Abstract
Condition monitoring and diagnosis systems capable of identifying machining system dejects and their location are essential for unmanned machining. Unattended (or minimally manned) machining would resu1t in increased capital equipment utilization, thus substantially reducing the manufacturing costs. A review of tool monitoring systems and techniques and their components and the Multiple Principle Component fuzzy neural network for tool condition monitoring machining are presented.
Keywords
- Hide Layer
- Acoustic Emission
- Tool Condition
- Fuzzy Neural Network
- Sensor Fusion
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Elbestawi, M.A., Dumitrescu, M. (2006). Tool Condition Monitoring in Machining - Neural Networks. In: Information Technology For Balanced Manufacturing Systems. BASYS 2006. IFIP International Federation for Information Processing, vol 220. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36594-7_2
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DOI: https://doi.org/10.1007/978-0-387-36594-7_2
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