Use Cases
ROB4GREEN brings together advanced robotics and Artificial Intelligence (AI) to enable smarter, more flexible and sustainable industrial processes. Through three real-world use cases — Wind Blade Decommissioning, Tyre Retreading, and Electronics Remanufacturing — the project demonstrates how AI-driven collaborative robotic solutions can support dismantling, remanufacturing and recycling, improving resource efficiency, reducing waste and accelerating the transition towards a circular economy.
Smart Dismantling and Recycling of Wind Turbine Blades
Pilot Partner: ACCIONA
Overview
The growing number of wind turbines reaching the end of their operational life creates significant challenges for the sustainable management of large composite structures. Wind turbine blades are particularly difficult to process due to their size, heterogeneous material composition, and demanding dismantling and recycling operations.
ROB4GREEN addresses these challenges through an intelligent, robot-assisted approach designed to improve the efficiency, precision, and sustainability of wind turbine blade dismantling and material recovery.
Main Challenges
Large and variable blade geometries: End-of-life wind turbine blades differ in size, shape, and structural characteristics, making standardised dismantling processes difficult.
Energy- and time-intensive processing: Conventional pretreatment, cutting, and material segregation operations require substantial time and energy.
Complex composite materials: The heterogeneous composition of blades complicates cutting, separation, and the recovery of cleaner material streams suitable for recycling.
ROB4GREEN Solution
In collaboration with ACCIONA, ROB4GREEN combines 3D scanning, material analysis, adaptive cutting plans, and robotic cutting and segregation to support more intelligent and resource-efficient dismantling operations. These technologies enable the robotic process to account for variations in blade geometry and materials, while supporting improved material separation and downstream recycling.
Expected Impact
Over 50% cleaner material stream for recycling.
20% reduction in pretreatment energy consumption.
30% reduction in preprocessing and segregation time.
AI-Enabled Heavy Vehicle Tyres Retreading
Pilot Partner: Michelin
Overview
Tyre retreading plays an important role in extending the service life of heavy vehicle tyres, reducing material consumption, and limiting waste generation. Nevertheless, retreading remains a complex industrial process characterised by significant variability in incoming tyre conditions, a strong dependence on operator expertise, and physically and cognitively demanding operations.
ROB4GREEN aims to transform tyre retreading through AI-enabled inspection, intelligent repair assessment, and robotic processing, supporting a more efficient, sustainable, and human-centred remanufacturing process.
Main Challenges
Strong reliance on operator expertise: Damage inspection, repair assessment, and processing operations depend heavily on experienced workers, while limited digitalisation restricts process consistency.
High physical and cognitive workload: Operators must perform demanding repair tasks while continuously assessing tyre conditions and processing requirements.
Variability in incoming tyres: Differences in tyre types, wear patterns, and damage characteristics require flexible inspection and repair strategies rather than standardised processing operations.
ROB4GREEN Solution
Together with Michelin, ROB4GREEN integrates AI-based damage detection, repair assessment, robotic skiving and filling, and automated validation and documentation. The pilot aims to improve the consistency and efficiency of tyre repair operations while supporting operators in complex decision-making and physically demanding tasks.
This human-centred approach combines intelligent automation with operator expertise to improve resource efficiency and extend tyre life.
Expected Impact
15% increase in the number of tyres processed.
20% reduction in energy consumption.
15% material savings.
Robotic Recovery of Automotive Electronics
Pilot Partner: Valeo
Overview
The increasing complexity and diversity of automotive electronics create significant challenges for the efficient recovery and reuse of valuable electronic components. Printed circuit board assemblies (PCBAs) contain components that may be suitable for recovery, but their identification, desoldering, and extraction typically involve manual, skill-intensive operations.
ROB4GREEN addresses these limitations by introducing AI-powered component identification and automated robotic disassembly, supporting more efficient component recovery, improved traceability, and circularity in automotive electronics.
Main Challenges
Manual and skill-intensive desoldering: Component recovery requires precise operations and specialised expertise, making conventional disassembly labour-intensive.
Limited traceability and inefficient processing: Insufficient digital tracking and time-consuming disassembly operations reduce process efficiency and complicate component management.
High variability in electronic assemblies: Differences in PCB layouts, component types, and configurations make automated identification and extraction particularly challenging.
ROB4GREEN Solution
In collaboration with Valeo, ROB4GREEN develops a robotic recovery process integrating AI-based component identification, automatic desoldering and parts extraction, and sorting and traceability solutions.
The pilot follows an intelligent workflow, starting with PCBA loading and automated component recognition, followed by robotic extraction and digital tracking of recovered parts. This approach aims to reduce manual intervention, increase operational efficiency, and support the reuse and recovery of valuable electronic components.
Expected Impact
Over 90% component detection.
Over 75% automatic disassembly.
Over 75% reduction in operator time.