Development of an artificial intelligence model for wire electrical discharge machining of Inconel 625 in biomedical applications - Pasupuleti Thejasree; Natarajan Manikandan; Neeraj Sunheriya; Jayant Giri; Rajkumar Chadge; T. Sathish; Ajay Kumar; Muhammad Imam Ammarullah (IET Collaborative Intelligent Manufacturing)
Abstract Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro‐Fuzzy Inference System for forecasting WEDM parameters using grey‐based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision‐making in the production of high‐quality biomedical devices and components.