@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} } @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} } @inproceedings{HartmannDobhanEngelmannetal., author = {Hartmann, J{\"u}rgen and Dobhan, Alexander and Engelmann, Bastian and Eberhardt, Lars and Heusinger, Moritz and Raab, C and Schleif, Frank-Michael and T{\"u}rk, M.}, title = {Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten: Digitalkonferenz}, 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} } @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} } @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} } @article{HofmannEberhardtHeusingeretal., author = {Hofmann, Jan and Eberhardt, Lars and Heusinger, Moritz and Dobhan, Alexander and Engelmann, Bastian and Schleif, Frank-Michael}, title = {Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten}, series = {FHWS Science Journal}, volume = {5}, journal = {FHWS Science Journal}, number = {2}, issn = {2196-6095}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:863-opus-20012}, pages = {135 -- 142}, abstract = {Mit einem Umsatz von 103 Milliarden Euro ist die Metallindustrie eine der gr{\"o}ßten deutschen Industriebranchen. Diese ist von volatilen Marktbedingungen und hohem Wettbewerb gepr{\"a}gt [1][2]. Kleine und mittlere produzierende Unternehmen (sogenannte KMU) sehen zunehmend gravierende Probleme bei der Einhaltung von Lieferterminen bedingt durch hohe Durchlaufzeiten in der Produktion [3]. Neben kaufm{\"a}nnischen Planungssystemen zur Erstellung von Produktionspl{\"a}nen nutzen Unternehmen als Planungsgrundlage weiterhin Excel mit 31 \% und manuelle Prozesse mit 10 \% [4]. Gleiches gilt f{\"u}r Produktwechselvorg{\"a}nge auf Maschinen (R{\"u}sten). Aufgrund dieser Aspekte ist es notwendig, die Rentabilit{\"a}t der KMU in der Metallindustrie zu steigern. Das wird durch effiziente Produktionsplanung und -steuerung, sowie der daraus resultierenden hohen Reaktionsf{\"a}higkeit und Flexibilit{\"a}t realisiert. Daher ist die Produktionsplanung auf die Markt- und Kundenanforderungen und die Anlageneffektivit{\"a}t auf ein hohes und stabiles Niveau auszurichten [5]. Hier bietet die Erfassung von Echtzeitdaten eine ad{\"a}quate Reaktion auf die genannten Anforderungen. Ebenfalls liefert sie großes Potenzial f{\"u}r die Produktionsplanung und -steuerung, um die Disposition und Koordination von Arbeitsauftr{\"a}gen zu optimieren. Weiterhin werden St{\"o}rgr{\"o}ßen oder unvorhergesehene Planungsabweichungen reduziert [4][6]. Zus{\"a}tzlich ist eine erh{\"o}hte Transparenz und Verbesserung menschlicher Entscheidungsprozesse notwendig. Dies kann durch datengetriebene Methoden unterst{\"u}tzt und sichergestellt werden [7]. Ein Ansatz zur Optimierung des Produktionsergebnisses kann durch die Erh{\"o}hung der Anlagenproduktivit{\"a}t selbst realisiert werden. Dazu muss die Verf{\"u}gbarkeit der Anlagen durch Lokalisierung und Reduzierung von Verlusten erh{\"o}ht werden. Die Umr{\"u}stungsprozesse tragen stark negativ zur Verf{\"u}gbarkeit einer Produktion bei. Eine Steigerung der Gesamtanlageneffektivit{\"a}t (overall equipment effectiveness oder kurz OEE) in einer Fertigungsumgebung ist jedoch m{\"o}glich durch eine intelligente Nutzung von Sensordaten mit Techniken wie z. B. Machine Learning (ML).}, language = {de} } @inproceedings{KlehrEngelmannSchleifetal., author = {Klehr, Lukas and Engelmann, Bastian and Schleif, Frank-Michael and Regulin, Daniel}, title = {Contextualized Segmentation of Milling Processes Using Discrete Rule-Based Pattern Recognition}, series = {Engineering Applications of Neural Networks - 26th International Conference, EANN 2025, Limassol, Cyprus, June 26-29, 2025, Proceedings, Part II}, volume = {2582}, booktitle = {Engineering Applications of Neural Networks - 26th International Conference, EANN 2025, Limassol, Cyprus, June 26-29, 2025, Proceedings, Part II}, editor = {Iliadis, Lazaros S. and Maglogiannis, Ilias and Kyriacou, Efthyvoulos and Jayne, Chrisina}, doi = {10.1007/978-3-031-96199-1\_18}, pages = {238 -- 254}, 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} }