@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} } @article{EngelmannSchmittMilleretal., author = {Engelmann, Bastian and Schmitt, Simon and Miller, Eddi and Br{\"a}utigam, Volker and Schmitt, Jan}, title = {Advances in machine learning detecting changeover processes in cyber physical production systems}, series = {Journal of Manufacturing and Materials Processing}, volume = {4}, journal = {Journal of Manufacturing and Materials Processing}, number = {4}, pages = {108 -- 108}, abstract = {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.}, 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{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} } @article{StuehmTornowSchmittetal., author = {St{\"u}hm, Kai and Tornow, Alexander and Schmitt, Jan and Grunau, Leonard and Dietrich, Franz and Dr{\"o}der, Klaus}, title = {A novel gripper for battery electrodes based on the Bernoulli-principle with integrated exhaust air compensation}, series = {Procedia CIRP}, volume = {23}, journal = {Procedia CIRP}, pages = {161 -- 164}, language = {en} } @incollection{SchmittInkermannStechertetal., author = {Schmitt, Jan and Inkermann, David and Stechert, Carsten and Raatz, Annika and Vietor, Thomas}, title = {Requirement oriented reconfiguration of parallel robotic systems}, series = {Robotic Systems-Applications, Control and Programming}, booktitle = {Robotic Systems-Applications, Control and Programming}, pages = {387 -- 410}, language = {en} } @article{JanSchmitt, author = {Jan Schmitt, Kai St{\"u}hm, Annika Raatz, Klaus Dr{\"o}der}, title = {Simulating production effects on lithium-ion batteries}, series = {AABC}, journal = {AABC}, language = {en} } @incollection{CerdasGerbersAndrewetal., author = {Cerdas, Felipe and Gerbers, Roman and Andrew, Stefan and Schmitt, Jan and Dietrich, Franz and Thiede, Sebastian and Dr{\"o}der, Klaus and Herrmann, Christoph}, title = {Disassembly planning and assessment of automation potentials for lithium-ion batteries}, series = {Recycling of Lithium-Ion Batteries: The LithoRec Way}, booktitle = {Recycling of Lithium-Ion Batteries: The LithoRec Way}, pages = {83 -- 97}, language = {en} } @article{HerrmannRaatzAndrewetal., author = {Herrmann, Christoph and Raatz, Annika and Andrew, Stefan and Schmitt, Jan}, title = {Scenario-based development of disassembly systems for automotive lithium ion battery systems}, series = {Advanced Materials Research}, volume = {907}, journal = {Advanced Materials Research}, pages = {391 -- 401}, language = {en} } @article{SchmittRaatzDietrichetal., author = {Schmitt, Jan and Raatz, Annika and Dietrich, Franz and Dr{\"o}der, Klaus and Hesselbach, J{\"u}rgen}, title = {Process and performance optimization by selective assembly of battery electrodes}, series = {CIRP Annals}, volume = {63}, journal = {CIRP Annals}, number = {1}, pages = {9 -- 12}, language = {en} } @article{SchillingSchmittDietrichetal., author = {Schilling, Antje and Schmitt, Jan and Dietrich, Franz and Dr{\"o}der, Klaus}, title = {Analyzing Bending Stresses on Lithium-Ion Battery Cathodes induced by the Assembly Process}, series = {Energy Technology}, volume = {4}, journal = {Energy Technology}, number = {12}, pages = {1502 -- 1508}, language = {en} } @article{SchmittRaatz, author = {Schmitt, Jan and Raatz, Annika}, title = {Failure Mode Based Design and Optimization of the Electrode Packaging Process for Large Scale Battery Cells}, series = {Advanced Materials Research}, volume = {907}, journal = {Advanced Materials Research}, pages = {309 -- 319}, language = {en} } @phdthesis{Schmitt, author = {Schmitt, Jan}, title = {Untersuchungen zum Herstellungsprozess des Elektrode-Separator-Verbunds f{\"u}r Lithium-Ionen Batteriezellen}, language = {en} } @article{SchmittSeitzScherdeletal., author = {Schmitt, Jan and Seitz, Philipp and Scherdel, Christian and Reichenauer, Gudrun}, title = {Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis}, series = {Computational Materials Science}, volume = {218}, journal = {Computational Materials Science}, issn = {1879-0801}, abstract = {Material