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A platform for research: civil engineering, architecture and urbanism
Optimizing the machining conditions in turning hybrid aluminium nanocomposites adopting teaching–learning based optimization and MOORA technique
In this study, hybrid nanocomposites of aluminium (NHAMMCs) made from AA5052 are fabricated via stir casting route by reinforcing 12 wt% Si3N4 and 0.5 wt% of graphene to study its machining characteristics through traditional turning process. The machining factors taken for the consideration in this work are rate of feed, machining speed and machining depth and nose radius. A mixed level Latin square orthogonal array (L18 21, 37) is considered for designing the experimental array. Multi-Objective optimization based on ratio analysis (MOORA) method is adopted for optimizing tool wear, surface roughness, and resultant cutting force. A population-based meta-heuristic optimization procedure; teaching–learning based optimization (TLBO) is also implemented to optimize the outputs. Observation presents that all the considered input factors have a significant influence on the measured outputs. The performance of TLBO algorithm outplays the MOORA method as observed for the results of validation experiment.
Optimizing the machining conditions in turning hybrid aluminium nanocomposites adopting teaching–learning based optimization and MOORA technique
In this study, hybrid nanocomposites of aluminium (NHAMMCs) made from AA5052 are fabricated via stir casting route by reinforcing 12 wt% Si3N4 and 0.5 wt% of graphene to study its machining characteristics through traditional turning process. The machining factors taken for the consideration in this work are rate of feed, machining speed and machining depth and nose radius. A mixed level Latin square orthogonal array (L18 21, 37) is considered for designing the experimental array. Multi-Objective optimization based on ratio analysis (MOORA) method is adopted for optimizing tool wear, surface roughness, and resultant cutting force. A population-based meta-heuristic optimization procedure; teaching–learning based optimization (TLBO) is also implemented to optimize the outputs. Observation presents that all the considered input factors have a significant influence on the measured outputs. The performance of TLBO algorithm outplays the MOORA method as observed for the results of validation experiment.
Optimizing the machining conditions in turning hybrid aluminium nanocomposites adopting teaching–learning based optimization and MOORA technique
Int J Interact Des Manuf
Raj, Praveen (author) / Biju, P. L. (author) / Deepanraj, B. (author) / Senthilkumar, N. (author)
2024-07-01
13 pages
Article (Journal)
Electronic Resource
English
Sistem Pendukung Keputusan Rekomendasi Pembelian Perumahan Menerapkan Metode MOORA
| BASE | 2023