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Simulating a Virtual Machining Model in an Agent-Based Model for Advanced Analytics.

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Summary
This summary is machine-generated.

This study introduces a virtual machining model for milling operations, generating realistic machine-monitoring data through simulation. This approach enhances manufacturing efficiency by predicting performance impacts and filling data gaps, reducing the need for expensive real-world data acquisition.

Keywords:
MTConnectSTEP-NCadvanced analyticsdata generatormanufacturing simulationmillingsimulation

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Area of Science:

  • Manufacturing Engineering
  • Computational Modeling
  • Data Analytics

Background:

  • Real-time machine-monitoring data is crucial for manufacturing efficiency but expensive to acquire.
  • Simulation offers a cost-effective alternative for generating machine-monitoring data.
  • Virtual manufacturing uses computer models to mimic real manufacturing systems.

Purpose of the Study:

  • To introduce a virtual machining model for 3-axis milling operations.
  • To enable the generation of machine-monitoring data from process plans.
  • To integrate the virtual machining model into an agent-based simulation for complex shop-floor modeling.

Main Methods:

  • Developed a digital mock-up of a 3-axis milling machine.
  • Generated energy consumption data using physics-based equations.
  • Utilized STEP-NC and MTConnect for data representation.
  • Integrated the model into an agent-based simulation environment.

Main Results:

  • The virtual machining model successfully generates machine-monitoring data.
  • The model provides energy consumption data based on physical principles.
  • Integration with agent-based modeling facilitates complex shop-floor simulations.
  • Data analytics can be applied to predict shop-floor performance.

Conclusions:

  • Virtual machining models offer a viable solution for acquiring machine-monitoring data.
  • The developed model aids in anticipating the impact of manufacturing system modifications.
  • Agent-based modeling enhances the complexity and utility of virtual manufacturing environments.
  • This work lays the foundation for advanced shop-floor performance prediction.