TY - JOUR A1 - Pfeifer, Tilo A1 - Dussler, G A1 - Engelmann, Bastian T1 - Visualisierungsmethoden für Mikrostrukturen mit dem konfokalen Laser-Scanning-Mikroskop JF - VDI-Berichte Y1 - 2003 SP - 175 EP - 185 ER - TY - JOUR A1 - Schmitt, Robert A1 - Engelmann, Bastian T1 - Keine Ermessensfrage!: Warum fähige Messgeräte wichtige Voraussetzung für Oberflächenoptimierungen sind JF - Metalloberfläche Y1 - 2005 VL - 59 IS - 9 SP - 39 EP - 42 ER - TY - JOUR A1 - Schmitt, Robert A1 - Hafner, Philip A1 - Engelmann, Bastian T1 - Strategies for the non-destructive characterization of thin layers with Scanning Acoustic Microscopy JF - tm - Technisches Messen Y1 - 2007 VL - 74 IS - 6 SP - 365 EP - 373 ER - TY - CHAP A1 - Bosse, L A1 - Driessen, Sascha A1 - Engelmann, Bastian A1 - Gillner, A A1 - Poprawe, R A1 - Pfeifer, Tilo A1 - Schmitt, Robert T1 - Inline-Verfahren T2 - Montage hybrider Mikrosysteme: Handhabungs-und Fügetechniken für die Klein-und Mittelserienfertigung Y1 - 2005 U6 - https://doi.org/10.1007/3-540-27536-3_16 SP - 187 EP - 218 ER - TY - CHAP A1 - Pfeifer, Tilo A1 - Schmitt, Robert A1 - Driessen, Sascha A1 - Engelmann, Bastian T1 - Offline-Verfahren T2 - Montage hybrider Mikrosysteme: Handhabungs-und Fügetechniken für die Klein-und Mittelserienfertigung Y1 - 2005 U6 - https://doi.org/10.1007/3-540-27536-3_15 SP - 175 EP - 186 ER - TY - JOUR A1 - Driessen, Sascha A1 - Engelmann, Bastian T1 - Methoden und Sensoren zur überwachung von Handhabungs-und Fügeprozessen in der Mikrosystemtechnik (Methods and Sensors for the Control of Handling and Assembly Processes in Microtechnology) JF - tm - Technisches Messen Y1 - 2004 VL - 71 IS - 7-8 SP - 404 EP - 416 ER - TY - CHAP A1 - Pfeifer, Tilo A1 - Dussler, G. A1 - Engelmann, Bastian T1 - Methods and sensors for the observation of micro assembly T2 - Conference Proceeding of Micro System 2003 in Munich Y1 - 2003 SP - 139 EP - 145 ER - TY - CHAP A1 - Pfeifer, Tilo A1 - Driessen, Sascha A1 - Engelmann, Bastian T1 - Control of the Assembly of Hybrid Micro Systems T2 - Conference Proceeding of Photonics in Measurement Y1 - 2004 SP - 357 EP - 368 ER - TY - CHAP A1 - Pfeifer, Tilo A1 - Engelmann, Bastian T1 - Process observation of the assembly of hybrid microsystems T2 - Conference Proceeding of 12th International Conference on Experimental Mechanics in Bari Y1 - 2004 ER - TY - CHAP A1 - Rosilius, Maximilian A1 - Wirsing, Benedikt A1 - von Eitzen, Ingo A1 - Wilhelm, Markus A1 - Schmitt, Jan A1 - Engelmann, Bastian A1 - Bräutigam, Volker T1 - Evaluation of Visual Requirements and Software-Design for Immersive Visibility in Industrial Applications T2 - 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) N2 - Currently, many sources predict increasing use of AR technology in the industrial environment. The task of immersive productive assistance systems is to provide information contextually to the industrial user. Therefore, it is essential to explore the factors and effects that influence the visibility and the corresponding quality of this information. Caused by the technical limitations of additive display technology and application conditions, this new approach has evaluated the immersive visibility of Landolt Rings in various greyscales against ambient illuminance levels on different industrial-like surfaces, coupled with and without a white virtual background. For this purpose, an empirical study in a within-subjects-design with full factorial experimental design (n=23) was conducted on Microsoft HoloLens 2 hardware. The mean values of the main effects indicate that visibility is significantly affected by ambient illuminance (best results at lower level), greyscale (best results at middle level) and virtual background (best results with background). In contrast, the choice of surface is shown to have no statistically significant effect on visibility, however it affects the response time. Additionally, cross-interactions of variables were analyzed and lead to a design recommendation for immersive industrial