Advances in Remanufacturing

Implementing remanufacturing as a strategy to achieve circularity in manufacturing companies offers significant benefits in terms of both environmental sustainability and financial performance. However, the development of sustainable remanufacturing systems is accompanied by various complexities and challenges. Companies engaged in remanufacturing must consider solutions that address the three dimensions of sustainability: economic, social, and environmental. Moreover, they need to tackle the unique characteristics associated with remanufacturing systems. This paper aims to investigate aspects covered in existing studies related to the development of sustainable remanufacturing systems. The methodology involves a literature review focusing on three aspects: (1) the triple bottom line of sustainability; (2) capabilities required for establishment of a sustainable remanufacturing system; (3) enablers that can support the development of a sustainable remanufacturing system. By classifying the published literature and conducting a thorough analysis, this paper provides valuable insights for practitioners and researchers, facilitating the creation and accumulation of knowledge in the field of sustainable remanufacturing systems. Furthermore, the paper aims to underscore the significance of this area of research and identify potential avenues for future investigation.

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), rapidly evolving in both academia and practice, allow for improved manufacturing processes thanks to data analysis. These technologies provide benefits to production systems in several ways by enabling resilience and improving sustainable growth. However, the manufacturing challenges and issues need to be revised according to the new trends provided by remanufacturing, i.e., an emerging and new “mode” of manufacturing able to bring used products to a “like-new” state, potentially profitable and less environmentally harmful compared to the classical manufacturing systems. This research work, methodologically based on a scoping literature review, provides the state-of-the-art related to AI, ML, and DL use in remanufacturing contexts, identifying the main field of applications and their challenges and limitations. The findings revealed an increasing interest in the topic in the last three years. Most of the studies focused on disassembly and inspection processes, whereas further applications (e.g., repair, demand forecasting, cost prediction, etc.) have not been fully investigated and need further research. DL represents the most widely used technique (followed by ML). Even though the literature confirmed that AI-based methods could increase productivity and lower time and costs, further attention needs to be paid to real industrial case study applications.

Remanufacturing offers benefits to the environment, society, and the economy. Disassembly is the first and most critical operation in remanufacturing. With advancing technology and increasing labour costs, there has been an interest in automating disassembly. Robotic disassembly sequence planning and line balancing are two extensively studied topics in this area. This article establishes a connection between robotic disassembly sequence planning and line balancing and identifies trends through an extensive literature review and bibliometric analysis, shedding light on the research landscape. The findings reveal significant gaps in the literature. While artificial intelligence and machine learning are used in robotic disassembly sequence planning, their integration into robotic disassembly line balancing remains unexplored. Robotic disassembly sequence planning predominantly uses a single-objective approach, while robotic disassembly line balancing adopts a multiobjective nondominated approach, with the genetic algorithm, evolutionary algorithm, and bees algorithm as the most commonly used metaheuristics. These results offer insights and directions for further research in this domain.

This paper deals with a comprehensive literature review on the topics of remanufacturing and reverse logistics, with a specific focus on the usage of computer simulation and optimization techniques. When dealing with the management of backward flows, challenges such as product complexity and variability, uncertainty in demand and supply, and high logistics and operational costs exists; to tackle these issues, strategies and techniques for optimizing processes have been explored; computer simulation was demonstrated to be a powerful tool for facing these issues. A total of 77 documents published from 2018 to 2023 are analysed, using advanced bibliometric and network analysis techniques. Outcomes highlight the challenges and opportunities associated with remanufacturing and reverse logistics and emphasise the role of simulation-based optimization in enhancing the efficiency and effectiveness of remanufacturing operations.

