ADD-reAM Research Team - addream-project.nl

Meet The ADD-reAM Research Team

WP1: Next-Generation Industrial AM for Repair and Remanufacturing

Starting soon

PhD Researcher

Task 1.1: Selection and Integration of AM in Existing and New Factories

Juno Ninan

PhD Researcher

Task 1.2: Supply chain design and coordination for AM-Based repair/remanufacturing

Damla Yuksel

PostDoc Researcher

Task 1.3: Design and management of reverse supply chain for AM

Ghazi Abid

PhD Researcher

Task 1.5: Sustainable spare parts management using AM

Amin Najafqolian

PhD Researcher

Task 1.6: Integrated quality control framework for AM: enhancing remanufacturing decision-making and process optimization

This work package focuses on integrating advanced AM technologies into real-world circular manufacturing systems. It addresses both technological integration and country-level considerations. WP1 will explore the selection and integration of AM technologies in existing and new factory environments (Task 1.1), the design of circular AM-based supply chains (Task 1.2) and the development of reverse logistics models (Task 1.3). Task 1.4 will focus on developing decision-support tools for optimizing AM-based repair and remanufacturing processes, ensuring efficient operations and Task 1.5 will investigate ways to integrate AM with forward production, coordinating repair activities with ongoing production lines to improve efficiency. Additionally, WP1 will address challenges in quality assurance for AM-based repair and remanufacturing enhancing decision-making and process optimization (Task 1.6). These efforts will inform WP2 on design principles, process optimization and logistics arrangements, ensuring efficient integration of AM into the circular economy.

Task 1.1: Selection and Integration of AM in Existing and New Factories

Project Leader: Sebastian Thiede (University of Twente)

Description:

While research on AM processes has made significant advancements, there is a lack of studies on factory design tools that fully integrate circular manufacturing principles. Most existing tools focus on optimizing material usage and energy efficiency in isolated processes but do not address the broader factory system, including closed-loop supply chains and reverse logistics. Developing such tools is crucial for transitioning AM into circular manufacturing. Limited case studies show potential for improved resource efficiency, but more research is needed to create scalable, real-world solutions. This task will explore systemic factors for integrating AM into circular manufacturing, covering the entire process chain from material preparation to post-processing within a circular factory framework.

Research questions:

RQ1.1.1: How can the planning and operation of AM process chains and factories be facilitated to align with circular manufacturing principles, ensuring sustainability, efficiency and resource circularity?
RQ1.1.2: What are the key technical, logistical and workforce-related challenges in developing a fully circular AM factory system, how can these be systematically addressed through system design and operation?
RQ1.1.3: What assessment models and design tools can be developed to support the implementation of circular AM factories, considering technical, economic and environmental objectives?

Task 1.2: Supply chain design and coordination for AM-Based repair/remanufacturing

Project Leader: Dirk Pieter van Donk (University of Groningen)

Description:

Supply chain management research increasingly critiques the dominant focus on growth and profits over social and environmental outcomes. Despite growing attention to sustainability, supply chains have only become “less unsustainable.” Recently, there has been a shift toward regenerative and circular supply chains, but most research in this area remains conceptual, with few real-world examples. This is partly because many businesses continue to follow non-sustainable paths. Initial studies touch on elements of circular chains, but much work is still needed. A systems approach that considers a network of interconnected organizations, rather than a single orchestrator firm, is increasingly recognized. The introduction of new technologies, like AM, requires a concurrent redesign of both the supply chain and broader network. This task aims to develop design principles for circular supply chains using remanufacturing and AM technologies for spare parts, focusing on cooperation and governance within such networks.

Research questions:

RQ1.2.1: Which elements and insights from current models do fit with the specific characteristics of remanufacturing and the use of AM technologies?
RQ1.2.2: Can we identify within current circular chains which models for its design, cooperation, information sharing and governance are effective?
RQ1.2.3: How to co-create with industrial partners, the actual circular chain(s) along with the various coordination, information sharing and governance structures?

