PRINCIPAL COMPONENT ANALYSIS AND SELF-ORGANIZING MAP FOR VISUAL CLUSTERING OF MACHINE-PART CELL FORMATION IN CELLULAR MANUFACTURING SYSTEM
نویسندگان
چکیده
منابع مشابه
Machine-Part cell formation through visual decipherable clustering of Self Organizing Map
Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the ...
متن کاملApplication of Principal Component Analysis in Machine-Part Cell Formation
The present paper applied Principal Component Analysis (PCA) for grouping of machines and parts so that the part families can be processed in the cells formed by those associated machines. An incidence matrix with binary entries has been chosen to apply this methodology. After performing the eigenanalysis of the principal component and observing the component loading plot of the principal compo...
متن کاملanalysis and interpretation of bearing vibration data using principal component analysis and self - organizing map
induction motor bearing is one of the key parts of the machine and its analysis and interpretation are important for fault detection. in the present work vibration signal has been taken for the classification i.e. bearing is healthy (h) or defective (d). for this purpose, clustering based classification of bearing vibration data has been carried out using principal component analysis (pca) and ...
متن کاملMachine-cell and Part-family Formation in Cellular Manufacturing Using a Two-phase Clustering Algorithm ⋆
This paper presents a two-phase clustering algorithm for machine-cell and partfamily formation in the design of cellular manufacturing systems. The proposed algorithm begins with the determination of initial cluster centers via a linear assignment method using the least similar group representatives in its first phase. A fuzzy C-means clustering method is followed in its second phase for part-f...
متن کاملThe Principal Components Analysis Self-Organizing Map
We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Systems Research Forum
سال: 2011
ISSN: 1793-9666,1793-9674
DOI: 10.1142/s179396661100028x