A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
Researchers propose a framework for self-adaptive digital twins, addressing concept drift and model degradation. The framework uses robust model predictive control for continual validation and updating.
- The framework addresses concept drift and model degradation in digital twins
- It uses robust model predictive control for continual validation and updating
- The approach is demonstrated in a case study on additive manufacturing
- It has potential applications in various industries where digital twins are used
Digital twins are virtual replicas of physical systems, relying on surrogate models to mirror real-time behavior. However, these models can degrade as operating conditions change, a phenomenon known as concept drift.
The proposed framework addresses this challenge by integrating robust model predictive control with continual validation and updating mechanisms. This allows digital twins to adapt to changing conditions and maintain their predictive performance.
The framework is demonstrated in a case study on additive manufacturing, showcasing its potential to improve the accuracy and reliability of digital twins in various applications.
The approach has implications for industries where digital twins are used to optimize performance, predict maintenance, and improve decision-making, such as manufacturing, healthcare, and finance.
Improves accuracy and reliability of digital twins
Enhances decision-making and optimization in various industries
Potential for increased adoption and investment in digital twin technology
Advances the field of digital twins and their applications
- concept drift
- The phenomenon of machine learning models degrading over time due to changes in operating conditions
- aleatoric uncertainty
- The uncertainty inherent in a system or process, as opposed to epistemic uncertainty which is due to lack of knowledge
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