on-line tool wear estimation through artificial neural networks
Publish Year: 1391
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICNMO01_306
تاریخ نمایه سازی: 19 اسفند 1391
Abstract:
On-line tool conditioning monitoring (TCM) is an essential feature of modern sophisticated and automated machine tools. Appropriate and timely decision for tool-change is urgently required in the machining systems. Ample researches have been carried out in this direction. Recently artificial neural networks (NN) are applied for this purpose in conjunction with suitable sensory systems. Its fast processing capability is well-suited for quick estimation of tool condition and corrective measure to be taken. The present work uses back-propagation type training and feed-forward testing procedures for the neural networks. Three models using different force parameters are tried to monitor tool wear on-line. The close estimation of the modeled output to the actual wear value demonstrates the possibility of successful tool wear monitoring
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Authors
R Askari
Islamic Azad University of Semnan ,Department of Mechanical Engineering, Semnan, Iran
Mohammad Jafar Ostad Ahmad Ghorabi
Islamic Azad University of Semnan ,Department of Mechanical Engineering, Semnan, Iran
N Askari
Technical and Vocational College Sama, Chamestan, Mazandaran, Iran
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