Optimal Weight Design of a Spur Gear Train Using Rao Algorithms

2020 ◽  
pp. 351-362
Author(s):  
R. Venkata Rao ◽  
Rahul B. Pawar
Keyword(s):  
Spur Gear ◽  
Gear Train ◽  
Optimal Weight ◽  
Weight Design

A grey wolf optimizer approach for optimal weight design problem of the spur gear

2018 ◽  
Vol 51 (6) ◽  
pp. 1013-1027 ◽  
Author(s):  
Murat Dörterler ◽  
İsmail Şahin ◽  
Harun Gökçe
Keyword(s):  
Design Problem ◽  
Spur Gear ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Optimal Weight ◽  
Weight Design

A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system

2021 ◽  
Vol 63 (5) ◽  
pp. 442-447
Author(s):  
Hammoudi Abderazek ◽  
Ferhat Hamza ◽  
Ali Riza Yildiz ◽  
Liang Gao ◽  
Sadiq M. Sait
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Spur Gear ◽  
Ant Lion Optimization ◽  
Lion Algorithm ◽  
Weight Design ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Teaching Learning ◽  
Lion Optimization Algorithm

Abstract Metaheuristic optimization algorithms have gained relevance and have effectively been investigated for solving complex real design problems in diverse fields of science and engineering. In this paper, a recent meta-heuristic approach inspired by human social concepts, namely the queuing search algorithm (QSA), is implemented for the first time to optimize the main parameters of the spur gear, in particular, to minimize the weight of a single-stage spur gear. The effectiveness of the algorithm introduced is examined in two steps. First, the algorithm used is compared with descriptions in previous studies and indicates that the final results obtained by QSA lead to a reduction in gear weight by 7.5 %. Furthermore, the outcomes obtained are compared with those for the other five algorithms. The results reveal that the QSA outperforms the techniques with which it is compared such as the sine-cosine optimization algorithm, the ant lion optimization algorithm, the interior search algorithm, the teaching-learning-based algorithm, and the jaya algorithm in terms of robustness, success rate, and convergence capability.

Discrete Optimal Design Formulations: Application to Gear Train Design

1992 ◽  
Author(s):  
Leonard P. Pomrehn ◽  
Panos Y. Papalambros
Keyword(s):  
Optimal Design ◽  
Spur Gear ◽  
Gear Train ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Interesting Aspect ◽  
Model Formulation ◽  
Design Models ◽  
Different Types

Abstract The use of discrete variables in optimal design models offers the opportunity to deal rigorously with an expanded variety of design situations, as opposed to using only continuous variables. However, complexity and solution difficulty increase dramatically and model formulation becomes very important. A particular problem arising from the design of a gear train employing four spur gear pairs is introduced and formulated in several different ways. An interesting aspect of the problem is its exhibition of three different types of discreteness. The problem could serve as a test for a variety of optimization or artificial intellegence techniques. The best known solution is included in this article, while its derivation is given in a sequel article.

scholarly journals 3D-Modeling and Motion Simulation of Composite Wheel Gears Transmission Based on UG

2018 ◽  
Vol 175 ◽  
pp. 03006
Author(s):  
Mingxia Zhao
Keyword(s):  
3D Modeling ◽  
Rotational Speed ◽  
Spur Gear ◽  
Transmission Mechanism ◽  
Gear Train ◽  
Gear Pair ◽  
Calculation Formula ◽  
Motion Simulation ◽  
Gear Trains ◽  
Simulation Parameters

Taking the compound gear trains as an example, the principle of the transmission mechanism was analyzed, and the rotational speed of the key gears in the compound gear trains was calculated by using the calculation formula of transmission ratio to obtain the simulation parameters of UG movement. The gear tool box in UG was applied to complete the modeling and meshing assembly of the bevel gear and spur gear, the rotation pair and gear pair was to motion simulation, the gear transmission state could have visually observed by motion simulation, and then the chart was analyzed to verify the design rationality of the gear train.

Tooth Contact Analysis of Planetary Gear Trains with Equally Spaced Planets

2010 ◽  
Vol 43 ◽  
pp. 279-282
Author(s):  
Kai Xu ◽  
Xiao Zhong Deng ◽  
Jian Jun Yang ◽  
Guan Qiang Dong
Keyword(s):  
Planetary Gear ◽  
Transmission Error ◽  
Spur Gear ◽  
Contact Analysis ◽  
Gear Train ◽  
Tooth Contact Analysis ◽  
Planetary Gear Train ◽  
Tooth Contact ◽  
Gear Trains ◽  
Equal Space

Based on Tooth Contact Analysis (TCA), a feasible approach for Transmission Error (TE) of planetary gear train is proposed in this paper. With a view to getting the total TE curve of the planetary gear train, a specific analysis of the TE from the planetary gear train with only one planet should be proceed firstly, the second step is to calculate each phase difference of planets in the gear train. The applicable conditions for the simplified calculation are spur gear or involute gear pairs in the gear train. Due to equal space between them, planets have the same phase angle.

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