Evolutionary computing for metals properties modelling

This is a post print version of the article, the official published version can be obtained from the link below.During the last decade Genetic Programming (GP) has emerged as an efficient methodology for teaching computers how to program themselves. This paper presents research work which utilizes GP for developing mathematical equations for the response surfaces that have been generated using hybrid modelling techniques for predicting the properties of materials under hot deformation. Collected data from the literature and experimental work on aluminium are utilized as the initial training data for the GP to develop the mathematical models under different deformation conditions and compositions.Financial support from the UK EPSRC (Engineering and Physical Sciences Research Council) under grant number GR/R70514/01 was used in this study

Hybrid modelling methodology applied to microstructural evolution during hot deformation of aluminium alloys

This is the post print version of this article. The official published version can be accessed from the link below.This paper considers how data based neurofuzzy modelling techniques for the poorly understood relationships between changing process histories and the evolution of the internal state variables of dislocation density, subgrain size and subgrain boundary misorientation can be combined with physically-based models to investigate the effects of the internal state variables on the flow stress and recrystallisation behaviour. The model uses genetic algorithms to optimise the constants and is validated for data on a range of aluminium-magnesium alloys of both high and commercial purity. It is shown that this hybrid modelling methodology supported by a knowledge base offers a flexible way to develop the microstructrural modelling as more data and better understanding of the evolution of the internal state variables become available.Financial support from the UK Engineering and Physical Sciences Research Council was used in this study

Modelling of dynamic recrystallisation of 316L stainless steel using a systems approach

This is the post print version of the article. The official published version can be obtained from the link below.Dynamic recrystallisation (DRX) is an important aspect for industrial applications in hot metal working. Although DRX has been known for more than thirty years, its mechanisms have never been precisely investigated, in part because it was not readily possible to make local texture measurements. In the present work, the material behaviour during DRX is investigated and modelled based on the microstructure of 316L stainless steel. The developed model is based on a constitutive equation Modelling technique which incorporates the strain, strain rate and instantaneous temperature for predicting the flow stress of material being deformed under hot conditions.Financial support from the UK EPSRC (Engineering and Physical Sciences Research Council) for their financial support under grant number GR/R70514/01 was used for this study

Modelling the flow behaviour, recrystallisation and crystallographic texture in hot deformed Fe-30wt%Ni Austenite

Abstract: The present work describes a hybrid modelling approach developed for predicting the flow behaviour, recrystallisation characteristics and crystallographic texture evolution in a Fe-30wt%Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 and 1050ºC and strain rates between 0.1 and 10 s-1. The evolution of grain structure, crystallographic texture and dislocation substructure was characterised in detail for a deformation temperature of 950ºC and strain rates of 0.1 and 10 s-1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modelling method utilises a combination of empirical, physically-based and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallisation behaviour is predicted from the measured microstructural state variables of internal dislocation density, subgrain size and misorientation between subgrains using a physically-based model. The texture evolution is modelled using artificial neural networks

Effect of changing strain rate on flow stress during hot deformation of type 316L stainless steel

ompression testing has been used with ramped changes in strain rate during deformation, and with changes in strain rate between double deformations to study their effects on flow stress. No systematic deviations from a mechanical equation of state were found for ramped increase or decrease in strain rate, even at the highest experimental ramping rates. In the two deformation tests, static recovery between deformations reduced the initial flow stress below the value for an equation of state by an amount dependent on time. The reduction was increased when strain rate was increased for the second deformation, and the strain interval required to re-establish the equation of state flow stress is uniquely related to the initial reduction in stress

Mesoscale modeling and simulation of microstructure evolution during dynamic recrystallization of a Ni-based superalloy

Microstructural evolution and plastic flow characteristics of a Ni-based superalloy were investigated using a simulative model that couples the basic metallurgical principle of dynamic recrystallization (DRX) with the twodimensional (2D) cellular automaton (CA). Variation of dislocation density with local strain of deformation is considered for accurate determination of the microstructural evolution during DRX. The grain topography, the grain size and the recrystallized fraction can be well predicted by using the developed CA model, which enables to the establishment of the relationship between the flow stress, dislocation density, recrystallized fraction volume, recrystallized grain size and the thermomechanical parameters

Campylobacter jejuni transcriptome changes during loss of culturability in water

Background: Water serves as a potential reservoir for Campylobacter, the leading cause of bacterial gastroenteritis in humans. However, little is understood about the mechanisms underlying variations in survival characteristics between different strains of C. jejuni in natural environments, including water. Results: We identified three Campylobacter jejuni strains that exhibited variability in their ability to retain culturability after suspension in tap water at two different temperatures (4°C and 25°C). Of the three strains C. jejuni M1 exhibited the most rapid loss of culturability whilst retaining viability. Using RNAseq transcriptomics, we characterised C. jejuni M1 gene expression in response to suspension in water by analyzing bacterial suspensions recovered immediately after introduction into water (Time 0), and from two sampling time/temperature combinations where considerable loss of culturability was evident, namely (i) after 24 h at 25°C, and (ii) after 72 h at 4°C. Transcript data were compared with a culture-grown control. Some gene expression characteristics were shared amongst the three populations recovered from water, with more genes being up-regulated than down. Many of the up-regulated genes were identified in the Time 0 sample, whereas the majority of down-regulated genes occurred in the 25°C (24 h) sample. Conclusions: Variations in expression were found amongst genes associated with oxygen tolerance, starvation and osmotic stress. However, we also found upregulation of flagellar assembly genes, accompanied by down-regulation of genes involved in chemotaxis. Our data also suggested a switch from secretion via the sec system to via the tat system, and that the quorum sensing gene luxS may be implicated in the survival of strain M1 in water. Variations in gene expression also occurred in accessory genome regions. Our data suggest that despite the loss of culturability, C. jejuni M1 remains viable and adapts via specific changes in gene expression