The growing demand for electric vehicles exacerbates concerns over the environmental implications of lithium-ion battery waste, which poses risks to both ecological systems and public health. While remanufacturing has been acknowledged as a viable, sustainable pathway for mitigating these issues, existing literature lacks a comprehensive investigation into the role of Industry 5.0 technologies in optimising this process. To achieve this goal, this study compares and evaluates the potential of different Industry 5.0 technologies and approaches to support the remanufacturing process of lithium-ion batteries. Specifically, we apply the AHP-PROMETHEE method to identify the most critical and influential Industry 5.0 prospects that should be prioritised for development and implementation. The novelty of our approach lies in the identification of critical Industry 5.0 imperatives that can enable efficient and effective remanufacturing processes. The analysis is supported by a comprehensive review of the relevant literature. The results of our study provide important implications for policymakers, battery manufacturers, and remanufacturing companies. By prioritising key Industry 5.0 technologies like digital twins, the Internet of Everything, and blockchain, this study shows that carmakers can significantly improve efficiency and sustainability in battery remanufacturing. This paper contributes to the emerging research on the integration of Industry 5.0 technologies in the remanufacturing process of lithium-ion batteries. Our next step is to explore the potential of the identified technologies in real-life applications and to evaluate their impact on the sustainability and efficiency of the remanufacturing process of lithium-ion batteries.

In the era of the circular economy (CE), more and more attention is paid to some production strategies such as Remanufacturing. This strategy aims to promote the creation of new lives for products considered at the end of their life and, therefore, reduce the industrial impact on the sustainability matrixes. Remanufacturing has grown in importance and relevance in the last few years, especially at an industrial glance, as evidenced by the birth of numerous divisions of primary Original Equipment Manufacturers (OEM) addressing remanufacturing. In this context, problems related to the practical consequences of the decisions related to the remanufacturing processes are relevant for the industrial decision-makers. Thus, the development of tools supporting the decision-makers in the remanufacturing field has increased in the last few years. This paper contributes to the identification and selection of the most suitable decision-making methods for several specific issues of Remanufacturing. To achieve this aim, a systematic review of the international scientific literature is conducted, providing practical insights for practitioners and proposing potential directions for further research development to enhance this knowledge domain.

Product remanufacturing and other End-of-Life (EOL) approaches still remain topical issues in research and policymaking. As observed in the literature, global challenges such as climate change and the subsequent need to reduce greenhouse gas emissions have contributed to driving this interest. While electric and hybrid vehicles are expected to rise in the vehicle market, electric vehicle batteries remain of interest as they typically account for 30–40% of the value of an electric vehicle. While this is a growing the concern, the end-of-life use of these products remains a concern. Remanufacturing, which prepares end-of-use products and their components for use through a controlled industrial process, bringing it to “as good as new”. Thus, remanufacturing can be a suggested end-of-life approach for the electric vehicle battery as well as the remanufacturable components in the electric vehicle. While existing studies show consumer knowledge of environmental issues, there are no studies that assess the environmental knowledge of consumers. This scoping review asks the question: from existing research, what are the environmental considerations and end-of-life approaches considered in consumer uptake of electric vehicles? While the broad focus is on electric vehicles, we narrow this interest to electric vehicle batteries for the reasons of component value. A PRISMA scoping review is conducted, and from this, various relevant themes necessary to investigate consumer acceptance and preferences for electric vehicle battery remanufacturing are identified.

Disassembly, a key step in remanufacturing, is almost always performed manually due to uncertainties associated with end-of-life products. In some cases, such as EV battery disassembly, the operator could be exposed to safety hazards. Teleoperation is an effective solution to this safety issue. This paper proposes a teleoperated disassembly system that integrates augmented reality (AR) and digital twin technology. The system establishes bidirectional human-in-the-loop communication between the physical system (the teleoperated robot) and the virtual model. Using AR, the system can provide real-time robot status information to the operator by superimposing a virtual robot on the physical robot and displaying relevant data either in the form of text or a line chart. Furthermore, the proposed approach allows operators to enter the control loop when anomalies are detected, enabling them to receive real-time feedback while remotely controlling the robot. Experiments have demonstrated the effectiveness of the system in monitoring robot status during complex disassembly and offering a more intuitive approach for remotely controlling the robot, thereby improving the safety and efficiency of disassembly operations.

