Multi-objective genetic optimisation for self-organising fuzzy logic control

This is the post-print version of the article. The official published version can be accessed from the link below.A multi-objective genetic algorithm is developed for the purpose of optimizing the rule-base of a Self-Organising Fuzzy Logic Control algorithm (SOFLC). The tuning of the SOFLC optimization is based on selection of the best shaped performance index for modifying the rule-base on-line. A comparative study is conducted between various methods of multi-objective genetic optimisation using the SOFLC algorithm on the muscle relaxant anaesthesia system, which includes a severe non-linearity, varying dynamics and time-delay

Diagnosis and decision-making for awareness during general anaesthesia

This is the post-print version of the article. The official published version can be obtained from the link below.We describe the design process of a diagnostic system for monitoring the anaesthetic state of patients during surgical interventions under general anaesthesia. Mid-latency auditory evoked potentials (MLAEPs) obtained during general anaesthesia are used to design a neuro-fuzzy system for the determination of the level of unconsciousness after feature extraction using multiresolution wavelet analysis (MRWA). The neuro-fuzzy system proves to be a useful tool in eliciting knowledge for the fuzzy system: the anaesthetist's expertise is indirectly coded in the knowledge rule-base through the learning process with the training data. The anaesthetic depth of the patient, as deduced by the anaesthetist from the clinical signs and other haemodynamic variables, noted down during surgery, is subsequently used to label the MLAEP data accordingly. This anaesthetist-labelled data, used to train the neuro-fuzzy system, is able to produce a classifier that successfully interprets unseen data recorded from other patients. This system is not limited, however, to the combination of drugs used here. Indeed, the similar effects of inhalational and analgesic anaesthetic drugs on the MLAEPs demonstrate that the system could potentially be used for any anaesthetic and analgesic drug combination. We also suggest the use of a closed-loop architecture that would automatically provide the drug profile necessary to maintain the patient at a safe level of sedation

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

A combined neuro fuzzy-cellular automata based material model for finite element simulation of plane strain compression

This paper presents a modelling strategy that combines Neuro-Fuzzy methods to define the material model with Cellular Automata representations of the microstructure, all embedded within a Finite Element solver that can deal with the large deformations of metal processing technology. We use the acronym nf-CAFE as a label for the method. The need for such an approach arises from the twin demands of computational speed for quick solutions for efficient material characterisation by incorporating metallurgical knowledge for material design models and subsequent process control. In this strategy, the cellular automata hold the microstructural features in terms of sub-grain size and dislocation density which are modelled by a neuro-fuzzy system that predicts the flow stress. The proposed methodology is validated on a two dimensional (2D) plane strain compression finite element simulation with Al-1% Mg alloy. Results from the simulations show the potential of the model for incorporating the effects of the underlying microstructure on the evolving flow stress fields. In doing this, the paper highlights the importance of understanding the local transition rules that affect the global behaviour during deformation