Task 1.3: Design and management of reverse supply chain for AM

Project Leader: Engin Topan (University of Twente)

Description:

Integrating AM into reverse logistics poses challenges, particularly due to the inflexibility of traditional reverse supply chains in accommodating AM’s dynamic repair processes, such as collection, disassembly and inspection. Developing flexible, integrated reverse logistics models that align with AM’s capabilities for repair and remanufacturing, while coordinating with forward production, is critical. AM’s benefits, including reduced downtime, increased parts availability and cost savings, depend on overcoming logistics integration challenges, such as optimal equipment placement and redundancy to prevent disruptions. The literature often overlooks these AM-specific logistics challenges, instead seeing AM solving logistics problems by distance reduction. While studies suggest a shift toward decentralized supply chains, with AM equipment positioned near assets, enhancing responsiveness and flexibility, there is limited research on structuring reverse logistics networks for decentralized, dynamic AM needs. Despite some research on optimizing static supply chains with AM, there is a gap in models addressing dynamic, decentralized supply chains, especially in contexts with moveable AM facilities and shifting installed bases. Additionally, AM repairs at lower levels of product structure introduce new logistics challenges, as conventional supply chains are designed for higher-level replacements. This task aims to develop quantitative models for designing and managing dynamic reverse supply chains using AM for repair and remanufacturing. We address both cross-facility and intra-facility challenges, validating our methods through dynamic reverse supply chain design and integration of forward and reverse flows within manufacturing environments. The goal is to reduce material waste, improve resource utilization and enhance parts availability and customer satisfaction.

Research questions:

RQ1.3.1: How should we design and deploy quantitative decision support models to design dynamic reverse supply chains that adapt to rapidly changing needs and conditions of the supply chain operations (using e.g. mobile 3D printers)?
RQ1.3.2: How should we integrate reverse logistics flows with existing forward and reverse production and logistics processes?
RQ1.3.3: Which quantitative models and solution algorithms provide the best support for choices regarding part repair and replacement levels in the product structure repair when deploying additive manufacturing (including part consolidation and direct repair)?

Task 1.4: Condition assessment and recommendation

Project Leader: Remco Dijkman, Zaharah Bukhsh (TU Eindhoven)

Description:

Reliable condition assessment of components is essential for decisions on repair, remanufacturing or disposal. Estimating Remaining Useful Life (RUL) is key, with methods categorized as data-driven (e.g., deep learning models), model-driven (e.g., deterioration modelling) or hybrid approaches (e.g., physics-informed neural networks). While data-driven RUL methods are well-studied, there has been limited focus on efficient data collection, aggregation and cleaning. Accurate data from multiple sources is vital for timely predictions. Current RUL methods mostly address operational components, but assessing failed components for potential repair or repurposing is equally important. This task will develop a data-centric approach for RUL prediction and quality assessment, addressing challenges like noisy or incomplete data. The goal is to integrate RUL estimation with optimization methods to make cost-effective and sustainable decisions. Finally, these approaches will be evaluated in industrial case studies to ensure real-world applicability.

Research question:

RQ1.4.1: How to take a data-centric approach to develop robust and adaptive RUL and quality assessment models integrated with optimization methods to improve decision-making in remanufacturing processes for both operational and failed components?

Task 1.5: Sustainable spare parts management using AM

Project Leader: Rob Basten (TU Eindhoven)

Description:

The goal of this task is to ensure the availability of spare parts using a sustainable approach that minimizes the use of new materials and energy. There is extensive literature on spare parts inventory control. More recently, research has focused on using AM to reduce inventory levels, particularly in the context of spare parts. A paper on the application of AM in spare parts supply chains is by Khajavi et al. which uses scenario analysis to examine how AM impacts supply chain design. Westerweel et al. extend this research by developing a model to determine whether it is beneficial to deploy AM capacity during military missions to lower spare part stock requirements. To repair or refurbish products, spare parts are essential. These parts can either be newly purchased, potentially incorporating recycled materials, or produced by repairing or remanufacturing failed components. AM can be employed in the latter case. The challenge lies in ensuring that sufficient spare parts are available when needed, while using minimal virgin material and energy. This requires decisions regarding the location of repair and remanufacturing facilities, the placement and quantity of spare parts and how to handle incoming failed parts.

