Optimize Volume for Planetary Gear Train by using Algorithm Based Artificial Immune System

Yaw, M. W.; Chong, K. H.

doi:10.30534/ijatcse/2020/191932020

Cited by 6 publications

(9 citation statements)

References 4 publications

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“…These findings are supported by the observation of Srinivasan et al [28]. The significant of this study as shown that the time of cut increases also the energy consumption of the computer numerical control (CNC) machine, and the optimisation of the individual parameters will assist the time variation to reduce the energy consumption [29]. Al-Tawalbeh, and Feilat [30] shows that the power consumption in the economic system is very significant in sustainable development of manufacturing industry.…”

Section: Resultssupporting

confidence: 79%

Experimental Analysis of the Influence of Depth of Cut, Time of Cut, and Machining Speed on Vibration Frequency during Turning of AL1060 Alloy

Okokpujie¹,

Ohunakin²,

Bolu³

et al. 2020

IJATCSE

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Machining vibration is a significant concern in the manufacturing industry, as vibration cannot be eliminated during metal to metal turning operations. It can be controlled to the minimum when a proper study is carryout before the turning process. This study aims to experimentally analyse the effect of the individual parameters such as depth of cut (DOC) and cutting speed have on the machining vibration during turning of AL1060 alloy. A computer numerical control (CNC) program was applied to investigate the effects of the different depths of cut such as 1 mm, 1.5 mm, 2 mm, 2.5 mm, and 3 mm. Moreover, cutting speed of 200 m/min, 350 m/min, 500 m/min, and 600 m/min with a constant feed rate of 5 mm/rev have on the machining vibration during the turning process. DTO 32105 frequency analyser an MXC-1600 digital frequency counter was employed to measure the machining vibration for a specific time between 15 seconds to 500 seconds. The result shows that as the depth of cut continually increases, the machining vibration increases and the minimum machining vibration of 131.8 Hz was obtained at cutting speed of 600 m/min, depth of cut of 1 mm, and cutting time of 15 sec. The Pareto chart was further used to analysis the average vibration frequency at the optimised cutting speed of 600 m/min. The surface quality and the cutting tool life can be significantly enhanced with the study of the individual machining parameters.

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Section: Resultssupporting

confidence: 79%

Experimental Analysis of the Influence of Depth of Cut, Time of Cut, and Machining Speed on Vibration Frequency during Turning of AL1060 Alloy

Okokpujie¹,

Ohunakin²,

Bolu³

et al. 2020

IJATCSE

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show abstract

“…6 Thus, these difficulties have driven the use of meta-heuristic optimizers for design optimization of planetary gears. [6][7][8]10,11,[13][14][15][16][17][18][19] Most of the studies in the literature are related to single-stage planetary gear sets and therefore there is a 1 literature gap for the volume minimization of multistage planetary gears. This study is conducted to measure the performance of recent nature-inspired methods in the design optimization of multistage planetary gear mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…6 Thus, these difficulties have driven the use of meta-heuristic optimizers for design optimization of planetary gears. 6–8,10,11,13–19…”

Section: Introductionmentioning

confidence: 99%

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Optimization of planetary gearbox using nature inspired meta-heuristic optimizers

Top,

Dorterler,

Sahin

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this study, a detailed research was carried out to solve the two-stage planetary gearbox design optimization problem by using nature-inspired optimization methods. Seven different nature-inspired meta-heuristic optimizers were applied to minimize the volume of the planetary gear. The recent optimizers, Grey Wolf Optimizer, Artificial Bee Colony, Multi-verse Optimizer, Cuckoo Search, Whale Optimization Algorithm and Salp Swarm Algorithm methods were applied to the problem for the first time. The optimal volume values obtained with the seven different optimizers are much better than the best values known in the literature. The best fitness value obtained is 30% better than the best known in the literature.

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“…The main goal is to reduce the weight and power loss of the construction and produce gearbox with enhanced service life of the components. In this regard, in recent years a lot of research has been directed towards the optimal design of the gear trains [3,4]. In this way, researchers have considered the weight minimization of the single stage spur gears under the constraints, such as gear bending strength and shafts torsional strength [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm

Sedak

Rosić

2021

Applied Sciences

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This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm between particle swarm optimization and differential evolution algorithms has been proposed and applied to considered problem. Here, the mutation operators from the differential evolution algorithm have been incorporated into the velocity update equation of the particle swarm optimization algorithm, with the adaptive population spacing parameter employed to select the appropriate mutation operator for the current optimization condition. It has been shown that the proposed algorithm successfully obtains the solutions of the non-convex Pareto set, and reveals key insights in reducing the weight, improving efficiency and preventing premature failure of gears. Compared to other well-known algorithms, the numerical simulation results indicate that the proposed algorithm shows improved optimization performance in terms of the quality of the obtained Pareto solutions.

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Optimize Volume for Planetary Gear Train by using Algorithm Based Artificial Immune System

Cited by 6 publications

References 4 publications

Experimental Analysis of the Influence of Depth of Cut, Time of Cut, and Machining Speed on Vibration Frequency during Turning of AL1060 Alloy

Experimental Analysis of the Influence of Depth of Cut, Time of Cut, and Machining Speed on Vibration Frequency during Turning of AL1060 Alloy

Optimization of planetary gearbox using nature inspired meta-heuristic optimizers

Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm

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