Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

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.

Parameter Selection and Optimization of an Intelligent Ultrasonic-Assisted Grinding System for SiC Ceramics

“…We next compared the proposed 1DCNFN with different models containing BPNN [8], ANFIS [10], FNN [11], and 1DCNN [21] to verify the prediction effectiveness. Here, the 1DCNN (used for comparison) has two convolutional layers, one pooling layer, and one fully connected layer; its architecture 1DCNN is detailed in Table VIII.…”

“…Step 3：Evaluate the fitness value of each particle by Eq. (21) and find out the personal best and global best .…”

“…The most popular deep learning architecture is the convolutional neural network (CNN), composed of a convolutional layer, a pooling layer, and a fully connected layer. Lin et al [21] employed one-dimensional CNN (1DCNN) to predict the surface roughness in the milling process. The authors applied a convolution filter to automatically extract vibration signal raw data and reported that their 1DCNN provided highly accurate predictions.…”

Modelling Surface Roughness in the Function of Torque When Drilling

“…Mathew et al [16] showed that a tool wear span VB (mm), when milling EN-8 medium carbon steel, can be connected with the level of an acoustic emission expressed through a mean stress value AE (mV), while Lin et al [17] showed that it is possible to use ANNs for the surface roughness prediction based on a vibration signal when milling medium carbon steel S45C.…”

Elevator Fault Detection Using Profile Extraction and Deep Autoencoder Feature Extraction for Acceleration and Magnetic Signals

“…A deep learning network can self-learn the relevant features from multiple signals [21]. Deep learning algorithms are frequently used in areas such as bearing fault diagnosis [22], machine defect detection [23], vibration signal analysis [24], computer vision [25] and image classification [26]. Autoencoding is a process for nonlinear dimension reduction with natural transformation architecture using feedforward neural network [27].…”