@inproceedings{KranzKristhofenSchirmeretal., author = {Kranz, Philipp and Kristhofen, Dennis and Schirmer, Fabian and Rose, Chad G. and Schmitt, Jan and Kaupp, Tobias}, title = {Gesture vs. Touch Control for Unforeseen Situations of Human-Robot Collaborative Assembly}, series = {2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)}, booktitle = {2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)}, publisher = {IEEE}, doi = {10.1109/HRI61500.2025.10974148}, pages = {1433 -- 1437}, language = {en} } @inproceedings{SchirmerKranzRoseetal., author = {Schirmer, Fabian and Kranz, Philipp and Rose, Chad G. and Willert, Volker and Schmitt, Jan and Kaupp, Tobias}, title = {Utilizing Eye Gaze for Human-Robot Collaborative Assembly}, series = {2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)}, booktitle = {2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)}, publisher = {IEEE}, doi = {10.1109/HRI61500.2025.10974041}, pages = {1603 -- 1607}, language = {en} } @article{MillerEngelmannKauppetal., author = {Miller, Eddi and Engelmann, Bastian and Kaupp, Tobias and Schmitt, Jan}, title = {Advanced Cascaded Scheduling for Highly Autonomous Production Cells with Material Flow and Tool Lifetime Consideration using AGVs}, series = {Journal of Machine Engineering}, journal = {Journal of Machine Engineering}, issn = {2391-8071}, language = {en} } @inproceedings{SeitzSchmittEngelmann, author = {Seitz, Philipp and Schmitt, Jan and Engelmann, Bastian}, title = {Evaluation of proceedings for SMEs to conduct I4.0 projects}, series = {Procedia Cirp}, volume = {86}, booktitle = {Procedia Cirp}, pages = {257 -- 263}, language = {en} } @inproceedings{SchirmerKranzSchmittetal., author = {Schirmer, Fabian and Kranz, Philipp and Schmitt, Jan and Kaupp, Tobias}, title = {Anomaly Detection for Dynamic Human-Robot Assembly: Application of an LSTM-based autoencoder to interpret uncertain human behavior in HRC}, series = {Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction}, booktitle = {Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction}, doi = {10.1145/3568294.3580100}, pages = {881 -- 883}, language = {en} } @inproceedings{MillerKauppSchmitt, author = {Miller, Eddi and Kaupp, Tobias and Schmitt, Jan}, title = {Cascaded Scheduling for Highly Autonomous Production Cells with AGVs}, series = {Manufacturing Driving Circular Economy: Proceedings of the 18th Global Conference on Sustainable Manufacturing, October 5-7, 2022, Berlin ; Lecture Notes in Mechanical Engineering}, booktitle = {Manufacturing Driving Circular Economy: Proceedings of the 18th Global Conference on Sustainable Manufacturing, October 5-7, 2022, Berlin ; Lecture Notes in Mechanical Engineering}, editor = {Kohl, Holger and Seliger, G{\"u}nther and Dietrich, Franz}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-28838-8}, doi = {https://doi.org/10.1007/978-3-031-28839-5_43}, pages = {383 -- 390}, abstract = {Highly autonomous production cells are a crucial part of manufacturing systems in industry 4.0 and can contribute to a sustainable value-adding process. To realize a high degree of autonomy in production cells with an industrial robot and a machine tool, an experimental approach was carried out to deal with numerous challenges on various automation levels. One crucial aspect is the scheduling problem of tasks for each resource (machine tool, tools, robot, AGV) depending on various data needed for a job-shop scheduling algorithm. The findings show that the necessary data has to be derived from different automation levels in a company: horizontally from ERP to shop-floor, vertically from the order handling department to the maintenance department. Utilizing that data, the contribution provides a cascaded scheduling approach for machine tool jobs as well as CNC and robot tasks for highly autonomous production cells supplied by AGVs.}, language = {en} } @article{MillerCeballosEngelmannetal., author = {Miller, Eddi and Ceballos, Hector and Engelmann, Bastian and Schiffler, Andreas and Batres, Rafael and Schmitt, Jan}, title = {Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning}, series = {2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop}, journal = {2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop}, pages = {1 -- 8}, language = {en} } @article{SeitzScherdelReichenaueretal., author = {Seitz, Philipp and Scherdel, Christian and Reichenauer, Gudrun and Schmitt, Jan}, title = {Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis}, series = {Computational Materials Science}, volume = {218}, journal = {Computational Materials Science}, pages = {111984 -- 111984}, language = {en} } @article{ScherdelMillerReichenaueretal., author = {Scherdel, Christian and Miller, Eddi and Reichenauer, Gudrun and Schmitt, Jan}, title = {Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML)}, series = {Processes}, volume = {9}, journal = {Processes}, number = {4}, pages = {672 -- 672}, language = {en} } @inproceedings{WehnertSchaeferSchmittetal., author = {Wehnert, Kira-Kristin and Sch{\"a}fer, S and Schmitt, Jan and Schiffler, Andreas}, title = {C7. 