Manufacturing process monitoring using time-frequency representation and transfer learning of deep neural networks

On-line process monitoring increases product quality, improves process stability, and lowers costs in manufacturing. This paper presents a study of using time-frequency representation and deep neural networks to enable real-time, intelligent manufacturing process monitoring. Acoustic emission (AE) signals are obtained during machine turning operations, and transformed into a time-frequency (TF) representation (image) format. Deep neural networks are then applied to the images for process classification according to the operation's spindle speed, feed rate, and depth of cut. The signals are nonstationary with frequency content varying in time. Four commonly used time-frequency analysis methods are discussed and compared, such as the short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert-Huang transform (HHT), and Wigner–Ville distribution (WVD). The study finds that the multi-resolution capability of the CWT technique allows it to render more accurate and richer details of the signals. Classification of machine-turning-operation processes through conventional monitoring techniques is challenging. First, there are not theories directly relating the signal characteristics to the physical process. Also, important signal components associated with the physics of the process are masked by heavy noise from the lathe. These lead to difficulties in selecting features to be extracted, and obtaining reliable data of these features. These obstacles are overcome by deep neural networks that are capable of learning and extracting meaningful features in an automatous fashion. A transfer learning approach is adopted in this study by using high-performance, representative deep neural networks previously developed for image classification and recognition, including ShuffleNet, GoogLeNet, ResNet18, ResNet50, VGG16, and DenseNet201. They are applied to the TF images obtained during the machine turning operations for process classification. The VGG16 network yields the highest classification accuracy at 92.67% when applied to processes of 12 classes, which demonstrates the potential and feasibility of the proposed method for satisfactory monitoring performance. Lastly, it is shown that the classification accuracy can be improved by using shallower networks modified from the VGG-16 network to mitigate the overfitting issue. A classification accuracy of 95.58% is achieved by removing two convolutional layers.