Estimation of damage in high strength steels

Das, Arijit; Sivaprasad, Sobha; Tarafder, Mita; Das, Suchandan K; Tarafder, S.

doi:10.1016/j.asoc.2012.09.016

Cited by 18 publications

(7 citation statements)

References 37 publications

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“…In ductile fracture route, a mode of failure normally occurs through continual damage accumulation involving three distinct stages: void nucleation, growth and coalescence to shape a continuous fracture path/ridge. [4][5][6][7][8][9][10][11][12][13][14] The central philosophy of the inter-relationship between the deformation properties and the fracture process of different alloys under diverse conditions has been well documented by the present author elsewhere. [1][2][3][9][10][11][12][13][14] For ductile fracture, the engineering properties are principally determined by the interaction of stress and strain fields with the correspondingly developed microstructural features of the material.…”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6][7][8][9][10][11][12][13][14] The central philosophy of the inter-relationship between the deformation properties and the fracture process of different alloys under diverse conditions has been well documented by the present author elsewhere. [1][2][3][9][10][11][12][13][14] For ductile fracture, the engineering properties are principally determined by the interaction of stress and strain fields with the correspondingly developed microstructural features of the material. The connectivity between dimple size distribution/number densities and precipitate size/shape distribution has been established for a number of alloys reported by Goods and Brown in their elegant study.…”

Section: Introductionmentioning

confidence: 99%

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Contribution of deformation-induced martensite to fracture appearance of austenitic stainless steel

Das

2016

Materials Science and Technology

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Fracture surface commonly carries the evidence of high-energy (ductile/tough) and low-energy (brittle) regions involved in fracture history, the macroscopic appearance of a fracture surface has often been utilised to qualitatively evaluate toughness of materials. For metastable austenitic stainless steels (AISI 304LN), the degree of martensitic transformation affects the fracture appearance and thus depends critically on the strain rate. The two dimensional ductile tearing ridge pattern quantified from many tensile fractographs are observed to predict the nature of disparity in deformation and fracture responses with systematic variation in strain rate of the steel under ambient atmosphere. The spatial distribution of deformation-induced martensite under tension at various stress/strains and strain rates strongly influences void nucleation, growth, coalescence and hence, keeps the impression on the ductile tearing ridge morphologies and dimple geometries on the fracture surface, where the initial inclusion content was constant.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contribution of deformation-induced martensite to fracture appearance of austenitic stainless steel

Das

2016

Materials Science and Technology

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“…Considering the fact that there is not an obvious physical determinant between these characteristic parameters and the spheroidization grade, the numerical fitting method can be used to evaluate their relationship. Currently, ANN is a valid way to fit the mathematical relationships, especially for multiple-input and multiple-output systems (Bhadeshia, 1999), which are different from the conventional material-behavior-evaluation techniques (Mandal et al, 2009; Das et al, 2013). Furthermore, many quantitative mathematical models are established using the combination of both image analysis and ANN, with which the problems presented in the industrial field have been successfully resolved (Buessler et al, 2014; Poonnoy et al, 2014; Samtas, 2014).…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography

Chen

Ren

et al. 2016

Microsc Microanal

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To eliminate the effect of subjective factors during manually determining the pearlite spheroidization grade of steel by analysis of optical metallography images, a novel method combining image mining and artificial neural networks (ANN) is proposed. The four co-occurrence matrices of angular second moment, contrast, correlation, and entropy are adopted to objectively characterize the images. ANN is employed to establish a mathematical model between the four co-occurrence matrices and the corresponding spheroidization grade. Three materials used in coal-fired power plants (ASTM A315-B steel, ASTM A335-P12 steel, and ASTM A355-P11 steel) were selected as the samples to test the validity of our proposed method. The results indicate that the accuracies of the calculated spheroidization grades reach 99.05, 95.46, and 93.63%, respectively. Hence, our newly proposed method is adequate for automatically detecting the pearlite spheroidization grade of steel using optical metallography.

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“…A neural network is the simple regression method where a flexible non-linear function is fitted with the experimentally measured data, the details of which have been comprehensively reported and critically reviewed in the literature [20,[57][58][59]. Neural networks have been applied successfully to model various complex systems, without the formation of an explicit model or parameterised formula [57][58][59][60][61][62][63][64][65][66]. The Bayesian framework of a neural network is exploited in this study.…”

Section: Artificial Intelligencementioning

confidence: 99%

Calculation of ductility from pearlite microstructure

Das

2018

Materials Science and Technology

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Ductility in drawn pearlitic steels’ wire is dependent on microstructural features, carbon concentration, transformation temperature and strain. An extensive literature review is performed and investigated that there are conflicting results about the effect of interlamellar spacing, pearlite colony size and prior-austenitic grain size on the toughness of these steels. This may be attributed to the differences in compositions, heat treatment, drawing conditions, the methodology applied for measuring microstructural entities and inaccuracy in measurements. Currently, a neural network model is created to correlate the complex relationship between tensile ductility with its influencing parameters in a variety of cold drawn pearlitic steels. The model has been applied to confirm that the calculations are reasonable in the context of metallurgical theories and other data published in the literature.

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Estimation of damage in high strength steels

Cited by 18 publications

References 37 publications

Contribution of deformation-induced martensite to fracture appearance of austenitic stainless steel

Contribution of deformation-induced martensite to fracture appearance of austenitic stainless steel

Automatic Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography

Calculation of ductility from pearlite microstructure

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