@incollection{MillerSchmittKauppetal., author = {Miller, Eddi and Schmitt, Anna-Maria and Kaupp, Tobias and Batres, Rafael and Schiffler, Andreas and Schmitt, Jan}, title = {A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling}, series = {Lecture Notes in Mechanical Engineering}, booktitle = {Lecture Notes in Mechanical Engineering}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031774287}, issn = {2195-4356}, doi = {10.1007/978-3-031-77429-4_59}, pages = {535 -- 543}, abstract = {Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense.}, language = {en} } @article{EngelmannSchmittMilleretal., author = {Engelmann, Bastian and Schmitt, Simon and Miller, Eddi and Br{\"a}utigam, Volker and Schmitt, Jan}, title = {Advances in machine learning detecting changeover processes in cyber physical production systems}, series = {Journal of Manufacturing and Materials Processing}, volume = {4}, journal = {Journal of Manufacturing and Materials Processing}, number = {4}, pages = {108 -- 108}, abstract = {The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.}, language = {en} } @article{NeuberSchmittEngelmannetal., author = {Neuber, Till and Schmitt, Anna-Maria and Engelmann, Bastian and Schmitt, Jan}, title = {Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {24}, pages = {10015 -- 10015}, language = {en} } @inproceedings{SchmittMillerSchiffleretal., author = {Schmitt, Anna-Maria and Miller, Eddi and Schiffler, Andreas and Schmitt, Jan}, title = {Energy Prediction for CNC Machines Using G-Code Evaluation, Machine Learning and a Real-World Training Part}, series = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, booktitle = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, doi = {10.1109/ICMRE64970.2025.10976308}, pages = {340-347}, language = {en} } @article{EngelmannSchmittTheilackeretal., author = {Engelmann, Bastian and Schmitt, Anna-Maria and Theilacker, Lukas and Schmitt, Jan}, title = {Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing}, series = {Journal of Manufacturing and Materials Processing}, volume = {2024}, journal = {Journal of Manufacturing and Materials Processing}, language = {en} } @article{EngelmannSchmittHeusingeretal., author = {Engelmann, Bastian and Schmitt, Anna-Maria and Heusinger, Moritz and Borysenko, Vladyslav and Niedner, Niklas and Schmitt, Jan}, title = {Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data}, series = {Applied Artificial Intelligence}, journal = {Applied Artificial Intelligence}, publisher = {Taylor \& Francis}, language = {en} } @article{SchmittMillerEngelmannetal., author = {Schmitt, Anna-Maria and Miller, Eddi and Engelmann, Bastian and Batres, Rafael and Schmitt, Jan}, title = {G-code evaluation in CNC milling to predict energy consumption through Machine Learning}, series = {Advances in Industrial and Manufacturing Engineering}, volume = {2024}, journal = {Advances in Industrial and Manufacturing Engineering}, number = {8}, abstract = {Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.}, language = {en} } @inproceedings{MillerSchmittKauppetal., author = {Miller, Eddi and Schmitt, Anna-Maria and Kaupp, Tobias and Schiffler, Andreas and Schmitt, Jan}, title = {Deep Reinforcement Learning for Adaptive Job Shop Scheduling in Robotic Cells: Handling Disruptions Effectively}, series = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, booktitle = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, language = {en} } @inproceedings{SchmittAntonovSchmittetal., author = {Schmitt, Anna-Maria and Antonov, Anna and Schmitt, Jan and Engelmann, Bastian}, title = {Classification of Production Process Phases with Multivariate Time Series Techniques}, series = {2024 22nd International Conference on Research and Education in Mechatronics (REM)}, booktitle = {2024 22nd International Conference on Research and Education in Mechatronics (REM)}, doi = {10.1109/REM63063.2024.10735481}, language = {en} } @article{KieflFischerSchmitt, author = {Kiefl, Sophia and Fischer, Sophie and Schmitt, Jan}, title = {Self-employed and stressed out? The impact of stress and stress management on entrepreneurs' mental health and performance}, series = {Frontiers in Psychology}, volume = {15}, journal = {Frontiers in Psychology}, publisher = {Frontiers}, doi = {10.3389/fpsyg.2024.1365489}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-56040}, pages = {15}, language = {en} } @article{OchsWehnertHartmannetal., author = {Ochs, Dennis and Wehnert, Kira-Kristin and Hartmann, J{\"u}rgen and Schiffler, Andreas and Schmitt, Jan}, title = {Sustainable Aspects of a Metal Printing Process Chain with Laser Powder Bed Fusion (LPBF)}, series = {Procedia CIRP}, volume = {98}, journal = {Procedia CIRP}, publisher = {Elsevir}, pages = {613 -- 618}, abstract = {Production companies are getting more and more aware of the relevancy of energy costs and the environmental impact of their manufactured products. Hence, the knowledge about