Calibration framework for digital twins improves prediction accuracy

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Pusan National University researchers reveal new calibration framework for digital twins
This innovative framework accounts for both parameter uncertainty and discrepancy, key issues that affect prediction accuracy of digital twins of automated material handling systems in semiconductor and display fabrication industries, improving decision-making capabilities and production performance. Credit: Prof. Soondo Hong from Pusan National University

To manage increasingly complex manufacturing systems, involving material flows across numerous transporters, machines, and storage locations, the semiconductors and display fabrication industries have implemented automated material handling systems (AMHSs). AMHSs typically involve complex manufacturing steps and control logic, and digital twin models have emerged as a promising solution to enhance the visibility, predictability, and responsiveness of production and material handling operation…



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