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Approximate Optimal Control (ApOpCon_SBO)
Model predictive control has many industrially relevant advantages but are not often used in mechatronic systems.
In-process control for improved robustness and quality in laser-based AM of metal parts (AMControl_SBO)
The project results are expected to lead to reduced variability and increased confidence in AM applications.
In-situ sensing based digital machine twin for enabling monitoring and compensation (DiGiMa_SBO)
We will amongst other develop a digital twin of a machine and its calibration methods.
A Hybrid SLAM approach for autonomous mobile systems (HySLAM_SBO)
The ide is to include a semantic information in the map to make localization more robust to changes in the environment.
A Digital Twin for Health Monitoring and Predictive Maintenance (DigiTwin_SBO)
In the era of IoT, there is a need for an estimation of the remaining useful life of defected components.
Control with Human in the Loop Learning (CHiLL_SBO)
The project aims at including the human in the control loop of a system.
Product Inspection with Little Supervision (PILS_SBO)
Except for very simple cases with little variation, computer vision systems require an extensive training data set to perform well.
Programming by User Demonstration (Proud_SBO)
The programmation of robots still accounts for 35% of the total cost.
Optimized parts supply for flexible assembly workstations (OPtiPartS_SBO)
The increasing number of product variants to be produced, makes the optimization of interal logistics processes increasingly complex.
Cost-effective validation of non-automotive autonomous vehicles (AutoTwin Validation_SBO)
We propose a virtual twin approach based on the combination of virtual testen and real-life measurements.
Design and validation of modular motion architectures (Modular Architectures_SBO)
Drive train modularity can reduce production costs, design costs and development time.
Adaptive thin film lubrication by Smart Nano-Fluids (Smart-Lubrication_SBO)
Bearings often operate under dynamic operating conditions leading to increased wear and finally premature failure.
Mixed Integer Optimal Control for the design of mechatronic systems (MixControl_SBO)
Methods to solve control problems with continuous decisions variables are mature, but there are sharp limitations when discrete variables are included.
Adaptive Mechatronic Systems Using Run-time Reconfiguration of the Software Architecture (ADAPTIVE_SBO)
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.
Smart handling of heavy and repetitive loads (SMARTHANDLER_ICON)
Manipulating bulky objects safely can be tricky.
Faster assembly and maintenance through Augmented Reality (FAMAR_ICON)
Manufacturing companies are looking for ways to provide operators and technicians with adequate training.
Ergonomic Monitoring and Improvement (ERGO-EyeHand_ICON)
Musculoskeletal disorders are the most important category of work-related diseases in many industrialized countries.
Optical beam-shaping for high productivity/quality laser aided manufacturing (BEAMSHAPE_ICON)
Beam shaping would enable to tailor the beam to the particular needs of the envisaged production process.
Selection & design of torque ripple reduction concepts for drivetrains (Torque Ripple Reduction_ICON)
Companies that design and manufacture drivetrains need more performant and cost-effective solutions for torque ripple reduction.
Context Aware Control (ConACon_ICON)
The project will deliver tools for automated context identification and deployment of context aware controllers.
Value driven Concept design for product-manufacturing co-design of mechatronic product families (VaProFam_ICON)
We are looking to create formal methods and tools to support value engineering processes.
Designer Centric Computational Design Synthesis (DC-CDS_ICON)
We will extend the existing CDS tooling by allowing to deal with uncertainty and by improving result visualisation.
The TETRA project "PRU Code Generation"
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.
Multi-System Learning Control (MultiSysLeCo)
We want to have optimal performance under all conditions and not end up with controllers that often yield poor system performance.