Metalworking Industry: Quality Management via Edge Computing-Based Cloud Monitoring System

This study uses the edge computing system for fast quality screening in the metal processing industry, and the Internet of Things technology is responsible for delivering data to the cloud for visualization and analysis. First, the system consists of optical components and embedded systems. Further, a fast Fourier transform is used to make the image have frequency characteristics. However, the convolution operation between the random kernel model and the image is the main means of feature extraction. In order to evaluate the performance and convergence of the proposed method, a rapid screening mechanism for good/bad products is defined. Finally, the data is passed to the cloud (ThingSpeak) platform for visualization through the MQTT protocol, and the content is subscribed to the content by the background host to perform the quality decision of the fuzzy inference system. The result is released back to the cloud. The experimental results in the industrial example show that the proposed method can accurately and quickly complete the quality inspection of surface roughness, and the feature distribution is easy to understand. At the same time, the edge computing system has the advantages of instant response and low cost, while the Internet of Things technology brings more management and analysis convenience. In future research, the unsupervised learning algorithm based on convolutional neural networks is a potential application, which can learn the quality of good or bad through a large amount of data.