Naar betrouwbare en autonome data-gedreven modellering voor machine-niveau Digital Twins: van fysisch-gedreven ontwerp tot betrouwbaarheidsanalyse

Abstract

Modern industrial systems require increasing efficiency and flexibility to remain competitive. To achieve this, decision support systems are essential for assisting humans in steering manufacturing, helping operators meet productivity and quality goals while minimizing the environmental impact. Digital Twins (DTs) are a key enabling technology for these systems, as they can monitor the process and predict key performance indicators (KPIs) such as energy consumption and product quality across different scenarios. Conventionally, physics-based (PB) models are regarded as the gold standard for building DTs thanks to their interpretability and reliability. However, as industrial systems grow in complexity, modeling from first principles becomes prohibitive. Data-driven regression models offer a practical alternative by leveraging Machine Learning (ML) techniques to model the system directly from data. ML techniques can automatically capture general non-linear input-output relationships, which makes them significantly more flexible. Significant efforts have been made to digitalize factories within the Industry 4.0 paradigm, a prerequisite for training ML-based models. However, even with digitalization, a paradox arises: truly informative data remains scarce. Industries are typically risk-averse, limiting the exploration of new operating regimes to protect safety and maintain a stable production. As a result, historical data is typically confined to a small range and number of conditions, while the models must operate reliably in unseen scenarios to support human decision-making. Ironically, these are the scenarios where standard ML-based regression models lack trustworthiness, often lacking the robustness and physical consistency required to operate safely. This thesis therefore investigates how data-driven regression models can be made more trustworthy by design, by integrating prior knowledge and automatically selecting robust models, with a focus on building DTs for industrial machines. This research aligns with the EU Ethics Guidelines for Trustworthy Artificial Intelligence (AI) [55]. While these guidelines outline seven key requirements for trustworthiness, here the scope is limited to three pillars: human agency and oversight, technical robustness and safety, and transparency. Addressing these three pillars, the first part of the thesis focuses on integrating prior physical knowledge and human rationale into the modeling process. It starts by drawing theoretical connections between diverse practices in physics-informed ML, making it easier for practitioners to choose between methods. Specifically, it demonstrates that adding physical equations as input features is a special case of architectural inductive biases, and that data augmentation, with synthetic data generated prior to training, is a special case of soft constraints, the two becoming equivalent when the prior knowledge can be written explicitly as target values. It then proposes a framework designed to embed human decision-making rationale directly into the architecture and training process of models used for continuous manufacturing. Specifically, the system's time-series behavior is reduced to a static mapping. This preserves partial interpretability and aligns with operators' mental models, while enabling plug-and-play deployment over existing legacy control systems. To ensure physical consistency, the framework induces known monotonic relationships by regularizing the model's Jacobian matrix. It also emphasizes sensitivity to decision variables, a critical aspect often overlooked in conventional approaches. The framework is validated both on historical data and during a seven-month real-time deployment at a large-scale wooden fiberboard manufacturer. There, DTs were designed for five physically distinct process stages using the same approach, demonstrating its generality. The results show that the proposed framework enhances both model responsiveness and physical consistency compared to standard training, which often yields models with negligible sensitivity and limited practical utility. Extending the focus on technical robustness, the second part of the thesis complements the first by improving the robustness of data-driven regression models against distribution shifts by design. It addresses this challenge through a novel data splitting strategy named Leave-Boundary-Out (LBO). First, theoretical foundations are established, showing that out-of-distribution (OOD) regions can be exploited for more effective model selection. Because the sensitivity of the validation loss to hyperparameters is often amplified in these regions, sample-size requirements are reduced. The LBO algorithm then leverages this insight to select models with superior extrapolation performance. Validated on multiple synthetic and real-world benchmarks from diverse engineering domains, the results using LBO show that models tuned for extrapolation consistently outperform standard approaches in OOD scenarios. Importantly, for the Polynomial Lasso pipeline, models simpler than those tuned using standard approaches tend to extrapolate better under both the L0- and L1-norm complexity measures, with LBO acting as a regularization mechanism that favors OOD robustness over purely fitting in-distribution data; for SVR the trend was ambiguous. Additionally, LBO helps localized methods such as Radial Basis Function (RBF) kernel-based regressors learn more generalizable trends, which could prove valuable for optimization frameworks that largely rely on such models. Overall, this thesis provides theoretical, methodological, and experimental foundations demonstrating that trustworthy data-driven regression modeling requires tailored approaches based on use-case needs. Furthermore, it offers validated directions for achieving this in industrial and engineering contexts, such as DT development for process and product optimization.