This PhD position is part of the academic network working on ecodesign, an international research and training initiative. The network aims to advance digital and ecodesign methodologies that enable scalable circular economy practices, aligned with European sustainability, resource efficiency, and Digital Product Passport (DPP) policies.
Digital Product Passports are emerging as a cornerstone of European circular economy policy, aiming to make product‑related information accessible and usable across the entire value chain. While existing platforms such as the International Dismantling Information System (IDIS) already provide guidance for safety and disassembly procedures, the accessibility, consistency, and structure of such information vary significantly. Updates are frequent, yet information formats are often fragmented, non-standardized, and not tailored to different user groups, hindering rapid localization and contextual use of instructions, especially in real‑world repair, remanufacturing, and recycling workflows.
At the same time, Augmented and Virtual Reality (AR/VR) technologies have demonstrated strong potential to support complex manual operations by providing interactive, in‑situ digital working instructions (DWIs). However, current AR/VR solutions are typically developed as standalone applications, manually authored, and weakly connected to product‑level data. As a result, they are difficult to scale, maintain, and align with regulatory DPP requirements or evolving product configurations.
The objective of this PhD project is to design DPP data structures, guidelines, and enabling frameworks that support AI‑ and AR/VR‑assisted reuse, repair, remanufacturing, and recycling of digital working instructions.
The research will investigate which product, process, and lifecycle information must be embedded within DPPs to enable automatic or semi‑automatic generation of DWIs, real‑time, context‑aware guidance via AR and VR interfaces, and bidirectional information exchange between operators, AI systems, and DPP infrastructures.
A central research focus will be the exploration of dynamic and machine‑interpretable data formats that allow DWIs to be generated, updated, aligned, and synchronized across different use contexts, ranging from customer‑facing self‑repair scenarios to professional circular business operations.
In addition, the project will explore the use of (multi‑modal) Large Language Models (LLMs) and other AI techniques to support the automated generation and adaptation of DWIs from DPP data. This research will explicitly assess which types of information remain critical to standardize and store in DPPs when AI‑assisted instruction generation is introduced.
By connecting AI, AR/VR technologies, and DPP frameworks, this project will advance the state of the art in digital design tools for circular economy applications. The expected outcome is a set of scalable DPP frameworks capable of supporting automatically generated DWIs, recommendations for DWI information requirements and standardization, and practical guidelines for integrating these solutions into industrial workflows.
- Validation and industrial relevance
The developed methodologies will be validated through industrial case studies in collaboration with European manufacturing and remanufacturing companies. For this, the PhD includes two mandatory international secondments (2-3 months), providing direct exposure to industrial design practice, advanced manufacturing technologies, and real world remanufacturing constraints.
To validate the developed technologies and DPP framework, the candidate is expected to build on commercial and prior research-developed projection-based AR digital work instruction systems. Diverse human-robot cooperative setups equipped with both op mounted dual projection systems and robot-mounted projection systems will be available to perform validation testing on case study products with small user groups and for the purpose of demonstration in the newly established remanufacturing light house laboratories at Flanders Make @ KU Leuven, in Belgium, Heverlee, managed by the LCE research group.
Through this combination of methodological development, laboratory-based experimental validation, and industrial collaboration, the objective is to generate actionable design knowledge that supports the goal of advancing sustainable and policy-aligned circular DPPs and manufacturing strategies.
