Tool health monitoring of a milling process using acoustic emissions and a resnet deep learning model

HIGHLIGHTS

• who: Mustajab Ahmed and collaborators from the Department of Engineering Sciences, National University of Sciences and Technology, Riyadh, Saudi Arabia have published the research: Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model, in the Journal: Sensors 2023, 23, x FOR PEER REVIEW of /2023/
• what: This work provides a non-destructive approach using AE burst signals for analyzing tool condition under MQL to address this issue. Through the use of airborne acoustic emissions from an end-milling machine, this study provides a novel approach to an online . . .
  

   

  

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