Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3762
Title: RECOGNITION OF CHATTER WITH NEURAL NETWORKS FOR TURNING PROCESS
Authors: Kumar, Arvind
Keywords: MECHANICAL & INDUSTRIAL ENGINEERING
CHATTER
NEURAL NETWORKS
TURNING PROCESS
Issue Date: 1997
Abstract: Chatter, ' defines as self-excited vibrations during cutting action, deteriorates Surface finish, reduces tool life, and damages machine tools. A brief study about chatter analysis which is capable of giving the mechanism of chatter in machine tool and methods for detection and prediction of chatter has been presented in the present work. A new approach chatter recognition using neural network has been developed which provides an easy implementation and speed computation. This procedure uses two synthetically trained neural networks to recognize the harmonic acceleration signals and to estimate their frequency respectively. Based on comparison of chatter frequency and natural frequency of the system, a procedure. for chatter recognition and prediction has been developed. An experimental verification has also been carried out on the ° turning process data. The importance of the recognition of chatter in machining can be seen in the quality of product and in the development of unmanned machine tool operations
URI: http://hdl.handle.net/123456789/3762
Other Identifiers: M.Tech
Appears in Collections:MASTERS' DISSERTATIONS (MIED)

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