Research questions:

RQ1.5.1: When to order, repair and remanufacture spare parts and how many spare parts to stock?
RQ1.5.2: How to determine what to do with an incoming failed part?
RQ1.5.3: Where to position spare parts and facilities in the supply chain?
RQ1.5.4: How does the addition of AM impact the repair and remanufacturing of spare parts in practice?

Task 1.6: Integrated quality control framework for AM: enhancing remanufacturing decision-making and process optimization

Project Leader: Mehrdad Mohammadi (TU Eindhoven)

Description:

Current remanufacturing processes using AM face challenges in maintaining consistent quality due to fragmented quality control (QC) practices. Existing approaches often treat IQC (initial quality control) and IPQC (in-process quality control) separately and real-time monitoring systems are underutilized for process optimization. Most research treats AM process optimization as a static problem, fixing settings in advance. However, AM optimization is inherently an online problem, requiring real-time adjustments to ensure part quality. This task aims to develop an integrated QC framework that combines IQC with IPQC through real-time process optimization and feedback loops. IQC determines whether a part can be remanufactured or should be recycled. If remanufactured, IPQC utilizes real-time monitoring and optimization, adjusting set-points based on material behaviour during the AM process. A feedback loop enables continuous process improvement. Figure 4 illustrates the integrated QC framework for AM with feed forward and feedback information. Key challenges include ensuring accurate manufacturability decisions, deploying real-time monitoring and control systems and creating a generalizable QC framework applicable to various AM technologies. This approach ensures dynamic adjustments for consistent quality throughout the remanufacturing process.

Research questions:

RQ1.6.1: How can an effective IQC framework be developed to determine quality criteria and set-points for returned parts to determine their suitability for remanufacturing using AM?
RQ1.6.2: What can an effective IPQC framework be developed to monitor and optimize the AM process in real-time to ensure successful manufacturing and consistent part quality?
RQ1.6.3: How can feedback loops be designed to integrate IQC and IPQC frameworks for continuous QC improvement?
RQ1.6.4: How can the integrated QC framework be generalized to different AM technologies and what guidelines should be included in a White Paper to assist policymakers?

WP2: Design for Remanufacturing Using AM

Vacancy

PhD Researcher

Task 2.1: Material selection for durability and multi-cycle use in AM

Xiaochen Ding

PhD Researcher

Task 2.2: Utilizing AM in design for repair and remanufacturing

Pepijn Goossens

PhD Researcher

Task 2.3: Design for product repair focusing on AM capabilities

Vacancy

PhD Researcher

Task 2.4: Repair and remanufacturing using next-generation AM technologies

WP2
Task 2.1
Task 2.2
Task 2.3
Task 2.4

This work package focuses on product and material levels. WP2 aims to ensure that products designed for remanufacturing are compatible with AM capabilities and that an accessible material database supports the integration of these technologies. Task 2.1 focuses on developing a systematic methodology for selecting materials in AM-based repair and remanufacturing, prioritizing durability, circularity and performance across multiple remanufacturing cycles. It aims to integrate computational tools and material databases to create an accessible framework for optimal material selection in repair processes. Task 2.2 aims to develop a framework that links product architecture to manufacturing technologies, focusing on design aspects that facilitate remanufacturing and will complement Task 2.3, that will develop guidelines for AM-supported repair by revisiting current design-for-repair methods, including Disassembly Maps, to incorporate AM-specific requirements. Task 2.4 explores the use of next-generation AM technologies to enhance the precision, efficiency and sustainability of repairing and remanufacturing complex products.

