Automated optimal design of a two-stage helical gear reducer

Tudose, Lucian; Buiga, Ovidiu; Ştefanache, Cornel; Sóbester, András

doi:10.1007/s00158-010-0504-z

Cited by 27 publications

(12 citation statements)

References 13 publications

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“…Gologlu and Zeyveli [8] demonstrated the capability of GA, for obtaining minimum volume of two stage helical gearbox. Zhang et al [9] conducted design optimization on a bevel gear, through a software based on GA. Tudose et al [10] optimized two stage coaxial helical gearbox by using GA. Tamboli et al [11] minimized volume of a helical gear pair. They obtained energy efficient helical gear pair using particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Advanced design optimization on straight bevel gears pair based on nature inspired algorithms

Ebenezer¹,

Ramabalan

Navaneethasanthakumar³

2019

SN Appl. Sci.

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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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Section: Introductionmentioning

confidence: 99%

Advanced design optimization on straight bevel gears pair based on nature inspired algorithms

Ebenezer¹,

Ramabalan

Navaneethasanthakumar³

2019

SN Appl. Sci.

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“…Gologlu and Zeyveli [4] reduced volume of the gear train in a two stage gear drive preliminary design. Tudose et al [5] automated a two stage transmission design taking into account helical gear, including shafts, bearings and housing. He applied a two phase evolutionary method for solving he same.…”

Section: Introductionmentioning

confidence: 99%

Practical Optimal Design on Two Stage Spur Gears Train Using Nature Inspired Algorithms

Ebenezer¹,

Ramabalan²,

Kumar³

2019

IJEAT

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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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“…From the latest application of these methods in this topic, Marjanovic et al [7] developed a practical approach to optimize spur gears trains, or Golabi et al [8] applied a non-linear programming to obtain parameters regarding to the optimum design of gear box. As far as evolutionary techniques of the optimization are considered in the traditional class of methods, a two-phase evolutionary algorithm was employed to optimize the design of a two-stage helical gear reducer [9]. Recently, in a similar manner, Padmanabhan et al [10] applied selective breeding algorithm, as one of these types of techniques, to optimize the volume of a two-stage gear reducer.…”

Section: Introductionmentioning

confidence: 99%