development processes are highly iterative and driven by the experience and intuition of the researcher. This can lead to time consuming procedures. Data-driven approaches such as Machine Learning can support decision processes with trained and validated models to predict certain output parameter. In a multifaceted process chain of material synthesis of electrochemical materials and their characterization, Machine Learning has a huge potential to shorten development processes. Based on this, the contribution presents a novel approach to utilize data derived from Small-Angle X-ray Scattering (SAXS) of SiO_2 matrix materials for battery anodes with Neural Networks. Here, we use SAXS as an intermediate, high-throughput method to characterize sol-gel based porous materials. A multi-step-method is presented where a Feed Forward Net is connected to a pretrained autoencoder to reliably map parameters of the material synthesis to the SAXS curve of the resulting material. In addition, a direct comparison shows that the prediction error of Neural Networks can be greatly reduced by training each output variable with a separate independent Neural Network.}, language = {en} } @inproceedings{SchmittBruhnRaatz, author = {Schmitt, Jan and Bruhn, Matthias and Raatz, Annika}, title = {Comparative analysis of pneumatic grippers for handling operations of crystalline solar cells}, series = {Proceedings of the IASTED Asian Conference on Power and Energy Systems, AsiaPES}, booktitle = {Proceedings of the IASTED Asian Conference on Power and Energy Systems, AsiaPES}, pages = {386 -- 392}, language = {en} } @inproceedings{HerrmannRaatzMennengaetal., author = {Herrmann, Christoph and Raatz, Annika and Mennenga, Mark and Schmitt, Jan and Andrew, Stefan}, title = {Assessment of automation potentials for the disassembly of automotive lithium ion battery systems}, series = {Leveraging Technology for a Sustainable World: Proceedings of the 19th CIRP Conference on Life Cycle Engineering, University of California at Berkeley, Berkeley, USA, May 23-25, 2012}, booktitle = {Leveraging Technology for a Sustainable World: Proceedings of the 19th CIRP Conference on Life Cycle Engineering, University of California at Berkeley, Berkeley, USA, May 23-25, 2012}, pages = {149 -- 154}, language = {en} } @inproceedings{SchmittTreuerDietrichetal., author = {Schmitt, Jan and Treuer, F and Dietrich, F and Dr{\"o}der, K and Heins, T-P and Schr{\"o}der, U and Westerhoff, U and Kurrat, M and Raatz, A}, title = {Coupled mechanical and electrochemical characterization method for battery materials}, series = {2014 IEEE Conference on Energy Conversion (CENCON)}, booktitle = {2014 IEEE Conference on Energy Conversion (CENCON)}, pages = {395 -- 400}, language = {en} } @inproceedings{SchreiberSklyarenkoSchlueteretal., author = {Schreiber, Frank and Sklyarenko, Yevgen and Schl{\"u}ter, Kathrin and Schmitt, Jan and Rost, Sven and Raatz, Annika and Schumacher, Walter}, title = {Tracking control with hysteresis compensation for manipulator segments driven by pneumatic artificial muscles}, series = {2011 IEEE international conference on robotics and biomimetics}, booktitle = {2011 IEEE international conference on robotics and biomimetics}, pages = {2750 -- 2755}, language = {en} } @inproceedings{SchmittHauptKurratetal., author = {Schmitt, Jan and Haupt, Hannes and Kurrat, Michael and Raatz, Annika}, title = {Disassembly automation for lithium-ion battery systems using a flexible gripper}, series = {2011 15th International Conference on Advanced Robotics (ICAR)}, booktitle = {2011 15th International Conference on Advanced Robotics (ICAR)}, pages = {291 -- 297}, language = {en} } @inproceedings{SchmittLastLochteetal., author = {Schmitt, Jan and Last, Philipp and Lochte, Christian and Raatz, Annika and Hesselbach, J{\"u}rgen}, title = {TRoBS - a biological inspired robot}, series = {2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)}, booktitle = {2009 IEEE International Conference on Robotics and Biomimetics (ROBIO)}, pages = {51 -- 56}, language = {en} }