the energy intensity of new process technologies as metal printing becomes increasingly crucial. Therefore, data about the energy intensity of entire process chains allow a detailed assessment of the life cycle costs and environmental impact of metal printed parts. As metal printing with Laser Powder Bed Fusion (LPBF) is applied from rapid prototyping to serial manufacturing processes more and more, sustainability data are useful to support a valid scale-up scenario and energetic improvements of the 3D-printing machinery as well as peripheral aggregates used in the process chain. The contribution aims to increase the transparency of the LPBF process chain in terms of its energy consumption. Therefore a generalized model to assess sustainability aspects of metal printed parts is derived. For this purpose, the LPBF process chain with the essential pre-, main- and post-processes is evaluated regarding its energy intensity. Here, the sub-processes, for example wet and dry cleaning of the printer, sieving of the metal powder or sand-blasting of the part are analyzed as well as the main printing process. Based on the derived experimental data from an installed, industry-like process chain, a model is created, which tends to generalize the experimental findings to evaluate other metal printed parts and process chain variants in terms of their energy intensity.}, language = {en} } @inproceedings{ChenBremerSchmittetal., author = {Chen, Mei-Hui and Bremer, Peik and Schmitt, Jan and Tsai, Kune-muh}, title = {Reentrant Flow Shop Production Control with the Theory of Constraints for Wafer Fabrication Having Parallel Machines}, series = {Proceedings of the 28th International Symposium on Logistics (ISL 2024) Building Sustainable Connectivity through Logistics and Supply Chains}, booktitle = {Proceedings of the 28th International Symposium on Logistics (ISL 2024) Building Sustainable Connectivity through Logistics and Supply Chains}, isbn = {978-0-85358-354-7}, pages = {131-132}, abstract = {Purpose of this paper: In a production line with capacity constraint resources (CCR) or bottleneck stations, the production control and the throughput rate are determined by the CCR station. In this study, we consider a production system with parallel machines and there are many rounds of reentrants during the production processes, and can be regarded as a reentrant flow shop process (RFS). In most RFS systems, the release of jobs is a push type without considering the WIP in each layer of reentrance. To resolve the excessive WIP in an RFS system, we consider the theory of constraints (TOC) and implement the DBR (drum, bottleneck and rope) to model RFS scheduling and to define appropriate buffer sizes at the CCR stations of diverse reentrant layers. Design/methodology/approach STS: We obtain production data of a type of semiconductor chip where the photolithography process is the most frequent reentrant. We designed nine scenarios based on the current RFS of the case company by applying the TOC and considering the time differences in parallel machines. A simulation model for the photolithography process was established using AutoMod simulation. Thirty simulation runs were performed for the mean and standard deviation. The goal is to identify the best scenario among the nine proposed ones. Findings: The simulation results demonstrated that Scenario 9, which involves dynamic buffers and assigning higher priority to products with more processing reentrance layers, achieved the highest throughput of finished products. The throughput of finished products in Scenario 9 is 22\% higher than in the base scenario. It also shows improvements in maximum product stack-up and input-output ratio compared to the base scenario. Value: The paper studies dispatching rules for reentrant flow lines having parallel machines with data from the semiconductor industry, and this type of production system is common for producing a wide range of electronic devices. With limited buffer sizes on the shop floor, the number of WIPs is significant, especially for reentrant flow shops, to avoid being clogged with too many WIPs. Implementing DBR modeling from TOC, the nine scenarios demonstrate the effects of controlling buffer sizes on system performances. Research limitations/implications: The nine scenarios are designed based on the case company situations and may not be readily implemented in other industries. Moreover, the DBR modeling of the buffer sizes and CCR station schedule control should also be adjusted when employed in other applications.