Task 2.1: Material selection for durability and multi-cycle use in AM

Project Leader: Constantinos Goulas (University of Twente)

Description:

One important aspect missing in current research is the lack of methodology for appropriate selection of materials for repair and remanufacturing using AM. Traditional material selection methods often fail to address key constraints, particularly durability and circularity from a lifecycle perspective. Material selection in AM is complex due to the diverse range of materials (metals, polymers, ceramics, composites) and the varying demands of different AM technologies, such as printability, layer adhesion and anisotropic mechanical properties. Materials used in AM must meet functional requirements—such as mechanical strength, thermal stability and wear resistance—while also being compatible with the specific AM technology. Although new alloys and composites have been developed for enhanced durability, there is still no comprehensive framework for material selection in AM-based repair and remanufacturing that balances performance, design constraints and sustainability. This research aims to develop a systematic methodology for selecting materials capable of withstanding multiple remanufacturing cycles without significant degradation. Factors such as thermal stress, fatigue, corrosion, erosion and mechanical wear impact material durability in AM. For repair applications, materials must demonstrate long-term performance and repair tolerance. Several computational tools, like ANSYS Granta Edupack, enable material comparisons based on specific constraints, but they require in-depth knowledge of materials science and manufacturing processes. This research will integrate image recognition technology with material databases (e.g., ANSYS Granta Edupack) to create a robust methodology for selecting optimal materials for AM-based repair. Users will input data and images of damaged components and the methodology will identify suitable materials and AM processes based on circularity, durability and performance. The resulting materials database will be made available to users in the form of an app, offering accessible, scientifically backed solutions for reducing waste and extending product lifecycles.

Research questions:

RQ2.1.1: How can material selection for additive manufacturing (AM) be optimized to ensure durability and circularity in repair and remanufacturing applications?
RQ2.1.2: What design constraints specific to AM processes should be considered during material selection to ensure successful repair and remanufacturing of components?
RQ2.1.3: How can an image-based app effectively integrate a material properties database to recommend optimal materials and AM processes for component repair and remanufacturing?

Task 2.2: Utilizing AM in design for repair and remanufacturing repair/remanufacturing

Project Leader: Ruud Balkenende (TU Delft)

Description:

Using AM for spare part production can ensure that spare parts are available for a long time. Instead of keeping a large inventory of physical spare parts, a digital file of each spare part can be stored online and produced on demand. This will save costs and waste from unused parts while making them available for a longer period. However, spare parts are currently not designed for AM. Using AM to produce parts that were initially designed for injection moulding introduces one major challenge: translating the design from one manufacturing method to another. Both the overall product complexity and specific part requirements, such as fine details and flexibility, can make it difficult to reproduce injection moulded parts with AM. Moreover, redesigning spare parts for AM after the initial production gives minimal possibilities for design changes and creates an increased workload, so design methods need to be developed that address spare parts already in the early design stages. This means that parts should be designed for both injection moulding and AM. As both technologies have different specs, this implies that parts might not be physical copies but should be functionally equivalent. An important implementation gap is further that most companies have not yet adopted design strategies facilitating remanufacturing at scale. Access to technical knowledge is not a barrier, whereas integrating this knowledge into the existing design process is. This implies that Design for Remanufacturing needs to be embedded into existing processes.

Research questions:

RQ2.2.1: Which design aspects determine feasibility of remanufacturing and upgrading, for replacement spare parts as well as reconstructed parts?
RQ2.2.2: How can product and part design facilitate the production of 3D-printed spare parts with equivalent functionality in the design of original parts?
RQ2.2.3: How can product-service systems be designed that enable implementation of design for AM spare parts? With as an underlying sub-RQ: Which opportunities and barriers regarding implementation are perceived by stakeholders?

