Design and Volume Optimization of High-Speed Helical Gear Pair by using Cohort Intelligence Algorithm

 

Design and Volume Optimization of High-Speed Helical Gear Pair by using Cohort Intelligence Algorithm
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-11
Year of Publication : 2023
Author : Pratik Patil, Shailendra Shisode, Omkar Kulkarni
DOI : 10.14445/22315381/IJETT-V71I11P226

How to Cite?

Pratik Patil, Shailendra Shisode, Omkar Kulkarni, "Design and Volume Optimization of High-Speed Helical Gear Pair by using Cohort Intelligence Algorithm," International Journal of Engineering Trends and Technology, vol. 71, no. 11, pp. 247-256, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I11P226

Abstract
Gears are the most fundamental unit for mechanical power transmission and play an important role in industrial applications. High-speed gearboxes are widely used in different applications, such as steam and gas turbines, pumps, compressors, etc. In this case study, a high-speed gearbox with a helical gear pair is considered using the DIN and AGMA standards, along with design factors including the face width, number of teeth on the pinion and gear, module, and helix angle. The DIN and AGMA standards are used to calculate the various gear geometry parameters, such as size and strength. A multivariable and constrained optimization problem is presented with a derived objective function. The volume minimization is performed using the cohort intelligence algorithm in MATLAB, and the results obtained are found to be satisfactory. Cohort intelligence is a modern technique that is applied for the optimization of different mechanical parts, systems, and processes. An optimized set of parameters models a helical gear pair in CAD software. The optimized design is then validated using FEA software, which shows that the stress value in the gear pair is below the allowable stress limit for the given material.

Keywords
Helical gear pair, Nature-inspired optimization algorithm, Cohort Intelligence Algorithm (CI), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and FEA.

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