}, language = {en} } @inproceedings{RosiliusWirsingvonEitzenetal., author = {Rosilius, Maximilian and Wirsing, Benedikt and von Eitzen, Ingo and Wilhelm, Markus and Schmitt, Jan and Engelmann, Bastian and Br{\"a}utigam, Volker}, title = {Evaluation of Visual Requirements and Software-Design for Immersive Visibility in Industrial Applications}, series = {2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, booktitle = {2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, pages = {234 -- 239}, abstract = {Currently, many sources predict increasing use of AR technology in the industrial environment. The task of immersive productive assistance systems is to provide information contextually to the industrial user. Therefore, it is essential to explore the factors and effects that influence the visibility and the corresponding quality of this information. Caused by the technical limitations of additive display technology and application conditions, this new approach has evaluated the immersive visibility of Landolt Rings in various greyscales against ambient illuminance levels on different industrial-like surfaces, coupled with and without a white virtual background. For this purpose, an empirical study in a within-subjects-design with full factorial experimental design (n=23) was conducted on Microsoft HoloLens 2 hardware. The mean values of the main effects indicate that visibility is significantly affected by ambient illuminance (best results at lower level), greyscale (best results at middle level) and virtual background (best results with background). In contrast, the choice of surface is shown to have no statistically significant effect on visibility, however it affects the response time. Additionally, cross-interactions of variables were analyzed and lead to a design recommendation for immersive industrial applications.}, language = {en} } @article{FischerRosiliusSchmittetal., author = {Fischer, Sophie and Rosilius, Maximilian and Schmitt, Jan and Br{\"a}utigam, Volker}, title = {A brief review of our agile teaching formats in entrepreneurship education}, series = {Sustainability}, volume = {14}, journal = {Sustainability}, number = {1}, doi = {https://doi.org/10.3390/su14010251}, pages = {251 -- 251}, abstract = {Companies are confronted with increasingly demanding environments, including globalization, technologization, intergenerationality, and crises such as the coronavirus pandemic. To accept uncertainties as a challenge and to recognize opportunities for development, well-educated and resilient founders are needed who can foster innovation and sustainable development within society and the economy. The majority of today's entrepreneurs have an academic background. Hence, institutions for higher education need to provide comprehensive educational offerings and support initiatives to train and sensitize future entrepreneurs. Therefore, since 2013, agile teaching formats have been developed in our project at a Bavarian university of applied sciences. In two stages, we founded a limited company for hands-on experimentation with entrepreneurship and also conceptualized an elective course and an annual founders' night. Based on a theoretical model and continuous teaching evaluations, we adjusted the individual modules to suit the target group. The objective is to promote the acquisition of key competencies and exert a positive influence on the startup quotient in the region. There are six startups by students who can be traced back to our project. This indicates that a target-group-oriented educational program encourages motivation and awareness of entrepreneurial thinking and action among students.}, language = {en} } @inproceedings{MeyerPechtlBremeretal., author = {Meyer, Maximilian and Pechtl, Lin and Bremer, Peik and Schmitt, Jan}, title = {Towards position-based technologies for digitized process management on the shop floor}, series = {Proceedings of the 24th International Symposium on Logistics - Supply Chain Networks vs Platforms: Innovations, Challenges and Opportunities, W{\"u}rzburg}, booktitle = {Proceedings of the 24th International Symposium on Logistics - Supply Chain Networks vs Platforms: Innovations, Challenges and Opportunities, W{\"u}rzburg}, pages = {232 -- 241}, abstract = {Position-based technologies, e.g Ultrawideband (UWB), WiFi or Bluetooth, for indoor localisation purposes are already commercially available. With the highly increasing digitalization of industrial processes, the potential of these technologies comes into focus of process management research. This paper aims to structure position-based technologies according to their potential to support shop-floor process management. For this purpose, a framework with the following dimensions is developed: technical performance properties of the IPS, economical aspects, relevant use-case requirements, and appropriate data aggregation strategies. Following this, two representable use-cases show the implementation of an indoor-positioning-system and the data aggregation in order to derive relevant process data from position data.