Intelligent Feedrate Optimization using an Uncertainty-aware Digital Twin within a Model Predictive Control Framework
Heejin Kim,
Raed Kontar,
Chinedum Okwudire
Abstract:The future of intelligent manufacturing machines involves autonomous selection of process parameters to maximize productivity while maintaining quality within specified constraints. To effectively optimize process parameters, these machines need to adapt to existing uncertainties in the physical system. This paper proposes a novel framework and methodology for feedrate optimization that is based on a physics-informed data-driven digital twin with quantified uncertainty. The servo dynamics are modeled using a d… Show more
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