Abstract
This presented article focuses on surface characterization and assessing the satisfactory machining condition of WEDMed Inconel 625. This work material has been received remarkable attention to the industrial and academic organization for its end use applications. WEDM is well-known machining process for intricate shape cutting and machining hard materials. The experimental design was planned according to L27 orthogonal array (OA), by varying controllable process parameter (i.e. Wire-Tension, Wire-speed, Flushing-Pressure, Discharge-Current and Spark-on Time), each parameter varied at four discrete levels, within the selected parametric domain. WEDMed surfaces have been investigated with a focus to the surface characterization of selected machined surface through captured images from scanning electron microscope (SEM). Eventually, multi-response optimization of process parameters was sought by using a combination of nonlinear regression modelling, fuzzy inference system (FIS) with Teaching Learning-Based Optimization (TLBO) algorithm. The obtained TLBO result was compared with the Genetic algorithm (GA). The results show that optimization algorithms are effective tools for getting satisfactory optimal machining conditions during WEDM process of Inconel 625.
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