TY - JOUR A1 - Miller, Eddi A1 - Ceballos, Hector A1 - Engelmann, Bastian A1 - Schiffler, Andreas A1 - Batres, Rafael A1 - Schmitt, Jan T1 - Industry 4.0 and International Collaborative Online Learning in a Higher Education Course on Machine Learning JF - 2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop Y1 - 2021 SP - 1 EP - 8 ER - TY - JOUR A1 - Lang, Silvio A1 - Engelmann, Bastian A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - A simplified machine learning product carbon footprint evaluation tool JF - Cleaner Environmental Systems N2 - 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. KW - Management, Monitoring, Policy and Law KW - Environmental Science (miscellaneous) KW - Renewable Energy, Sustainability and the Environment KW - Environmental Engineering Y1 - 2024 U6 - https://doi.org/10.1016/j.cesys.2024.100187 SN - 2666-7894 VL - 13 PB - Elsevier BV ER - TY - JOUR A1 - Miller, Eddi A1 - Borysenko, Vladyslav A1 - Heusinger, Moritz A1 - Niedner, Niklas A1 - Engelmann, Bastian A1 - Schmitt, Jan T1 - Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning JF - Sensors Y1 - 2021 VL - 21 IS - 17 SP - 5896 EP - 5896 ER - TY - JOUR A1 - Schuster, Florian A1 - Engelmann, Bastian A1 - Sponholz, Uwe A1 - Schmitt, Jan A1 - Engineering, Institute Digital T1 - Human acceptance evaluation of AR-assisted assembly scenarios JF - Journal of Manufacturing Systems Y1 - 2021 VL - 61 SP - 660 EP - 672 ER - TY - CHAP A1 - Schuster, Florian A1 - Sponholz, Uwe A1 - Engelmann, Bastian A1 - Schmitt, Jan T1 - A user study on AR-assisted industrial assembly T2 - 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) Y1 - 2020 SP - 135 EP - 140 ER - TY - JOUR A1 - Martinez, Mario A1 - Schmitt, Anna-Maria A1 - Schiffler, Andreas A1 - Engelmann, Bastian T1 - Production Data Set for five-Axis CNC Milling with multiple Changeovers JF - Scientific Data N2 - 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. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1038/s41597-025-05294-0 SN - 2052-4463 VL - 12 IS - 1 PB - Springer Science and Business Media LLC ER - TY - JOUR A1 - Schmitt, Anna-Maria A1 - Engelmann, Bastian T1 - A Series Production Data Set for Five-Axis CNC Milling JF - Data N2 - 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. Y1 - 2024 U6 - https://doi.org/10.3390/data9050066 SN - 2306-5729 VL - 9 IS - 5 PB - MDPI ER - TY - JOUR A1 - Schmitt, Anna-Maria A1 - Engelmann, Bastian T1 - A Series Production Data Set for Five-Axis CNC Milling JF - Data Y1 - 2024 UR - https://doi.org/10.3390/data9050066 VL - 2024 IS - 9 SP - 1 EP - 9 ER -