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Title: Work roll system optimisation using thermal analysis and genetic algroithm
Author: Tafesse Azene, Yoseph
ISNI:       0000 0004 2713 8704
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2011
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In today‟s highly competitive business environment it is vital to have smart and robust decision making framework for companies to be competitive or even to stay in the business. Profit margin increases is no longer a result of producing and bring more products to the market. Instead it is also a result of reducing cost, in particular tooling cost. In order to succeed with this, industry need to look in to innovative intelligent systems to enhance their process development so that maximum utilisation of tools can be achieved. Tooling is part of a process hence having an optimal process design is one ideal strategy for best utilising of tools. In design optimisation however presence of uncertainty in design variables and in the mathematical model (used for representing the real life process) is inevitable. For reliable design solution to be found this process complexity need to be addressed. The aim of this research is to understand work roll system optimisation and thermal issues within rolling system design, understand uncertainties and sources of uncertainties and develop Genetic Algorithm (GA) based solution frameworks so that a conscious decision, that prolong tool life can be made. The thesis has proposed a framework for generating approximate models from numeric finite element (FE) data. Using the proposed framework a number of quantitative work roll system thermal analysis and optimisation models were generated and used in subsequent optimisation process. In the absence of a suitable multi-pass model that exhibits the features of a multi-stage process; confident decision making will not be possible. Hence the research has developed a quantitative multi-pass model to simulate the work roll system thermal analysis and optimisation problem that represents the relationships as well as inherited features between passes. The research has developed a Genetic Algorithm based optimisation framework that deals with the constraint quantitative problem as well as the uncertainty, in the design space and fitness function. The research also proposed a post GA result analysis methodology for identifying the final best optimal design solution for the research many objective high dimensional work roll system problems in presence of uncertainty The performance of the proposed frameworks was studied and analysed through case studies. The research also identifies future research directions in the subject area.
Supervisor: Roy, Rajkumar Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available