Brunel University Research Archive (BURA) >
College of Engineering, Design and Physical Sciences >
Dept of Electronic and Computer Engineering >
Dept of Electronic and Computer Engineering Theses >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4495

Title: An intelligent manufacturing system for heat treatment scheduling
Authors: Al-Kanhal, Tawfeeq
Advisors: Abbod, MF
Keywords: Genetic algorithm (GA)
Heat treatment operation
Scheduling
Neuro fuzzy system
Particle swarm optimisation (PSO)
Publication Date: 2010
Publisher: Brunel University School of Engineering and Design PhD Theses
Abstract: This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.
Description: This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.
URI: http://bura.brunel.ac.uk/handle/2438/4495
Appears in Collections:Brunel University Theses
Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Theses

Files in This Item:

File Description SizeFormat
FulltextThesis.pdf2.88 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.