Multi-Objective Optimization of Two-Stage Helical Gear Train Using NSGA-II

Sanghvi, Rajesh Chandrakant; Vashi, A. S.; Patolia, H. P.; Jivani, R. G.

doi:10.1155/2014/670297

Cited by 19 publications

(27 citation statements)

References 12 publications

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“…Load carrying capacity-It is a functional characteristic of the gear. In this work, the optimum values for minimum volume obtained in this research are used to find the load carrying capacity at both the stages [15]. The constraint condition is formulated in such a way that, this calculated value should be more than the minimum allowed load carrying capacity of the gear [29].…”

Section: Contact Ratio (Cr)mentioning

confidence: 99%

“…It should be more than minimum value of the gear [15]. It is given by, (21) (21) Where (22) where deformation factor = , is the sum of error between meshing teeth, .…”

Section: Load Carrying Capacitymentioning

confidence: 99%

“…Zhang, et al, [21] optimized spur gear reducer design based on GA. Li et al [7] solved multi-objective optimization design of reducer through an adaptive GA . Sanghvi et al [15] determined the optimum volume of a two stage gear train and optimum load delivering capacity of the gear by Non Sorted Genetic Algorithm (NSGA-II). Barbieri et al [8] investigated modifications spur gear pair profile by GA. Zhang et al [9] established a numerical model for getting optimal volume design of a gear drive by GA and MATLAB toolbox.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Contact Ratio (Cr)mentioning

confidence: 99%

“…It should be more than minimum value of the gear [15]. It is given by, (21) (21) Where (22) where deformation factor = , is the sum of error between meshing teeth, .…”

Section: Load Carrying Capacitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ebenezer¹,

Ramabalan²,

Kumar³

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…The optimization results ought to be a set of optimal solutions rather than an optimal solution. This set is known as the Pareto optimal set, and its corresponding tradeoff in objective space is known as the Pareto optimal frontier, which is made up of the Pareto optimal points (Sanghvi et al 2014;Huang et al 2007;Hu and Yu 2009). Designers could select an optimized scheme which satisfies the requirements of all disciplines from the Pareto optimal set.…”

Section: Introductionmentioning

confidence: 99%

Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft

Pan

Huang

et al. 2017

J.Aerosp. Technol. Manag.

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An optimization strategy is constructed to solve the aerodynamic and structural optimization problems in the conceptual design of double-swept flying wing aircraft. Aircraft preliminary aerodynamic and structural design optimization is typically based on the application of a deterministic approach of optimizing aerodynamic performance and structural weight. In aerodynamic optimization, the objective is to minimize induced drag coefficient, and the structural optimization aims to find the minimization of the structural weight. In order to deal with the multiple objective optimization problems, an optimization strategy based on collaborative optimization is adopted. Based on the optimization strategy, the optimization process is divided into system level optimization and subsystem level optimization. The system level optimization aims to obtain the optimized design which meets the constraints of all disciplines. In subsystem optimization, the optimization process for different disciplines can be executed simultaneously to search for the consistent schemes. A double-swept configuration of flying wing aircraft is optimized through the suggested optimization strategy, and the optimization results demonstrate the effectiveness of the method.

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“…Deb and Jain demonstrated the use of NSGA-II in solving the original problem involving mixed discrete and real-valued parameters and more than one objective [19]. Sanghvi et al investigated multiobjective optimization of a two-stage helical gear train by using NSGA-II [20].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Steering Mechanism for Rotary Steering System Using Modified NSGA-II and Fuzzy Set Theory

Niu

et al. 2015

Mathematical Problems in Engineering

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Due to the complicated design process of gear train, optimization is a significant approach to improve design efficiency. However, the design of gear train is a complex multiobjective optimization with mixed continuous-discrete variables under numerous nonlinear constraints, and conventional optimization algorithms are not suitable to deal with such optimization problems. In this paper, based on the established dynamic model of steering mechanism for rotary steering system, the key component of which is a planetary gear set with teeth number difference, the optimization problem of steering mechanism is formulated to achieve minimum dynamic responses and outer diameter by optimizing structural parameters under geometric, kinematic, and strength constraints. An optimization procedure based on modified NSGA-II by incorporating dynamic crowding distance strategies and fuzzy set theory is applied to the multiobjective optimization. For comparative purpose, NSGA-II is also employed to obtain Pareto optimal set, and dynamic responses of original and optimized designs are compared. The results show the optimized design has better dynamic responses with minimum outer diameter and the response decay decreases faster. The optimization procedure is feasible to the design of gear train, and this study can provide guidance for designer at the preliminary design phase of mechanical structures with gear train.

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Multi-Objective Optimization of Two-Stage Helical Gear Train Using NSGA-II

Cited by 19 publications

References 12 publications

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

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

Application of Multidisciplinary Design Optimization on Advanced Configuration Aircraft

Multiobjective Optimization of Steering Mechanism for Rotary Steering System Using Modified NSGA-II and Fuzzy Set Theory

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