}, language = {en} } @article{FischerSchmitt, author = {Fischer, Sophie and Schmitt, Jan}, title = {Regionalstudie Auswirkungen des globalen Klimawandels in Mainfranken}, doi = {10.57714/fpkt-vt47}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-19959}, pages = {176}, abstract = {Mainfr{\"a}nkische Unternehmen und Handwerksbetriebe stehen aufgrund expandierender Wertsch{\"o}pfungsketten in einem starken Abh{\"a}ngigkeitsverh{\"a}ltnis zu ihren global verteilten Zulieferern. Dadurch ist die Wertsch{\"o}pfungskette anf{\"a}llig f{\"u}r externe St{\"o}rungen, die unter anderem durch den Klimawandel ausgel{\"o}st werden. Zugleich sind die Erw{\"a}rmungsraten in der bayerischen Region derzeit deutlich h{\"o}her als im bundesweiten Durchschnitt. Die Entwicklung von strategischen Maßnahmen zur Klimaanpassung ist demnach unerl{\"a}sslich, um die unternehmensspezifischen Risiken des Klimawandels zu antizipieren und zu minimieren. Die Klimaanpassung ist in vielen kommunalen, wirtschaftlichen und gesellschaftlichen Bereichen eine der gr{\"o}ßten Herausforderungen der Gegenwart. Die vorliegende Regionalstudie beabsichtigt daher eine Abgrenzung der Verh{\"a}ltnisse und Wechselwirkungen des globalen Klimawandels, der Auswirkungen in der bayerischen Region Mainfranken sowie der aufkommenden Ver{\"a}nderungsbedarfe f{\"u}r gesellschaftliche, politische und wirtschaftliche Akteure. Ein umfassender grundlagentheoretischer {\"U}berblick dient zun{\"a}chst der Einordnung wichtiger Ansatzpunkte aus politischer Perspektive und stellt Angebote f{\"u}r Wirtschaftsakteure dar. Ferner werden die wirtschaftlichen Auspr{\"a}gungen nach Handwerksbetrieben und Unternehmen differenziert, sowie deren Rolle f{\"u}r Wachstum und Innovation in der Region, als auch in Bezug auf den Klimawandel und die erforderliche Anpassung aufgezeigt. Die Ergebnisse einer quantitativen Erhebung veranschaulichen, dass neben der Wirtschaftlichkeit und physischen Ressourcen, menschliche Faktoren zur Erh{\"o}hung der organisationalen Klimaresilienz beitragen. Um als wirtschaftliche Organisationen einen ad{\"a}quaten Umgang mit dem Klimawandel zu finden, sind bspw. zun{\"a}chst F{\"u}hrungskr{\"a}fte aufgefordert einen systemischen Ansatz mit umfangreichen Maßnahmen zu Klimaschutz und Klimaanpassung zu entwickeln, auf ihre Wirksamkeit zu erproben und ganzheitlich zu integrieren. Mit der vorliegenden Regionalstudie wird deutlich, dass neben dem Klimaschutz, der Reduzierung klimasch{\"a}dlicher Treibhausgasemissionen, die Klimaanpassung ein unverzichtbarer Aspekt ist, um die wirtschaftliche Effizienz zu erhalten und sogar zu erh{\"o}hen, indem die ver{\"a}nderten Anforderungen als Entwicklungschancen wahrgenommen werden.}, language = {de} } @inproceedings{HartmannDzemkoEngelmannetal., author = {Hartmann, J{\"u}rgen and Dzemko, Mikita and Engelmann, Bastian and Schmitt, Jan}, title = {Toward Shifted Production Strategies Through Additive Manufacturing: A Technology and Market Review for Changing Value Chains}, series = {7th CIRP Global Web Conference (86)}, volume = {86}, booktitle = {7th CIRP Global Web Conference (86)}, doi = {10.1016/j.procir.2020.01.029}, pages = {228 -- 233}, abstract = {In the last decade many different additive manufacturing (AM) technologies for metal, plastic or ceramic processing raise from research to commercialization. As a result, AM grows into different business areas and transforms structures and processes. Hence, the contribution tends to show the change in added values though the availability of different additive manufacturing technologies based on a technology screening and market research. Regarding the named purpose, a broad market research of 83 companies and 339 printer models has been conducted to find patterns of AM technology market share and regions to structure indicators such as accuracy by processed material classes with a specified AM method. Printing materials as metal, plastic, ceramic and carbon have been considered. The categorization is done by the AM principles: power bed fusion, material extrusion, vat photopolymerization and …}, language = {en} } @article{SchmittEngelmannManghisietal., author = {Schmitt, Jan and Engelmann, Bastian and Manghisi, Vito Modesto and Wilhelm, Markus and Uva, Antonello and Fiorentino, Michele}, title = {Towards gestured-based technologies for human-centred smart factories}, series = {International Journal of Computer Integrated Manufacturing}, volume = {36}, journal = {International Journal of Computer Integrated Manufacturing}, number = {1}, issn = {1362-3052}, pages = {110 -- 127}, abstract = {Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, - monitoring or - control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator - ErgoTakt - derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers' ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.