Parametric investigation in electrical discharge drilling of Ti-alloy using ANFIS

The need for precise and efficient manufacturing processes is rapidly growing worldwide for the conversion of biomaterials into highly accurate and precise artificial implants and medical devices. Ti-based alloys are particularly used for implants and bone plate materials because they have good mechanical as well as biological properties. Predicting increased productivity during EDD of Ti-6Al-4V alloy with less tool damage and improved dimensional accuracy of the drilled hole is proposed in the study. Process performance was evaluated in terms of metal removal rate (MRR), tool wear rate (TWR), and hole taper (HT), as functions of discharge current, pulse-on time, pulse-off time, and dielectric pressure. A hybrid modeling approach combining Adaptive Neuro-Fuzzy Inference System (ANFIS) model integrating artificial neural networks (ANN) and fuzzy logic (FL) was employed to capture the non-linear relationships among variables. The model exhibited a close agreement with experimental results, with prediction errors 1.04% for MRR, 5.65% for TWR and 4.12% for HT. Discharge current and dielectric pressure were identified as the most influential parameters. While the proposed approach effectively predicts process behavior within the experimental range, its applicability beyond the tested parameter domain requires further validation. The study demonstrates the potential of hybrid modeling for achieving enhanced precision and efficiency in the fabrication of biomedical components.