Hybrid Computational Intelligence Schemes in Complex Domains: An Extended Review
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
AI approaches for cutting tool diagnosis in machining processes
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A Neural-Fuzzy Pattern Recognition Algorithm Based Cutting Tool Condition Monitoring Procedure
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Expert Systems with Applications: An International Journal
Context sensitive recognition of abrupt changes in cutting process
Expert Systems with Applications: An International Journal
The application of B-Spline neurofuzzy networks for condition monitoring of metal cutting tool
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
Tool wear condition monitoring in drilling processes using fuzzy logic
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Tool wear estimation using an analytic fuzzy classifier and support vector machines
Journal of Intelligent Manufacturing
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Wavelet transforms and fuzzy techniques are used to monitor tool breakage and wear conditions in real time according to the measured spindle and feed motor currents, respectively. First, continuous and discrete wavelet transforms are used to decompose the spindle and feed ac servo motor current signals to extract signal features so as to detect the breakage of drills successfully. Next, the models of the relationships between the current signals and the cutting parameters are established under different tool wear states. Subsequently, fuzzy classification methods are used to detect tool wear states based on the above models. Finally, the two methods above are integrated to establish an intelligent tool condition monitoring system for drilling operations. The monitoring system can detect tool breakage and tool wear conditions using very simple current sensors. Experimental results show that the proposed system can reliably detect tool conditions in drilling operations in real time and is viable for industrial applications.