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 -