Advanced monitoring of machining operations

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Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, background): “In order to exclude a catastrophic tool failure (Pan et al., 2021), it is necessary to track the progress of wear of the cutting tool from the beginning (Teti et al., 2010).”
Cloud-enabled prognosis for manufacturing (2015, background): “An Artificial Neural Network (ANN) provides an estimated output result for the remaining useful life of a machine or component based on measured condition-monitoring data or event data rather than a physical understanding of the wear or failure mechanism [19].”
Tool condition monitoring based on numerous signal features (2011, positive): “It is generally acknowledged that reliable process condition monitoring based on a single signal feature (SF) is not feasible [1].”
A review of flank wear prediction methods for tool condition monitoring in a turning process (2012, methodology): “Teti et al. [12] reviewed various AE techniques [94 – 96] applied for tool wear monitoring and proposed that due to a wide sensor dynamic range, AE can detect most of the phenomena in machining, although significant data acquisition and signal processing is required.”