Task 2.3: Design for product repair focusing on AM capabilities

Project Leader: Bas Flipsen (TU Delft)

Description:

Design for repair has received increasing attention over the past 5 years and assessment of product repairability through scoring systems that evaluate design features is now feasible. However, the opportunities of facilitating repair through AM are hardly addressed and focus on tedious, often not sustainable, self-repair, for which the initial design was never intended. The current methods and tools for design for repair therefore needs to be readdressed to derive guidelines for AM-supported repair. Also, the scoring systems need to be revisited to ensure that AM-facilitated repair is treated fairly. One of the most notable design aspects in repair is design for disassembly and reassembly. In a highly repairable product, the components that fail most often should be easily accessible for repair or replacement. We recently developed the Disassembly Map method to guide designers to a range of improvement opportunities. This map, however, doesn’t cover specifics of AM repair. Especially dealing with modules, consisting of multiple parts is potentially blocking AM for repair and remanufacturing. Further, although we expect commonalities with design for remanufacturing (Task 2.2), tensions between design strategies for repair and remanufacturing need to be resolved to avoid unfavourable trade-offs between repair and remanufacturing.

Research questions:

RQ2.3.1: How can AM facilitated repair opportunities be incorporated in design methods and repairability
scoring systems?
RQ2.3.2: How can the Disassembly Map method be improved to incorporate the use of AM made spare parts?
RQ2.3.3: Which tension occur between design for repair and design for remanufacturing and how can these
be resolved?

Task 2.4: Repair and remanufacturing using next-generation AM technologies

Project Leader: Ian Gibson (University of Twente)

Description:

Over the past 20 years, AM technologies have matured to the point where they are now widely accepted and regularly integrated into industry resource strategies, particularly in the context of Industry 4.0. However, AM continues to evolve, expanding to include a broader range of length scales (from microns to meters), composites, multi-materials and embedded components such as gears, sensors and actuators, further enhancing functionality. New AM products may include micron scale surface coatings, with porous and complex internal geometric structures, built in such a way that they may be highly functional but correspondingly difficult to recycle. The goal of Task 2.4 is to explore the application of next-generation AM technologies in the repair and remanufacturing of complex products. By leveraging advancements in AM, this task aims to improve the precision, efficiency and flexibility of repair and remanufacturing processes while minimizing material waste and energy consumption. It focuses on utilizing the latest developments in AM technologies to repair and remanufacture products, particularly those with intricate geometries and complex material requirements. Current AM processes offer significant potential for repairing high-value components that are difficult or impossible to fix using traditional methods. This task will identify and test cutting-edge AM techniques that enhance the repair and remanufacturing of products, aiming for improved cost-effectiveness and sustainability.

Research questions:

RQ2.4.1: What AM technologies are likely to be introduced into the manufacturing industry?
RQ2.4.2: How are these technologies potentially going to positively or negatively impact the environment?
RQ2.4.3: What tools can we adopt to ensure that designers understand the positive and negative aspects of AM?
RQ2.4.4: How do we ensure that products can be effectively remanufactured using AM?

WP3: Regulatory, economic, environmental, educational and social innovations

Luna Frank

PhD Researcher

Task 3.2: Legislation and policy for circular economy through AM

Shashank Bhardwaj

PhD Researcher

Task 3.3: Environmental and societal impact of repair and remanufacturing using AM

Starting soon

PhD Researcher

Task 3.4: Standardization and Intellectual Property (IP) for AM in repair and remanufacturing

Aiswarya Sunil

PhD Researcher

Task 3.5: Consumer adoption of AM-based repair and remanufactured products

Starting Soon

PhD Researcher

Task 3.7: Geopolitical and socioeconomic mapping of AM in the Netherlands

This work package aims to address broader economic, environmental, legal, educational and societal challenges associated with integrating AM into circular manufacturing. WP3 builds upon the technical advancements in WP1 and WP2 by developing viable business models (Task 3.1) that leverage AM’s potential for repair and remanufacturing. It will also address regulatory (Task 3.2) and IP (Task 3.4) issues that may arise from the adoption of AM in industrial processes, providing the necessary policy recommendations to support AM integration. Moreover, WP3 will research how AM’s potential for repair and remanufacturing thrives on social values related to prolonged product lifespan, ownership and sustainable consumption patterns (Task 3.5). Required training and education (Task 3.6) to enable a resilient, localized repair industry will be researched too. The outputs from WP3, such as LCA and SLCA studies (Task 3.3), legal frameworks (Task 3.2 and 3.4) and training concepts (Task 3.6), will provide essential feedback to WP1 and WP2, ensuring that the technological solutions developed are economically viable, legally compliant, applicable to industrial procedures and aligned with sustainability goals.

