@misc{10787, abstract = {{Cyber-physical production systems have emerged with the rise of Industry 4.0 in different industrial fields. Especially the food sector, where inhomogeneous input products like beer/yeast suspensions with different qualities and properties have yet slowed down automation, has potential for this evolution. This contribution presents optimization methods for a dynamical cross-flow filtration plant which is driven by an advanced control concept in combination with data driven product monitoring via inline near infrared spectroscopy (NIR) in order to improve energy savings and filtration performance. Using a hierarchical control and optimization structure, the non stationary batch process is steered towards a high production rate with low energy consumption for a variety of different input products.}}, author = {{Tebbe, Jörn and Pawlik, Thomas and Trilling-Haasler, Marc and Löbner, Jannis and Lange-Hegermann, Markus and Schneider, Jan}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}}, isbn = {{978-1-6654-9314-7 }}, issn = {{1935-4576}}, keywords = {{Spectroscopy, Production systems, Filtration, Velocity control, Optimization methods, Cyber-physical systems, Nonhomogeneous media}}, location = {{Lemgo}}, pages = {{1--7}}, publisher = {{IEEE}}, title = {{{Holistic optimization of a dynamic cross-flow filtration process towards a cyber-physical system}}}, doi = {{10.1109/INDIN51400.2023.10217913}}, year = {{2023}}, } @misc{10788, abstract = {{For process monitoring, an adequate data preprocessing is crucial to link accessible inline process data with offline measured target variables. Literature, however, does not provide systematic preprocessing strategies. The effects of five different preprocessing strategies on data from a Dielectric Spectroscopy system applied to the Viable Cell Density (VCD) of a mammalian cell cultivation were thus evaluated. Single-frequency measurements are typically used to model the VCD over the growth phase using linear regression or the Cole-Cole model and served as a reference. As multi-frequency measurement is promising to model the VCD beyond the growth phase using Partial Least Squares Regression (PLSR), we further aimed to determine, whether replacing linear regression by PLSR shows comparable modeling performance. All five preprocessing strategies led to comparable results. Exemplary, when using capacitance values at a frequency of 3347 kHz, linear regression resulted in a R2 of 0.90 and a standard deviation of 0.4 % on average. Both normalization techniques had the same positive effect on the results of PLSR. The order of smoothing and normalization was irrelevant for both regression methods. Comparing the results of linear regression and PLSR, the latter obtained on average 9 % better results. Therefore, we concluded that PLSR is preferable over linear regression and is potentially suitable to model the VCD beyond the growth phase, which is suggested to be investigated based on more data sets.}}, author = {{Ramm, Selina and Hernández Rodriguez, Tanja and Frahm, Björn and Pein-Hackelbusch, Miriam}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}}, isbn = {{978-1-6654-9314-7}}, issn = {{1935-4576}}, keywords = {{Spectroscopy, Smoothing methods, Systematics, Phase measurement, Linear regression, Data models, Dielectric measurement}}, location = {{Lemgo}}, pages = {{1--6}}, publisher = {{IEEE}}, title = {{{Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities}}}, doi = {{10.1109/INDIN51400.2023.10218012}}, year = {{2023}}, } @misc{12706, abstract = {{Vaseline, also referred to as petrolatum, is a colloidal dispersion of liquid-crystalline structures of hydrocarbons derived from petroleum. It has long been recognized for its versatile applications in the pharmaceutical industry, with its use in the formulation of various topical medications, wound care products, and drug delivery systems. For pharmaceutical use, petrolatum has to meet the quality standards described in its Pharmacopoeia monograph. The comprised test ranges allow for a broad range of Vaseline qualities on the market, while the tests themselves only poorly discriminate between grades. The only differentiating properties are related to the melting behavior, which is tested via drop point analysis, and the consistency, addressed in the functionality-related characteristics section. In this study, we propose the hypothesis that Near-infrared spectroscopy (NIRS) could be a comparably simple method to evaluate the crystalline behavior of Vaseline qualities. We expect such information to provide additional details for Vaseline quality discrimination. This discrimination would allow the most suitable petroleum jelly to be selected for an existing formulation when the previous one needs to be replaced; for example, due to a manufacturer change. We demonstrate that NIRS in transmission and reflectance mode obtained by traditional continuous spectra acquisition and fragmented NIR spectra acquisition through multi-optical, multi-modal excitation, respectively, can both serve as a basis for detecting Vaseline quality differences, which we have further proven by thermal analysis and tests with semisolid formulations. Additionally, we demonstrate that a lower-cost multi-optical spectrometer in reflectance mode can detect Vaseline quality differences in rotated samples.