An approach for design optimization of helical gear pair with balanced specific sliding and modified tooth profile

Rai, Paridhi; Barman, Asim Gopal

doi:10.1007/s00158-019-02198-7

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…Finally, for the problem (4), Fig. 14 shows the POFs obtained with DE-NSGA-II, NSGA-II, WASF-GA and MOEA/D in this work, with NSGA-II in [56] (coincide with those obtained in this work) and with ISPAES in [58], as far as we know all the multi-objective methods that have been applied to this problem (4). It can be seen that DE-NSGA-II finds the whole POF A-E, while NSGA-II finds only section B-E, NSDE and ISPAES section C-E and WASF-GA and MOEA/D section D-E.…”

Section: De-nsga-ii Vs Nsga-ii (Repair Ii)supporting

confidence: 68%

“…Using multi-objective optimization concepts, the problem (3) can be redefined as a multi-objective optimization problem as in (4). To this end, a new objective function, that minimizes the maximum size of any of the four gears, has been added.…”

Section: Gear Train Design Problem Preliminary Studymentioning

confidence: 99%

“…The problem (4) can be solved by explicit enumeration using 6 bits (which allow to represent 2 6 = 64 different binary solutions) for the binary coding of the values of each variable x i , i = 1, 2, 3, 4. This implies an exploration space of 64 4 = 16, 777, 216 solutions. Considering that the values of each variable are restricted to the range [12,60] and reducing the numerous solutions of the decision space that produce identical solutions in the objective space (overlapping solutions [59]), a total of 662,165 feasible non-overlapping solutions are obtained.…”

Section: Gear Train Design Problem Preliminary Studymentioning

confidence: 99%

“…Stochastic in nature, evolutionary algorithms (EAs) in their version of genetic algorithms (GAs) [1]- [3] have been applied effectively in science, engineering and engineering design [4]- [7]. The evolution of a population of solutions, using a parent selection process first, and reproduction operations (crossower and mutation) on the selected parents later, provides the GAs with great skill to find an approximate optimal solution to problems of high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

et al. 2020

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This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.

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Section: De-nsga-ii Vs Nsga-ii (Repair Ii)supporting

confidence: 68%

Section: Gear Train Design Problem Preliminary Studymentioning

confidence: 99%

Section: Gear Train Design Problem Preliminary Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

et al. 2020

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“…Modified parameters can be obtained by applying the complex method [25][26][27] to minimize the meshing impact and uniformly distribute teeth surface load. Subsequently, several typical examples were utilized to verify the correctness of this modification method via comparison of the loaded transmission error, maximum impact force, and load distribution factor before and after modification [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Optimal analysis of gear modification fitting in alternating time domain aiming at minimizing meshing-in impact of teeth-pair contact interface

Wang¹,

An²,

Wang³

et al. 2021

J. vibroeng.

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In a pair of actual gears alternating meshing process, the transmission system errors and thermal elastic deformation of the gear teeth can cause gears in the meshing zone to form high contact pairs inconsistently mesh-in and mesh-out points to deviate from the theoretical line, which will lead to sudden changes in meshing velocity and an instantaneous meshing effect. Offline meshing impact generates large additional loads, and it increases the vibration and noise of gear transmission system. Further, the gear teeth mesh-in impact is significantly greater than that of gear teeth mesh out. Therefore, to analyze the impact with the minimum value of gear teeth mesh in, the initial mesh-in points need to be determined, and the meshing impact velocity and impact force of teeth meshing-out need to be calculated. The optimized meshing tooth-pair contact interface (CI) anti-impact numerical calculation mode is validated using the loaded teeth contact analysis method.

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Optimization Design of Helical Cylindrical Gears

Guogeng,

Xiaowen,

Suzhen

2024

Mechanisms and Machine Science

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An approach for design optimization of helical gear pair with balanced specific sliding and modified tooth profile

Cited by 14 publications

References 25 publications

Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

Optimal analysis of gear modification fitting in alternating time domain aiming at minimizing meshing-in impact of teeth-pair contact interface

Optimization Design of Helical Cylindrical Gears

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