Optimizing cutting parameters with Industrial IoT system for automated continuous dry milling

Abstract

Applying the combination of the Industrial Internet of Things (IIoT) and noise technology to automated milling operation systems, this study aims to understand the relationship between cutting noise, vibration, the surface roughness of the workpieces, and tool wear in the automatic processing process. Taguchi method is first used for acquiring the optimal cutting parameters; IIoT, accelerometer, and noise meter are integrated into automated cutting systems, under different cutting parameters, to acquire vibration and noise data in the cutting process; and the surface roughness of workpiece and end mill wear is measured in the experimental process. Finally, the data analysis results explain the causation among cutting noise, vibration, surface roughness of the workpiece, and tool wear. It is proved by experiments that indicate a strong linear and positive correlation between vibration and noise generated during continuous milling that a single noise meter can replace the use of three vibration sensors, and a cutting noise value of 82.812db indicates the best timing to replace the tool in this case. Such results could provide optimized tool change timing and reduce processing costs, and they are expected to offer valuable opinions to automated cutting processing.