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Researchers have developed a brand new Bayesian calibration framework that considerably improves the accuracy of digital twin fashions for automated materials dealing with programs (AMHSs) by addressing each parameter uncertainty and system discrepancy.
The framework makes use of sparse area knowledge and probabilistic modeling to calibrate digital twins, outperforming standard fashions and enabling quicker, extra dependable predictions in complicated manufacturing environments.
The tactic has been validated by way of empirical testing, utilized at Samsung Show, and is designed to scale throughout numerous industries looking for correct, self-adaptive digital twin options.
PRESS RELEASE — Digital twins for automated materials dealing with programs (AMHSs) of semiconductor and show fabrication industries undergo from parameter uncertainty and discrepancy. This results in inaccurate predictions, in the end affecting efficiency. To deal with this, researchers have developed a brand new Bayesian calibration framework that concurrently accounts for each parameter uncertainty and discrepancy, enhancing the prediction accuracy of digital twin fashions. This revolutionary framework holds nice potential for enhancing digital twin applicability throughout various industries.
To handle more and more complicated manufacturing programs, involving materials flows throughout quite a few transporters, machines, and storage areas, the semiconductors and show fabrication industries have carried out automated materials dealing with programs (AMHSs). AMHSs usually contain complicated manufacturing steps and management logic, and digital twin fashions have emerged as a promising resolution to boost the visibility, predictability, and responsiveness of manufacturing and materials dealing with operation programs. Nonetheless, digital twins don’t at all times absolutely mirror actuality, probably affecting manufacturing efficiency and should end in delays.
Digital twins of AMHSs face two main points: parameter uncertainty and discrepancy. Parameter uncertainty arises from real-world parameters which might be troublesome to measure exactly however are important for correct modeling. For instance, the acceleration of an automatic automobile in AMHSs can range barely within the area however is mounted within the digital twin. Discrepancy, alternatively, originates from the distinction in operational logic between the real-world system and the digital twin. That is particularly necessary since digital twins usually simplify or resemble the true processes, and discrepancies collected over time result in inaccurate predictions. Regardless of its significance, most performance-level calibration frameworks overlook discrepancy and focus solely on parameter uncertainty. Furthermore, they usually require a considerable amount of area knowledge.
To deal with this hole, a analysis staff led by Professor Soondo Hong from the Division of Industrial Engineering at Pusan Nationwide College, South Korea, developed a brand new Bayesian calibration framework. “Our framework permits us to concurrently optimize calibration parameters and compensate for discrepancy,” explains Prof. Hong. “It’s designed to scale throughout giant sensible manufacturing unit environments, delivering dependable calibration efficiency with considerably much less area knowledge than standard strategies.” Their research was made obtainable on-line on Might 08, 2025, and revealed in Quantity 80 of the Journal of Manufacturing Programs on June 01, 2025.
The researchers utilized modular Bayesian calibration for numerous working situations. Bayesian calibration can use sparse real-world knowledge to estimate unsure parameters whereas additionally accounting for discrepancy. It really works by combining area observations and obtainable prior information with digital twin simulation outcomes by way of probabilistic fashions, particularly Gaussian processes, to acquire a posterior distribution of calibrated digital twin outcomes over numerous working situations. They in contrast the efficiency of three fashions: a field-only surrogate that predicts real-world habits straight from noticed knowledge; a baseline digital twin mannequin utilizing solely calibrated parameters; and the calibrated digital twin mannequin accounting for each parameter uncertainty and discrepancy.
The calibrated digital twin mannequin considerably outperformed the field-only surrogate and confirmed concrete enhancements in prediction accuracy over the baseline digital fashions. “Our strategy permits efficient calibration even with scant real-world observations, whereas additionally accounting for inherent mannequin discrepancy.” notes Prof. Hong, “Importantly, it provides a sensible and reusable calibration process validated by way of empirical experiments, and might be personalized for every facility’s traits.”
The developed framework is a sensible and reusable strategy that can be utilized to precisely calibrate and optimize digital twins, in any other case hindered by scale, discrepancy, complexity, or the should be versatile for widespread cross-industry software. This strategy precisely predicted area system responses for large-scale programs with scarce area observations and supported speedy calibration of future manufacturing schedules in real-world programs. The calibration system can also be apt for discrepancy-prone digital fashions that behave in a different way than their real-world counterparts attributable to simplified logic or code. Excessive-complexity manufacturing and materials dealing with environments, the place handbook optimization is difficult, may also profit from this calibration framework. It additionally permits the event of reusable and sustainable digital twin frameworks that may be utilized to totally different industries. Moreover, this strategy is being utilized and scaled at Samsung Show, the place the researchers have carefully collaborated with operation groups to customise the framework for the real-world complexities.
General, this novel framework has the potential to alter the applicability and effectivity of AMHSs. Trying forward, Prof. Hong concludes, “Our analysis provides a pathway towards self-adaptive digital twins, and sooner or later, has sturdy potential to change into a core enabler of sensible manufacturing.”
