İmalat hücreleri tasarımında bulanık mantık yaklaşımı
Fuzzy logic approach to manufacturing cell design
- Tez No: 136399
- Danışmanlar: DOÇ. DR. ORHAN TORKUL
- Tez Türü: Yüksek Lisans
- Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
- Anahtar Kelimeler: Grup Teknolojisi, Hücresel İmalat, Bulanık Mantık
- Yıl: 2003
- Dil: Türkçe
- Üniversite: Sakarya Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Endüstri Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 89
Özet
ÖZET
Özet (Çeviri)
FUZZY LOGIC APPROACH TO MANUFACTURING CELL DESIGN SUMMARY Keywords : Group Technology, Cellular Manufacturing, Fuzzy Logic, Fuzzy Clustering Group Technology and Cellular Manufacturing are applied in three different types. These are; i. Forming part families, ii. Forming machine cells, iii. Forming part families and machine cells simultaneously Several algorithms and techniques are improved to achieve above problems of group technology. Artificial intelligence technologies are commonly in use in clustering problems as in other manufacturing issues. In this study, fuzzy logic approach is studied in design of part families and machine cells simultaneously. The goal of this study is to compare manufacturing cell design which made of fuzzy logic approach with the crisp methods. In this study, in which design of manufacturing cell with fuzzy logic approach is presented, contains five chapters. In the scope of the thesis, after a general introduction, philosophy of group technology and cellular manufacturing will be mentioned, studies about the problem of part family and cell formation which made until now and techniques that used in these studies will be explained. Thirdly, fuzzy sets in fuzzy logic will be mentioned shortly, fuzzy clustering algorithm will be dwelled upon which based on fuzzy clusters. Furthermore, how the algorithm modeled in frame of part family and cell formation will be discussed. In the last two chapters, the solution of fuzzy clustering algorithm of examples which one of them is put into practice by classical methods and the other is a real data set and comparison of performances with rank order clustering algorithm that is a classical method will be presented. It has been seen from the result of the study, fuzzy clustering solutions are more efficient than the crisp method for selected data sets. x
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