@inproceedings{LodesSchiendorfer2022, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks}, booktitle = {Proceedings of the European Conference of the PHM Society 2022}, editor = {Do, Phuc and Michau, Gabriel and Ezhilarasu, Cordelia}, publisher = {PHM Society}, address = {State College}, isbn = {978-1-936263-36-3}, doi = {https://doi.org/10.36001/phme.2022.v7i1.3331}, pages = {294 -- 305}, year = {2022}, abstract = {Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100\%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting.}, language = {en} } @inproceedings{LodesSchiendorfer2024, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {A multi-layer machine learning architecture for near real-time inference in manufacturing based on Apache Kafka and selective classification}, booktitle = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Facchinetti, Tullio and Cenedese, Angelo and Lo Bello, Lucia and Vitturi, Stefano and Sauter, Thilo and Tramarin, Federico}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-6123-0}, doi = {https://doi.org/10.1109/ETFA61755.2024.10711030}, year = {2024}, language = {en} } @article{LodesHassDachtleretal.2025, author = {Lodes, Lukas and Hass, Erik and Dachtler, Kristina and Schiendorfer, Alexander}, title = {SmartManPy - Open Source Synthetic Manufacturing Data}, volume = {2025}, journal = {Procedia Computer Science}, number = {253}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1877-0509}, doi = {https://doi.org/10.1016/j.procs.2025.01.245}, pages = {1830 -- 1839}, year = {2025}, abstract = {Data Science projects in manufacturing often suffer from difficult access to the necessary high-quality data, such as sensor readings or product quality records. In addition, strict rules due to data protection and intellectual property concerns hinder broad use of the provided data for innovative research, especially in the academic context. To enable the research and evaluation of approaches for a variety of Industry 4.0 use cases, we developed SmartManPy, an extension to the ManPy discrete event simulation environment. We extended the scope of the project to meet our needs and are now able to simulate production lines and the resulting data in just a few lines of code. SmartManPy (Smart Manufacturing Python) includes complex synthetic feature generation, quality control and failures based on recorded features, and data drift simulation. We additionally added a basic environment to train AI agents using reinforcement learning. Our code is released to the public on GitHub.}, language = {en} } @inproceedings{LodesSchiendorfer2023, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {A Deep Learning Bootcamp for Engineering \& Management Students}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v207/lodes23a.html}, pages = {32 -- 36}, year = {2023}, language = {en} }