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 - 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 - Scherdel, Christian A1 - Miller, Eddi A1 - Reichenauer, Gudrun A1 - Schmitt, Jan T1 - Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML) JF - Processes Y1 - 2021 VL - 9 IS - 4 SP - 672 EP - 672 ER - TY - CHAP A1 - Miller, Eddi A1 - Kaupp, Tobias A1 - Schmitt, Jan ED - Kohl, Holger ED - Seliger, Günther ED - Dietrich, Franz T1 - Cascaded Scheduling for Highly Autonomous Production Cells with AGVs T2 - Manufacturing Driving Circular Economy: Proceedings of the 18th Global Conference on Sustainable Manufacturing, October 5-7, 2022, Berlin ; Lecture Notes in Mechanical Engineering N2 - Highly autonomous production cells are a crucial part of manufacturing systems in industry 4.0 and can contribute to a sustainable value-adding process. To realize a high degree of autonomy in production cells with an industrial robot and a machine tool, an experimental approach was carried out to deal with numerous challenges on various automation levels. One crucial aspect is the scheduling problem of tasks for each resource (machine tool, tools, robot, AGV) depending on various data needed for a job-shop scheduling algorithm. The findings show that the necessary data has to be derived from different automation levels in a company: horizontally from ERP to shop-floor, vertically from the order handling department to the maintenance department. Utilizing that data, the contribution provides a cascaded scheduling approach for machine tool jobs as well as CNC and robot tasks for highly autonomous production cells supplied by AGVs. Y1 - 2023 SN - 978-3-031-28838-8 SN - 978-3-031-28839-5 U6 - https://doi.org/https://doi.org/10.1007/978-3-031-28839-5_43 SP - 383 EP - 390 PB - Springer CY - Cham ER - TY - JOUR A1 - Miller, Eddi A1 - Engelmann, Bastian A1 - Kaupp, Tobias A1 - Schmitt, Jan T1 - Advanced Cascaded Scheduling for Highly Autonomous Production Cells with Material Flow and Tool Lifetime Consideration using AGVs JF - Journal of Machine Engineering Y1 - 2023 UR - https://doi.org/10.36897/jme/171749 SN - 2391-8071 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 - 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 -