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Updated: Sep 16, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

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Published on: June 1, 2022

Reconfigurable model clusters for scalable modelling of feed drive dynamics.

Mohammadmahdi Mehrabi¹, Keivan Ahmadi²

¹Department of Mechanical Engineering, University of Victoria, Victoria, V8W 2Y2, Canada.

Scientific Reports

|July 4, 2025

Summary

This study introduces a new method for modeling machine tool feed drives using pre-calibrated models and Bayesian selection. This approach simplifies and scales the creation of machine tool digital twins and shadows.

Keywords:

Bayesian model updating Feed drives Machine tool dynamics

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Area of Science:

Mechanical Engineering
Control Systems Engineering
Computational Modeling

Background:

Accurate modeling of machine tool feed drives is crucial for performance optimization and predictive maintenance.
Traditional methods require extensive individual machine calibration, hindering scalability.
The increasing demand for digital twins and shadows necessitates more efficient modeling techniques.

Purpose of the Study:

To develop a novel, scalable approach for modeling machine tool feed drive dynamics.
To reduce the need for individual machine calibration in developing digital twins and shadows.
To leverage Bayesian model selection for accurate representation of machine behavior.

Main Methods:

Utilized a cluster of pre-calibrated models for a fleet of similar machines or varying conditions.
Applied Bayesian model selection to assimilate internal controller signals into the model cluster.
Selected an optimal combination of models to represent individual machines accurately.

Main Results:

Demonstrated the effectiveness of the approach through numerical simulations with known ground truths.
Showcased the potential to simplify and scale feed drive modeling significantly.
Identified key technical and operational considerations for practical implementation.

Conclusions:

The proposed approach offers a simplified and scalable solution for feed drive modeling in machine tools.
Facilitates large-scale development of machine tool digital twins and shadows without extensive calibration.
Provides a foundation for broader application by outlining necessary technical and operational considerations.