Task 3.1: Circular business models for AM in repair and remanufacturing

Project Leader: Timber Haaker (Saxion University of Applied Science)

Description:

Additive Manufacturing (AM) provides unique capabilities for on-demand production of spare parts, repairing damaged components, and customizing products to extend their lifespan. AM-based circular business models, which integrate AM technologies with circular economy principles, are gaining attention for minimizing waste and maximizing resource efficiency. AM facilitates easier repair, remanufacturing and on-demand production, supporting product-as-a-service models. These models extend product lifecycles, reduce waste and improve resource use, but more empirical research is needed on their economic viability and environmental impacts across industries. Transitioning to AM circular models poses challenges due to supply chain dependencies and uncertainties in returns and risks. Therefore, developing design strategies and decision-support tools tailored to AM-enabled circular models is a key area for future research, focusing on economic, social and ecological value creation.

Research questions:

RQ3.1.1: What are existing and future business models patterns for AM based circular business models and what are the involved design options?
RQ3.1.2: What are possible AM-based circular business models for actual AM applications as envisaged in the project’s demonstrators?
RQ3.1.3: How can we model AM-based circular business models to support decision-making by clarifying the impact of options (decisions) and scenarios (external influences) on economic, ecological, and social value?

Task 3.2: Legislation and policy for circular economy through AM

Project Leader: Leonie Reins (Erasmus School of Law)

Description:

The 2020 Circular Economy Action Plan (CEAP) and related legislative instruments, such as Ecodesign, extended producer responsibility and Right to Repair, provide the foundation for this task. However, existing legal frameworks are criticized for being too focused on linear lifespans, lacking substantial changes to challenge the status quo. Several obstacles to a circular economy are identified: policymakers tend to favour soft law, which is often ineffective and prone to greenwashing. A significant tension exists between consumer protection laws—which prioritize providing new products to consumers—and environmental goals, which emphasize repair and reuse. Furthermore, IP rights restrict access to necessary information and spare parts for remanufacturing, while inconsistent tax provisions and the influence of lobbying further hinder progress. To address, it is clear that these regulatory barriers, including conflicts between consumer law and environmental goals, the limited availability of spare parts and fragmented legislation, impede the adoption of circular practices, especially in AM. The solution lies in creating binding legal frameworks with universally accepted circular economy principles. Strong, clear goals must guide policy frameworks, with less reliance on self-regulation. Expanding circular policies to other sectors will support a broader transition, ensuring that AM and circular economy practices advance together, with clear legal pathways for their implementation.

Research questions:

RQ3.2.1: What are the existing regulatory provisions that can be considered an obstacle to Circular Economy through AM?
RQ3.2.2: How can a regulatory framework (be designed to) facilitate the advancement of the Circular Economy through AM?

Task 3.3: Environmental and societal impact of repair and remanufacturing using AM

Project Leader: Stefano Cucurachi (University of Leiden)

Description:

This task focuses on assessing the environmental and social benefits of repair and remanufacturing using AM, including reduced energy consumption, lower raw material use and decreased emissions compared to new product manufacturing. It will provide LCA and SLCA studies of selected alternatives, comparing them with systems that lack AM solutions. These assessments will be conducted within a Safety and Sustainability by Design (SSbD) framework, evaluating both current and future technological maturity while accounting for uncertainties. We will apply SSbD principles to minimize the use of harmful materials and reduce impacts on human health, climate and the environment throughout the lifecycle. Existing LCA metrics will be extended to include socio-economic and circular sustainability aspects. The task also aims to harmonize trade-offs across indicators for better decision-making and develop open-source software to support these assessment.

