@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} } @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} } @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} } @article{MartinezSchmittSchiffleretal., author = {Martinez, Mario and Schmitt, Anna-Maria and Schiffler, Andreas and Engelmann, Bastian}, title = {Production Data Set for five-Axis CNC Milling with multiple Changeovers}, series = {Scientific Data}, volume = {12}, journal = {Scientific Data}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2052-4463}, doi = {https://doi.org/10.1038/s41597-025-05294-0}, abstract = {Abstract This data descriptor contains information about an extensive production data set for a five-axis CNC milling process. Three geometrically different products were manufactured and relevant features from the numerical control of the machine were recorded. The recorded manufacturing process contains the preparation of the machine for the next product (changeover) as well as the machining process (production). The experimental manufacturing was organized with the aid of a changeover matrix to ensure that all possible changeover combinations for the three products were considered. The production was repeated five times, resulting in 30 manufacturing sessions and five complete changeover matrices. The data set was recorded in a laboratory environment. A rich feature set including i.e. the NC-code of the products, tool information, and a Jupyter notebook is provided with the data set.}, language = {en} } @article{MillerBarthelmeSchiffleretal., author = {Miller, Eddi and Barthelme, Christine and Schiffler, Andreas and Engelmann, Bastian and Schmitt, Jan}, title = {Internationalisierung in Pandemiezeiten, technische M{\"o}glichkeiten, Lehr- und Forschungskonzepte mal anders gedacht}, 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-20035}, pages = {143 -- 146}, abstract = {Eines der zentralen strategischen Ziele unserer Hochschule ist die Internationalisierung, sowie der »internationalisation@home«. Als die weltweite Corona-Pandemie die Pr{\"a}senzlehre und -forschung ebenso wie den internationalen Austausch von Studierenden und Forschenden zu Beginn 2020 quasi zum Erliegen brachte wurden die Rufe nach digitalen Angeboten im internationalen Bereich schnell laut. Vor diesem Hintergrund reagierte der »Deutsche Akademische Auslandsdienst (DAAD)« mit der kurzfristig ins Leben gerufenen F{\"o}rderlinie »International Virtual Academic Collaboration« (IVAC), um internationale Hochschulkooperationen und weltweite Mobilit{\"a}t unter digitalen Vorzeichen strategisch zu gestalten und auszubauen [1].}, language = {de} } @article{Engelmann, author = {Engelmann, Bastian}, title = {Towards Material-Batch-Aware Tool Condition Monitoring}, series = {Journal of Manufacturing and Materials Processing}, volume = {5}, journal = {Journal of Manufacturing and Materials Processing}, number = {4}, issn = {2504-4494}, abstract = {In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.}, language = {en} } @article{SchmittEngelmann, author = {Schmitt, Anna-Maria and Engelmann, Bastian}, title = {A Series Production Data Set for Five-Axis CNC Milling}, series = {Data}, volume = {9}, journal = {Data}, number = {5}, publisher = {MDPI}, issn = {2306-5729}, doi = {10.3390/data9050066}, abstract = {The described data set contains features from the machine control of a five-axis milling machine. The features were recorded during thirteen series productions. Each series production includes a changeover process in which the machine was set up for the production of a different product. In addition to the timestamps and the twenty recorded features derived from Numerical Control (NC) variables, the data set also contains labels for the different production phases. For this purpose, up to 23 phases were assigned, which are based on a generalized milling process. The data set consists of thirteen .csv files, each representing a series production. The data set was recorded in a production company in the contract manufacturing sector for components with real series orders in ongoing industrial production.}, language = {en} } @article{SchmittEngelmann, author = {Schmitt, Anna-Maria and Engelmann, Bastian}, title = {A Series Production Data Set for Five-Axis CNC Milling}, series = {Data}, volume = {2024}, journal = {Data}, number = {9}, pages = {1 -- 9}, language = {en} } @article{GeorgeBijuSchmittEngelmann, author = {George Biju, Vinai and Schmitt, Anna-Maria and Engelmann, Bastian}, title = {Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines}, series = {Sensors}, volume = {2024}, journal = {Sensors}, language = {en} }