@incollection{MillerSchmittKauppetal., author = {Miller, Eddi and Schmitt, Anna-Maria and Kaupp, Tobias and Batres, Rafael and Schiffler, Andreas and Schmitt, Jan}, title = {A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling}, series = {Lecture Notes in Mechanical Engineering}, booktitle = {Lecture Notes in Mechanical Engineering}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031774287}, issn = {2195-4356}, doi = {10.1007/978-3-031-77429-4_59}, pages = {535 -- 543}, abstract = {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.}, language = {en} } @article{NeuberSchmittEngelmannetal., author = {Neuber, Till and Schmitt, Anna-Maria and Engelmann, Bastian and Schmitt, Jan}, title = {Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {24}, pages = {10015 -- 10015}, language = {en} } @inproceedings{SchmittMillerSchiffleretal., author = {Schmitt, Anna-Maria and Miller, Eddi and Schiffler, Andreas and Schmitt, Jan}, title = {Energy Prediction for CNC Machines Using G-Code Evaluation, Machine Learning and a Real-World Training Part}, series = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, booktitle = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, doi = {10.1109/ICMRE64970.2025.10976308}, pages = {340-347}, language = {en} } @article{EngelmannSchmittTheilackeretal., author = {Engelmann, Bastian and Schmitt, Anna-Maria and Theilacker, Lukas and Schmitt, Jan}, title = {Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing}, series = {Journal of Manufacturing and Materials Processing}, volume = {2024}, journal = {Journal of Manufacturing and Materials Processing}, language = {en} } @article{EngelmannSchmittHeusingeretal., author = {Engelmann, Bastian and Schmitt, Anna-Maria and Heusinger, Moritz and Borysenko, Vladyslav and Niedner, Niklas and Schmitt, Jan}, title = {Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data}, series = {Applied Artificial Intelligence}, journal = {Applied Artificial Intelligence}, publisher = {Taylor \& Francis}, language = {en} } @article{SchmittMillerEngelmannetal., author = {Schmitt, Anna-Maria and Miller, Eddi and Engelmann, Bastian and Batres, Rafael and Schmitt, Jan}, title = {G-code evaluation in CNC milling to predict energy consumption through Machine Learning}, series = {Advances in Industrial and Manufacturing Engineering}, volume = {2024}, journal = {Advances in Industrial and Manufacturing Engineering}, number = {8}, abstract = {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.}, language = {en} } @inproceedings{MillerSchmittKauppetal., author = {Miller, Eddi and Schmitt, Anna-Maria and Kaupp, Tobias and Schiffler, Andreas and Schmitt, Jan}, title = {Deep Reinforcement Learning for Adaptive Job Shop Scheduling in Robotic Cells: Handling Disruptions Effectively}, series = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, booktitle = {2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)}, language = {en} } @inproceedings{SchmittAntonovSchmittetal., author = {Schmitt, Anna-Maria and Antonov, Anna and Schmitt, Jan and Engelmann, Bastian}, title = {Classification of Production Process Phases with Multivariate Time Series Techniques}, series = {2024 22nd International Conference on Research and Education in Mechatronics (REM)}, booktitle = {2024 22nd International Conference on Research and Education in Mechatronics (REM)}, doi = {10.1109/REM63063.2024.10735481}, language = {en} }