Research questions:

RQ3.3.1: How do the environmental impacts of AM-based repair and remanufacturing compare to traditional manufacturing methods across their full life cycle?
RQ3.3.1: What are the social implications, including job creation, health and safety impacts and social acceptance, of using AM for repair and remanufacturing?
RQ3.3.3: How can an integrated SSbD framework improve decision-making for sustainable manufacturing practices?

Task 3.4: Standardization and Intellectual Property (IP) for AM in repair and remanufacturing

Project Leader: Rudi Bekkers (TU Eindhoven)

Description:

Repair and remanufacturing using AM offers substantial benefits, but their success depends on integration into existing techno-economic institutions, particularly standardization and Intellectual Property (IP). Standardization is crucial for AM with a lack of standards hindering AM’s broader acceptance. Seifi et al. emphasize its role in quality assurance and certification, essential because AM parts may differ from originals, impacting performance and safety. To ensure consistency, Gao et al. argue that AM industries must establish standards for materials, processes and testing. The complex relationship between AM and IP has also raised concerns. While Gao et al. highlight how 3D printing marketplaces challenge traditional IP protection, Steenhuis & Pretorius suggest that initial IP protections may have limited AM’s growth. Solutions like encryption via watermarking have been proposed, but their effectiveness varies by application. So far, the existing work on standardisation and IP in the context of AM has been rather open-ended. The literature agrees that there are significant challenges, but these are formulated in a rather general sense and are currently not very specific. This is partly due to AM being a broad field in terms of application, ranging from prototyping, regular manufacturing, 3D printing marketplaces, downloadable open-source projects and much more. This task focuses specifically on the use of AM in repair and remanufacturing. By narrowing down from the broad concept of AM, we can address much more specific questions. At the same time, even the context of AM in repair and remanufacturing is diverse. We therefore will define, in collaboration with stakeholders, a number of use cases that are defined by dimensions, such as (1) the application area, (2) the value chain in question and (3) the AM technology to be used.

Research questions:

RQ3.4.1: How can it be ensured that parts used for repair and remanufacturing produced by AM meet the required quality and satisfy requirements?
RQ3.4.2: Who is entitled to repair or remanufacture products on the European market and does the regulatory framework require clarifications or improvements?

Task 3.5: Consumer adoption of AM-based repair and remanufactured products

Project Leader: Ruth Mugge (TU Delft)

Description:

A circular economy encourages changes in consumer behaviour, such as adopting repair practices and purchasing remanufactured products. This task focuses on repair and refurbishment, where used consumer products re-enter the market with different warranty rights compared to new products. Remanufacturing involves collecting, testing, cleaning and restoring used products for resale, thereby reducing environmental impact. However, consumers face barriers to adopting circular behaviours. For repair, challenges include time, effort, lack of knowledge, high costs and perceived low product value. Repair and remanufacturing are also met with scepticism, as consumers often associate it with lower quality and wear. As a result, repair is rarely considered and repair occupies a small share of the consumer market. This task explores how AM can increase consumer adoption of repair and remanufacturing. AM offers unique opportunities for customization, fast and cost-effective repairs and enhanced product value. However, consumer perceptions of AM in repair and remanufacturing contexts remain unclear, as does their likelihood of adopting these solutions based on product design integration. Accordingly, this task aims to answer the following research questions:

Research questions:

RQ3.5.1: What motivators and barriers do consumers experience regarding AM-based repair and remanufacturing?
RQ3.5.2: Which design strategies will increase consumer adoption of AM-based repair and remanufacturing?

