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Gears are used to transmit mechanical power in systems such as automotives, automation and machine tools. The demand for lighter and optimally designed gears is high in power transmission systems, as they save material, energy and also considerably influence performance. Hence, in this paper, a bevel gear pair is optimized. The problem consists of a non linear objective function, four design variables and eight inequality constraints. The objective is to minimize the volume of the gear. The design variables are: number of teeth, module, face width and diameter of the shaft, which is a new addition. Apart from considering regular mechanical constraints, six other additional critical constraints on contact ratio, load carrying capacity, power loss, root not cut, no involute interference and line of action are also included. Nature inspired algorithms, namely, simulated annealing, fire fly, cuckoo search and fmincon solver are employed in MATLAB environment. Results of simulation are analysed, compared and validated with literature.

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The accurate design of spur gear drive has a tremendous impact on size, weight, transmission and machine performance. Also, the demand for lighter gears is high in power transmission systems, as they save material and energy. Hence this paper presents an enhanced method to solve a two stage spur gear optimization problem. It consists of a mathematical model with a nonlinear objective function and 11 constraints. A two stage spur gear is considered. To obtain minimum volume of spur gear drive is objective of the problem. The considered design variables are: Module, number of teeth, base width of the gears and, shaft diameter and power. Besides considering regular mechanical constraints based on American Gear Manufacturers Association (AGMA) requisites, six more additional critical constraints on contact ratio, load carrying capacity, power loss, root not cut, no involute interference and line of action are imposed on the drive. Nature inspired optimization algorithms, namely, Simulated Annealing (SA), Firefly (FA) and MATLAB solver fmincon are used to find solution in MATLAB environment. Simulation results are analyzed, compared with literature and validated

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