Application of Hybrid Artificial Intelligence Approach for Forecast of Performance Metrics in Sustainable Wire Electro Discharge Machining of SAE 1010 Low Carbon Steel

SAE 1010's high quality makes it a top pick for use in automotive applications, especially in headed fasteners and bolts. Its most common application is in automobiles, but it has many exciting potential uses in other fields of technology as well. Alternative methods of material removal have been the key to solving many previously intractable machining difficulties. It has several practical uses in aircraft engineering and has great potential for usage in other technical fields. It might be difficult to manufacture complex shaped components using conventional approaches of machining. To avoid such problems, numerous state-of-the-art machining techniques have been created. Wire Electrical Discharge Machining (WEDM) is a type of EDM that can be used for this purpose. In this explorative study, Taguchi's approach is engaged to investigate the WEDM of SAE 1010 from a green perspective. This investigation uses a natural dielectric fluid to lessen its impact on the natural world. This explorative analysis examined the impact that factors have on the WEDM method. Material removal rate, surface roughness, and tolerance errors are just some of the many performance indicators taken into account in this investigation. An artificial intelligence technology based on a hybrid technique projected the chosen output measure. The study's findings that the model is both efficient and precise in its forecast could prove useful to the manufacturer as it sets goals for key performance metrics.