Performance Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms for Optimizing Power Loss in a Worm Gear Mechanism

Sabarinath, P.; Thansekhar, M.R.; Saravanan, R.

doi:10.1007/978-81-322-2119-7_44

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…The authors strive to achieve maximum power and efficiency, as well as minimum total weight and center distance applying the method: Swarm intelligence optimization of worm and worm gear design. Sabarinath et al [8] optimize worm gear drives according to the "power loss" criterion by using the following two algorithms: Differential Evolution and Particle Swarm Optimization algorithms. The results obtained are compared with the outputs of: genetic algorithm and analytical method.…”

Section: Significant Advanced Methods For Worm Gear Optimization Rese...mentioning

confidence: 99%

Advanced Design Optimization Procedures for Worm Gear Drives

Dobreva¹,

Dobrev²,

Mollova³

2023

Atlantis Highlights in Engineering

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Concerning the optimization design of worm drives, it is especially important to achieve maximum output power of the gear set, which depends particularly intensively on the efficiency coefficient of the worm gear meshing. The purpose of the study presented is to create advanced design optimization procedures concerning the output power of the worm gear, taking into account nine design variable parameters and several significant mechanical constraints. The variable parameters selected are: number of starts for the worm, number of teeth of the worm gear, center distance, sliding speed and friction coefficient for the meshing, lead and tooth friction angles, permissible contact stresses for the pair of worm and worm gear materials, appropriate values of the safety factors. The research methodology applied includes four different stages, which are briefly described. The research results obtained are analyzed. The conclusions and recommendations deduced are related and focused to the objectives of future research work of the author's team.

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Section: Significant Advanced Methods For Worm Gear Optimization Rese...mentioning

confidence: 99%

Advanced Design Optimization Procedures for Worm Gear Drives

Dobreva¹,

Dobrev²,

Mollova³

2023

Atlantis Highlights in Engineering

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“…The design of the closed coil helical spring is considered with the Number of active coils(N), wire diameter(d), mean coil diameter of spring (D), minimum wire diameter( min), maximum working load ( max), preload compressive force( p),allowable shear stress(S), Spring index (C),modulus of rigidity(G), perturbance factor ( ), Stress factor or Wahl factor(C ), maximum perturbance factor ( max), maximum outside diameter of spring ( max), spring stiffness ( ), Free length ( ),deflection under the maximum load ( l), deflection under preload ( ), deflection from preload to maximum load ( ) and allowable maximum deflection under preload ( ) respectively [2,3]…”

Section: Formulation Of Problemmentioning

confidence: 99%

Volume Minimization of a Closed Coil Helical Spring Using ALO, GWO, DA, FA, FPA, WOA, CSO. BA, PSO and GSA

J¹,

Stephen²

2021

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“…A detailed study regarding the design, modeling, structural analysis and manufacturing of helical gear in marine engines has been reported in Venkatesh et al [3]. Sabarinath et al [4][5][6] used two important metaheuristic algorithms namely Particle swarm optimization (PSO) and Differential Evolution (DE) for minimizing weight of a belt pulley system [4], minimizing the power loss of worm gear mechanism [5] and minimizing weight of a helical compression spring [6]. Geem et al [7] introduced harmony search (HS) which is based on the music improvisation process of musicians in the hunt for a magnificent harmony.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Design Optimization of Helical Gear Pair Using Adaptive Parameter Harmony Search Algorithm

Sabarinath¹,

Thansekhar²,

Jeganathan³

et al. 2015

AMM

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Mechanical design engineers design products by selecting the best possible materials and geometries that satisfies the specific operational requirements of the design. It involves lot of creativity and aesthetics to make better designs. A gear design makes the designer to compromise many design variables so as to arrive the best performance of a gear set. The best possible way for multi variable, Multiobjective gear design is to try design optimization. For many complex engineering optimization problems multi objective design optimization methods are used to simplify the design problem. In this paper, multiobjective design of helical gear pair transmission with objective functions namely volume of the small and large helical gear and opposite number of overlap ratio is taken into account. The design variables considered are normal module, helix angle, gear width coefficient and teeth number of small helical gear. A recent meta-heuristic algorithm namely parameter adaptive harmony search algorithm is applied to solve this problem using the weighted sum approach. It is evident from the results that the proposed approach is performing better than other algorithms.

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Performance Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms for Optimizing Power Loss in a Worm Gear Mechanism

Cited by 6 publications

References 8 publications

Advanced Design Optimization Procedures for Worm Gear Drives

Advanced Design Optimization Procedures for Worm Gear Drives

Volume Minimization of a Closed Coil Helical Spring Using ALO, GWO, DA, FA, FPA, WOA, CSO. BA, PSO and GSA

Multi Objective Design Optimization of Helical Gear Pair Using Adaptive Parameter Harmony Search Algorithm

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