Disassembly is a significant step in remanufacturing of acquired End-of-Life (EOL) multi peripheral (MP) imaging device. Most of the cases disassembly processes are done manually, which is very exhaustive and has low disassembly efficiency. Recently, robotic disassembly sequence planning (DSP) has widely been studied. It is mainly designed to improve disassembly efficiency and to minimize disassembly cost and time before executing the disassembly process. Further, Human–Robot collaboration (HRC) has gained considerable attention to handle difficulties in DSP in remanufacturing. Therefore, in this paper, DSP of EOL of MP device for Human–robot collaboration is developed by the Stackelberg game theoretic model. This game-theoretic model can be able to manage the volatile and uncertain features of human operators to obtain human-oriented HRC disassembly. In this work, firstly, the Stackelberg model is developed to generate feasible disassembly sequences of EOL of MP device. Further we have presented comparison between game theory and non-game theory based HRC approach.

This paper presents the results of an experimental study conducted in the field of human–robot interaction (HRI) aimed at evaluating the impact of the presence of a robot on human performance. Experiments were carried out in a virtual reality (VR) environment and were about a disassembly-reassembly task in the aerospace field. A sample of 78 engineering students participated in the project, divided into two subgroups, one working in co-existence with a robot and the other performing the same task without the presence of the robot. The parameter monitored during the experiments to compare the groups was the total completion time of the disassembly-reassembly task. The results obtained show that the two groups have similar behaviour. Statistically, there are no significant differences between the groups, and qualitative analyses show very similar behaviour concerning the learning experience on the task.

With the increasing concern of recycling the end of life products, robotic disassembly is being paid much attention nowadays. Generally, there are many uncertainties in disassembly process, and these uncertainties should definitely be considered. Cognitive capability forming is useful to solve the uncertainties during the robotic disassembly process. In this paper, cognitive digital twin model of robot disassembly process is built, and its cognitive capability is validated. An ontology modeling-based method is proposed to achieve the cognitive capability of the digital twin. During the robotic disassembly process, the cognitive capability addresses uncertainties of different fasteners with similar product structure. In order to verify the proposed method, the case studies are conducted in robotic disassembly process of clutches, and results show that cognitive digital twin is able to resolve the uncertainties of product fasteners in the robot disassembly process and improved the success rate in disassembling the products with similar structure.

Robotic pick-and-place process is a typical robotic manufacturing process, which is widely used in many applications. In the meanwhile, sustainable manufacturing is being paid much attention and the energy consumption during the robotic pick-and-place process could be further improved to enhance the sustainability. However, the payload deployed on the industrial robot is always uncertain and the existing energy-efficient trajectory optimization method could not be dynamically used for this situation. In this paper, digital twin-based energy-efficient trajectory optimization for robotic pick-and-place process under uncertain load is proposed. The digital twin for robotic pick-and-place process under uncertain payload is firstly built. Afterwards, the proximal policy optimization algorithm is used to realize energy-efficient trajectory optimization under uncertain payload. Finally, case study is conducted to validate the proposed method. The results show the effectiveness of the digital twin model and the Proximal Policy Optimization algorithm in reducing the energy consumption of the robotic pick-and-place process, which improves the sustainability of robotic pick-and-place process.

In recent years, the diversification and individuation of market demand are becoming more and more obvious, which puts forward higher requirements for the flexibility of the manufacturing system. For cyber-physical systems, flexibility refers to the ability to be scalable and reconfigurable. The traditional industrial reconfiguration method is inefficient and lacks practical deployment, it can no longer achieve the flexibility required in practical manufacturing processes. To address these problems, this paper proposes a reconfiguration solution based on an optimization algorithm and the OPC UA discovery mechanism. The optimization algorithm is used to fast generate the optimal combination of system resources for minimizing the makespan. OPC UA discovery enables the plug-and-produce functionality that can accelerate new device integration. The proposed solution can provide decision-making support for the rapid reconfiguration of the manufacturing system and also allow for the dynamic integration of newly inserted devices into the manufacturing system. Finally, the feasibility of the solution is verified by a demonstration of reconfiguration in robotic manufacturing systems.

