CNC machining data repository: Geometry, NC code & high-frequency energy consumption data for aluminum and plastic
Markus Brillinger1, Muaaz Abdul Hadi1, Stefan Trabesinger1,2
1Pro2Future GmbH, Sandgasse 34, Graz 8010, Austria.
Data in Brief
|July 18, 2025
Summary
This study introduces a new CNC machining dataset with geometry, NC code, and energy data. This resource aids smart manufacturing research in optimizing processes and energy efficiency.
Area of Science:
- Manufacturing Engineering
- Data Science
- Machine Learning
Background:
- High-quality datasets are crucial for advancing machine learning and optimizing manufacturing processes.
- Data-driven approaches are increasingly vital for improving energy efficiency and predictive maintenance in manufacturing.
Purpose of the Study:
- To introduce a comprehensive CNC machining data repository.
- To provide researchers and engineers with valuable data for manufacturing analytics and smart manufacturing applications.
Main Methods:
- The dataset comprises three key categories: product geometry, NC code, and high-frequency energy consumption data.
- The data was collected from CNC machining operations.
Main Results:
- The repository integrates diverse data types essential for advanced manufacturing analytics.
- This dataset enables the analysis of energy consumption patterns during machining.
Conclusions:
- The public availability of this dataset will accelerate the development of data-driven solutions in smart manufacturing.
- This resource facilitates benchmarking and the enhancement of digital twin models with real-world machining data.
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