## 4 Results and Discussion  
In this study, an IoT solution based on the establishment, installation, testing, and implementation of experiments in the Robot and Motor Control Laboratory at the Asia Eastern University of Science and Technology, Mechanical Engineering (AEUST-ME, Taiwan), was built. Related experiments to create a solution using IoT are proposed to monitor the status of workpieces on the production line. As shown in Figure 11, AEUST proposed a physical map of the electromechanical integration production line platform. The quality of the product is improved through the relationship between the data of various types of sensors and the color recognition of the image. In the first step of the initial construction, the original machine is based on the traditional PLC architecture, and its speed of operation, product color recognition, self-inspection, and repair or learning mechanisms are lacking. Therefore, the establishment of a new IoT in the signal of the existing network communication interception sensor is integrated, which is unified for analogue/digital signal conversion. To avoid excessive surge signals, the impact of the electronic circuit on the burning out of the electronic components  
Figure 11: Physical visual image results of the cloud servo platform to remote and monitor: (**a**) color-recognition image of the production object, (**b**) night monitoring image: black and white image, (**c**) day monitoring image: color image. Figure 10: Neural network regulated the fractional order PID controller to feedback the signal of the motor torque, and transmit the upload of each join datum to the IoT. is clear. Adding a rectifier and limit-function circuit for signal interception can avoid this issue. Thus, the converted signal can be sent to the cloud network control system with assured integrity. Furthermore, the construction and functionality of the PLC equipment and machines are enhanced. A supervised machine learning mechanism was proposed. This is a general supervised learning process used to generate a global model that corresponds to the input objects and the expected output. Based on the case in this study, the optimized production line transfer mode of similar processes has been solved in the past, and new inference methods are sought to solve the best path of the current production line. This is the process of the machine learning method used to solve current production line problems. Figure 12 shows the face recognition results constructed by applying the neural theory. The Raspberry PI system is detected by fog computing that outputs the recognition signal, which includes the human wear mask and motion-tracking detection. By synchronously uploading data to the cloud computing controller for calculation and monitoring, the functions of factory population monitoring are achieved. During the visualization process to repair a rejected product, pre-shipment inspection is necessary. The system uses real-time location data and shipment urgency to track progress. It can repair and reduce human costs after required additional delivery procedures. In the future, these challenges will include image analysis of production-line workers and equipment during the test and analysis of the relationship between these data and test log data. To use these results, it is necessary to further reduce the scrap rate of finished products. In addition, it is difficult to reduce indirect costs and extend the visualization plan to the entire supply chain and between other factories. To create a solution using an IoT structure, both design and experiments are proposed to monitor the status of workpieces on a smart factory platform. As shown in Figure 13, we propose a physical map of the electromechanical integration platform. The quality of the product is improved through the relationship between the data of various types of fluxgate sensors, color recognition of the image, and acoustic and vibration signals. The converted signal can be sent to the cloud network control system with assured integrity.