Secondary phase distribution analysis via finite body tessellation

Boselli,; Pitcher,; Gregson,; Sinclair, Robert

doi:10.1046/j.1365-2818.1999.00483.x

Cited by 45 publications

(53 citation statements)

References 12 publications

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“…The morphology and distribution (in both size and spatial arrangement) of the TiB 2 particles of the composite were quantified on two-dimensional sections using a finitebody tessellation method, [14] based on scanning electron microscopy (SEM) of the material. A finite-body tessellation consists of a network of cells such that every point within a cell is closer to the interface of the corresponding particle than any other.…”

Section: A Materials and Microstructuresmentioning

confidence: 99%

“…It has been found previously that such interface-based finite-body tessellation provides a valuable method of assessing particle distributions in common particulate MMC microstructures, offering significant advantages over Dirichlet tessellation and nearest-neighbor methods. [14,15,16] A variety of parameters relating to particle morphology and spatial distribution may then be derived for each individual particle, for instance, "local" area fraction (ratio of particle area and corresponding tessellation cell area), and mean near-neighbor distance (average of the interface-to-interface separations with all particles that share a cell edge around each individual particle of interest). In particular, the coefficient of variation of mean near-neighbor distance (COV(d mean )), defined as the ratio of the standard deviation and the average of mean near-neighbor distances, has been identified in previous work as a particle-morphologyindependent parameter to quantify homogeneity of particle distributions.…”

Section: A Materials and Microstructuresmentioning

confidence: 99%

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Fatigue crack growth in a particulate TiB2-reinforced powder metallurgy iron-based composite

Yang

Sinclair

2003

Metall Mater Trans A

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Fatigue crack growth behavior has been examined in a particulate titanium diboride (TiB 2 )-reinforced iron-based composite that had been produced via a mechanical alloying process. Comparison with equivalent unreinforced material indicated that fatigue crack growth resistance in the composite was superior to monolithic matrix material in the near-threshold regime. The composite exhibited relatively low crack closure levels at threshold, indicative of a high intrinsic (effective) threshold growth resistance compared to the unreinforced iron. The lower closure levels of the composite were consistent with reduced fracture surface asperity sizes, attributable to the reinforcement particles limiting the effective slip distance for stage I-type facet formation. The observed shielding behavior was rationalized in terms of recent finite-element analysis of crack closure in relation to the size of crack wake asperities and the crack-tip plastic zone. The different intrinsic fatigue thresholds of the composite and unreinforced iron were closely consistent with the influences of stiffness and yield strength on cyclic crack-tip opening displacements. Cracks in the composite were generally seen to avoid direct crack-tip-particle interaction.

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Section: A Materials and Microstructuresmentioning

confidence: 99%

Section: A Materials and Microstructuresmentioning

confidence: 99%

Fatigue crack growth in a particulate TiB2-reinforced powder metallurgy iron-based composite

Yang

Sinclair

2003

Metall Mater Trans A

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“…A large number of secondary electron images (SEI) were obtained from the cracked surface to examine qualitatively the type of particles participating in early fatigue initiation. The fi nite body tessellation technique (FBT) [12] was used to investigate quantitatively the geometrical features of short-crack initiating particles. Figure 2 shows scanning electron microscope (SEM) images of lining surfaces of fatigued fl at bar specimens.…”

Section: Experimental Workmentioning

confidence: 99%

Microstructural modelling of fatigue initiation in aluminium-bearing alloys

Syngellakis¹,

Ali²,

Reed³

2013

Int. J. CMEM

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Microstructural fatigue initiation in Al-Sn-Si-Cu-Ni bearing lining alloys is reported and investigated. The secondary phases of such alloys comprise fi ne and relatively few Sn and Si particles as well as a large number of hard AlNi 3 -type intermetallics, frequently encapsulated within soft Sn layers. During fatigue tests, these particles were observed to initiate short fatigue cracks. Through elasto-plastic fi nite element analysis of ideal microstructures but with realistic geometric and mechanical property data, critical values of key stress and strain components within the matrix, the Sn layers, and the particles were predicted and linked to microstructural features associated with observed fatigue initiation. These modelling results indicate the extent to which either the hydrostatic stresses or plastic shear strains may be responsible for fatigue crack initiation in the Sn layers, as well as the optimum microstructural characteristics that would minimise tensile stresses, which are responsible for brittle particle fracture.

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“…In each case a classification approach has been followed, whereby prediction as to whether a particular particle will fall into the fatigue initiation class or the non-initiation class is made as a function of finite body tessellation image analysis parameters that measure, size, shape clustering etc of the particles. 50 Unlike many classification techniques, which place an emphasis on obtaining a good classification rate (e.g. 100% successful classification of those particles associated with crack initiation), the SUPANOVA approach also provides enhanced model transparency and hence aids model interpretation (e.g.…”

Section: Physical and Mechanical Propertiesmentioning

confidence: 99%

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering – a survey

Reed¹,

Starink²,

Gunn³

et al. 2009

Materials Science and Technology

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Many adaptive numerical modelling (ANM) techniques such as artificial neural networks (including multilayer perceptrons), support vector machines and Gaussian processes have now been applied to a wide range of regression and classification problems in materials science. Materials science offers a wide range of industrial applications and hence problem complexity levels from well physically characterised systems (e.g. high value, low volume products) to high volume low cost applications with intrinsic scatter due to commercial manufacturing processes. The authors review a number of recent examples in the literature, with the aim of identifying best practice in the use of these techniques as part of a multistrand modelling approach. The importance of understanding the basic principles of these modelling techniques and how they can link with other modelling strategies is emphasised. In particular the authors wish to identify the importance of hybrid physically based ANM in taking the field forward, which can range from, at the most basic level, careful data selection and data preprocessing to a full integration of physically based models with advanced ANM. A number of case studies are presented to illustrate the main points of the paper. Keywords: Neural networks, Support vector machines, Gaussian processes, Adaptive numerical modelling, Data driven techniques, Hybrid modelling, Applications in materials science IntroductionThe links between processing variables, microstructure evolution, resultant properties and hence mechanical performance are core tenets of materials engineering and a variety of modelling approaches have been applied to these materials modelling challenges. In some cases, a clear physical basis for such models has now been established, but often these can apply only to well defined conditions, which may not be representative of either genuine industrial (scaled up) production processes, or materials performance under realistic service conditions. The advantages of physically based models are:(i) they are robust to interpolation and extrapolation (as long as the underlying physical mechanisms being modelled are in operation) (ii) they should not require large amounts of data, as typically they only have a limited number of fitted parameters; ideally, a physically based model may have no fitted parameters (iii) the mechanistic verifications derived from physically based modelling may also offer insights into novel materials optimisation strategies (iv) a physically based model is relatively transparent, i.e. the mathematical underlying relationships between input and output parameters may be written in a reasonably clear mathematical form: this is part of a loosely defined group of methods which the authors term 'white box'. However, in a truly integrated processing-performance model, with the levels of uncertainty and complexity associated with industrial practice and complex service conditions, expressing these interlinked processes purely on physics based principles is unlikely to be feasible. ...

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Secondary phase distribution analysis via finite body tessellation

Cited by 45 publications

References 12 publications

Fatigue crack growth in a particulate TiB2-reinforced powder metallurgy iron-based composite

Fatigue crack growth in a particulate TiB2-reinforced powder metallurgy iron-based composite

Microstructural modelling of fatigue initiation in aluminium-bearing alloys

Invited review: Adaptive numerical modelling and hybrid physically based ANM approaches in materials engineering – a survey

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