Multi-objective design optimisation of rolling bearings using genetic algorithms

Gupta, Shantanu; Tiwari, Rajiv; Nair, Shivashankar B.

doi:10.1016/j.mechmachtheory.2006.10.002

Cited by 126 publications

(55 citation statements)

References 10 publications

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“…Представлена оптимальная конструкция конкретного цилиндрического роликового подшип-ника, полученная с помощью эволюционных алго-ритмов. В работе [6] рассматривается оптимальное проектирование подшипников качения с целью удов-летворения различных ограничений: геометрических, кинематических и прочностных, в то время, как це-левыми характеристиками качества являются срок службы и надежность. Это вызывает необходимость разработки оптимальной методологии проектирова-ния в виде задачи многоцелевой оптимизации.…”

Section: анализ литературных данных и постановка задачиunclassified

“…а задача сводится к задаче безусловной минимизации функций (5) или (6). Предлагаемый подход предполагает собой форму-лировку задачи анализа в виде 3-х мерной контакт -ной задачи для двух упругих тел.…”

Section: рис 1 расчетная схема узла подшипника качения а бunclassified

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Design and optimization of the shape of the roller generatrix of double-row railway roller bearing

Хавин

Ягудин

2014

EEJET

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Section: анализ литературных данных и постановка задачиunclassified

Section: рис 1 расчетная схема узла подшипника качения а бunclassified

Design and optimization of the shape of the roller generatrix of double-row railway roller bearing

Хавин

Ягудин

2014

EEJET

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“…Internal parameters include ball diameter (D b ), pitch The purpose of optimization is to evaluate the above mentioned internal geometry to maximize the performance of the bearing. Gupta et al [4] presented the optimization aspects of rolling element bearings by using a non-dominated sorting genetic algorithm (NSGA-II). The design parameters, objective functions and constraints for defining feasible design parameter space considered by the authors are shown below.…”

Section: Example 2: Optimization Of a Radial Ball Bearingmentioning

confidence: 99%

Mechanical Design Optimization Using the Existing Optimization Techniques

Rao

Savsani²

2012

Springer Series in Advanced Manufacturing

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In this chapter, an effort is made to verify if any improvement in the solution is possible by employing advanced optimization techniques to some of the mechanical element design optimization problems available in the research literature. Seven different mechanical element design problems, like optimization of a gear train, radial ball bearing, Belleville spring, multi-plate disc clutch brake, robot gripper, hydrostatic thrust bearing and four stage gear train, are considered in this chapter for the analysis. The considered problems were solved by using either GA or PSO or by both the methods by the other researchers. These problems are tested by using other advanced optimization techniques such as ABC, DE, BBO and AIA in this book. PSO is applied to the problems to which only GA was applied. The descriptions of different mechanical elements considered in this work are given in the next section. Description of Different Mechanical DesignOptimization Problems Example 1: Optimization of a Gear TrainThis problem is taken from Yokota et al.

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“…Papers dealing with the optimization of bearing features have appeared only lately, aimed at maximizing one or several performance properties of ball bearings [5][6][7][8][9][10][11], cylindrical roller bearings [12,13] and tapered roller bearings [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The optimized bearing yielded better fatigue life as compared to standard catalogues. This study was further evolved [9] to achieve multi-objective optimization of the bearing in terms of static load capacity, fatigue life and elastohydrodynamic film thickness under inner and Article published by EDP Sciences Optimum value of φ for δ ≤ δ lim outer geometric constraints. Angular-contact ball bearings were optimized by Savsani et al [10] for maximum static and dynamic loading ratings and minimum film thickness using a modified particle swarm optimization technique.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of paired tapered roller bearings under centred radial and axial static loads

Dragoni

2015

Mechanics & Industry

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-Based on the relationships from the ISO 76 standard, this paper optimizes the internal dimensions of tapered roller bearings for maximum static load capacity. A bearing system formed by two identical bearings is assumed, subjected to whatever combination of centred radial and axial forces. It is shown that the static capacity increases linearly with the roller infill, with the ratio of roller length to roller diameter and with the square of the pitch diameter of the roller set. Further, given the ratio of axial to radial force, an optimal contact angle exists which maximizes the static capacity of the bearing pair, regardless of the actual bearing size and ratio of roller diameter to pitch diameter. The optimization procedure can either be used to design custom-made bearings or to pick from manufacturers' catalogues the bearing with the best contact angle for any assigned loading condition.

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Multi-objective design optimisation of rolling bearings using genetic algorithms

Cited by 126 publications

References 10 publications

Design and optimization of the shape of the roller generatrix of double-row railway roller bearing

Design and optimization of the shape of the roller generatrix of double-row railway roller bearing

Mechanical Design Optimization Using the Existing Optimization Techniques

Optimal design of paired tapered roller bearings under centred radial and axial static loads

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