Three‐dimensional fatigue crack growth modelling in a helical gear using extended finite element method

Rad, Ahmad Amiri; Forouzan, Mohammad Reza; Dolatabadi, A. Sadeghi

doi:10.1111/ffe.12140

Cited by 40 publications

(9 citation statements)

References 32 publications

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“…Agarwal et al [60] used ANSYS to determine the crack initiation point and propagation direction to calculate the gear life by using Paris' law, but they mainly studied the influence of slag inclusion on the gear life. Although Rad et al's method [61] was similar to many previous methods, it was based on linear elastic fracture mechanics to simulate crack propagation and then used Paris' law to calculate the RUL of gear, but their method mainly gave better guidance in modeling, and using their modeling method can save a lot of time. In most cases, using Paris' law to predict crack degradation life requires knowing the initial length of the crack and the intensity factor.…”

Section: Digital Twin-driven Physical Model-based Prediction Methods Of Gear Remaining Useful Lifementioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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As a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of physical model-based one, it includes prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of digital twin-driven virtual model-based one, it includes non-deep learning methods, and deep learning methods. Non-deep learning methods include wiener process, gamma process, hidden Markov model (HMM), regression-based model, proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN), etc. It mainly summarizes the performance degradation and life test of various models in gear, and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.

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Section: Digital Twin-driven Physical Model-based Prediction Methods Of Gear Remaining Useful Lifementioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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“…11 considered the effect of moving gear tooth load on the crack initiation life and Amiri et al. 12 calculated the fatigue life of a helical gear.…”

Section: Introductionmentioning

confidence: 99%

Optimisation methodology for lightweight gears to be produced by additive manufacturing techniques

Mura

Curá

Pasculli

2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this work, a new design procedure to optimise lightweight gears is presented. Lightweight gears find application in all industrial fields where the weight of the structure is a key factor. Thanks to the new additive manufacturing techniques, it is possible to better distribute the wheel material and therefore increase gear performance. The proposed procedure acts on the material distribution in order to enhance wheel dynamic performance and reduce weight. The new design procedure has been applied to a real airplane gear in order to evaluate if the optimisation procedure can be useful to improve wheel performance. Results show improved performance of the optimised wheel respect to the reference wheel. The optimised wheel geometry is supposed to be manufactured by additive manufacturing techniques.

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“…Kramberger et al investigated the effect of rim thickness on bending fatigue life of a thin‐rimmed spur gear for truck gearbox by finite element and boundary element methods, and Amiri Rad et al calculated the fatigue life of a helical gear by means of the extended finite element method (XFEM).…”

Section: Introductionmentioning

confidence: 99%

Influence of high speed on crack propagation path in thin rim gears

Curá

Mura

Rosso

2016

Fatigue Fract Eng Mat Struct

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A B S T R A C T Thin rim gears need accurate design as cracks nucleated at the tooth root fillet may propagate in a safe way (through the tooth) or in catastrophic way (through the rim). Crack propagation direction is mainly influenced by both wheel geometry parameters and crack initiation point. For specific geometry configurations, crack propagation path may be influenced also by other parameters such as the centrifugal load. For this reason, in this work the effect of the centrifugal load (proportional to wheel speed), related to the bending one, has been investigated. The stress field at the tooth root fillet and near the crack has been considered to evaluate the crack initiation point and to explain the propagation direction. This research activity has been carried out by means of numerical models (traditional 2D and 3D finite elements and extended finite elements (XFEM)). Results show that both crack initiation point and crack propagation path are strongly influenced by the centrifugal load entity; this effect is mainly evident in the uncertainty zone of the backup ratio.A = bending stress coefficient B = centrifugal stress coefficient C R = ISO blank factor F = force applied on the tooth at HPSTC H B = rim thickness H T = tooth height L = face width M = normal gear modulus m b = backup ratio m W = web ratio W = web thickness σ b = bending stress σ c = centrifugal stress σ yield = yield stress τ = shear stress

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Three‐dimensional fatigue crack growth modelling in a helical gear using extended finite element method

Cited by 40 publications

References 32 publications

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Optimisation methodology for lightweight gears to be produced by additive manufacturing techniques

Influence of high speed on crack propagation path in thin rim gears

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