}, language = {en} } @article{SchmittSeitzScherdeletal., author = {Schmitt, Jan and Seitz, Philipp and Scherdel, Christian and Reichenauer, Gudrun}, 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}, issn = {1879-0801}, abstract = {Material development processes are highly iterative and driven by the experience and intuition of the researcher. This can lead to time consuming procedures. Data-driven approaches such as Machine Learning can support decision processes with trained and validated models to predict certain output parameter. In a multifaceted process chain of material synthesis of electrochemical materials and their characterization, Machine Learning has a huge potential to shorten development processes. Based on this, the contribution presents a novel approach to utilize data derived from Small-Angle X-ray Scattering (SAXS) of SiO_2 matrix materials for battery anodes with Neural Networks. Here, we use SAXS as an intermediate, high-throughput method to characterize sol-gel based porous materials. A multi-step-method is presented where a Feed Forward Net is connected to a pretrained autoencoder to reliably map parameters of the material synthesis to the SAXS curve of the resulting material. In addition, a direct comparison shows that the prediction error of Neural Networks can be greatly reduced by training each output variable with a separate independent Neural Network.}, language = {en} } @article{FischerKeuppPaethetal., author = {Fischer, Sophie and Keupp, Luzia and Paeth, Heiko and G{\"o}hlich, Michael and Schmitt, Jan}, title = {Climate adaptation as organizational learning: a grounded theory study on manufacturing companies in a Bavarian Region}, series = {Education Sciences}, volume = {12}, journal = {Education Sciences}, number = {1}, pages = {22 -- 22}, language = {en} } @article{FischerGoehlichSchmitt, author = {Fischer, Sophie and G{\"o}hlich, Michael and Schmitt, Jan}, title = {Interrelationships of climate adaptation and organizational learning: Development of a measurement model}, series = {Journal of Pedagogical Research}, volume = {6}, journal = {Journal of Pedagogical Research}, number = {5}, pages = {130 -- 152}, language = {en} } @incollection{Schmitt, author = {Schmitt, Jan}, title = {Ein integriertes Vorgehensmodell zur Implementierung eines nachhaltigen Wertstoffmanagements}, series = {CSR in S{\"u}ddeutschland: Unternehmerischer Erfolg und Nachhaltigkeit im Einklang}, booktitle = {CSR in S{\"u}ddeutschland: Unternehmerischer Erfolg und Nachhaltigkeit im Einklang}, pages = {93 -- 105}, language = {de} } @incollection{EngelmannSchmitt, author = {Engelmann, Bastian and Schmitt, Jan}, title = {Industrie 4.0 f{\"u}r Studierende des Wirtschaftsingenieurwesens}, series = {Kompetenzen f{\"u}r die digitale Transformation 2020: Digitalisierung der Arbeit-Kompetenzen-Nachhaltigkeit 1. Digitalkompetenz-Tagung}, booktitle = {Kompetenzen f{\"u}r die digitale Transformation 2020: Digitalisierung der Arbeit-Kompetenzen-Nachhaltigkeit 1. Digitalkompetenz-Tagung}, pages = {265 -- 273}, language = {de} } @article{WilhelmManghisiUvaetal., author = {Wilhelm, Markus and Manghisi, Vito Modesto and Uva, Antonello and Fiorentino, Michele and Br{\"a}utigam, Volker and Engelmann, Bastian and Schmitt, Jan}, title = {ErgoTakt: A novel approach of human-centered balancing of manual assembly lines}, series = {Procedia CIRP}, volume = {97}, journal = {Procedia CIRP}, doi = {https://doi.org/10.1016/j.procir.2020.05.250}, pages = {354 -- 360}, abstract = {Although the increasing use of automation in industry, manual assembly stations are still common and, in some situations, even inevitable. Current practice in manual assembly lines is to balance them using the takt-time of each workstation and harmonize it. However, this approach mostly does not include ergonomic aspects and thus it may lead to workforce musculoskeletal disorders, extended leaves, and demotivation. This paper presents a holistic human-centric optimization method for line balancing using a novel indicator ̶ the ErgoTakt. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and its balance in time. The authors used a custom version of the ErgoSentinel Software and a Microsoft Kinect depth camera to perform online and real-time ergonomic assessment. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-value and the cycle time of each assembly workstation with respect to the worker's ability. The paper presents the concept, the system-setup and preliminary evaluation of an assembly scenario. The results demonstrate that the new approach is feasible and able to optimize an entire manual assembly process chain in terms of both, economic aspects of a well-balanced production line as well as the ergonomic issue of long term human healthy work.}, language = {en} } @inproceedings{SchmittHillenbrandKranzetal., author = {Schmitt, Jan and Hillenbrand, Andreas and Kranz, Philipp and Kaupp, Tobias}, title = {Assisted human-robot-interaction for industrial assembly: Application of spatial augmented reality (sar) for collaborative assembly tasks}, series = {Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction}, booktitle = {Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction}, pages = {52 -- 56}, language = {en} }