ArticleScientific reports2026
Experimental investigations on hybrid manufacturing: WEDM of WAAM-fabricated stainless-steel components using ANFIS modelling.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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