High-Cycle Fatigue Behaviour of Ultrafine Grained 5052 Al Alloy Processed Through Cryo-Forging

Yogesha, K. K.; Joshi, Amit; Raviraj,; Raja, A.; Jayaganthan, R.

doi:10.1007/978-3-030-05728-2_14

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…On the other hand, influence of cryo-rolling followed by annealing is attributed to the combined recovery and recrystallisation process, which enhances the plastic zone size near the crack tip due to crack tip/precipitate interaction resulting in enhanced fatigue crack growth resistance at low temperature annealing [28]. The similar fatigue behaviour in other Al alloys were also reported in the literature [16,29,30]. These differences in the Paris law regime were clearly interpreted by ML algorithms.…”

Section: Back Propagation Neural Networksupporting

confidence: 66%

Prediction of Fatigue Crack Growth Behaviour in Ultrafine Grained Al 2014 Alloy Using Machine Learning

Raja

Chukka

Jayaganthan

2020

Metals

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The present work investigates the relationship between fatigue crack growth rate (da/dN) and stress intensity factor range (∆K) using machine learning models with the experimental fatigue crack growth rate (FCGR) data of cryo-rolled Al 2014 alloy. Various machine learning techniques developed recently provide a flexible and adaptable approach to explain the complex mathematical relations especially, non-linear functions. In the present work, three machine algorithms such as extreme learning machine (ELM), back propagation neural networks (BPNN) and curve fitting model are implemented to analyse FCGR of Al alloys. After tuning of networks with varying hidden layers and number of neurons, the trained models found to fit well to the tested data. The three tested models are compared with each other over the training as well as testing phase. The mean square error for predicting the FCG of cryo-rolled Al 2014 alloy by BPNN, ELM and curve fitting methods are 1.89, 1.84 and 0.09 respectively. While the ELM models outperform the rest of models in terms of training time, curve fitting model showed best performance in terms of accuracy over testing data with least mean square error (MSE). In terms of local optimisation, back propagation neural networks excel the other two models.

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Section: Back Propagation Neural Networksupporting

confidence: 66%

Prediction of Fatigue Crack Growth Behaviour in Ultrafine Grained Al 2014 Alloy Using Machine Learning

Raja

Chukka

Jayaganthan

2020

Metals

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“…According to Eq. (3), the strain rate of superplastic deformation has an inverse relationship with grain size to the power "p." Moreover, with the advent of severe plastic deformation methods, fabrication of ultrafine grain (UFG) and nano-sized grains can be obtained using cryo forming [8,9], multiaxial forging, equal-channel angular pressing (ECAP), friction stir processing (FSP), and high-pressure torsion (HPT). According to the Hall-Petch relationship, improvement in room-temperature mechanical properties in different alloys was studied extensively [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Effect of Grain Size on Superplastic Deformation of Metallic Materials

Raja¹,

Jayaganthan²,

Tiwari³

et al. 2020

Aluminium Alloys and Composites

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The superplastic deformation exhibited by metals with different grain sizes and their corresponding deformation mechanism influences the industrial metalforming processes. The coarse-grained materials, which contain grain size greater than 20 μm, exhibited superplastic deformation at high homologous temperature and low strain rate of the order of 10 −4 s −1. Fine grain materials (1-20 μm) are generally considered as favorable for superplastic deformation. They possess highstrain-rate sensitivity "m" value, approximately, equal to 0.5 at the temperature of 0.5 times the melting point and at a strain rate of 10 −3 to 10 −4 s −1. Ultrafine grains (100 nm to less than 1 μm) exhibit superplasticity at high strain rate as well as at low temperature when compared to fine grain materials. It is attributed to the fact that both temperature and strain rates are inversely proportional to the grain size in Arrhenius-type superplastic constitute equation. The superplastic phenomenon with nano-sized grains (10 nm to less than 100 nm) is quite different from their higher-scale counterparts. It exhibits high ductility with high strength. Materials with mixed grain size distribution (bimodal or layered) are found to exhibit superior superplasticity when compared to the homogeneous grain-sized material. The deformation mechanisms governing these superplastic deformations with different scale grain size microstructures are discussed in this chapter.

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“…The major difference in deformation mechanism between conventional practices and severer plastic deformation is that, during traditional plastic deformation method formation of continuous evolution of dislocation structure which does not support grain refinement where as in SPD it evidences a changeover from continuous dislocation mechanism to microlocalized flow which strongly supports grain refinement intern helps to improve the mechanical properties of the materials to prepare them fit for a variety of applications [1][2][3][4]. There are different SPD methods in order to obtain ultra-fine grain structured materials such as equal-channel angular pressing(ECAP) [5], high pressure torsion extrusion [6], multidirectional forging (MDF) process [7], Constrained groove pressing (CGP) [8], accumulative roll-bonding (ARB) [9], repetitive corrugation and straightening (RCS) [10,11] etc. ECAP is one of the major SPD process which helps to process the material to get ultrafine grains structure through grain refinement for the betterment of mechanical properties of the material.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Property Enhancement of Aluminium Alloy Aa 7068 Processed Through an Effective Grain Refinement Process Called Ecap

Shivashankara¹,

S²,

Gopi³

2021

IJEAST

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In the present scenario there is an immense need for highly designated material with better mechanical and physical properties for a variety of engineering applications. This article discusses a well-known severe plastic deformation process called equal channel angular pressing (ECAP), which was conducted on Aluminium alloy AA7068 at elevated temperature 220°C with repetitive ECAP cycles using route A, up to four passes in order to improve the mechanical properties of the material by producing ultrafine grained (UFG) microstructure. The microstructure of homogenized and ECAP processed samples were analyzed using optical microscope and mechanical properties were examined through tensile and microhardness tests. Investigation results showed an improvement in tensile strength and hardness of ECAP processed samples with a considerable amount of grain refinement up to 15 µm after four ECAP passes as compared with unprocessed samples.

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High-Cycle Fatigue Behaviour of Ultrafine Grained 5052 Al Alloy Processed Through Cryo-Forging

Cited by 9 publications

References 34 publications

Prediction of Fatigue Crack Growth Behaviour in Ultrafine Grained Al 2014 Alloy Using Machine Learning

Prediction of Fatigue Crack Growth Behaviour in Ultrafine Grained Al 2014 Alloy Using Machine Learning

Effect of Grain Size on Superplastic Deformation of Metallic Materials

Mechanical Property Enhancement of Aluminium Alloy Aa 7068 Processed Through an Effective Grain Refinement Process Called Ecap

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