ConvLSTM deep learning signal prediction for forecasting bending moment for tool condition monitoring

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Abstract

This paper presents a new method using a Convolutional Long Short-Term Memory (ConvLSTM) network to predict the future sensor signals for cutting tool bending moment in milling. Tool bending moment is directly correlated to cutting forces and can be used for tool condition monitoring. Bending moment from a sensory tool holder was obtained and transformed into uniform sized time-frequency domain data to be used as frame inputs to the network. Future frames of sensor signals are predicted and validated by comparing the predicted frames with the ground truth. The investigations show the potential of the proposed method for predicting future signals which can be used for tool condition monitoring.
Original languageEnglish
Pages (from-to)1071-1076
Number of pages6
JournalProcedia CIRP
Volume107
DOIs
Publication statusPublished - 26 May 2022

Keywords

  • Deep learning
  • End milling
  • Sensors
  • machining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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