Digital Twin for CNC Power Dataset

The study titled "Digital Twin Framework with Human and Machine Intelligence for Power Consumption Prediction in CNC Machining" utilizes the "Digital_Twin_CNC_Dataset" for CNC power prediction. This dataset includes extensive experiments conducted on the modified Kakino path, focusing on various cutting parameters.

Here is a breakdown of the dataset:

1. Kakino_ap1: Contains experiments varying spindle speed and feed rate, with a constant depth of cut of 1.
2. Kakino_ap2: Contains experiments varying spindle speed and feed rate, with a constant depth of cut of 2.
3. Kakino_ap3: Contains experiments varying spindle speed and feed rate, with a constant depth of cut of 3.
4. Kakino_ap4: Contains experiments varying spindle speed and feed rate, with a constant depth of cut of 4.
5. Kakino_ap5: Contains experiments varying spindle speed and feed rate, with a constant depth of cut of 5.

Additionally, the dataset includes:

a. NC Program: Details of the specific NC program used to execute the modified Kakino path on the workpiece.
b. ADT_Result: Summarized results from all cutting parameters, validating the applied digital twin formulation for specific spindle speeds and depths of cut.
c. Real World: Video recordings of the cutting process in the actual machining environment.
e. Virtual Simulation: Simulated environment for conducting virtual tests of the cutting process.

This comprehensive dataset supports the development and validation of predictive models for power consumption in CNC machining,
leveraging digital twin technology enhanced by human and machine intelligence.