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Usinagem
- USI 63 |
| Title
: |
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INFLUENCE
OF THE FORCE COMPONENTS ON THE PERFORMANCE OF ARTIFCIAL NEURAL NETWORKS
FOR TOOL WEAR MONITORING |
| Abstract
: |
|
A
large number of works have been presented describing the results from
research with Artificial Neural Networks (ANNs) for tool wear monitoring. In
these works the ANNS were “fed” with different types of information,
frequently with values from measurements of force and acceleration. The
correlation between these values and the tool wear state have been shown in
the literature but their influence in the performance of the ANNs, and most
of all, the correlation between the results from the ANNs and the tool wear
were not clearly presented. In the present work the authors show the results
of experiments and analysis to find out the force components that gives more
contribution for the performance of the ANNs tested and trained with data
from tool wear tests. These data were sampled during the turning of
non-ductile steel bars, with different types of tools and several cutting
conditions.
|
| Autores
: |
Canabrava Filho, José S.
Barrow, George
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| Trabalho
Completo : |
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1o.
Cobef | Comissão Organizadora |
Palestras | Sessões Técnicas |
Autor | Revisores |
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