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Adaptive Optimization of Wire EDM Process for Ti6Al4V Alloy Using ANFIS and AI Technique
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Author(s): V. Senthilkumar (SRM TRP Engineering College, Trichy, India)and A. Nagadeepan (SRM TRP Engineering College, Trichy, India)
Copyright: 2025
Pages: 10
Source title:
Harnessing AI for Control Engineering
Source Author(s)/Editor(s): Mohamed Arezki Mellal (M'Hamed Bougara University, Algeria)
DOI: 10.4018/979-8-3693-7812-0.ch005
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Abstract
Wire Electrical Discharge Machining (WEDM) is a non-traditional machining process widely used for machining difficult-to-cut materials such as titanium alloys. This study proposes an intelligent optimization approach using Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize WEDM parameters for machining titanium alloy (Ti6Al4V) with coated wires. The key parameters investigated include pulse-on time, pulse-off time, peak current, and wire tension. The results demonstrate that the ANFIS model accurately predicts the optimal parameters, achieving a material removal rate (MRR) of up to 5.22 mm3/min (increased by 15%) and a surface roughness (Ra) as low as 3.60 µm (reduced by 12%). The proposed approach significantly improves machining efficiency and surface quality, reducing the need for costly experimental trials. This study highlights the potential of ANFIS in optimizing WEDM processes for industrial applications, particularly in aerospace and biomedical industries where titanium alloys are extensively used.
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