Research foci

My main research is to automate parameter optimisation of industrial machinery by including existing expert knowledge into advanced machine learning algorithms. In our case study we try to implement our concepts for the case of additive manufacturing (colloquially referred to as 3D printing) using FDM machines. An important role in finding an optimal parameter settings plays predicting the quality, that results from applying a given setting.

  • evolutionary rule-based learning (supervised and reinforcement learning)
  • unsupervised deep learning for feature extraction
  • 3D printing / additive manufacturing
  • adaptive systems

Publications

  • Increasing Reliability in FDM Manufacturing. Heider, M. (2019). In Draude, C., Lange, M. & Sick, B. (Hrsg.), INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge). Bonn: Gesellschaft für Informatik e.V., pp. 483-491.
  • Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge. Nordsieck, R.; Heider, M.; Angerer, A. and Hähner, J. (2019). In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 406-413. DOI: 10.5220/0007953204060413
  • Robot gardens: An augmented reality prototype for plant-robot biohybrid systems. S. von Mammen, H. Hamann, and M. Heider. In Proceedings of the 22nd ACM Symposium on Virtual Reality Software and Technology (VRST), (Munich, Germany), pp. 139-142, ACM Press, November 2016.

Curriculum/Vitae

since 2019 Research Assistant with the chair for Organic Computing
2015–2018 Master programme Computer Science and Information-oriented Business Management at the University of Augsburg
2012–2016 Bachelor programme Computer Science at the University of Augsburg

Courses / teaching

(applied filters: | Semester: current | Institution: Organic Computing | Lehrende: Michael Heider | Typen: )
name semester type
Seminar über Ad-hoc- und Sensornetze WS 2019/20 Seminar
Seminar über Organic Computing WS 2019/20 Seminar
Studentische Arbeiten am Lehrstuhl Organic Computing WS 2019/20 sonstige

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