Parametric optimization in micro-EDM drilling of Ti–46.5Al–5Nb–2Mo–0.3B alloy using teaching-learning-based optimization algorithm

Purpose The present study is aimed at enhancing the machining performance of titanium aluminide alloy (Ti–46.5Al–5Nb–2Mo–0.3B), known for its poor machinability but critical use in aerospace and automotive sectors. The objective is to identify the optimal machining conditions that could increase the material removal rate (MRR) while minimizing tool wear rate (TWR) during micro-electrical discharge drilling (micro-EDD). Design/methodology/approach Experiments were designed using a Taguchi L27 orthogonal array design using five crucial machining parameters namely: tool rotational speed, discharge current, pulse-on duration, pulse-off duration and power. Machining efficiency and tool performance was assessed by examining two important output responses, that is MRR and TWR. Teaching-Learning-Based Optimization (TLBO) algorithm was employed to optimize the EDD machining process. TLBO was utilized to reconcile the opposing objectives of increasing the MRR and lowering the TWR. Findings The optimal fitness value of 0.962 was obtained, for a tool rotational speed of 180 RPM, discharge current of 4 A, pulse-on duration of 9 µs, pulse-off length of 6 µs and power of 1000 W. Experimental validation revealed a robust relationship between predicted and experimental outcomes, with percentage errors within 5%, thereby confirming the efficacy of the TLBO methodology. This study demonstrates the effectiveness of the TLBO algorithm for multi-objective optimization in EDD processes, providing a framework for improving machining performance in advanced materials, such as titanium aluminides. Practical implications The study presents a validated optimization framework that can be directly applied to improve machining efficiency and tool longevity in industrial settings. This is particularly valuable for manufacturers working with advanced aerospace-grade alloys, enabling more precise, cost-effective and reliable production of micro-features. Originality/value This work is novel in its application of the TLBO algorithm for optimizing the micro-EDD process of titanium aluminide alloys, which are known for their challenging machinability. Unlike conventional optimization approaches, TLBO provides a robust, parameter-free method that adapts well to complex multi-objective problems, making it especially suitable for advanced manufacturing applications.