This work relates to the application of Artificial Intelligence to
tool wear monitoring. The main objective is to develop an
intelligent condition monitoring system able to detect when a
cutting tool is worn out. It is used a combined Expert System and
Neural Network able to process data coming from external sensors
and combine this with information from the knowledge base and
thereafter estimate the wear state of the tool. The novelty of this
work is mainly associated with the configuration of the proposed
system. With the combination of sensor-based information and
inference rules, the result is an on-line system that can learn
from experience and can update the knowledge base pertaining to
information associated with different cutting conditions. Two
neural networks resolve the problem of interpreting the complex
sensor inputs while the Expert System, keeping track of previous
success, estimates which of the two neural networks is more
reliable. In this study an on-line tool wear monitoring system for
turning processes has been developed which can reliably estimate
the tool wear under common workshop conditions.
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