@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} } @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} } @article{JanSchmitt, author = {Jan Schmitt, Kai St{\"u}hm, Annika Raatz, Klaus Dr{\"o}der}, title = {Simulating production effects on lithium-ion batteries}, series = {AABC}, journal = {AABC}, language = {en} } @article{HerrmannRaatzAndrewetal., author = {Herrmann, Christoph and Raatz, Annika and Andrew, Stefan and Schmitt, Jan}, title = {Scenario-based development of disassembly systems for automotive lithium ion battery systems}, series = {Advanced Materials Research}, volume = {907}, journal = {Advanced Materials Research}, pages = {391 -- 401}, language = {en} } @article{StuehmTornowSchmittetal., author = {St{\"u}hm, Kai and Tornow, Alexander and Schmitt, Jan and Grunau, Leonard and Dietrich, Franz and Dr{\"o}der, Klaus}, title = {A novel gripper for battery electrodes based on the Bernoulli-principle with integrated exhaust air compensation}, series = {Procedia CIRP}, volume = {23}, journal = {Procedia CIRP}, pages = {161 -- 164}, language = {en} } @article{SchmittRaatzDietrichetal., author = {Schmitt, Jan and Raatz, Annika and Dietrich, Franz and Dr{\"o}der, Klaus and Hesselbach, J{\"u}rgen}, title = {Process and performance optimization by selective assembly of battery electrodes}, series = {CIRP Annals}, volume = {63}, journal = {CIRP Annals}, number = {1}, pages = {9 -- 12}, language = {en} } @article{SchmittPosseltDietrichetal., author = {Schmitt, Jan and Posselt, G and Dietrich, F and Thiede, S and Raatz, A and Herrmann, C and Dr{\"o}der, K}, title = {Technical performance and energy intensity of the electrode-separator composite manufacturing process}, series = {Procedia CIRP}, volume = {29}, journal = {Procedia CIRP}, doi = {10.1016/j.procir.2015.02.016}, pages = {269 -- 274}, language = {en} } @article{SchmittRaatz, author = {Schmitt, Jan and Raatz, Annika}, title = {Failure Mode Based Design and Optimization of the Electrode Packaging Process for Large Scale Battery Cells}, series = {Advanced Materials Research}, volume = {907}, journal = {Advanced Materials Research}, pages = {309 -- 319}, language = {en} } @article{SchillingSchmittDietrichetal., author = {Schilling, Antje and Schmitt, Jan and Dietrich, Franz and Dr{\"o}der, Klaus}, title = {Analyzing Bending Stresses on Lithium-Ion Battery Cathodes induced by the Assembly Process}, series = {Energy Technology}, volume = {4}, journal = {Energy Technology}, number = {12}, pages = {1502 -- 1508}, language = {en} } @article{SchmittInkermannRaatzetal., author = {Schmitt, Jan and Inkermann, D and Raatz, A and Hesselbach, J and Vietor, T}, title = {Dynamic reconfiguration of parallel mechanisms}, series = {New trends in mechanism science: analysis and design}, journal = {New trends in mechanism science: analysis and design}, pages = {557 -- 565}, 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 Media SA}, issn = {1664-1078}, doi = {https://doi.org/10.3389/fpsyg.2024.1365489}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-55955}, abstract = {Introduction: Entrepreneurs play a central role in economic and social stability, yet the start-up rate in Germany has declined in recent years, possibly due to the stress associated with entrepreneurial endeavors. Stressors such as financial uncertainty and time pressure are prevalent among entrepreneurs and negatively affect their psychological well-being. However, research on stress management strategies among self-employed individuals remains limited. Methods: This pilot study conducted a quantitative analysis with 117 self-employed participants in Germany. The study focused on typical entrepreneurial work demands and selected stress coping mechanisms. Results: The analysis revealed a significant correlation between quantitative demands and mental exhaustion. Furthermore, a high positive correlation between presenteeism and workload suggests that presenteeism may partially explain the variance in workload. These findings underscore how high job demands can lead to self-endangering behaviors that are detrimental to mental health. Discussion: Although no significant moderating effect of proactive coping on the relationship between job demands and mental exhaustion was observed, significant negative correlations between proactive coping and both job demands and mental exhaustion suggest a potential protective role of proactive coping against work-related stress. This study highlights the importance of understanding stress coping strategies among self-employed individuals and their impact on entrepreneurial success and mental well-being. Further research in this area is warranted to develop effective interventions to support the well-being and productivity of self-employed individuals in Germany.}, 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} } @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} } @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} } @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} } @article{SchirmerKranzRoseetal., author = {Schirmer, Fabian and Kranz, Philipp and Rose, Chad G. and Schmitt, Jan and Kaupp, Tobias}, title = {Towards Dynamic Human-Robot Collaboration: A Holistic Framework for Assembly Planning}, series = {Electronics}, volume = {14}, journal = {Electronics}, number = {1}, publisher = {MDPI}, issn = {2079-9292}, doi = {10.3390/electronics14010190}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-57825}, abstract = {The combination of human cognitive skills and dexterity with the endurance and repeatability of robots is a promising approach to modern assembly. However, efficiently allocating tasks and planning an assembly sequence between humans and robots is a manual, complex, and time-consuming activity. This work presents a framework named "Extract-Enrich-Assess-Plan-Review" that facilitates holistic planning of human-robot assembly processes. The framework automatically Extracts data from heterogeneous sources, Assesses the suitability of each assembly step to be performed by the human or robot, and Plans multiple assembly sequence plans (ASP) according to boundary conditions. Those sequences allow for a dynamic adaptation at runtime and incorporate different human-robot interaction modalities that are Synchronized, Cooperative, or Collaborative. An expert remains in the loop to Enrich the extracted data, and Review the results of the Assess and Plan steps with options to modify the process. To experimentally validate this framework, we compare the achieved degree of automation using three different CAD formats. We also demonstrate and analyze multiple assembly sequence plans that are generated by our system according to process time and the interaction modalities used.}, language = {en} } @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} } @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} }