Fog Computing Based Hybrid Deep Learning Framework in effective inspection system for smart manufacturing

Most sensors have been taken up, which resulted in a massive data size, with the continuously growing IoT (Internet of Things) devices and communications infrastructure in development. The inspection of the manufacturer to identify product defects is one of the most common examples. In order to develop an effective inspection system with greater precision, this paper has been proposed a Fog Computing based Hybrid Deep-Learning Framework (FC-HDLF), that can find possible defective products. Since a large number of assembly lines can occur in a single factory, one of the main problems is how these data are processed in real-time. The system can handle incredibly large amounts of data by discharging the load from the central servers to the fog nodes. In this paper, there are two obvious advantages. Next, the Convolutional Neural Network (CNN) model is adapted to the fog computing environment, which improves its calculation performance considerably. The other is that a model of control is built that can display the form and extent of the defect simultaneously. A decision-making framework for multi-agents is built to ensure a production process architecture to optimize production processes.