Monitoring of tool wear using artificial neural networks

K. Venkatesh; MengChu Zhou; R. Caudill; AMER AUTOMAT CONTROL COUNCIL

doi:10.1109/ACC.1994.735022

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Conference proceeding

Monitoring of tool wear using artificial neural networks

K. Venkatesh, MengChu Zhou, R. Caudill and AMER AUTOMAT CONTROL COUNCIL

Proceedings of 1994 American Control Conference - ACC '94, Vol.3, pp.2565-2569 vol.3

1994

DOI: https://doi.org/10.1109/ACC.1994.735022

Abstract

Adaptive control

Artificial neural networks

Condition monitoring

Machine tools

Machining

Manufacturing systems

Surface finishing

Switches

Training data

Wearable sensors

An online scheme for tool wear monitoring using artificial neural networks (ANNs) has been proposed. Motivated by the fact that the tool wear at a given instance of time depends on the tool wear value at previous instance of time, memory was included in ANN. With this aim, an ANN without memory, an ANN with one phase memory, and an ANN with two are investigated in this study. The advantages and unique characteristics of the proposed tool wear modeling scheme with earlier methods of tool wear estimation are discussed.

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Details

Title: Monitoring of tool wear using artificial neural networks
Creators - without role: K. Venkatesh - New Jersey Institute of Technology
MengChu Zhou - New Jersey Institute of Technology
R. Caudill - New Jersey Institute of Technology
AMER AUTOMAT CONTROL COUNCIL
Publication Details: Proceedings of 1994 American Control Conference - ACC '94, Vol.3, pp.2565-2569 vol.3
Publisher: IEEE
Identifiers: 9938795308331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Conference proceeding