Optimization of EDM Process Parameters through Teaching Learning Based Optimization Algorithm

Abstract

Electrical Discharge Machining (EDM) is a non-traditional machining process
where intricate and complex shapes can be machined. Only electrically conductive
materials can be machined by this process and is one of the important machining
processes for machining high strength, temperature-resistant (HSTR) alloys. For
achieving the best performance of the EDM process, it is crucial to carry out parametric
design responses such as Material Removal Rate, Tool Wear Rate, Gap Size etc. It is
essential to consider most number of input parameters to get the better result. In the
present work Teaching-Learning-Based optimization (TLBO) algorithm has been applied
for multi-objective optimization of the responses of EDM process. The optimization
performance of the TLBO algorithm is compared with that of other population-based
algorithms, e.g., genetic algorithm (GA), ant colony optimization (ACO), and artificial
bee colony (ABC) algorithm. It is observed that the TLBO algorithm performs better than
the others with respect to the optimal process response values.