A solution method for optimal weight design problem of the gear using genetic algorithms

Takao, Yoshihiro; Taguchi, Takeaki; Gen, Mitsuo

doi:10.1016/s0360-8352(98)00149-1

Cited by 104 publications

(66 citation statements)

References 1 publication

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“…Based on the same constraints, Osman et al [2] deduced some of the gear size ratio expressions in another way, discovering that only three In addition, advanced stochastic methods such as the genetic algorithms (GAs), the simulated annealing algorithms (SAAs) and the particle swarm optimization algorithms (PSOAs) are intensively used in some GTOs mainly because these methods can handle various types of design variables, objectives and constraints easily, requiring no information of functional derivatives. Yokota et al [16] optimized the weight of a gear set with an improved GA. Marcelin [17] proposed a GA combined with a penalty selection method to optimize gear pairs. Mendi et al [18] used a GA with a static penalty function incorporated in the fitness function to minimize the volume of a single-stage gearbox.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that the PSOA and the SAA were more effective and efficient than the GA. However, numerous trials, especially for inexperienced designers, might have to be performed to appropriately determine several parameters for the advanced stochastic methods used in those studies [16][17][18][19][20][21][22][23] since the efficacies of those methods significantly depend on such parameters. Thus, such a requirement might actually increase the total times for those methods to obtain global optima.…”

Section: Introductionmentioning

confidence: 99%

“…The enumeration technique is preferred to the random methods [12][13][14][15] and the advanced stochastic methods [16][17][18][19][20][21][22][23] because the number of the possible solutions to the 2DET could be well limited considering practical manufacturing requirements and a high-performance computer could scan and evaluate all the candidates efficiently. In addition, multiple criteria are simultaneously optimized for the 2DET using the MMPO to find global optima since the MMPO could well represent the real optimization purposes of designers in an intuitive, reasonable and comprehensive way.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enumerative Optimization Procedure for the Gear Train Optimization Problem of a Two-Speed Dedicated Electric Transmission

Chen

Liu

et al. 2017

Energies

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Abstract:Gear train optimization problems (GTOPs) can be very difficult. This paper proposes an enumerative optimization procedure (EOP) for the GTOP of a two-speed dedicated electric transmission (2DET) for electric vehicles (EVs). The EOP combines enumeration with the Min-Max Principle of Optimality (MMPO). First, the requirements of the EV and the requirements of manufacture and operation were checked in a dedicated order to obtain the feasible region of the GTOP. Then, the MMPO was implemented within the feasible region to reveal the global optimum in terms of the performance of the EV, the load capacity of the gears and the size of the gear train (GT). Results demonstrated that the EOP was effective in determining the feasible region and simultaneously and globally optimizing multiple criteria for the GTOP. The idea of combining enumeration with optimization, as the EOP presents, may be helpful to solve other GTOPs and provide global optima that are immediately practical and applicable.Keywords: gear train optimization problem (GTOP); enumerative optimization procedure (EOP); two-speed dedicated electric transmission (2DET); electric vehicle (EV); Min-Max Principle of Optimality (MMPO)

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enumerative Optimization Procedure for the Gear Train Optimization Problem of a Two-Speed Dedicated Electric Transmission

Chen

Liu

et al. 2017

Energies

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“…Constraints are center distance, deflection of worm and beam strength of worm gear. Yokota et al [8] formulated an optimal weight design problem of a gear for a constrained bending strength of gear, torsional strength of shafts, and each gear dimension as a nonlinear integer programming (NIP) problem and solved it directly by using an improved GA. The efficiency of the proposed method is confirmed by showing the improvement in weight of gears and space area.…”

Section: Introductionmentioning

confidence: 99%

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

Sanghvi¹,

Vashi²,

Patolia³

et al. 2014

Journal of Optimization

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Gears not only transmit the motion and power satisfactorily but also can do so with uniform motion. The design of gears requires an iterative approach to optimize the design parameters that take care of kinematics aspects as well as strength aspects. Moreover, the choice of materials available for gears is limited. Owing to the complex combinations of the above facts, manual design of gears is complicated and time consuming. In this paper, the volume and load carrying capacity are optimized. Three different methodologies (i) MATLAB optimization toolbox, (ii) genetic algorithm (GA), and (iii) multiobjective optimization (NSGA-II) technique are used to solve the problem. In the first two methods, volume is minimized in the first step and then the load carrying capacities of both shafts are calculated. In the third method, the problem is treated as a multiobjective problem. For the optimization purpose, face width, module, and number of teeth are taken as design variables. Constraints are imposed on bending strength, surface fatigue strength, and interference. It is apparent from the comparison of results that the result obtained by NSGA-II is more superior than the results obtained by other methods in terms of both objectives.

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“…It has been proven that GAs are more successful than the others, especially when large numbers of jobs and machines are considered. Yokota [18] formulated an optimal weight design problem of a gear for a constrained bending strength of gear, torsional strength of shafts and each gear dimension as a nonlinear programming problem, and solved it directly by keeping the nonlinear constraint by using an improved genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Allocating optimal index positions on tool magazines using genetic algorithms

Dereli¹,

Filiz²

2000

Robotics and Autonomous Systems

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This paper presents an optimisation system software developed for the determination of optimal index positions of cutting tools on the automatic tool changer (ATC) or turret magazine of CNC machine tools. Position selection is performed using a genetic algorithm (GA) which takes a list of cutting tools assigned to certain machining operations together with total number of index positions available on the ATC or turret magazines and, catalogue value of indexing time as the input, and then randomly generates an initial population of position sets (chromosomes). New chromosomes are generated using genetic operators: crossover, reproduction and mutation. A fitness function is used to evaluate the goodness of each chromosome in terms of total tool-indexing time. Based on the fitness values, the next generation is formed from the newly generated sequences and old population. As the iterations are continued, the better sequences with higher fitness values (lower total-indexing times) dominate and the system converges to an optimal positioning set. The system is implemented in C programming language and on a PC. It can be used as stand-alone system or as an integrated module of a process planning system called OPPS-PRI (Optimised Process Planning System for PRIsmatic parts) developed for prismatic parts.

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A solution method for optimal weight design problem of the gear using genetic algorithms

Cited by 104 publications

References 1 publication

Enumerative Optimization Procedure for the Gear Train Optimization Problem of a Two-Speed Dedicated Electric Transmission

Enumerative Optimization Procedure for the Gear Train Optimization Problem of a Two-Speed Dedicated Electric Transmission

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

Allocating optimal index positions on tool magazines using genetic algorithms

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