Optimization of Spur Gear Systems with Hybrid Metal Matrix Composites

Authors:
P. Jai Rajesh, V. Balambica, M. Achudhan

Addresses:
Department of Mechatronics, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Department of Mechanical, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India.

Abstract:

This study optimises and analyses a single-stage spur gear gearbox made of Hybrid Metal Matrix Composite (HMMC) with AL6060, Si3N4, and BN. The goal is to improve gearbox efficiency, weight, and structural integrity while meeting or exceeding industry standards. Particle Swarm Optimisation (PSO), Firefly, MOTLBO, Hybrid MOTLBO_PSO, Hybrid PSO_Firefly, and Orca are used to find the best gear settings. Design parameters, including face width (b), pitch diameters (d1 and d2), number of teeth (Z1), module (m), and input power (Pin), are optimised. After optimisation, the efficiency, weight, accuracy rank, and overall rank of each algorithm are assessed using a sensitivity analysis. In optimising gear design, the Orca algorithm strikes the best balance between efficiency and weight. To evaluate structural integrity, standard and optimised gears undergo Finite Element Analysis (FEA). The optimised gear has a lower weight of 181.98 grams, a higher centre distance of 4.95 cm, a higher efficiency of 96.233%, and lower maximum stress (7.90E+08 Pa) and deformation (2.65E-05 mm) than the normal gear. This study shows that multi-algorithm optimisation improves spur gearbox performance, especially when using advanced composite materials such as HMMC. The discoveries help build efficient and lightweight gear systems, advancing mechanical engineering and gear gearbox technology.

Keywords: Grey Wolf Optimiser; Simulated Annealing; Spur Gears; Mechanical Systems; Gear Systems; Finite Element Analysis (FEA); Particle Swarm Optimisation (PSO); Gearbox Technology.

Received on: 10/10/2024, Revised on: 05/12/2024, Accepted on: 01/03/2025, Published on: 15/12/2025

DOI: 10.64091/ATIAS.2025.000249

AVE Trends in Intelligent Applied Sciences, 2025 Vol. 1 No. 4 , Pages: 197-206

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