000			03430nam a2200385 4500
005			20260817163248.0
008			030798 th eng
035			_a.b11425325
099		9	_aAIT Thesis no.IE-95-02
100	1		_aMohan, A. Madhu
245	1	0	_aART1 neural networks for machine cell and part family formation
260			_aBangkok : _bAsian Institute of Technology, _c1995
300			_a71 leaves : _bill.
490	1		_aThesis ; _vno. IE-95-02
502			_aThesis (M.Eng.) - Asian Institute of Technology, 1995
500			_aA thesis report submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Engineering and Technology
520			_aAn increasingly competitive and changing environment and ever-changing customer preferences have compelled manufacturers to look for ways to increase productivity. One area under examination is design of a production system based on Group Technology. Group Technology is a modern manufacturing philosophy that advocates the 'product organization' as against the 'production organization' in conventional systems. A more practical and effective approach for implementing GT is the Cluster Analysis approach. This study looks into the application of a Neural Network for forming machine and part family formation. Unlike the non-learning algorithms, the neural models are able to learn and store the learned patterns. For the classification problem, ARTl neural network was used as it is flexible and unsupervised. The input to the neural network is the machine-component incidence matrix is based on the production flow analysis. A computer program in TURBO C is used for executing the algorithm. The experiments are carried out on eight sets of data taken from literature. Initially the original similarity criteria designed for ARTl by its originators was used. Later, another criteria based on Hamming distance was incorporated. In most of the cases the results were same as that of the earlier reference works. From the results obtained, it was clear that ARTl is effective in cluster identification. The actual clustering depends on the way of presentation of input patterns. Even though different clusters were identified there was not significant change in the performance indices. Hence, it can be used as an effective tool in the Computer Integrated Manufacturing environment, as it gives flexibility for the machine cells formed.
650	1	0	_aNeural networks (Computer science)
700	1		_aNagarur, N. N., _eChairperson
700	1		_aPandey, P. C., _eExamination committee
700	1		_aTabucanon, Mario T., _eExamination Committee
710	2		_aAsian Institute of Technology, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. IE-95-02
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B15202
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902			_a240404
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