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Model predictive control has many industrially relevant advantages but are not often used in mechatronic systems.
The project results are expected to lead to reduced variability and increased confidence in AM applications.
We will amongst other develop a digital twin of a machine and its calibration methods.
The ide is to include a semantic information in the map to make localization more robust to changes in the environment.
In the era of IoT, there is a need for an estimation of the remaining useful life of defected components.
The project aims at including the human in the control loop of a system.
Except for very simple cases with little variation, computer vision systems require an extensive training data set to perform well.
The programmation of robots still accounts for 35% of the total cost.
The increasing number of product variants to be produced, makes the optimization of interal logistics processes increasingly complex.
We propose a virtual twin approach based on the combination of virtual testen and real-life measurements.
Drive train modularity can reduce production costs, design costs and development time.
Bearings often operate under dynamic operating conditions leading to increased wear and finally premature failure.
Methods to solve control problems with continuous decisions variables are mature, but there are sharp limitations when discrete variables are included.
Software architects need to reason about a set of operation modes at design time while the middleware has to be configured to allow these mode switches.
Manipulating bulky objects safely can be tricky.
Manufacturing companies are looking for ways to provide operators and technicians with adequate training.
Musculoskeletal disorders are the most important category of work-related diseases in many industrialized countries.
Beam shaping would enable to tailor the beam to the particular needs of the envisaged production process.
Companies that design and manufacture drivetrains need more performant and cost-effective solutions for torque ripple reduction.
The project will deliver tools for automated context identification and deployment of context aware controllers.
We are looking to create formal methods and tools to support value engineering processes.
We will extend the existing CDS tooling by allowing to deal with uncertainty and by improving result visualisation.
The goal is to develop methods that lower the cost and time-to-market and increase the quality of data acquisition for Industry 4.0 applications.
We want to have optimal performance under all conditions and not end up with controllers that often yield poor system performance.