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Related Experiment Video

Updated: Sep 18, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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Production Data Set for five-Axis CNC Milling with multiple Changeovers.

Mario Martinez1, Anna-Maria Schmitt1, Andreas Schiffler1

  • 1Technical University of Applied Sciences Würzburg-Schweinfurt, Institute of Digital Engineering (IDEE), Schweinfurt, 97421, Germany.

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|June 23, 2025
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Summary

This study details a comprehensive five-axis CNC milling production dataset, covering machine preparation and machining for three distinct products. The data facilitates analysis of manufacturing processes and optimization strategies.

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Mechanical Engineering

Background:

  • CNC milling is a critical automated manufacturing process.
  • Data-driven approaches are essential for optimizing production efficiency.
  • Standardized datasets are needed for developing and validating milling process models.

Purpose of the Study:

  • To present an extensive production dataset for five-axis CNC milling.
  • To provide a comprehensive record of machine preparation (changeover) and production phases.
  • To enable further research and development in CNC manufacturing.

Main Methods:

  • Manufacturing of three geometrically distinct products using a five-axis CNC machine.
  • Systematic recording of machine control features during changeover and production.
  • Experimental design utilizing a changeover matrix to cover all product combinations.
  • Repetition of production runs to ensure data robustness.

Main Results:

  • A detailed dataset encompassing NC-code, tool information, and process parameters.
  • Inclusion of a Jupyter notebook for data analysis and reproducibility.
  • Comprehensive data covering 30 manufacturing sessions and five changeover matrices.

Conclusions:

  • The dataset provides a valuable resource for analyzing five-axis CNC milling processes.
  • Enables research into optimizing changeover times and production efficiency.
  • Supports the development of advanced manufacturing analytics and simulation tools.