Version 1
: Received: 30 October 2023 / Approved: 30 October 2023 / Online: 1 November 2023 (09:13:39 CET)
Version 2
: Received: 2 November 2023 / Approved: 2 November 2023 / Online: 3 November 2023 (06:35:49 CET)
Version 3
: Received: 5 November 2023 / Approved: 6 November 2023 / Online: 6 November 2023 (07:38:22 CET)
How to cite:
Kim, H.; Kontar, R.; Okwudire, C. Intelligent Feedrate Optimization using an Uncertainty-aware Digital Twin within a Model Predictive Control Framework. Preprints2023, 2023110041. https://doi.org/10.20944/preprints202311.0041.v3
Kim, H.; Kontar, R.; Okwudire, C. Intelligent Feedrate Optimization using an Uncertainty-aware Digital Twin within a Model Predictive Control Framework. Preprints 2023, 2023110041. https://doi.org/10.20944/preprints202311.0041.v3
Kim, H.; Kontar, R.; Okwudire, C. Intelligent Feedrate Optimization using an Uncertainty-aware Digital Twin within a Model Predictive Control Framework. Preprints2023, 2023110041. https://doi.org/10.20944/preprints202311.0041.v3
APA Style
Kim, H., Kontar, R., & Okwudire, C. (2023). Intelligent Feedrate Optimization using an Uncertainty-aware Digital Twin within a Model Predictive Control Framework. Preprints. https://doi.org/10.20944/preprints202311.0041.v3
Chicago/Turabian Style
Kim, H., Raed Kontar and Chinedum Okwudire. 2023 "Intelligent Feedrate Optimization using an Uncertainty-aware Digital Twin within a Model Predictive Control Framework" Preprints. https://doi.org/10.20944/preprints202311.0041.v3
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 digital twin, which incorporates the known uncertainty in the physics-based models and predicts the distribution of contour error using a data-driven model that learns the unknown uncertainty on-the-fly by sensor measurements. Using the quantified uncertainty, the proposed feedrate optimization maximizes productivity while maintaining quality under desired servo error constraints and stringency (i.e., the tolerance for constraint violation under uncertainty) using a model predictive control framework. Experimental results obtained using a 3-axis desktop CNC machine tool and a desktop 3D printer demonstrate significant cycle time reductions of up to 38% and 17% respectively, while staying close to the error tolerances compared to the existing methods.
Keywords
smart manufacturing; feedrate optimization; digital twin; CNC milling; 3D printing
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Chinedum Okwudire
Commenter's Conflict of Interests: Author