Tradeoff analysis in minimum volume design of multi-stage spur gear reduction units

Thompson, David F.; Gupta, Shubhagm; Shukla, Aditya

doi:10.1016/s0094-114x(99)00036-1

Cited by 66 publications

(35 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thompson et al [4] presented a generalized optimal design formulation with multiple objectives which is applicable to a gear train of arbitrary complexity. The methodology was applied to the design of two-stage and threestage spur gear reduction units, subject to identical loading conditions and design criteria.…”

Section: Introductionmentioning

confidence: 99%

A Study on Optimization of Planetary Gear Trains

Filiz

Olguner

Evyapan³

2017

Acta Phys. Pol. A

View full text Add to dashboard Cite

This study presents optimization of planetary gear train in a specific configuration. General characteristics of planetary gear trains are discussed briefly. A compound configuration for planetary gear train is selected and an optimization study is performed for this configuration. For the given input power, motor speed and overall gear ratio, modules, facewidths, teeth numbers of gears are found, satisfying the condition of minimum kinetic energy of the gear trains. In optimization, the objective is set to minimization of kinetic energy. Allowable bending stress and allowable contact stress are considered as design constraints. Minimum teeth number for a given pressure angle, center distance, recommendation on the facewidth, limitations on teeth ratios are considered as geometrical and kinematical constraints. The Matlab ® Optimtool optimization toolbox is used. Results for certain operating conditions are obtained and tabulated.

show abstract

Section: Introductionmentioning

confidence: 99%

A Study on Optimization of Planetary Gear Trains

Filiz

Olguner

Evyapan³

2017

Acta Phys. Pol. A

View full text Add to dashboard Cite

show abstract

“…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%

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

Chen

Liu

et al. 2017

Energies

View full text Add to dashboard Cite

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)

show abstract

“…Wang (1994) considered center distance, weight, tooth deflection, and gear life as objective functions. Thompson, Gupta, and Shukla (2000) optimized for minimum volume and surface fatigue life. Kurapati and Azarm (2000) optimized a gearbox for minimum volume and minimum stress in the output shaft.…”

Section: Introductionmentioning

confidence: 99%

Gearbox design for uncertain load requirements using active robust optimization

Avigad¹,

Purshouse

Fleming

2015

Engineering Optimization

View full text Add to dashboard Cite

Design and optimization of gear transmissions have been intensively studied, but surprisingly the robustness of the resulting optimal design to uncertain loads has never been considered. Active Robust (AR) optimization is a methodology to design products that attain robustness to uncertain or changing environmental conditions through adaptation. In this study the AR methodology is utilized to optimize the number of transmissions, as well as their gearing ratios, for an uncertain load demand. The problem is formulated as a bi-objective optimization problem where the objectives are to satisfy the load demand in the most energy efficient manner and to minimize production cost. The results show that this approach can find a set of robust designs, revealing a trade-off between energy efficiency and production cost. This can serve as a useful decision-making tool for the gearbox design process, as well as for other applications.

show abstract

Tradeoff analysis in minimum volume design of multi-stage spur gear reduction units

Cited by 66 publications

References 4 publications

A Study on Optimization of Planetary Gear Trains

A Study on Optimization of Planetary Gear Trains

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

Gearbox design for uncertain load requirements using active robust optimization

Contact Info

Product

Resources

About