Optimizing machining responses of homologous TiNiCu shape memory alloys using hybrid ANN-GA approach. (2022)

• Record Type: Journal Article
• Authors: Roy, Abhinaba; Sachin, B.; Raghavendra, T.; Rao, Charitha M.; Naik, Gajanan M.; Soni, Hargovind; Mashinini, P.M.; Narendranath, S.
• Is Part Of: Materials today. Volume 62:Part 6(2022)
• Page Start: 4402
• Page End: 4410
• Publication Date: 2022
• Subjects: Wire EDM -- Shape memory alloys -- Artificial neural network -- Genetic algorithm -- Optimization; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.04.890
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Fabrication of shape memory alloys using wire electro discharge machining (WEDM) has gained popularity over the last few years. Most widely used machining parameters of WEDM process are pulse on time (Øon ), pulse off time (Øoff ), servo voltage (σ) and wire feed (ω). WEDM responses like material removal rate (MR ), surface roughness (SR ), kerf width (KW ) and recast layer thickness (LT ) have been evaluated by researchers to determine machining characteristics and are also considered for this study. These machining responses determine the quality of machining and are majorly influenced by thermal conductivity and melting temperature of the WEDM workpiece. Actuation behavior of shape memory alloys is a function of phase transformation characteristics which in turn depends on elemental composition of the selected alloys. Therefore, dissimilar machining responses of Ti50 Ni40 Cu10 and Ti50 Ni25 Cu25 have been observed even though similar machining input values were used. This study utilized artificial neural network (ANN) mapping to establish WEDM response function – which was used as fitness function to perform multi objective optimization using genetic algorithm (GA). It was found that ANN successfully predicted machining responses of selected homologous alloys and GA helped in identifying suitable input parameter values to optimize machining responses.