Multi-objective micro-geometry optimization of gear tooth supported by response surface methodology

Korta, Jakub; Mundo, Domenico

doi:10.1016/j.mechmachtheory.2016.11.015

Cited by 64 publications

(32 citation statements)

References 30 publications

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“…In the regions close to the tooth contact area, where a detailed description of the geometry is required to properly capture contact phenomena, the element size was set to 0.11 mm, based on a convergence analysis [4]. Considerably coarser mesh was created in the regions that are sufficiently far from the meshing teeth.…”

Section: Ste Estimation In Lightweight Gears By Nonlinear Fe Simulationsmentioning

confidence: 99%

“…Sidebands are instead clearly visible in the FFT analysis of the mesh stiffness derived from the STE curve according to (4) and shown in Figure 15. Dynamic sideband amplifications are observed also in the waterfall diagram shown in Figure 16.…”

Section: Effect Of Ste Harmonic Component Due To Discontinuities In Tmentioning

confidence: 99%

“…The use of FEM for 3D static and dynamic contact/impact analysis of gear drives was proposed by Lin et al in [3], while an example of FE-based optimization of gear microgeometry is reported in [4].…”

Section: Introductionmentioning

confidence: 99%

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A Study on the Dynamic Behaviour of Lightweight Gears

Shweiki

Palermo

Mundo

2017

Shock and Vibration

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This paper investigates the dynamic effects of mass reduction on a pair of spur gears. A one-Degree-of-Freedom (DOF) model of a mechanical oscillator with clearance-type nonlinearity and linear viscous damping is used to perform the investigations. Onedimensional (1D) gear pair models aim at studying the torsional gear vibrations around the rotational axes and can be used to simulate either gear whine or gear rattle phenomena. High computational efficiency is reached by using a spring-damper element with variable stiffness to model the gear meshing process. The angle-dependent mesh stiffness function is computed in a preparation phase through detailed Finite Element (FE) simulations and then stored in a lookup table, which is then interpolated during the dynamic simulation allowing for high computational efficiency. Nonlinear contact effects and influence of material discontinuities due to lightweighting are taken into account by FE simulations with high level of detail. Finally, the influence of gear body topology is investigated through a sensitivity analysis, in which analytical functions are defined to describe the time-varying mesh stiffness.

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Section: Ste Estimation In Lightweight Gears By Nonlinear Fe Simulationsmentioning

confidence: 99%

Section: Effect Of Ste Harmonic Component Due To Discontinuities In Tmentioning

confidence: 99%

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A Study on the Dynamic Behaviour of Lightweight Gears

Shweiki

Palermo

Mundo

2017

Shock and Vibration

Self Cite

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“…Indeed, they used a method combining the finite element method and semi analytical solution. In another work, Korta and Mundo [20] aimed to find optimal microgeometry modifications of tooth profile of a pair of identical spur gears, providing decreased values of peak-topeak transmission error and maximal contact stress along the meshing cycle. They used three kinds of metamodeling techniques: the Gaussian Process (stochastic), the Shepard k-Nearest (non parametric deterministic) and the Polynomial (parametric deterministic).…”

Section: Introductionmentioning

confidence: 99%

“…The analysis of references [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] highlights a common fact that the proposed design approaches employ exclusively one type of models, analytical or numerical (i.e. FEM) one, to evaluate the performances of any design candidate.…”

Section: Introductionmentioning

confidence: 99%

Optimum design of a spur gear using a two level optimization approach

Mahiddini¹,

Chettibi

Benfriha

et al. 2019

mech

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In this paper, we present a two level optimization approach in order to enhance the design process of a one-stage speed reducer. The proposed design methodology is performed using genetic algorithms which are judiciously combined with the use of :i) analytical models (1stlevel) and ii) Finite Element Method (FEM)based models ( 2nd level), to evaluate design candidates. Indeed, the use of CAD-CAE tools to develop higher fidelity FEM models allows to re-evaluate the attained first level designs, while accounting for new design parameters and advanced aspects which have been ignored in the first level. In order to minimize the computational burden, a metamodel based optimization technique is adopted at this second level. To illustrate the efficiency of the proposed approach, a case study of a spur gear based reducer is presented where the design of experiments is built using Hypercube Latin Sampling and surrogate models are constructed using Radial Basic Functions.

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