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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 techniques (AMHSs) by addressing each parameter uncertainty and system discrepancy.
The framework makes use of sparse subject knowledge and probabilistic modeling to calibrate digital twins, outperforming typical fashions and enabling sooner, extra dependable predictions in advanced manufacturing environments.
The tactic has been validated by means 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 techniques (AMHSs) of semiconductor and show fabrication industries undergo from parameter uncertainty and discrepancy. This results in inaccurate predictions, finally affecting efficiency. To handle this, researchers have developed a brand new Bayesian calibration framework that concurrently accounts for each parameter uncertainty and discrepancy, bettering the prediction accuracy of digital twin fashions. This revolutionary framework holds nice potential for enhancing digital twin applicability throughout numerous industries.
To handle more and more advanced manufacturing techniques, involving materials flows throughout quite a few transporters, machines, and storage areas, the semiconductors and show fabrication industries have applied automated materials dealing with techniques (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 techniques. Nevertheless, digital twins don’t at all times totally 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 car in AMHSs can differ barely within the subject however is fastened within the digital twin. Discrepancy, then again, originates from the distinction in operational logic between the real-world system and the digital twin. That is particularly vital since digital twins usually simplify or resemble the actual processes, and discrepancies gathered 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 typically require a considerable amount of subject 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 allows us to concurrently optimize calibration parameters and compensate for discrepancy,” explains Prof. Hong. “It’s designed to scale throughout massive good manufacturing facility environments, delivering dependable calibration efficiency with considerably much less subject knowledge than typical strategies.” Their examine was made accessible on-line on Might 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 subject observations and accessible prior data with digital twin simulation outcomes by means of 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 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 allows 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 means of empirical experiments, and may 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 must be versatile for widespread cross-industry utility. This strategy precisely predicted subject system responses for large-scale techniques with scarce subject observations and supported speedy calibration of future manufacturing schedules in real-world techniques. The calibration system can also be apt for discrepancy-prone digital fashions that behave in a different 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 allows the event of reusable and sustainable digital twin frameworks that may be utilized to completely different industries. Moreover, this strategy is being utilized and scaled at Samsung Show, the place the researchers have intently collaborated with operation groups to customise the framework for the real-world complexities.
Total, this novel framework has the potential to vary the applicability and effectivity of AMHSs. Wanting forward, Prof. Hong concludes, “Our analysis provides a pathway towards self-adaptive digital twins, and sooner or later, has sturdy potential to turn out to be a core enabler of good manufacturing.”
