U., Tlija, M. & Haber, R. Sustainability metrics targeted optimization and electric discharge process modelling by neural networks. Sci. Rep. 15, 3375. https://doi.org/10.1038/s41598-024-78883-5 (2025).
Hannan, A. et al. Machining performance, economic and environmental analyses and multi-criteria optimization of electric discharge machining for SS310 alloy. Sci. Rep. 14, 28930. https://doi.org/10.1038/s41598-024-79338-7 (2024).
Hurairah, M. A., Sana, M., Farooq, M. U. & Anwar, S. Genetic algorithm-based optimization of artificial neural network of process parameters and characterization of machining errors in graphene mixed dielectric. Arab. J. Sci. Eng. https://doi.org/10.1007/s13369-024-09029-y (2024).
Farooq, M. U., Ali, M. A., Anwar, S. & Bhatti, H. A. Process parameters optimization and performance analysis of micro-complex geometry machining on Ti6Al4V. Int. J. Interact. Des. Manuf. 18, 4573–4593. https://doi.org/10.1007/s12008-023-01711-z (2024).
Kokare, S. et al. Wire arc additive manufacturing of a high-strength low-alloy steel part: environmental impacts, costs, and mechanical properties. Int. J. Adv. Manuf. Technol. 134, 453–475 (2024).
Pasupuleti, T., Natarajan, M. & Silambarasan, R. Development of regression models for laser beam welding of Inconel 718 alloy thin sheets. SAE Tech. Pap. -28-0340 (2022). (2022).
Li, S. H., Kumar, P., Chandra, S. & Ramamurty, U. Directed energy deposition of metals: processing, microstructures, and mechanical properties. Int. Mater. Rev. 68, 605–647. https://doi.org/10.1080/09506608.2022.2097411 (2023).
Lim, J. S., Oh, W. J., Lee, C. M. & Kim, D. H. Selection of effective manufacturing conditions for directed energy deposition process using machine learning methods. Sci. Rep. 11, 24169. https://doi.org/10.1038/s41598-021-03622-z (2021).
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
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
Ethics declarations
Competing interest
The authors declare no competing interests.
Ethical approval
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.
Consent for publication
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.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-45952-w