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Intelligent Polishing Using Fuzzy Logic and Genetic Algorithm

Intelligent Polishing Using Fuzzy Logic and Genetic Algorithm


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About the Book

This dissertation, "Intelligent Polishing Using Fuzzy Logic and Genetic Algorithm" by Yiu-ming, Tsang, 曾耀明, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of the thesis entitled Intelligent polishing using fuzzy logic and genetic algorithm Submitted by TSANG YIU MING For the degree of Master of Philosophy At the University of Hong Kong In December 2006 Abstract Polishing is widely applied to different products. The quality of surface finish is very important as it affects not only surface appearance, but also product functionality. The mechanism of the polishing process is not thoroughly understood. It is difficult for polish workers to adjust parameter settings to control the quality of surface finish, and also to determine the parameter settings for the polishing process by the mathematical approach. Fuzzy logic has been applied in controlling processes with unknown mechanism and a fuzzy model can be optimized by genetic algorithms. In this thesis, an intelligent system using both fuzzy logic and genetic algorithms is proposed to generate parameter settings automatically with given inputs. i i The working principle of the intelligent system is that a parameter setting can be generated with the use of a knowledge base generated by the knowledge base module. The intelligent system is divided into two modules: the design of a knowledge base module and a parameter setting generation module. The knowledge base module consists of fuzzy rules and membership functions, which are derived from experiment data and are optimized by genetic algorithm. In the parameter setting generation module, parameter settings are generated with the use of the knowledge base. The proposed design is implemented. The implementation is divided into 3 parts: (a) collection of experimental data for the generation of the knowledge base; (b) the development of the knowledge base module; and (c) development of the parameter setting generation module. For the collection of experimental data, an experiment is conducted based on defined materials, and equipment and experimental procedures. For the development of the knowledge base module, fuzzy rules and membership functions are designed for the variables involved in the polishing process. Fitness function is used to select the best knowledge base. For the development of the parameter setting generation module, the parameter setting is selected with the use of the knowledge base and the input of required surface finish. The parameter setting is verified by confirmation experiment. Experimental results show that the parameter setting generated by the system is able to achieve the required surface finish. ii i With the use of the intelligent system, manufacturers can obtain instant technical support, thereby enables production lead times to be shortened. The quality of surface finish can be assured. Knowledge of the polish experts can be retained. Fuzzy rules and membership functions stored in the knowledge base can also act as polishing guidelines for hand polishing. The process automation can be speeded up. The intelligent system is expected to be applied to the determination of parameter settings for polishing other materials. The proposed system can also act as a foundation for incorporating cost factors in the design in the future. In addition, the system can be further developed by the identification of polishing features through a case matching process. Computational time is expected to be further reduced. (476 words)


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Product Details
  • ISBN-13: 9781361476482
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 176
  • Weight: 422 gr
  • ISBN-10: 1361476486
  • Publisher Date: 27 Jan 2017
  • Binding: Paperback
  • Language: English
  • Spine Width: 10 mm
  • Width: 216 mm


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