Abstract
A multiple sensor monitoring system, equipped with cutting force, acoustic emission and vibration sensing units, was employed in association with advanced procedures for signal analysis, sensor fusion and cognitive decision making for residual stress evaluation in turning of Inconel 718 nickel alloy. Two signal processing and feature extraction methodologies, based respectively on sensory data statistical evaluation and Principal Component Analysis, were applied to the sensor signals generated during experimental turning tests. The extracted features were combined into sensor fusion input feature vectors to be fed to neural network based pattern recognition paradigms for decision making on machined surface integrity in terms of residual stress conditions.
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