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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 discipline knowledge and probabilistic modeling to calibrate digital twins, outperforming typical fashions and enabling quicker, extra dependable predictions in advanced manufacturing environments.
The strategy has been validated via empirical testing, utilized at Samsung Show, and is designed to scale throughout numerous industries in search of correct, self-adaptive digital twin options.
PRESS RELEASE — Digital twins for automated materials dealing with programs (AMHSs) of semiconductor and show fabrication industries endure from parameter uncertainty and discrepancy. This results in inaccurate predictions, in the end affecting efficiency. To handle 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 advanced 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 advanced manufacturing steps and management logic, and digital twin fashions have emerged as a promising answer to boost the visibility, predictability, and responsiveness of manufacturing and materials dealing with operation programs. Nevertheless, digital twins don’t at all times absolutely replicate 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 are tough to measure exactly however are important for correct modeling. For instance, the acceleration of an automatic automobile in AMHSs can differ barely within the discipline however is fastened within the digital twin. Discrepancy, however, 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 discipline knowledge.
To handle this hole, a analysis crew 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 discipline knowledge than typical strategies.” Their examine was made out there on-line on Could 08, 2025, and revealed in Quantity 80 of the Journal of Manufacturing Methods on June 01, 2025.
The researchers utilized modular Bayesian calibration for numerous working eventualities. Bayesian calibration can use sparse real-world knowledge to estimate unsure parameters whereas additionally accounting for discrepancy. It really works by combining discipline observations and out there prior information with digital twin simulation outcomes via probabilistic fashions, particularly Gaussian processes, to acquire a posterior distribution of calibrated digital twin outcomes over numerous working eventualities. They in contrast the efficiency of three fashions: a field-only surrogate that predicts real-world habits immediately 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 affords a sensible and reusable calibration process validated via empirical experiments, and may be custom-made 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 discipline system responses for large-scale programs with scarce discipline observations and supported speedy calibration of future manufacturing schedules in real-world programs. The calibration system can be apt for discrepancy-prone digital fashions that behave in another way than their real-world counterparts on account of simplified logic or code. Excessive-complexity manufacturing and materials dealing with environments, the place handbook optimization is difficult, may 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.
Total, this novel framework has the potential to alter the applicability and effectivity of AMHSs. Trying forward, Prof. Hong concludes, “Our analysis affords a pathway towards self-adaptive digital twins, and sooner or later, has sturdy potential to turn into a core enabler of sensible manufacturing.”
