Abstract
Pattern recognition techniques have been implemented in real-time tool condition monitoring (TCM) systems to improve their robustness and reliability. The performance and accuracy of these techniques vary depending on their algorithm and the dataset properties. This research benchmarks six pattern recognition techniques to optimize the learning effort, classification accuracy and calculation time for TCM in milling of Al-Alloys using spindle-drive feedback. The techniques were tested using a generalized dataset where the tool condition has a dominant effect over the cutting conditions. The analysis demonstrated the high capability of the linear discriminant analysis technique compared to other techniques.
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