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An artificial-neural-networks-based in-process tool wear prediction system in milling operations
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Discrimination of the Tool Failure Patterns with Simultaneous Approach of Fuzzy Clustering, Principle Components and Multiple Regression Analysis
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Application of fast Haar transform and concurrent learning to tool-breakage detection in milling
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Prediction of cutting forms and tool breakage in milling from feed drive current measurements
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Using multivariate models to monitor end-mill wear and predict tool failure
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Monitoring end-mill wear and predicting tool failure using accelerometers
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