Fatigue Crack Propagation in a Gear Tooth in the Presence of an Inclusion

Agarwal, Vineet; Zagade, Pramod; Khan, Danish; Gautham, B. P.

doi:10.1080/15502287.2014.882434

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Pehan et al employed numerical calculation to investigate the location of the initial crack and discussed the influence of different initial crack locations on the fatigue crack propagation. Agarwal simulated the fatigue crack propagation in gear teeth, and Ma et al also analysed the 3D crack propagation and predicted the fatigue life of the gear …”

Section: Introductionmentioning

confidence: 99%

“…Pehan et al employed numerical calculation to investigate the location of the initial crack 3 and discussed the influence of different initial crack locations on the fatigue crack propagation. Agarwal simulated the fatigue crack propagation in gear teeth, 4 and Ma et al also analysed the 3D crack propagation and predicted the fatigue life of the gear. 5 In this paper, both experimental and numerical methods were employed to investigate the fatigue crack propagation in an involute spur gear tooth under constant amplitude loading.…”

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confidence: 99%

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Experimental and numerical investigation of fatigue crack growth in the cracked gear tooth

Zhang

et al. 2016

Fatigue Fract Eng Mat Struct

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The numerical simulation was conducted to analyse the fatigue crack growth in gear with the finite element codes ansys (ANSYS, Inc. Canonsburg, Pennsylvania, USA.) and franc3d (Fracture Analysis Consultants, Inc. Ithaca, New York, USA.), and the corresponding fatigue test was also carried out. During the simulation, the location of maximal stress induced by the external force was first determined by the code ansys, and then the obtained results were imported into the franc3d to analyse the crack growth. The analysed results were input into the codes ansys and franc3d again to compute the stress and the stress intensity factor in the following steps. After several rounds of analysis, the results of the fatigue crack propagation were obtained. The investigations show that the crack mode I is dominant during the crack growth and the stress intensity factor KI raises with increase of crack growth length and a series of quarter‐elliptical cross sections of the ruptured gear tooth are obtained. The simulation results are in good accordance with experimental findings.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Experimental and numerical investigation of fatigue crack growth in the cracked gear tooth

Zhang

et al. 2016

Fatigue Fract Eng Mat Struct

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“…In essence, the length and expansion direction of cracks are predicted directly by using Paris' law module in the software. 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.…”

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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“…Podrug et al 43 considered the effect of gear rim thickness, crack closure, and change in forces during rotation while the prediction of gear tooth cracks propagation life. Agarwal et al 53 studied the phenomena of fatigue crack propagation in the presence of inclusion in the gear tooth root. For the hard inclusions near to the original crack paths, the crack propagation was observed to be slower.…”

Section: Physics-based Approachesmentioning

confidence: 99%

A review on diagnostic and prognostic approaches for gears

Kundu

Darpe

Kulkarni

2020

Structural Health Monitoring

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Prognostics and health management has become a significant part of component life-cycle in modern industries. The prognostics and health management framework is implemented in the industries to identify the fault type, assess fault severity, and predict the future state or remaining useful life to optimize the maintenance activities. Three significant aspects of a prognostics and health management framework are diagnostics, prognostics, and decision making. This article presents a review of different types of diagnostic and prognostic approaches (i.e. physics-based, data-driven, and hybrid approaches) developed for the gears. The flow of information between diagnostics and prognostics parts of the framework is briefly discussed. Regarding the physics-based approaches, this article discusses different physics-based diagnostic and prognostic models developed for different types of gear failure modes such as crack, pitting, and wear. In the data-driven approaches, the article attempts to summarize the data processing techniques used for extracting fault-related information from the recorded raw vibration signal, health indicators developed for different kinds of gear failure modes, processing/selection approaches for best health indicators, fault classification, and fault prognostic models particularly developed for the gear. The article discusses how a hybrid approach can be developed by the integration of a data-driven diagnostics approach and a physics-based prognostics approach. Finally, uncertainty quantification of prognostic approaches, performance evaluation metrics, decision-making strategies, and future research and development perspectives are discussed. This article focuses on the diagnostic and prognostic approaches developed for gears, given the fact that these approaches for other components such as bearing and batteries are reviewed in the past.

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Fatigue Crack Propagation in a Gear Tooth in the Presence of an Inclusion

Cited by 8 publications

References 10 publications

Experimental and numerical investigation of fatigue crack growth in the cracked gear tooth

Experimental and numerical investigation of fatigue crack growth in the cracked gear tooth

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

A review on diagnostic and prognostic approaches for gears

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