Metalworking Industry: Quality Management via Edge ComputingBased 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 goodbad 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.

A smart surface inspection system using faster RCNN in cloudedge computing environment Abstract   Automated surface inspection has become a hot topic with the rapid development of machine vision technologies. Traditional machine vision methods need experts to carefully craft image features for defect detection. This limits their applications to wider areas. The emerging convolutional neural networks (CNN) can automatically extract features and yield good results in many cases. However, the CNNbased image classification methods are more suitable for flat surface texture inspection. It is difficult to accurately locate small defects in geometrically complex products. Furthermore, the computational power required in CNN algorithms is usually high and it is not efficient to be implemented on embedded hardware. To solve these problems, a smart surface inspection system is proposed using faster RCNN algorithm in the cloudedge computing environment. The faster RCNN as a CNNbased object detection method can efficiently identify defects in complex product images and the cloudedge computing framework can provide fast computation speed and evolving algorithm models. A real industrial case study is presented to illustrate the effectiveness of the proposed method. The results show that the proposed method can provide high detection accuracy within a short time.