Research Centre of Digital Labs & Production
The research group Digital Labs & Production connects people, spaces and processes in the life sciences. From mixed-reality digital twins via progressive web applications to machine-to-machine interfaces, we connect physical and digital worlds through data and analytics.
The centre combines specific methodological and technological expertise in the digitization and virtualization of laboratories, processes and production facilities in the life sciences. This includes, on the one hand, the networking of devices, processes and people using interfaces, data pipelines and data management and, on the other hand, the mapping of physical systems and infrastructures to models and simulation environments. Digital twins are a good example of how these topics interact.
Three research groups are active in this field.
Strategic, tactical and operational process optimization using modelling and simulation tools are the focus of the research group. This includes the modelling and simulation of the dynamics of heterogeneous, complex systems as well as the investigation, optimization and control of their behaviour.
The research group specializes in the development of systems for data aggregation, transformation and management. Processing pipelines are designed and implemented to take data from their sources (e.g. graphic user interfaces, wearable sensors, measuring probes) through preparation steps (incl. quality control and homogenization) to storage solutions, analysis and visualization of results and insights.
The research group combines dynamic physical structures with digital environments using sensor, actuators, and edge computing. The group supports automation and decentralized intelligent data processing in the life sciences by connecting people, machines, and contexts.
Reinforcement Learning Analysis Framework
The aim of this project is to implement a framework that facilitates the development of RL solutions for real-world applications. This is necessary since the academic literature usually focuses on specific algorithms and approaches differ widely for different regions in the highly complex RL problem space. ...
Smart Hospital – Integrated Framework, Tools & Solutions (SHIFT)
Simulation-based comparison of an end-to-end and a platform configuration for injection molding line
The project partner received two layout proposals for a new production line and wanted to compare them in terms of their suitability and performance. In the injection molding process, failure of individual injection molding cavities can occur, which leads to systematic or random missing parts. The system's modules ...
PE(K)O Sustain – Physically modified oils as sustainable alternative to tropical fats for the baking and sweet goods industry
Designing Business Models for the IoT
This project aims at developing a business model simulation software for evaluating IoT business models. The holistic approach leverages advanced simulation methods and will create new revenue opportunities for Swiss manufacturing companies.
Hollenstein, Lukas; Lichtensteiger, Lukas; Stadelmann, Thilo; Amirian, Mohammadreza; Budde, Lukas; Meierhofer, Jürg; Füchslin, Rudolf Marcel; Friedli, Thomas,
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, eds.,
Applied data science : lessons learned for the data-driven business.
Available from: https://doi.org/10.1007/978-3-030-11821-1_17
SGLWT Mitgliederversammlung 2018, Wädenswil, 13. April 2018.
Available from: https://doi.org/10.1007/978-1-4842-3790-8
2018(1), pp. 9.
Available from: https://doi.org/10.21256/zhaw-1445
Hollenstein, Lukas; Lötscher, Adrian; Luccarini, Fabian,
Deatcu, Christina; Schramm, Thomas; Zobel, Kay, eds.,
Tagungsband ASIM 2018, 24. Symposium Simulationstechnik.
24. Symposium Simulationstechnik (ASIM 2018), Hamburg, Deutschland, 4.-5. Oktober 2018.
Available from: https://doi.org/10.21256/zhaw-4956