Task 3.6: Model for embedding remanufacturing in the education system

Project Leader: Arie Paul van den Beukel (Saxion University of Applied Science)

Description:

AM has the potential to enhance supply chain resilience through on-demand repair and remanufacturing. However, realizing these benefits depends on education and training programs that develop the necessary expertise. In Europe, educational initiatives focus on circular product innovation, driven by policies like “Right to Repair.” While these programs address key areas such as design for disassembly and sustainable business models, they often neglect the skills needed for repair processes. AM technologies vary widely, each with distinct challenges and in some cases a lack of specific expertise, especially in metal-based AM, hinders broader adoption. A Dutch industry survey also identified a critical shortage of AM expertise, emphasizing the need for specialized training. The application of AM in repair and remanufacturing heightens the demand for tailored educational programs. This task will develop an educational framework addressing the skills required for AM-based repair and remanufacturing across academic and vocational levels in the Dutch education system. The framework will align with industry needs and support the integration of AM into sustainable supply chains, fostering a skilled workforce prepared for roles in the circular economy.

Research questions:

RQ3.6.1: What specific knowledge and skills are required for different stages of the repair and remanufacturing process using AM?
RQ3.6.2: How does an educational model address different disciplines, like product engineering, process operations and production planning? How do these disciplines require distinction in taught knowledge and skills to utilize AM for repair and remanufacturing?
RQ3.6.3: How can training programs be designed to simulate real-world repair challenges in which AM-technology is conducted?
RQ3.6.4: How can an educational model be applied at different levels ranging from academia, vocational programs to practice-oriented (on-the-job) training?

Task 3.7: Geopolitical and socioeconomic mapping of AM in the Netherlands

Project Leader: Karel van den Berghe (TU Delft)

Description:

In the current geopolitical turmoil, increasingly the need for a thorough industrial strategy is called upon (e.g. Draghi report European Commission, 2024). In general, within these strategies, the aim is to accelerate innovation, circularity and competitiveness across sectors and across institutions (e.g. Dutch industrial strategy). However, after decades of deregulation, no clear industrial strategy and even the closure of the Ministry of planning in The Netherlands (VROM), a decent understanding, let alone a steering of industry is lacking. This task builds upon earlier research developed to understand industrial ecosystems. It is situated within the field of economic geography and planning, where recently the attention for (the turmoil within) global production networks has increased. The innovation of this task lies in the combination a theorisation of geopolitics and planning, and mapping visualisation of ecosystems across sectors and across institutional boundaries. Via a mixed methods approach wherein quantitative data (e.g. socioeconomic and logistical datasets) will be combined with qualitative data (e.g. policy documents, interviews, expert sessions) an as complete as possible understanding of the current status of the Dutch manufacturing sector will be developed. Subsequently, using this understanding, pro-actively and in codesign with relevant public and private stakeholders, policy recommendations will be formulated to improve and achieve a successful CE Dutch manufacturing sector via AM.

Research questions:

RQ3.7.1: How are the Dutch AM manufacturing sector value chains currently embedded in existing global production networks?
RQ3.7.2: What policy recommendations are needed to change these networks to achieve a Dutch CE AM manufacturing sector?

WP4: Dissemination and Utilization

Work package 4 will utilize the outputs from WP1, WP2 and WP3 to develop a Technology Roadmap, a White Paper and three demonstrators (Task 4.1) that guide and showcase the integration of AM technologies into repair and remanufacturing processes. The Technology Roadmap (Deliverable D4.1.1) will provide strategic guidance for applying AM innovations in real-world settings, while the White Paper (Deliverable D4.1.2) will outline key regulatory and policy recommendations to facilitate AM adoption. The demonstrators will highlight how the technological, economic and regulatory innovations developed throughout the project can be applied in practical, industrial contexts, with hardware demonstrations located at the Advanced Manufacturing Centre, UT. Task 4.2 will focus on disseminating project outcomes to a broad audience, including academic researchers, industry stakeholders, policymakers and the public. This will be achieved through the publication of scientific papers, presentations at conferences and organizing workshops and symposia, ensuring that the knowledge and innovations from the project are effectively communicated and adopted, further promoting AM technologies in circular economy practices.