4 Application of Laser Line Scanners for Quality Control during Selective Laser Melting (SLM)}, series = {SMSI 2021-System of Units and Metreological Infrastructure}, booktitle = {SMSI 2021-System of Units and Metreological Infrastructure}, pages = {298 -- 299}, language = {en} } @article{SeitzSchmitt, author = {Seitz, Philipp and Schmitt, Jan}, title = {Alternating Transfer Functions to Prevent Overfitting in Non-Linear Regression with Neural Networks}, series = {Journal of Experimental \& Theoretical Artificial Intelligence}, journal = {Journal of Experimental \& Theoretical Artificial Intelligence}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-49199}, abstract = {In nonlinear regression with machine learning methods, neural networks (NNs) are ideally suited due to their universal approximation property, which states that arbitrary nonlinear functions can thereby be approximated arbitrarily well. Unfortunately, this property also poses the problem that data points with measurement errors can be approximated too well and unknown parameter subspaces in the estimation can deviate far from the actual value (so-called overfitting). Various developed methods aim to reduce overfitting through modifications in several areas of the training. In this work, we pursue the question of how an NN behaves in training with respect to overfitting when linear and nonlinear transfer functions (TF) are alternated in different hidden layers (HL). The presented approach is applied to a generated dataset and contrasted to established methods from the literature, both individually and in combination. Comparable results are obtained, whereby the common use of purely nonlinear transfer functions proves to be not recommended generally.}, language = {en} } @article{WeberWilhelmSchmitt, author = {Weber, Aleksej and Wilhelm, Markus and Schmitt, Jan}, title = {Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control}, series = {sensors}, volume = {24}, journal = {sensors}, number = {18}, publisher = {MDPI}, doi = {10.3390/s24185919}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-57575}, pages = {18}, abstract = {The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.}, language = {en} } @techreport{AsikainenEskolaSalinFischeretal., author = {Asikainen, Eveliina and Eskola-Salin, Nina and Fischer, Sophie and Giedraitien{\´e}, Vytautė and Beseckas, Povilas and Mairhofer, Stephanie and K{\"o}der, Lea and Schmitt, Jan and Walter, Holger and Guerrero-Perez, Olga and Bl{\´a}zquez-Parra, Elidia Beatriz and Bikuvien{\´e}, Ina and Lisina, Neringa and Tamulien{\´e}, Rasa and Liepinaitien{\´e}, Alina and M{\"a}kel{\"a}, Anne-Maria and Tahlo, Sanna and Selimaa, Hanna and Hager, Veronika and Ortega-Casanova, Joaquin and Mora-Segado, Patricia}, title = {Sustainability at HEIs: Mapping Good Practice}, doi = {10.57714/b75p-n548}, abstract = {Global climate change is a cognitive challenge for many people and often evokes negative associations due to its complexity and interactions with politics, social movements and economic developments. Therefore, the possession of green skills becomes central to the fight against climate change. The European Council conclusions recognize this urgency and underline the need for a transition to green skills. This recognition also extends to higher education, where institutions have a crucial role to play in tackling the climate crisis. Personal Green Skills in Higher Education (PeGSinHE) is an Erasmus+ KA2 project coordinated by Kauno Kolegija (KK, Lithuania), Tampere University of Applied Sciences (TAMK, Finland), Hochschule f{\"u}r Agrar- und Umweltp{\"a}dagogik (HAUP, Austria), Universidad de M{\´a}laga (UMA, Spain) and Technical University of Applied Sciences W{\"u}rzburg Schweinfurt (THWS, Germany). The strategically designed project aims not only to promote green skills among students and encourage personal behavioral change in line with the Sustainable Development Goals, but also to instill a sense of social responsibility in the partner institutions. The focus is on empowering lecturers at partner universities through innovative teaching and learning methods to effectively impart green skills to students. This report describes the objectives and methodology used to assess environmental and sustainability competencies in the higher education institutions involved in the project. Methodologically, the report uses an assessment template designed to provide a comprehensive overview of best practice and baseline levels of environmental and sustainability competencies. It advocates the involvement of key stakeholders from all five partner Higher Education Institutions to ensure a broad perspective on these practices and competences within their respective countries and organizations. Different methods and perspectives will be used to collect data to enable a holistic understanding of the topic. The joint completion of the assessment template serves as a catalyst for joint discussions on the level of environmental and sustainability competencies and the identification of best practices in each organization. The results show that national implementation strategies are relatively loose, although some competency descriptions set targets for undergraduate degree programs. Challenges faced by higher education staff include resource constraints, particularly lack of time, the need for a deeper understanding of sustainable development and pedagogical tools, and the need for improved opportunities for collaboration. Given the time and resource constraints of this study, the results must be considered preliminary. Nevertheless, they confirm the findings of previous studies.