High-frequency machine datasets captured via Edge Device from Spinner U5-630 milling machine.

Area of Science:

• Manufacturing Engineering
• Data Science

Background:

• High-frequency (HF) machine data acquisition is crucial for advanced manufacturing process analysis.
• Edge devices offer distributed data processing capabilities for real-time insights, unlike traditional cloud computing.

Purpose of the Study:

• To retrieve and analyze high-frequency machine data from a Spinner U5-630 milling machine.
• To demonstrate the utility of HF machine data for calculating energy consumption and other machining parameters.

Main Methods:

• Data was collected using an Edge Device from a Spinner U5-630 milling machine at a 500Hz sampling rate.
• Collected data comprised 23 .json files from two experimental parts, detailing machine operations.
• Energy consumption was calculated as one application of the analyzed HF machine data.

Main Results:

• Successfully retrieved comprehensive HF machine data (500Hz) from milling experiments.
• Demonstrated the feasibility of using this data to determine machine energy consumption.
• Highlighted the potential for extracting additional parameters like torque, speed, and NC code.

Conclusions:

• HF machine data acquired via Edge Devices provides a rich source for detailed manufacturing process analysis.
• The collected data enables accurate calculation of energy consumption and offers insights into various operational parameters.
• This approach facilitates enhanced understanding and optimization of milling processes.