Abstract
In many power transmission high-performance applications, the weight of the gear train is required to be minimized. The weight minimization of a spur gear train as an objective function is considered in this work. This problem contains six mixed type design variables such as four continuous variables, one discrete variable, and one integer variable. The optimum design of this gear train is verified with eight non-linear design constraints. In this work, a spur gear train problem is solved for two different sets of design variables’ ranges. Each design of a gear train obtained using the recently proposed Rao algorithms is compared with the designs obtained using other advanced optimization algorithms in previous studies such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), and grey wolf optimizer (GWO). The comparison revealed that the Rao algorithms gave better designs of the spur gear train with minimum weight.
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Appendix
Appendix
Nomenclature
- a :
-
Gear transmission ratio
- b :
-
Face width
- b w :
-
Web thickness
- d 1 , d 2 :
-
Pinion and gear shaft diameter
- D 1 , D 2 :
-
Pitch diameter of pinion and gear
- D i :
-
Rim inner diameter
- d o :
-
Boss outer diameter
- d p :
-
Diameter of drilled hole
- D r :
-
Diameter of gear dedendum circle
- H :
-
Hardness
- I :
-
Geometry factor
- K m :
-
Factor for mounting
- K ms :
-
Factor for mean stress
- K o :
-
Factor for overload
- K r :
-
Factor for bending reliability
- K v :
-
Factor for velocity
- l :
-
Boss length
- l w :
-
Rim thickness
- m :
-
Module
- n :
-
No. of drilled holes
- N 1 , N 2 :
-
Pinion and gear shaft speed
- S fe :
-
Surface fatigue strength
- Z 1 , Z 2 :
-
Pinion and gear teeth
- ρ :
-
Gear material density
- τ :
-
Shear strength of shafts
- ϕ :
-
Pressure angle
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Venkata Rao, R., Pawar, R.B. (2020). Optimal Weight Design of a Spur Gear Train Using Rao Algorithms. In: Pandit, M., Srivastava, L., Venkata Rao, R., Bansal, J. (eds) Intelligent Computing Applications for Sustainable Real-World Systems. ICSISCET 2019. Proceedings in Adaptation, Learning and Optimization, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-44758-8_33
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DOI: https://doi.org/10.1007/978-3-030-44758-8_33
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