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 - CHAP A1 - Schirmer, Fabian A1 - Kranz, Philipp A1 - Schmitt, Jan A1 - Kaupp, Tobias T1 - Anomaly Detection for Dynamic Human-Robot Assembly: Application of an LSTM-based autoencoder to interpret uncertain human behavior in HRC T2 - Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction Y1 - 2023 U6 - https://doi.org/10.1145/3568294.3580100 SP - 881 EP - 883 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 - Seitz, Philipp A1 - Scherdel, Christian A1 - Reichenauer, Gudrun A1 - Schmitt, Jan T1 - Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis JF - Computational Materials Science Y1 - 2023 VL - 218 SP - 111984 EP - 111984 ER - TY - JOUR A1 - Seitz, Philipp A1 - Schmitt, Jan T1 - Alternating Transfer Functions to Prevent Overfitting in Non-Linear Regression with Neural Networks JF - Journal of Experimental & Theoretical Artificial Intelligence N2 - In nonlinear regression with machine learning methods, neural networks (NNs) are ideally suited due to their universal approximation property, which states that arbitrary nonlinear functions can thereby be approximated arbitrarily well. Unfortunately, this property also poses the problem that data points with measurement errors can be approximated too well and unknown parameter subspaces in the estimation can deviate far from the actual value (so-called overfitting). Various developed methods aim to reduce overfitting through modifications in several areas of the training. In this work, we pursue the question of how an NN behaves in training with respect to overfitting when linear and nonlinear transfer functions (TF) are alternated in different hidden layers (HL). The presented approach is applied to a generated dataset and contrasted to established methods from the literature, both individually and in combination. Comparable results are obtained, whereby the common use of purely nonlinear transfer functions proves to be not recommended generally. KW - Machine learning; nonlinear regression; function approximation; overfitting; transfer function Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-49199 UR - https://doi.org/10.1080/0952813X.2023.2270995 ER - TY - GEN A1 - Fischer, Sophie A1 - Schmitt, Jan T1 - Planspiel MainKassandra BT - Klimaanpassung und Grundbegriffe spielerisch lernen N2 - Planspiel zur Klimaanpassung für Unternehmen, die mehr über die Wechselwirkungen des Klimawandels erfahren und zur strategischen und nachhaltigen Weiterentwicklung beitragen möchten. Mit einem spielerischen Ansatz werden direkte und indirekte Auswirkungen des Klimawandels simuliert und einzelne Teams aufgefordert, mit einem interaktiven Maßnahmenkatalog zielgerichtete Anpassungsstrategien zu entwickeln. Dabei gilt es Klimaereignisse und vorhandene Ressourcen zu beachten, denn der Spielsieg wird nur durch eine Balance zwischen ökonomischen und ökologischen Interessen erreicht. Die erworbenen Erfahrungen können dann direkt in die berufliche Praxis geführt und innerhalb von Arbeitsteams diskutiert werden. Beginnen Sie heute mit dem Umdenken und werden Sie kreativ, um Ihr Unternehmen vor den Folgen des Klimawandels zu schützen. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:863-opus-22271 ER -