Optimal Design of a Heavy Duty Helical Gear Pair Using Particle Swarm Optimization Technique

Tamboli, Ketan; Patel, Sunny; George, P.M.; Sanghvi, Rajesh Chandrakant

doi:10.1016/j.protcy.2014.08.065

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…Optimization has been applied to various gear forms such as helical, spur, bevel, spiral and worm type of gears by numerous researchers [7]. In pursuit of efficient methods, a computer-aided gear design method was introduced by Hofstetter et al [8], for the early development phase of a gear to capture the conceptual design decisions.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of a 2-stage spur gearbox using NSGA-II and decision-making methods

Maputi

Arora

2020

J Braz. Soc. Mech. Sci. Eng.

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Gears are crucial elements in mechanical systems and contribute to the overall performance of machinery. As such, optimization of gearbox transmission systems remains a challenging problem faced by researchers and designers for numerous years. In the present work, three objectives viz volume, power output and centre-distance are investigated simultaneously. A two-stage spur gearbox design problem is formulated with eight design variables viz. face-width (stage 1), face-width (stage 2), shaft diameter (stage 1), shaft diameter (stage 2), module, pinion teeth number(stage 1) and pinion teeth number (stage 2) considered. Two geometric and three design constraints are formulated. Pareto frontiers are generated using NSGA-II evolutionary algorithm. The Pareto frontier is investigated using decision tools viz. FUZZY, LINMAP and TOPSIS and the best solutions selected using the deviation index. Validation of results was done using previous reports in the literature and geometric modelling software. Variation and sensitivity studies indicate that module, pinion tooth number and face-width variables had a higher influence on volume as compared to power output and centre-distance. The combined optimization of volume, centre-distance and power output reveals key insights for the design of compact gearboxes.

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Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of a 2-stage spur gearbox using NSGA-II and decision-making methods

Maputi

Arora

2020

J Braz. Soc. Mech. Sci. Eng.

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“…Jing et al 10 used exact two-dimensional (2D) analytical method to calculate the magnetic field distribution and then to optimize a concentric magnetic gear through considering sinusoidal magnetizations. Due to most of power transmission systems requiring light weight, efficient, and low-cost elements, Tamboli et al 11 optimized a heavy-duty gear reducer with helical gear pair based on the minimum volume. Rao 12 used teaching learning based optimization (TLBO) and elitist teaching learning based optimization (ETLBO) algorithms to optimize a spur gear train for weight reduction under the contains of bending strength, surface durability, torsional strength, and center distance.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization design of spur gear based on NSGA-II and decision making

Qi-zhi

2019

Advances in Mechanical Engineering

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Optimization design of spur gear is a complicated work because the performance characteristics depend on different types of decision variables and objectives. Traditional single-objective optimization design of the spur gear always results in poor outcomes relative to other objectives due to objectives' competition with each other. Therefore, this study works on the spur gear design based on the multi-objective optimization model of elitist non-dominated sorting genetic algorithm (NSGA-II). In the model, gear module, teeth number, and transmission ratio are decision variables, while center distance, bearing capacity coefficient, and meshing efficiency are objectives. Final optimal solutions are picked out from Pareto frontier calculated from NSGA-II using the decision makers of Shannon Entropy, linear programming technique for multidimensional analysis of preference (LINMAP), and technique of order preference by similarity to an ideal solution (TOPSIS). Meanwhile, a deviation index is used to evaluate the reasonable status of the optimal solutions. From triple-objective and dual-objective optimization results, it is found that the optimal solution selected from LINMAP decision maker shows a relatively small deviation index. It indicates that LINMAP decision maker may yield better optimal solution. This study could provide some beneficial information for spur design.

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“…Furthermore, gear arrangement can be defined by axes, whether they are parallel or perpendicular, or by the number of stages forming a gear system. Gear types such as worm and wheel, spur, bevel, and helical have been investigated by researchers [12][13][14] while using various optimization methods. Single-stage, multistage, and multispeed gear systems have been investigated in literature [15,16] while considering numerous gear types.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of a three-stage transmission using advanced optimization techniques

Maputi

Arora

2019

Int. J. Simul. Multidisci. Des. Optim.

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Gear transmission systems are very important machine elements and their failure can lead to losses or damage of other mechanical components that comprise a machine or device. Since gears are applied in numerous mechanical devices, there is need to design and subsequently optimize them for intended use. In the present work, two objectives, viz., volume and center distance, are minimized for a rotary tiller to achieve a compact design. Two methods were applied: (1) analytical method, (2) a concatenation of the bounded objective function method and teaching–learning-based optimization techniques, thereby improving the result by 44% for the former and 55% for the latter. Using a geometric model and previous literature, the optimal results obtained were validated with 0.01 variation. The influence of design variables on the objective functions was also evaluated using variation studies reflecting on a ranking according to objective. Bending stress variation of 12.4% was less than contact stress at 51% for a defined stress range.

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Optimal Design of a Heavy Duty Helical Gear Pair Using Particle Swarm Optimization Technique

Cited by 23 publications

References 9 publications

Multi-objective optimization of a 2-stage spur gearbox using NSGA-II and decision-making methods

Multi-objective optimization of a 2-stage spur gearbox using NSGA-II and decision-making methods

Multi-objective optimization design of spur gear based on NSGA-II and decision making

Design optimization of a three-stage transmission using advanced optimization techniques

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