Applied Internet of Things to Analyze Vibration, Workpiece Roughness, and Tool Wear: Case Study of Successive Milling

Along with technology development and market change, automated production should be made easier and more intelligent to promote production efficiency and product quality as well as reduce labor and production costs. The introduction of the Internet of Things (IoT) is an important issue in automated processing. This study aims to apply the Industrial Internet of Things (IIoT) to automated processing systems for real-time monitoring of the condition of production lines and analyze the causal relationship between vibration, surface roughness, tool wear, and take successive milling of medium carbon steel workpieces as a case study. First, automated processing hardware equipment is set up, and software and hardware are required for installing IIoT; then, the IoT App is designed. Second, successive automated processing experiments are preceded. The Taguchi method is utilized in the processing process to find optimized cutting parameters to be the parameter setting values for successive cutting. Three accelerometers are used to detect vibration changes in the cutting process; meanwhile, IIoT is introduced to monitor the condition of the production line. Finally, Using big data analytics acquired in the experiments to verify the processing quality under optimized cutting parameters could make a 4.516% improvement and obtain the vibration value for the best tool change during successive processing as well as to realize the obtainment of current processing information through IIoT. The system would deliver tool change or processing abnormality alerts to users for real-time condition exclusion. To achieve the goal of remote monitoring and intelligent automatic processing.