NOW OPEN

Open Call #1

Attractive funding for your next product & service development

  • Data Collection
  • Creation of insights
  • Decision support
  • Automation

Deadlines

30 June 2025

Opening

11 September 2025

Submission deadline (17:00 CET)

What we offer

Benefits for SMEs/start-ups

Open Call Challenges

Combined AI and Data solutions for DATA COLLECTION

1.1 - Augmented sensing solution

The selected third party is expected to contribute to (one of) the following points (in applying to this challenge, please specify which point you would like to focus on):

  • Collaboration with Arçelik to develop an augmented sensing solution for its refrigerator production facility. The solution will enable accurate monitoring of forces, stresses, or other relevant physical quantities during plastic mold changes in the refrigerator production line. It will provide real-time feedback and predictive insights to reduce manual intervention and iterative adjustments during mold changes, minimize polyurethane waste, and reduce facility downtime. 
  • Collaboration with KU Leuven to develop and test augmented sensing solutions, specifically for mecha(tro)nic system level dynamics, and model-based augmented sensing in electro-thermo-mechanical applications in the manufacturing industry. The focus will be on the technological development of the augmented sensing solution (e.g., targeting force, stress, and torque estimation), with the availability of several industrially relevant laboratory setups and measurement equipment.

The selected third party is expected to contribute by providing:

  • Vegetation Recognition in Transmission Line Corridors: Utilizing multispectral/hyperspectral cameras, it shall be made possible to recognize vegetation types from imagery. This is important because different types of vegetation grow at different rates, making it easier to predict when intervention will be necessary.
  • Assessing Vegetation Health: With multispectral/hyperspectral cameras, it shall be made possible to assess the health of vegetation from imagery. This is crucial because healthy vegetation poses significantly less risk to infrastructure compared to diseased or damaged specimens.
  • Assessing Infrastructure Condition: Using multispectral/hyperspectral imagery, it shall be made possible to determine the condition of infrastructure. Discussions indicate that it is possible to detect the amount of biofilm and dirt on insulation, identify corrosion, detect overheating, and more.
  • Other Use Cases: Additional applications may also be considered.

Combined AI and Data solutions for creation of INSIGHTS

2.1 - Product-production Digital Twins

The selected third party is expected to contribute to (one of) the following points (in applying to this challenge, please specify which point you would like to focus on):

  • Collaboration with Arçelik to develop a product-production digital twin solution to optimise material mixing ratios based on real-time measurement of visual and mechanical properties of the different plastic source materials (virgin and recycled plastic)
  • Collaboration with Arçelik to combine augmented sensing (cf. challenge 1.1) with a product-production digital twin solution to optimise End-of-Life product refurbishment processes by assessing the health and predicting the remaining lifespan of components
  • Collaboration with KU Leuven to link the manufacturing process to product quality in mecha(tro)nic applications. The focus will be on the technological development of a dedicated product-production simulation framework for injection moulding manufacturing processes, with the availability of a fully equipped injection moulding laboratory facility.

The selected third party is expected to contribute by providing:

  • Product-production digital twin solution to optimise material mixing ratios based on real-time measurement of visual and mechanical properties of the different plastic source materials.

The selected third party is expected to contribute by providing:

  • Develop an optimization algorithm for optimal sizing of a microgrid components using a microgrid Digital Twin

  • Develop a microgrid grid energy control algorithms using predictions of energy needs and production

  • Implement and test the developed solutions in a digital environment with real world data and for different scenarios

Combined AI and Data solutions for DECISION SUPPORT

3.1 – Digital Twin-Enabled Smart Production Process Planning Tool

The selected third party is expected to contribute by providing:

  • Develop a solution that uses historical demand patterns and future demands to build more robust, flexible, and data-driven planning strategies.

  • The tool developed through this challenge will analyze customer demand trends over time to uncover patterns and enable the grouping and classification of customers based on their demand characteristics.

Combined AI and Data solutions for AUTOMATION

4.1- Automated energy management for parts production

The selected third party is expected to contribute by providing:

  • Develop a smart energy management strategy and algorithm based on the energy consumption models of the machine modes

  • Develop a module for monitoring and management of high energy intensive machines

  • Demonstration of the prototype on the industrial setup

The selected third party is expected to contribute by providing:

  • Provide an Energy Management System, based on energy consumption models and machine models.

  • Provide a monitoring and management module for the most energy intensive machines. Be able to demonstrate the prototype at pilot scale.

The selected third party is expected to contribute by providing:

  • Battery Assessment: Evaluation of battery performance, capacity, and overall condition.

  • Safety Measures for Battery Handling: Protocols and best practices to ensure safe handling, storage, and transportation of batteries.

  • Battery Defect Detection: Identification of manufacturing or operational defects in batteries using diagnostic tools and inspection methods.

  • Battery Health Monitoring: Assessment of battery state-of-health (SoH) using sensors and diagnostic data (e.g., temperature, voltage, internal resistance).

  • Battery Charging and Discharging Methods: Procedures and technologies for safe and efficient charging and discharging of batteries.

  • Decision-Making for Second Life, Reuse, or Recycling: Criteria and processes for determining whether a battery should be reused, repurposed for second-life applications, or sent for recycling.

The selected third party is expected to contribute by providing:

  • Develop a co-bot-assisted gasket installation solution

  • Integrate AI-based perception and control systems to allow the co-bot to adapt to different gasket shapes and models.

  • Ensure compliance with safety and ergonomic standards, by reducing physical strain on human workers and improving workplace safety with co-bot working.

  • Demonstrate human-robot collaboration capabilities, where the co-bot supports rather than replaces the human operator, ensuring intuitive interaction and task sharing.

How to apply

Get informed

Read the Guidelines for Applicants

Register

Access F6S platform

Submit

Submit your application through the F6S platform

Who can apply

  • Industry organisations providing the SMEs / Start-ups use-cases and high-TRL playgrounds for solution testing and validation
  • Integrators / engineering services providers that support the SMEs / Start-Ups in integration of the solutions
  • Research institutions, research infrastructures, non-profit organisations and charitable (scientific) foundations and public research centers

NOTE: Please read carefully the Guidelines for Applicants.

Open Call Evaluation Criteria

1

Novelty/ Innovation

2

Impact

3

Project planning

4

Expertise

Timeline