Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because the high wear rate cutting slower processing. The complexities prompted creation cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has potential to be useful for removal harder electrically conductive. In order create intricate designs, this method frequently employed. input factors, including pulse duration (on/off) peak current, were taken into account during experimental design process. material removal, surface roughness, dimensional deviation, GD&T errors opted as performance indicators. approach proposed by Taguchi was selected investigation process an Analysis Variance find out relative momentousness each factor. From analysis it perceived applied current predominant factor influences chosen output characteristics. aspiration article evolve decision-making model based on hybrid learning can adopted predict measures affect WEDM According findings, value ANFIS-GRG, predicted 0.7777, fact closer than any other value. ability help make variety different processes efficient. showed model’s functionality enhanced, helps producers well-informed decisions.