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Tool condition monitoring is crucial for manufacturing systems to maximize productivity, maintain part quality and reduce waste and cost. We provide a novel approach to detect tool wear in milling processes based on deep machine learning methods. For the first time, a 98% accuracy was achieved within totally new testing data that the system was not trained for. Also we reduced the calibration efforts by 75% compared to available literature. such performance has never been done before.

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This page is a summary of: A Generalized Multisensor Real-Time Tool Condition–Monitoring Approach Using Deep Recurrent Neural Network, Smart and Sustainable Manufacturing Systems, February 2019, ASTM International,
DOI: 10.1520/ssms20190020.
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