applications. Y1 - 2021 UR - https://www.researchgate.net/profile/Rosilius_Maximilian/publication/355896115_Evaluation_of_Visual_Requirements_and_Software-Design_for_Immersive_Visibility_in_Industrial_Applications/links/6189827807be5f31b7591290/Evaluation-of-Visual-Requirements-and-Software-Design-for-Immersive-Visibility-in-Industrial-Applications.pdf SP - 234 EP - 239 ER - TY - JOUR A1 - Engelmann, Bastian A1 - Schmitt, Simon A1 - Miller, Eddi A1 - Bräutigam, Volker A1 - Schmitt, Jan T1 - Advances in machine learning detecting changeover processes in cyber physical production systems JF - Journal of Manufacturing and Materials Processing N2 - 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. Y1 - 2020 UR - https://www.proquest.com/docview/2461685989?pq-origsite=gscholar&fromopenview=true VL - 4 IS - 4 SP - 108 EP - 108 ER - TY - JOUR A1 - Neuber, Till A1 - Schmitt, Anna-Maria A1 - Engelmann, Bastian A1 - Schmitt, Jan T1 - Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems JF - Sensors Y1 - 2022 VL - 22 IS - 24 SP - 10015 EP - 10015 ER - TY - JOUR A1 - Engelmann, Bastian A1 - Schmitt, Anna-Maria A1 - Theilacker, Lukas A1 - Schmitt, Jan T1 - Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing JF - Journal of Manufacturing and Materials Processing Y1 - 2024 UR - https://doi.org/10.3390/jmmp8010015 VL - 2024 ER - TY - JOUR A1 - Engelmann, Bastian A1 - Schmitt, Anna-Maria A1 - Heusinger, Moritz A1 - Borysenko, Vladyslav A1 - Niedner, Niklas A1 - Schmitt, Jan T1 - Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data JF - Applied Artificial Intelligence Y1 - 2024 UR - https://doi.org/10.1080/08839514.2024.2381317 PB - Taylor & Francis ER - TY - JOUR A1 - Schmitt, Anna-Maria A1 - Miller, Eddi A1 - Engelmann, Bastian A1 - Batres, Rafael A1 - Schmitt, Jan T1 - G-code evaluation in CNC milling to predict energy consumption through Machine Learning JF - Advances in Industrial and Manufacturing Engineering N2 - 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. KW - Machine Learning KW - CNC machine tools KW - G-code KW - Energy consumption Y1 - 2024 UR - https://doi.org/10.1016/j.aime.2024.100140 VL - 2024 IS - 8 ER - TY - CHAP A1 - Hartmann, Jürgen A1 - Dzemko, Mikita A1 - Engelmann, Bastian A1 - Schmitt, Jan T1 - Toward Shifted Production Strategies Through Additive Manufacturing: A Technology and Market Review for Changing Value Chains T2 - 7th CIRP Global Web Conference (86) N2 - 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 … Y1 - 2019 U6 - https://doi.org/10.1016/j.procir.2020.01.029 VL - 86 SP - 228 EP - 233 ER - TY - CHAP A1 - Schmitt, Anna-Maria A1 - Antonov, Anna A1 - Schmitt, Jan A1 - Engelmann, Bastian T1 - Classification of Production Process Phases with Multivariate Time Series Techniques T2 - 2024 22nd International Conference on Research and Education in Mechatronics (REM) Y1 - 2024 U6 - https://doi.org/10.1109/REM63063.2024.10735481 ER - TY - CHAP A1 - Hartmann, Jürgen A1 - Dobhan, Alexander A1 - Engelmann, Bastian A1 - Eberhardt, Lars A1 - Heusinger, Moritz A1 - Raab, C A1 - Schleif, Frank-Michael A1 - Türk, M. T1 - Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten: Digitalkonferenz Y1 - 2020 ER - TY - JOUR A1 - Schmitt, Jan A1 - Engelmann, Bastian A1 - Manghisi, Vito Modesto A1 - Wilhelm, Markus A1 - Uva, Antonello A1 - Fiorentino, Michele T1 - Towards gestured-based technologies for human-centred smart factories JF - International Journal of Computer Integrated Manufacturing N2 - 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. KW - gesture-based monitoring KW - gesture-based control KW - manufacturing Y1 - 2023 UR - https://doi.org/10.1080/0951192X.2022.2121424 SN - 1362-3052 VL - 36 IS - 1 SP - 110 EP - 127 ER - TY - CHAP A1 - Engelmann, Bastian A1 - Schmitt, Jan T1 - Industrie 4.0 für Studierende des Wirtschaftsingenieurwesens T2 - Kompetenzen für die digitale Transformation 2020: Digitalisierung der Arbeit-Kompetenzen-Nachhaltigkeit 1. Digitalkompetenz-Tagung Y1 - 2021 SP - 265 EP - 273 ER -