Alexander Bott, M.Sc.

Area of Research:

Industry 4.0
- Wear models for drive elements
- Sensor integration

General Tasks:

Supervision drive laboratory
Demo Center I4.0
Talent Pool

Test benches:

Curriculum Vitae:

seit 04/2022	Research associate at the Institute of Production Engineering (wbk) of the Karlsruhe Institute of Technology (KIT)
09/2019 – 03/2022	Master's degree (M.Sc) in mechanical engineering at the Karlsruhe Institute of Technology (KIT)
10/2015-08/2019	Bachelor studies (B.Sc) in mechanical engineering at the Karlsruhe Institute of Technology (KIT)
09/2013-09/2015	Technical Secondary School
07/2012-09/2013	Trained mechanic at Felsomat GmbH & Co. KG
09/2009-07/2012	Apprenticeship at Felsomat GmbH & Co. KG

Publications

Challenges in Implementing Gaia-X for Industrial Applications – Insights from Three Diverse Use Cases
Bott, A.; Behrendt, S.; Gleich, K.; Karimi, E.; Fleischer, J.; Lanza, G.
2025. Zeitschrift für wirtschaftlichen Fabrikbetrieb, 120 (3), 163–168. doi:10.1515/zwf-2025-1023

Optimization of Adversarial Reprogramming for Transfer Learning on Closed Box Models
Bott, A.; Siems, M.; Puchta, A.; Fleischer, J.
2025. IEEE Access, 13, 48999–49006. doi:10.1109/ACCESS.2025.3552219

Machine Learning-Driven RUL Prediction and Uncertainty Quantification for Ball Screw Drives in a Cloud-Ready Maintenance Framework
Bott, A.; Liu, B.; Puchta, A.; Fleischer, J.
2024. Journal of Machine Engineering, 24 (3), 17 – 31. doi:10.36897/jme/192681

A Gaia-X-based Ecosystem for Manufacturing: Lessons Learned from the Project Gaia-X4ICM
Erb, Y.; Teigeler, H.; Lins, S.; Sunyaev, A.; Gleich, K.; Behrendt, S.; Karimi, E.; Bott, A.; Lanza, G.; Schulze, V.; Fleischer, J.; Leander-Knoll, M.; Jaganathan, K.; Scheibenberger, K.; Frick, F.; Bubeck, W.; Neher, P.; Neumann, R.; Lechler, A.; Verl, A.; Riedel, O.; Andriushchenko, O.; Shcherbakov, O.; Hoppe, D.; Brinkmeier, D.
2024. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000174929

Experimental uncertainty evaluation by measuring a micro gear standard using focus variation
Gauder, D.; Gölz, J.; Bott, A.; Stein, M.; Lanza, G.
2024. Measurement Science and Technology, 35 (10), Art.-Nr.: 105013. doi:10.1088/1361-6501/ad5ea1

Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification
Bott, A.; Schreyer, F.; Puchta, A.; Fleischer, J.
2024. Machine Learning and Knowledge Extraction, 6 (3), 1969 – 1996. doi:10.3390/make6030097

Vom Verbrauchsmonitoring zur Verbrauchsprognose - Untersuchung des Umgangs produzierender Unternehmen mit Energieverbrauchsmonitoring und -vorhersage
Ströbel, R.; Bott, A.; Hutt, L.; Groß, S.; Fleischer, J.
2024. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, 119 (1-2), 80 – 84. doi:10.1515/zwf-2024-1009

Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability
Bott, A.; Anderlik, S.; Ströbel, R.; Fleischer, J.; Worthmann, A.
2024. Machines, 12 (3), Art.-Nr.: 153. doi:10.3390/machines12030153

Towards a Testing Framework for Machine Learning Model Deployment in Manufacturing Systems
Heider, I.; Baumgärtner, J.; Bott, A.; Ströbel, R.; Puchta, A.; Fleischer, J.
2024. 10th CIRP Conference on Assembly Technology and Systems (CIRP CATS 2024) Hrsg.: Fleischer , Jürgen; Jörg, Krüger, 127, 122–128. doi:10.1016/j.procir.2024.07.022

Simulation uncertainty determination of single flank rolling tests using monte carlo simulation and skin model shapes for zero defect manufacturing of micro gears
Gauder, D.; Bott, A.; Gölz, J.; Lanza, G.
2023. Computers in Industry, 146, Art.-Nr.: 103854. doi:10.1016/j.compind.2023.103854

Model-Based Diagnosis of Feed Axes with Contactless Current Sensing
Hansjosten, M.; Bott, A.; Puchta, A.; Gönnheimer, P.; Fleischer, J.
2023. M. Liewald, T. Bauernhansl, H.-C. Möhring & A. Verl (Eds.), Production at the Leading Edge of Technology: Proceedings of the 12th Concress of the German Academic Association for Production Technology (WGP), University of Stuttgart, October 2022. Hrsg.: Mathias Liewald, Thomas Bauernhansl, Hans-Christian Möhring, Alexander Verl, 314–323, Springer. doi:10.1007/978-3-031-18318-8_33

Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis
Ströbel, R.; Bott, A.; Wortmann, A.; Fleischer, J.
2023. Machines, 11 (11), Art.Nr.: 1032. doi:10.3390/machines11111032

Cyber-Physical Test Environment for the Identification of Interacting Wear Effects in Feed Axes
Bott, A.; Stöbel, R.; Osen, G.; Fleischer, J.
2023. Journal of Machine Engineering, 23 (1), 123–132. doi:10.36897/jme/162266

Generation of Artificial Learning Data to Train Functional Meta-Models of Micro Gears
Schiller, V.; Gauder, D.; Gölz, J.; Bott, A.; Wannenwetsch, M.; Lanza, G.
2022. Procedia CIRP, 114, 67–72. doi:10.1016/j.procir.2022.10.010

Inline qualification of focus variation metrology for a series production of micro gears = Inline-Qualifizierung der Fokusvariations-Messtechnik für die Serienproduktion von Mikrozahnrädern
Gauder, D.; Gölz, J.; Bott, A.; Jung, N.; Lanza, G.
2022. tm - Technisches Messen, 89 (9), 594–611. doi:10.1515/teme-2022-0047

Comparative analysis between single flank rolling test for micro gears and analytical simulation of optical measurements = Vergleichende Analyse zwischen der Einflankenwälzprüfung für Mikrozahnräder und der analytischen Simulation von optischen Messdaten
Gauder, D.; Gölz, J.; Bott, A.; Lanza, G.
2022. tm - Technisches Messen, 89 (9), 580–593. doi:10.1515/teme-2022-0041