@misc{12719, abstract = {{A human digital twin (HDT) is a virtual representation of a worker in cyberspace. Nevertheless, current research focusses mainly on HDTs for motoric work types, such as assembly. To fully integrate an HDT for workers in production, it is necessary that an HDT also displays cognitive processes like memorizing, thinking or reasoning. Such a concept can be used in information-based work, for example monitoring highly automated production systems, and contribute to the planning and control of production. Due to the high proportion of planning and decision-making processes, the efficiency of information-based work is determined in particular by the inner processes of the worker. An HDT can therefore help to describe current and future states of socio-technical work systems. Therefore, this paper presents a systematic literature review to explicitly derive the relevant components of an HDT for information-based work types. The elements of such an HDT and its environment are defined. Further, the current gaps in literature are identified. There are currently no real-world applications of such an HDT. Additionally, the value of multi-HDT systems must be evaluated more extensively.}}, author = {{Mordaschew, Viktoria and Latos, Benedikt and Tackenberg, Sven}}, booktitle = {{Procedia Computer Science}}, issn = {{1877-0509}}, keywords = {{Human-centric Production, Digital Twin, Systematic Literature Review}}, pages = {{2137--2146}}, publisher = {{Elsevier}}, title = {{{A Human Digital Twin for Worker-Centric Production}}}, doi = {{https://doi.org/10.1016/j.procs.2025.01.274}}, volume = {{253}}, year = {{2025}}, } @misc{12795, abstract = {{Including disabled workers in value-creating work processes is a fundamental and guaranteed human right and is, therefore, an essential goal of society. In Germany, sheltered workshops create the conditions for this inclusion since they are essential to companies' value chains. A central challenge is the inclusion of disabled workers in the value-creation processes, such as in manufacturing or assembly areas. The skills of disabled workers vary since they have individual impairments. Therefore, this paper presents a digital human model, a Human Digital Twin (HDT), for disabled workers. The model maps their skills and supports the production planning and assembly processes. (C) 2024 The Authors. Published by Elsevier B.V.}}, author = {{Mordaschew, Viktoria and Duckwitz, Sönke and Tackenberg, Sven}}, booktitle = {{5th International Conference on Industry 4.0 and Smart Manufacturing (ISM)}}, editor = {{Longo, F. and Shen, W. and Padovano, A.}}, issn = {{1877-0509}}, keywords = {{Human Digital Twin, Industry 4.0, Sheltered Workshops, Production Planning}}, location = {{Lisbon, PORTUGAL}}, pages = {{745--751}}, publisher = {{Elsevier BV}}, title = {{{A Human Digital Twin of Disabled Workers for Production Planning}}}, doi = {{10.1016/j.procs.2024.01.074}}, volume = {{232}}, year = {{2024}}, } @misc{9360, abstract = {{Background: Errors can have dangerous consequences, resulting in a preventive strategy in most company-based technical vocational education and training (TVET). On the contrary, errors provide a useful opportunity for learning due to mismatches of mental models and reality and especially to improve occupational safety and health (OSH). Objective: This article presents a didactic concept for developing a learning system based on learning from errors. Learners shall directly experience the consequences of erroneous actions through presenting error consequences in augmented reality to avoid negative, dangerous, or cost-intensive outcomes. Methods: Empirical data prove errors to be particularly effective in TVET. A formal description of a work system is systematically adopted to outline a connection between work, errors concerning OSH, and a didactic concept. A proof-of-concept systematically performs a use case for the developed learning system. It supports critical reflections from a technical, safety, and didactical perspective, naming implications and limitations. Results: By learning from errors, a work-based didactic concept supports OSH competencies relying on a learning system. The latter integrates digital twins of the work system to simulate and visualise dangerous error consequences for identified erroneous actions in a technical proof-of-concept. Results demonstrate the ability to detect action errors in work processes and simulations of error consequences in augmented reality. Conclusion: The technical learning system for OSH education extends existing learning approaches by showcasing virtual consequences. However, capabilities are limited regarding prepared learning scenarios with predefined critical errors. Future studies should assess learning effectiveness in an industrial scenario and investigate its usability.}}, author = {{Goppold, Marvin and Herrmann, Jan-Phillip and Tackenberg, Sven and Brandl, Christopher and Nitsch , Verena}}, booktitle = {{Work}}, issn = {{1875-9270 }}, keywords = {{Vocational education, digital twin, work system design}}, number = {{4}}, pages = {{1563--1575}}, publisher = {{IOS Press}}, title = {{{An error-based augmented reality learning system for work-based occupational safety and health education}}}, doi = {{10.3233/WOR-211243}}, volume = {{72}}, year = {{2022}}, } @misc{8384, abstract = {{ynamic simulation models are widely utilized to evaluate complex technical components and systems like electric drives or machines. They can support the development process of a production machine by avoiding an inadequate layout of components or an erroneous control design. However, the effort for building them is often too high for this purpose (lot size one). An automated model generation can be utilized to overcome the gap between efforts and advantages of dynamic simulations. This contribution presents an approach for simplifying the dynamic model generation of production machines by using the so-called Asset Administration Shell defined by the initiative Platform Industrie 4.0. The Asset Administration Shell was developed to aggregate all data necessary for maintaining the product across its life cycle. This includes component data and models as well as structural information about a machine. The generation process is performed by using the common FMI standard and a two-step procedure which allows the linkage of different simulation tools. The model generation is demonstrated by an example layout of a machine's internal direct current grid.}}, author = {{Göllner, D. and Pawlik, Thomas and Schulte, Thomas}}, booktitle = {{2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)}}, isbn = {{978-1-6654-3772-1 }}, issn = {{2157-3611}}, keywords = {{Digital Twin, Asset Administration Shell, Dynamic Simulation Model, Industry 4.0, Automated Model Generation}}, location = {{Online (Singapore)}}, pages = {{808--812}}, publisher = {{IEEE}}, title = {{{Utilization of the Asset Administration Shell for the Generation of Dynamic Simulation Models}}}, doi = {{10.1109/IEEM50564.2021.9673089}}, year = {{2021}}, } @inbook{3349, abstract = {{Model-based concepts and simulation techniques in combination with digital tools emerge as a key to explore the full potential of biopharmaceutical production processes, which contain several challenging development and process steps. One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch-to-batch variation, variabilities in cell behavior, and influences of changes in cultivation conditions necessitate adequate digital solutions to provide information about the current and near future process state to derive correct process decisions. For this purpose digital seed train twins have proved to be efficient, which digitally display the time-dependent behavior of important process variables based on mathematical models, strategies, and adaption procedures. This chapter will outline the needs for digitalization of seed trains, the construction of a digital seed train twin, the role of parameter estimation, and different statistical methods within this context, which are applicable to several problems in the field of bioprocessing. The results of a case study are presented to illustrate a Bayesian approach for parameter estimation and prediction of an industrial cell culture seed train for seed train digitalization.}}, author = {{Hernández Rodriguez, Tanja and Frahm, Björn}}, booktitle = {{Digital Twins Tools and Concepts for Smart Biomanufacturing}}, editor = {{Herwig, Christoph and Pörtner, Ralf and Möller, Johannes }}, isbn = {{978-3-030-71659-2}}, issn = {{1616-8542}}, keywords = {{Bayes, Digital twin, Parameter estimation, Seed train, Uncertainty}}, pages = {{97–131}}, publisher = {{Springer}}, title = {{{Digital Seed Train Twins and Statistical Methods}}}, doi = {{https://doi.org/10.1007/10_2020_137}}, volume = {{176}}, year = {{2021}}, }