}, language = {en} } @article{WilhelmLotterScherdeletal., author = {Wilhelm, Markus and Lotter, Frank and Scherdel, Christian and Schmitt, Jan}, title = {Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis}, series = {buildings}, volume = {14}, journal = {buildings}, number = {2}, publisher = {MDPI}, issn = {2075-5309}, doi = {10.3390/buildings14020340}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-56030}, pages = {14}, abstract = {In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt\%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.}, language = {en} } @article{LangEngelmannSchiffleretal.2024, author = {Lang, Silvio and Engelmann, Bastian and Schiffler, Andreas and Schmitt, Jan}, title = {A simplified machine learning product carbon footprint evaluation tool}, series = {Cleaner Environmental Systems}, volume = {13}, journal = {Cleaner Environmental Systems}, publisher = {Elsevier BV}, issn = {2666-7894}, doi = {10.1016/j.cesys.2024.100187}, year = {2024}, abstract = {On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.}, language = {en} } @inproceedings{GattulloDammaccoRuospoetal., author = {Gattullo, Michele and Dammacco, Lucilla and Ruospo, Francesca and Evangelista, Alessandro and Fiorentino, Michele and Schmitt, Jan and Uva, Antonio E}, title = {Design preferences on industrial augmented reality: a survey with potential technical writers}, series = {2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, booktitle = {2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, pages = {172 -- 177}, language = {en} } @article{MillerBorysenkoHeusingeretal., author = {Miller, Eddi and Borysenko, Vladyslav and Heusinger, Moritz and Niedner, Niklas and Engelmann, Bastian and Schmitt, Jan}, title = {Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning}, series = {Sensors}, volume = {21}, journal = {Sensors}, number = {17}, pages = {5896 -- 5896}, language = {en} } @article{SchusterEngelmannSponholzetal., author = {Schuster, Florian and Engelmann, Bastian and Sponholz, Uwe and Schmitt, Jan and Engineering, Institute Digital}, title = {Human acceptance evaluation of AR-assisted assembly scenarios}, series = {Journal of Manufacturing Systems}, volume = {61}, journal = {Journal of Manufacturing Systems}, pages = {660 -- 672}, language = {en} } @inproceedings{SchusterSponholzEngelmannetal., author = {Schuster, Florian and Sponholz, Uwe and Engelmann, Bastian and Schmitt, Jan}, title = {A user study on AR-assisted industrial assembly}, series = {2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, booktitle = {2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, pages = {135 -- 140}, language = {en} } @inproceedings{PfeufferFischerSchmittetal., author = {Pfeuffer, Katharina and Fischer, Sophie and Schmitt, Jan and Br{\"a}utigam, Volker}, title = {Human or Robot Resource Management? The Future of Work in the Digital Transformation of Manufactoring Companies}, series = {Proceedings of the Conference on Production Systems and Logistics: CPSL 2025}, booktitle = {Proceedings of the Conference on Production Systems and Logistics: CPSL 2025}, publisher = {publish-Ing.}, address = {Offenburg}, doi = {10.15488/18885}, abstract = {This article examines the transformative effects of Smart Factory technologies - such as human-robot collaboration, intelligent assistance systems and cyber-physical production systems - on organizational design, with a particular focus on central fields of action for Human Resources management (HRM) and operational management. A case study of a German automotive supplier is used to examine how digitalization and automation are changing human work and organizational structures. Two future scenarios for organizational models are proposed: the swarm organization, which consists exclusively of highly qualified employees while robots take over routine tasks, and the polarized organization, which is characterized by a division between highly qualified specialists and low-skilled employees. Each scenario brings different challenges and opportunities for HR management, as companies need to adapt to digital skills, new models of collaboration and the management of a highly specialized or polarized workforce. This paper provides a conceptual framework and actionable insights for HRM and production management to manage the shift towards advanced, automated organizational models and ensure a smooth transition to the Smart Factory of the future.}, language = {en} } @inproceedings{ErbeBrandmeierSchmittetal., author = {Erbe, Karin and Brandmeier, Melanie and Schmitt, Michael and Donbauer, Andreas and Liebscher, Jan-Andreas and Kolbe, Thomas}, title = {Detektion von Fahrradst{\"a}ndern in Luftbildern mittels Deep Learning}, series = {42. Wissenschaftlich-Technische Jahrestagung der DGPF. 5.-6. Oktober 2022 in Dresden}, volume = {30}, booktitle = {42. Wissenschaftlich-Technische Jahrestagung der DGPF. 5.-6. Oktober 2022 in Dresden}, editor = {Kersten, Thomas P. and Tilly, Nora}, issn = {0942-2870}, doi = {10.24407/KXP:1795622415}, pages = {27 -- 39}, language = {de} }