Evaluation of deep learning neural networks for surface roughness prediction using vibration signal analysis

Wan Ju Lin, Shih Hsuan Lo, Hong Tsu Young, Che Lun Hung*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

76 Scopus citations

Abstract

The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals' raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.

Original languageEnglish
Article number1462
JournalApplied Sciences (Switzerland)
Volume9
Issue number7
DOIs
StatePublished - 01 04 2019

Bibliographical note

Publisher Copyright:
© 2019 by the authors.

Keywords

  • CNN
  • Convolution neural network
  • DNN
  • Deep learning
  • Deep neural networks
  • FFT
  • Fast Fourier Transform
  • LSTM
  • Long short term memory network
  • Surface roughness
  • Vibration signals

Fingerprint

Dive into the research topics of 'Evaluation of deep learning neural networks for surface roughness prediction using vibration signal analysis'. Together they form a unique fingerprint.

Cite this