@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} }