Optimizing machining responses of homologous TiNiCu shape memory alloys using hybrid ANN-GA approach

Abhinaba Roy, B. Sachin, T. Raghavendra, Charitha M. Rao, Gajanan M. Naik, Hargovind Soni, P. M. Mashinini, S. Narendranath

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 Ti50Ni40Cu10 and Ti50Ni25Cu25 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.

Original languageEnglish
Pages (from-to)4402-4410
Number of pages9
JournalMaterials Today: Proceedings
Volume62
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Artificial neural network
  • Genetic algorithm
  • Optimization
  • Shape memory alloys
  • Wire EDM

ASJC Scopus subject areas

  • General Materials Science

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