The synthesis of hybrid mechanisms using genetic algorithms
This thesis presents a novel design methodology for the synthesis of hybrid mechanisms using Genetic Algorithms. GAs are a search and optimisation method which model the mechanics of population genetics to give a truly global search method. In parallel to the development of a suitable GA, the work a...
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ndltd-bl.uk-oai-ethos.bl.uk-3615152018-06-26T03:14:31ZThe synthesis of hybrid mechanisms using genetic algorithmsConnor, Andrew Miles1996This thesis presents a novel design methodology for the synthesis of hybrid mechanisms using Genetic Algorithms. GAs are a search and optimisation method which model the mechanics of population genetics to give a truly global search method. In parallel to the development of a suitable GA, the work also develops novel objective function criteria which go some way to providing an approximation to dynamic criteria whilst using only kinematic properties during calculations. This has considerable effect in reducing the time required to find a feasible solution. The thesis presents a set of results which validate the proposed methodology, both in terms of speed of convergence and quality of the final solutions obtained. The application chosen is the synthesis of a hybrid five bar path generating mechanism. A description is given of the development of a practical machine for a given test case, so as to illustrate that the solutions produced are feasible in terms of real world implementation. Results are presented which show the effectiveness of the machine. Finally, a critical analysis of both the methodology and the results is carried out. This highlights some areas in which the methodology could be improved by future work.621.8TJ Mechanical engineering and machineryLiverpool John Moores University10.24377/LJMU.t.00005570http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361515http://researchonline.ljmu.ac.uk/5570/Electronic Thesis or Dissertation |
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621.8 TJ Mechanical engineering and machinery |
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621.8 TJ Mechanical engineering and machinery Connor, Andrew Miles The synthesis of hybrid mechanisms using genetic algorithms |
description |
This thesis presents a novel design methodology for the synthesis of hybrid mechanisms using Genetic Algorithms. GAs are a search and optimisation method which model the mechanics of population genetics to give a truly global search method. In parallel to the development of a suitable GA, the work also develops novel objective function criteria which go some way to providing an approximation to dynamic criteria whilst using only kinematic properties during calculations. This has considerable effect in reducing the time required to find a feasible solution. The thesis presents a set of results which validate the proposed methodology, both in terms of speed of convergence and quality of the final solutions obtained. The application chosen is the synthesis of a hybrid five bar path generating mechanism. A description is given of the development of a practical machine for a given test case, so as to illustrate that the solutions produced are feasible in terms of real world implementation. Results are presented which show the effectiveness of the machine. Finally, a critical analysis of both the methodology and the results is carried out. This highlights some areas in which the methodology could be improved by future work. |
author |
Connor, Andrew Miles |
author_facet |
Connor, Andrew Miles |
author_sort |
Connor, Andrew Miles |
title |
The synthesis of hybrid mechanisms using genetic algorithms |
title_short |
The synthesis of hybrid mechanisms using genetic algorithms |
title_full |
The synthesis of hybrid mechanisms using genetic algorithms |
title_fullStr |
The synthesis of hybrid mechanisms using genetic algorithms |
title_full_unstemmed |
The synthesis of hybrid mechanisms using genetic algorithms |
title_sort |
synthesis of hybrid mechanisms using genetic algorithms |
publisher |
Liverpool John Moores University |
publishDate |
1996 |
url |
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361515 |
work_keys_str_mv |
AT connorandrewmiles thesynthesisofhybridmechanismsusinggeneticalgorithms AT connorandrewmiles synthesisofhybridmechanismsusinggeneticalgorithms |
_version_ |
1718706986218946560 |