}}, author = {{Fliedner, Niels Hendrik and Lohweg, Volker and Al-Karawi, Claudia and Pein-Hackelbusch, Miriam}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Dörksen, Helene and Scanzio, Stefano and Jasperneite, Jürgen and Wisniewski, Lukasz and Man, Kim Fung and Sauter, Thilo and Seno, Lucia and Trsek, Henning and Vyatkin, Valeriy }}, isbn = {{978-1-6654-9314-7}}, keywords = {{multimodal sensing, crystalline materials, microstructure, rotation measurement, PCA, calorimetry, pharmaceuticals, European Pharmacopoeia}}, location = {{Lemgo}}, publisher = {{IEEE}}, title = {{{A Novel Spectroscopic Approach for Vaseline Quality Discrimination}}}, doi = {{10.1109/indin51400.2023.10218318}}, year = {{2023}}, } @misc{12995, abstract = {{Due to Industry 4.0 developments, the demanded modularity of manufacturing systems generates additional manual efforts for security experts to guarantee a secure operation. The rising utilization of information and the frequent changes of system structures necessitate a continuous and automated security engineering, especially by application of the mandatory security risk assessments. Collecting the required information for these assessments and formalising expert knowledge shall improve the security of modular manufacturing systems in the future. In order to automate the security risk assessment process, this work proposes a method to determine the Target Security Level (SL-T) in conformance to the IEC 62443 standard based on the MITRE ATT&CK framework and the Intel Threat Agent Library (TAL).}}, author = {{Ehrlich, Marco and Bröring, Andre and Diedrich, Christian and Jasperneite, Jürgen and Kastner, Wolfgang and Trsek, Henning}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics : INDIN 2023 : 17-20 July 2023, Lemgo, Germany}}, editor = {{Jasperneite, Jürgen}}, isbn = {{978-1-6654-9314-7}}, keywords = {{Integrated circuits, Industries, Libraries, Security, Risk management, IEC Standards, Interviews}}, location = {{Lemgo}}, publisher = {{IEEE}}, title = {{{Determining the Target Security Level for Automated Security Risk Assessments}}}, doi = {{10.1109/indin51400.2023.10217902}}, year = {{2023}}, } @misc{13010, abstract = {{Especially in highly interdisciplinary fields such as automation engineering, contemporary programming education with tailored assignments and individual feedback is a major challenge for educational institutions due to the increasing number of students per teacher and the ever-increasing demand for computer science professionals. To address this gap, we present ”KIAAA” an AI Assistant for Automation Engineering Teaching, a work-in-progress approach for an integrated, customized, and AI-based learning support system for automation and programming courses based on instructor-defined course objectives. Thereby in the KIAAA system, the individual knowledge level of the students is determined and individually tailored virtual learning scenarios are generated based on the knowledge and learning profile of the students. These are iteratively adapted based on the answers given. To achieve this, KIAAA uses several AI components, a hybrid rule-based scenario generation component, a Help-DKT-based cognitive model, and a solution assessor that uses a combination of traditional code analysis methods and AI-based analyses methods for automated programming task assessment. These components are the main parts of KIAAA to generate customized programming scenarios as well as visualization and simulation based on a modern game and physics engine.}}, author = {{Eilermann, Sebastian and Wehmeier, Leon and Niggemann, Oliver and Deuter, Andreas}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Jasperneite, Jürgen}}, isbn = {{978-1-6654-9314-7}}, keywords = {{Visualization, Automation, Education, Games, Hybrid power systems, Task analysis, Artificial intelligence}}, location = {{Lemgo}}, publisher = {{IEEE}}, title = {{{KIAAA: An AI Assistant for Teaching Programming in the Field of Automation}}}, doi = {{10.1109/indin51400.2023.10218157}}, year = {{2023}}, }