Multi-objective optimization of WEDM of aluminum hybrid composites using AHP and genetic algorithm

Amresh Kumar, Neelkanth Grover, Alakesh Manna, Raman Kumar, Jasgurpreet Singh Chohan, Sandeep Singh, Sunpreet Singh, Catalin Iulian Pruncu

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
52 Downloads (Pure)


Aluminum hybrid composites have the potential to satisfy emerging demands of lightweight materials with enhanced mechanical properties and lower manufacturing costs. There is an inclusion of reinforcing materials with variable concentrations for the preparation of hybrid metal matrix composites to attain customized properties. Hence, it is obligatory to investigate the impact of different machining conditions for the selection of optimum parameter settings for aluminum-based hybrid metal matrix composite material. The present study aims to identify the optimum machining parameters during wire electrical discharge machining of samples prepared with graphite, ferrous oxide, and silicon carbide. In the present research work, five different process parameters and three response parameters such as material removal rate, surface roughness, and spark Gap are considered for process optimization. Energy-dispersive spectroscopy and scanning electron microscopy analysis reported the manifestation of the recast layer. Analytical hierarchy process and genetic algorithm have been successfully implemented to identify the best machining conditions for hybrid composites.

Original languageEnglish
Number of pages13
JournalArabian Journal for Science and Engineering
Early online date7 Jul 2021
Publication statusE-pub ahead of print - 7 Jul 2021


  • analytical hierarchy process
  • genetic algorithm
  • metal matrix composites
  • optimization
  • wire electrical discharge machining


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