Wear Prediction Of Hot Working Tools

The method of CAE neural network was used to predict the wear of hot forging tool. The data base for prediction consisted of experimental observations of the laboratory simulation of tribomechanical and tribotemperature conditions on the locally most loaded part of the forging tool during the applic...

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Bibliographic Details
Published in:WIT Transactions on Engineering Sciences 1999-01, Vol.24
Main Authors: Tercelj, M, Turk, R, Perus, I
Format: Article
Language:English
Subjects:
Computer simulation
Hot forging
Hot working
Laboratories
Neural networks
Thermal simulators
Tool wear
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Summary:The method of CAE neural network was used to predict the wear of hot forging tool. The data base for prediction consisted of experimental observations of the laboratory simulation of tribomechanical and tribotemperature conditions on the locally most loaded part of the forging tool during the application. Individual influence variables on the wear were computed by FEM. The laboratory simulation of the tool wear was performed on a module which was developed separately as appendix for the GLEEBLE 1500 simulator. The ability of CAE has shown to be vital in the prediction of wear on the basis of experimental phenomena
ISSN:1746-4471
1743-3533
DOI:10.2495/CON990401