2022/09/07 by Maximilian Kannapinn, Kannapinn, Maximilian, Minh Khang Pham +3
Engineering · #Advanced MEMS and NEMS Technologies #Advancements in Semiconductor Devices and Circuit Design #Artificial Intelligence (cs.AI) #Biological Physics (physics.bio-ph) #Computational Engineering #Electrostatic Discharge in Electronics #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Finance #Fluid Dynamics (physics.flu-dyn) #Systems and Control (eess.SY) #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2209.03062
openalex publication_date 2022/09/07 · openalex created_date 2022/09/30 · openalex updated_date 2026/07/28
One possible way of making thermal processing controllable is to gather real-time information on the product's current state. Often, sensory equipment cannot capture all relevant information easily or at all. Digital Twins close this gap with virtual probes in real-time simulations, synchronized with the process. This paper proposes a physics-based, data-driven Digital Twin framework for autonomous food processing. We suggest a lean Digital Twin concept that is executable at the device level, entailing minimal computational load, data storage, and sensor data requirements. This study focuses on a parsimonious experimental design for training non-intrusive reduced-order models (ROMs) of a thermal process. A correlation (R=-0.76) between a high standard deviation of the surface temperatures in the training data and a low root mean square error in ROM testing enables efficient selection of training data. The mean test root mean square error of the best ROM is less than 1 Kelvin (0.2 % mean average percentage error) on representative test sets. Simulation speed-ups of Sp ≈ 1.8E4 allow on-device model predictive control. The proposed Digital Twin framework is designed to be applicable within the industry. Typically, non-intrusive reduced-order modeling is required as soon as the modeling of the process is performed in software, where root-level access to the solver is not provided, such as commercial simulation software. The data-driven training of the reduced-order model is achieved with only one data set, as correlations are utilized to predict the training success a priori.