Optimizing the machining conditions in turning hybrid aluminium nanocomposites adopting teaching–learning based optimization and MOORA technique

• Published in: International journal on interactive design and manufacturing Vol. 18; no. 5; pp. 3089 - 3101
• Main Authors: Raj, Praveen, Biju, P. L., Deepanraj, B., Senthilkumar, N.
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Alloys; Aluminum; CAE) and Design; Carbon dioxide; Cavitation; Composite materials; Computer-Aided Engineering (CAD; Cutting force; Cutting wear; Deformation; Electronics and Microelectronics; Engineering; Engineering Design; Graphene; Heuristic methods; Industrial Design; Instrumentation; Learning; Mechanical Engineering; Metal fatigue; Multiple objective analysis; Nanocomposites; Nanoparticles; Optimization; Original Paper; Orthogonal arrays; Powder metallurgy; Silicon nitride; Surface roughness; Temperature; Tool wear; Turning (machining); Hybrid nanocomposite; Turning; TLBO; Dry machining; MOORA; Nose radius
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-023-01450-1

In this study, hybrid nanocomposites of aluminium (NHAMMCs) made from AA5052 are fabricated via stir casting route by reinforcing 12 wt% Si 3 N 4 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 (L 18 2 1 , 3 7 ) 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.