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Tool life prediction via SMB-enabled monitor based on BPNN coupling algorithms for sustainable manufacturing

Published online by Cambridge University Press:  03 July 2023

Wen-Yang Chang Open the ORCID record for Wen-Yang Chang [Opens in a new window]  and
Bo-Yao Hsu
Wen-Yang Chang*
Affiliation:
Department of Mechanical and Computer-Aided Engineering, National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yunlin County 632, Taiwan Smart Machinery and Intelligent Manufacturing Research Center, National Formosa University, Yunlin 632, Taiwan
Bo-Yao Hsu
Affiliation:
Department of Power Mechanical Engineering, National Formosa University, Yunlin 632, Taiwan
*
Corresponding author: Wen-Yang Chang; Email: wenyang@nfu.edu.tw
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Abstract

The predictive methods of tool wear are usually based on different algorithm predictors, different source data, and different sensing devices for remaining useful life (RUL). In general, it has challenges to model and ensure all of the cutting conditions that are suitable in the actual cutting process for sustainable manufacturing. In order to overcome the doing large amount of experimental data and predict different tool RULs, this study combines the analytical force modeling, the back-propagation neural network (BPNN) machine learning, and the current sensor which all are integrated in smart machine box (SMB) to realize the practical RUL prediction for on-line and real-time applications. The analytical model of the cutting force coefficients of shear and ploughing was established from average cutting forces, it could reduce the experimental number and predict the different cutting conditions. In general, the loading current of the cutting tool from a spindle motor is relatively easier acquired than the resultant forces. Thus, the loading currents of the spindle are used to train and predict the cutting forces using the BPNN model during intelligent manufacturing. The SMB architecture mainly performed the autonomous actions based on the edge layer, the fog layer, and the cloud layer via the TCP/IP, the MQTT protocol, and the unified communication library. Results showed that a predictive error for the ends of the tool life is about 3–10% that are based on the calculating of the cumulative current ratio.

Keywords

BPNNforce coefficientsloading currentpredictiontool life
Type
Research Article
Information
AI EDAM , Volume 37 , 2023 , e20
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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