Prediction of damage evolution in forged aluminium metal matrix composites using a neural network approach

Roberts, S.M.; Kusiak, J.; Liu, Y.L.; Forcellese, Archimede; Withers, Philip J.

doi:10.1016/s0924-0136(98)00153-8

Cited by 31 publications

(14 citation statements)

References 3 publications

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“…On the basis of previous work [5,12,14] one might expect the rate of damage to be greatest for large strain increments, at high strain rates and low forming temperatures. As a result we have (arbitrarily) taken damage, d, to increase according to the equation:…”

Section: The Damage Modelmentioning

confidence: 99%

An evaluation of recurrent neural network modelling for the prediction of damage evolution during forming

Xiong

Withers

2005

Journal of Materials Processing Technology

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Section: The Damage Modelmentioning

confidence: 99%

An evaluation of recurrent neural network modelling for the prediction of damage evolution during forming

Xiong

Withers

2005

Journal of Materials Processing Technology

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“…In the context of industrial material science, often only partial knowledge is available about the physical processes involved, although significant amounts of 'raw' data may be available from production records and may be used to construct a data driven model. In modelling of properties and processing of Al-based alloys AN modelling has been applied to processing-property relations in 2xxx and 7xxx plate [7,8,10,11], fatigue life of 2xxx panels [12], air bending of a 5xxx alloy [13], damage evolution in MMCs [14], hot working [15] and welding [16].…”

Section: 1mentioning

confidence: 99%

Predicting the Structural Performance of Heat-Treatable Al-Alloys

Starink

Sinclair

Reed

et al. 2000

MSF

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Advances in physically-based and adaptive numeric modelling (ANM) have lead to a significant elevation of the role of modelling in commercial alloy development and process optimisation. Within these two categories of modelling a wide variety of techniques exists, whilst hybrid approaches, taking advantage of the benefits of the both methods, are also being developed. In this paper various ANM techniques and physically-based models relevant for modelling and predicting the properties of heat treatable Al-based alloys are reviewed, and case studies involving the modelling of proof stress, toughness and conductivity of a range of Al-based alloys are presented. These case studies illustrate that successful modelling requires an in-depth understanding of the various modelling options. Selection of the optimum modelling approach is driven by a range of factors, such as size of database available (large databases are best analysed using adaptive numeric modelling approaches), and availability of sound physical understanding (the latter benefiting physically-based approaches). 1.Introduction Throughout the history of alloy development, the modelling of properties and microstructure-property relationships has lagged behind the commercialisation of new products and processes. In terms of agehardened Al-alloys, commercial aerospace applications were established by 1919, however the first basic models of age hardening, based on dislocation movement and dislocation pinning, were not developed until the 1940's [1,2], whilst the first detailed models of strengthening in multi-component Al-based alloys were only published in the 1980/90s [3,4,5]. In recent years, this time lag has been reduced, with modelling preceding or directly leading product development in some cases. A specific example is the formulation of new compositions of Ni-based superalloys by Rolls-Royce Plc. which are based on thermodynamic modelling, and the recent development of 7449 and 7040 Al-alloy plate which has been influenced by microstructure-property modelling [6]. The use of well-founded modelling approaches has clear advantages in directing product development in a cost effective manner.Whilst the above examples concern modelling through physical understanding of microstructures and microstructure development, modelling using adaptive numeric (AN) approaches such as artificial neural networks has recently made important advances. Current state-of-the-art adaptive numeric modelling combines flexible, adaptive algorithms for model construction with a rigorous mathematical/statistical basis [7,8].The purpose of the present paper is to highlight recent advances in physically-based and AN modelling, and to consider the relative benefits of the two approaches as well as hybrid approaches which use elements of both. In exploring this wide ranging and continuous field of modelling approaches we will start by introducing some of the better known techniques. On one side of the spectrum of modelling

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“…In the past few years there has been a constantly increasing interest for ANN modelling in different fields of materials science [5]. ANN models have been developed to model different correlations and phenomena in steels [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], aluminium alloys [23,24], Ni-base superalloys [25][26][27], mechanically alloyed materials [28], etc.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for modelling correlations in titanium alloys

Malinov

Sha

2004

Materials Science and Engineering: A

135

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Prediction of damage evolution in forged aluminium metal matrix composites using a neural network approach

Cited by 31 publications

References 3 publications

An evaluation of recurrent neural network modelling for the prediction of damage evolution during forming

An evaluation of recurrent neural network modelling for the prediction of damage evolution during forming

Predicting the Structural Performance of Heat-Treatable Al-Alloys

Application of artificial neural networks for modelling correlations in titanium alloys

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