TY - JOUR A1 - Stier, Simon P. A1 - Xu, Xukuan A1 - Gold, Lukas A1 - Möckel, Michael T1 - Ontology-Based Battery Production Dataspace and Its Interweaving with Artificial Intelligence-Empowered Data Analytics JF - Energy Technology N2 - Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia. KW - Batterie KW - Künstliche Intelligenz Y1 - 2024 UR - https://onlinelibrary.wiley.com/doi/epdf/10.1002/ente.202301305 U6 - https://doi.org/https://doi.org/10.1002/ente.202301305 VL - 2024 IS - 2301305 SP - 1 EP - 13 ER - TY - JOUR A1 - Xu, Xukuan A1 - Donghui, Li A1 - Bi, Jinghou A1 - Möckel, Michael T1 - AutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models JF - Scientific Reports N2 - Design of experiments (DOE) is an established method to allocate resources for efficient parameter space exploration. Model based active learning (AL) data sampling strategies have shown potential for further optimization. This paper introduces a workflow for conducting DOE comparative studies using automated machine learning. Based on a practical definition of model complexity in the context of machine learning, the interplay of systematic data generation and model performance is examined considering various sources of uncertainty: this includes uncertainties caused by stochastic sampling strategies, imprecise data, suboptimal modeling, and model evaluation. Results obtained from electrical circuit models with varying complexity show that not all AL sampling strategies outperform conventional DOE strategies, depending on the available data volume, the complexity of the dataset, and data uncertainties. Trade-offs in resource allocation strategies, in particular between identical replication of data points for statistical noise reduction and broad sampling for maximum parameter space exploration, and their impact on subsequent machine learning analysis are systematically investigated. Results indicate that replication oriented strategies should not be dismissed but may prove advantageous for cases with non-negligible noise impact and intermediate resource availability. The provided workflow can be used to simulate practical experimental conditions for DOE testing and DOE selection. KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1038/s41598-024-83581-3 ER - TY - CHAP A1 - Xu, Xukuan A1 - Möckel, Michael T1 - Machine Learning Based Early Rejection of Low Performance Cells in Li Ion Battery Production T2 - CACML '24: Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning N2 - Lithium-ion battery cell production is conducted through a multistep production process which suffers from a notable scrap rate. Machine learning (ML) based process monitoring provides solutions to mitigate the impact of substantial scrap rates by repeated multifactorial quality predictions (virtual quality gates) along the process line. This enables an early rejection of battery cells which are unlikely to reach required specifications, avoids further waste of resources at later process steps and simplifies recycling of rejected cells. A hierarchical architecture is used to apply ML algorithms first for process-adapted feature extraction which is guided by a priori knowledge on typical production anomalies. In a second step, these features are correlated with end-of-line quality control data using explainable ML methods. The resulting predictions may lead to pass or fail of a battery cell, or -in the context of flexible production- may also trigger adjustments of later process steps to compensate for detected deficiencies. An example ML based quality control concept is illustrated for a pilot battery cell production line. KW - Maschinelles Lernen Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1145/3654823.3654870 SP - 251 EP - 256 ER - TY - JOUR A1 - Zanotto, Franco M. A1 - Zapata Dominguez, Diana A1 - Ayerbe, Elixabete A1 - Boyano, Iker A1 - Burmeister, Christine A1 - Duquesnoy, Marc A1 - Eisentraeger, Marlene A1 - Florez Montaño, Jonathan A1 - Gallo-Bueno,, Alfonso A1 - Gold, Lukas A1 - Hall, Florian A1 - Kaden, Nicolaj A1 - Muerkens, Bernhard A1 - Otaegui, Laida A1 - Reynier, Yvan A1 - Stier, Simon A1 - Thomitzek, Matthias A1 - Turetskyy, Artem A1 - Vallin, Nicolas A1 - Wessel, Jacob A1 - Xu, Xukuan A1 - Abbasov, Jeyhun A1 - Franco, Alejandro A. T1 - Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities JF - Chemistry Europe - Batteries & Supercaps N2 - Lithium-ion battery (LIB) manufacturing requires a pilot stage that optimizes its characteristics. However, this process is costly and time-consuming. One way to overcome this is to use a set of computational models that act as a digital twin of the pilot line, exchanging information in real-time that can be compared with measurements to correct parameters. Here we discuss the parameters involved in each step of LIB manufacturing, show available computational modeling approaches, and discuss details about practical implementation in terms of software. Then, we analyze these parameters regarding their criticality for modeling set-up and validation, measurement accuracy, and rapidity. Presenting this in an understandable format allows identifying missing aspects, remaining challenges, and opportunities for the emergence of pilot lines integrating digital twins. Finally, we present the challenges of managing the data produced by these models. As a snapshot of the state-of-the-art, this work is an initial step towards digitalizing battery manufacturing pilot lines, paving the way toward autonomous optimization. KW - Batterie KW - Digitalisierung Y1 - 2022 UR - https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/batt.202200224 U6 - https://doi.org/https://doi.org/10.1002/batt.202200224 VL - 2022 IS - 5/9 SP - 1 EP - 35 ER -