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Tool wear monitoring using artificial neural networks

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Date Issued:
1992
Summary:
An on-line scheme for monitoring tool wear in unmanned machining operations using artificial neural networks (ANNs) is proposed. Various configurations of ANNs are studied to increase the accuracy of tool wear estimation. With this aim three configurations of the ANNs namely, an ANN without memory, an ANN with one phase memory, and an ANN with two phase memory are considered. Each ANN is trained to associate an input vector which consists of values of cutting conditions, with an output vector containing flank wear as a single output. The training data and evaluation data is generated using the popular analytical tool wear model. The performance of all the ANNs are compared by considering four different cases of evaluation data. The proposed scheme of tool wear modeling using ANNs is easily extendible to include other cutting parameters and can be implemented in real-time.
Title: Tool wear monitoring using artificial neural networks.
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Name(s): Kurapati, Venkatesh.
Florida Atlantic University, Degree grantor
Masory, Oren, Thesis advisor
College of Engineering and Computer Science
Department of Ocean and Mechanical Engineering
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1992
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 145 p.
Language(s): English
Summary: An on-line scheme for monitoring tool wear in unmanned machining operations using artificial neural networks (ANNs) is proposed. Various configurations of ANNs are studied to increase the accuracy of tool wear estimation. With this aim three configurations of the ANNs namely, an ANN without memory, an ANN with one phase memory, and an ANN with two phase memory are considered. Each ANN is trained to associate an input vector which consists of values of cutting conditions, with an output vector containing flank wear as a single output. The training data and evaluation data is generated using the popular analytical tool wear model. The performance of all the ANNs are compared by considering four different cases of evaluation data. The proposed scheme of tool wear modeling using ANNs is easily extendible to include other cutting parameters and can be implemented in real-time.
Identifier: 14868 (digitool), FADT14868 (IID), fau:11654 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 1992.
Subject(s): Neural networks (Computer science)
Flexible manufacturing systems
Power tools
Machine tools--Data processing
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/14868
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.