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Intelligent feedrate optimization using a physics-based and data-driven digital twin
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DOI：10.1016/j.cirp.2023.04.063.png)
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Intelligent manufacturing machines envisioned for the future must be able to autonomously select process parameters that maximize their speed while adhering to quality specifications. Accordingly, this paper pro-poses a framework and methodology for using a physics-based and data-driven digital twin of a feed drive to maximize feedrate while respecting kinematic and contour error limits. To correct for inaccuracies intro-duced by unmodeled dynamics and disturbances, the data-driven model is updated on-the -fly using sensor feedback. Experiments on a 3-axis CNC machine tool prototype are used to demonstrate up to 35% cycle time reduction without violating error tolerances compared to the status quo.& COPY; 2023 CIRP. Published by Elsevier Ltd. All rights reserved.
Keyword:
Computer numerical control (CNC)
Digital twin
Feedrate optimization
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