Science and Technology of Welding & Joining · 2001 · 19 citations · 4 references
Natural SciencesMaterials EngineeringMaterials ScienceMaterials CharacterizationMechanical Behavior Of MaterialsMetallurgyAlloysDuctilityFracture MechanicsFracture ToughnessSteel MaterialsStrength Of MaterialsFerrous AlloysHigh Strength Low Alloy SteelsLow Alloy SteelsNeural Network ModelingOxygen Concentration EffectsSteel Fracture ToughnessSteel ToughnessTensile Strength Estimation
In previous work by the present authors a model was developed for the estimation of the tensile properties (strength and ductility) of ferrous alloys intended for the welding of high strength low alloy steels used in the construction of ships. The method used to model the properties as a function of a large number of variables was based on a neural network within a Bayesian framework. This method is particularly useful when attempting to understand complex non-linear phenomena where the distribution of data within the input space is not obvious. In the present work, a similar approach is used to model the toughness (characterised by Charpy and dynamic tear tests) of the same alloys. The level of noise in the experimental data is perceived to be high, but it has nevertheless been possible to recognise reasonable trends and uncertainties when making predictions. For example, the toughness shows a non-linear deterioration as the oxygen concentration is increased; this behaviour is expected but can now be expressed quantitatively.
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David Mackay · Neural Computation · 1992 · 4.4K citations · Full text
A Practical Bayesian Framework for Backpropagation Networks
David Mackay · Neural Computation · 1992 · 2.9K citations
Neural Networks in Materials Science.
H. K. D. H. Bhadeshia · ISIJ International · 1999 · 675 citations · Full text
Natural Sciences, Materials Engineering, Materials Science +10