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Optimization Through Nature-Inspired Soft-Computing and Algorithm on ECG Process
Abstract
In the present research work selection of significant machining parameters depending on nature-inspired algorithm is prepared, during machining alumina-aluminum interpenetrating phase composites through electrochemical grinding process. Here during experimentation control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) are considered. The response data are initially trained and tested applying Artificial Neural Network. The paradoxical responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are accomplished individually by employing Cuckoo Search Algorithm. A multi response optimization for all the response parameters is compiled primarily by using Genetic algorithm. Finally, in order to achieve a single set of parametric combination for all the outputs simultaneously fuzzy based Grey Relational Analysis technique is adopted. These nature-driven soft computing techniques corroborates well during the parametric optimization of ECG process.
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