Digital twins for automated material handling systems (AMHSs) of semiconductor and display fabrication industries suffer from parameter uncertainty and discrepancy. This leads to inaccurate predictions, ultimately affecting performance. To address this, researchers have developed a new Bayesian calibration framework that simultaneously accounts for both parameter uncertainty and discrepancy, improving the prediction accuracy of digital twin models. This innovative framework holds great potential for enhancing digital twin applicability across diverse industries.

To manage increasingly complex manufacturing systems, involving material flows across numerous transporters, machines, and storage locations, the semiconductors and display fabrication industries have implemented … Read More → "Pusan National University Researchers Reveal New Calibration Framework for Digital Twins"