TY - CHAP A1 - Miller, Eddi A1 - Schmitt, Anna-Maria A1 - Kaupp, Tobias A1 - Batres, Rafael A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling T2 - Lecture Notes in Mechanical Engineering N2 - Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense. Y1 - 2025 SN - 9783031774287 U6 - https://doi.org/10.1007/978-3-031-77429-4_59 SN - 2195-4356 SP - 535 EP - 543 PB - Springer Nature Switzerland CY - Cham 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 - CHAP A1 - Schmitt, Anna-Maria A1 - Miller, Eddi A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - Energy Prediction for CNC Machines Using G-Code Evaluation, Machine Learning and a Real-World Training Part T2 - 2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE) Y1 - 2025 U6 - https://doi.org/10.1109/ICMRE64970.2025.10976308 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 - Miller, Eddi A1 - Schmitt, Anna-Maria A1 - Kaupp, Tobias A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - Deep Reinforcement Learning for Adaptive Job Shop Scheduling in Robotic Cells: Handling Disruptions Effectively T2 - 2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE) Y1 - 2025 UR - 10.1109/ICMRE64970.2025.10976238 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 - Schmitt, Jan A1 - Treuer, F A1 - Dietrich, F A1 - Dröder, K A1 - Heins, T-P A1 - Schröder, U A1 - Westerhoff, U A1 - Kurrat, M A1 - Raatz, A T1 - Coupled mechanical and electrochemical characterization method for battery materials T2 - 2014 IEEE Conference on Energy Conversion (CENCON) Y1 - 2014 SP - 395 EP - 400 ER - TY - JOUR A1 - Jan Schmitt, Kai Stühm, Annika Raatz, Klaus Dröder T1 - Simulating production effects on lithium-ion batteries JF - AABC Y1 - 2013 ER - TY - JOUR A1 - Herrmann, Christoph A1 - Raatz, Annika A1 - Andrew, Stefan A1 - Schmitt, Jan T1 - Scenario-based development of disassembly systems for automotive lithium ion battery systems JF - Advanced Materials Research Y1 - 2014 VL - 907 SP - 391 EP - 401 ER - TY - JOUR A1 - Stühm, Kai A1 - Tornow, Alexander A1 - Schmitt, Jan A1 - Grunau, Leonard A1 - Dietrich, Franz A1 - Dröder, Klaus T1 - A novel gripper for battery electrodes based on the Bernoulli-principle with integrated exhaust air compensation JF - Procedia CIRP Y1 - 2014 VL - 23 SP - 161 EP - 164 ER - TY - CHAP A1 - Schreiber, Frank A1 - Sklyarenko, Yevgen A1 - Schlüter, Kathrin A1 - Schmitt, Jan A1 - Rost, Sven A1 - Raatz, Annika A1 - Schumacher, Walter T1 - Tracking control with hysteresis compensation for manipulator segments driven by pneumatic artificial muscles T2 - 2011 IEEE international conference on robotics and biomimetics Y1 - 2011 SP - 2750 EP - 2755 ER - TY - CHAP A1 - Schmitt, Jan A1 - Grabert, Frank A1 - Raatz, Annika T1 - Design of a hyper-flexible assembly robot using artificial muscles T2 - 2010 IEEE International Conference on Robotics and Biomimetics Y1 - 2010 SP - 897 EP - 902 ER - TY - CHAP A1 - Schmitt, Jan A1 - Haupt, Hannes A1 - Kurrat, Michael A1 - Raatz, Annika T1 - Disassembly automation for lithium-ion battery systems using a flexible gripper T2 - 2011 15th International Conference on Advanced Robotics (ICAR) Y1 - 2011 SP - 291 EP - 297 ER - TY - CHAP A1 - Schmitt, Jan A1 - Inkermann, David A1 - Stechert, Carsten A1 - Raatz, Annika A1 - Vietor, Thomas T1 - Requirement oriented reconfiguration of parallel robotic systems T2 - Robotic Systems-Applications, Control and Programming Y1 - 2012 SP - 387 EP - 410 ER - TY - JOUR A1 - Schmitt, Jan A1 - Raatz, Annika A1 - Dietrich, Franz A1 - Dröder, Klaus A1 - Hesselbach, Jürgen T1 - Process and performance optimization by selective assembly of battery electrodes JF - CIRP Annals Y1 - 2014 VL - 63 IS - 1 SP - 9 EP - 12 ER - TY - JOUR A1 - Schmitt, Jan A1 - Posselt, G A1 - Dietrich, F A1 - Thiede, S A1 - Raatz, A A1 - Herrmann, C A1 - Dröder, K T1 - Technical performance and energy intensity of the electrode-separator composite manufacturing process JF - Procedia CIRP Y1 - 2015 U6 - https://doi.org/10.1016/j.procir.2015.02.016 VL - 29 SP - 269 EP - 274 ER - TY - JOUR A1 - Schmitt, Jan A1 - Raatz, Annika T1 - Failure Mode Based Design and Optimization of the Electrode Packaging Process for Large Scale Battery Cells JF - Advanced Materials Research Y1 - 2014 VL - 907 SP - 309 EP - 319 ER -