Development of Data Fusion Framework for Data-Driven Digital Twin in the Milling Process

Digital Twin Technology is being considered to optimize the milling process with the least modification in machining to produce the best fit product per the market need and lower the overall production cost. As of now, the researchers are implementing digital twin technology by opting for the use of industrial-grade sensors to create a digital model of the physical model. as the cost of these sensors is sustainably high medium and small-size manufacturers are unable to adopt this technology. This research has developed a cost-efficient and easy-to-use multi-sensor data fuse framework for sensing vibration, acoustic emission, tool temperature, and workpiece temperature. This will be useful for the manufacturers to create a digital model for the optimization of the milling process using machine-learning-based algorithms. This developed framework can also be used on other machines to optimize the machine.