As lowering manufacturing cost and improving resource sustainability is becoming more critical, remanufacturing has attained significant attention in the manufacturing industry. Disassembly is the first step and consumes the most time in remanufacturing. There are three ways to disassemble a part and they are: manual disassembly (MD), human–robot collaborative disassembly (HRCD), and robotic disassembly (RD). RD is a disassembly method using robots without any human intervention and is the focus of this research work. To implement RD, spatial information is crucial. As end-of-life (EOL) products are likely to be discontinued products, the digital models for these products are not likely to be available and determining spatial information using vision systems to facilitate RD may not be possible due to occlusion and inaccessibility caused by restrictive spatial environments such as car under bonnet. However, a digital model of EOL products (including their internal profiles) can be obtained using 3D scan or computed tomography (CT) scan and subsequently, sliced into layers to determine essential spatial information to facilitate RD. Therefore, this research work has proposed to explore the application of slicing technology to assist the RD of a car battery within a car engine compartment where a scanned digital model of the car engine (under the bonnet compartment) is sliced into layers and exported as a text file known as geometry-code (G-code) that contains a large amount of geometrical information that can be used to determine the geometry, location of each component and spatial availability information of an EOL product. The results show that the x, y, z coordinates extracted from G-code can be used to define the spatial availability and identify the barriers and facilitate the creation of collision free paths for RD.

This paper presents a study on image-based incremental learning for part recognition of used automotive parts, also known as cores. The use of Machine Learning (ML) in the recognition of used parts has proven to be effective in suggesting Original Equipment Number (OEN) based on images and logistics data of a core. This leads to a four-eye process where the worker and ML interact through an assistance system. In reverse logistics, the spectrum of parts handled is constantly changing, making it difficult to have a “complete” image or sensor-based data set. The study focuses on the ramp-up phase of an ML implementation project in a real-world automotive core sorting station. There are two stations equipped with sensors such as RGB cameras. The sorted parts were acquired over a period of one year. Incremental learning was employed to cope with the growing dataset and the growing number of classes to be identified without retraining a model from scratch. Open source and state-of-the-art incremental ML learning methods such as POD-Net and Foster were tested against the common joint training approach used for most benchmarks in computer vision. The best-fitting open-source method for this problem was identified as POD-Net used with a self-supervised pretrained ResNet50. For the ramp-up of an ML-based core recognition a combination of incremental learning and joint training was found to be useful. It starts learning from a small number of digitized parts (14 classes), while maintaining a high recognition accuracy rate throughout the year, with a final class count of 100 (an increase of approx. 600%), which is a subset of a real application problem. The results of this study show that the proposed method is efficient, plastic, and energy-saving. Thus, it is a promising approach for the recognition of used automotive parts.

Assessing the remaining useful life (RUL) of components is essential for decision-makers in various industries, including manufacturing, transportation, and aerospace. By providing a reliable estimation of how long a part can continue to operate effectively, RUL assessment can inform decisions regarding maintenance, repair, or replacement of the component. To develop a technique for estimating the RUL of a system that is both highly accurate and less time-consuming than current methods, firstly, using the principal component analysis (PCA) manner, features were extracted in a reduced format; then, a hybrid model, i.e., an ensemble model that combines linear, k-nearest neighbors (KNN), and Gaussian process regression (GPR) through weighted averages, was designed. The well-known C-MAPSS dataset from NASA was employed to evaluate the performance of the model. To optimize the model's performance, an optimization procedure, i.e., constrained nonlinear multivariable was applied to identify the tuned weight in the ensemble learning that minimized the root-mean-square error (RMSE) value. Findings indicated that the optimized hybrid model outperformed the linear, KNN, and GPR models separately, as well as many of the prior investigations dealing with the same dataset, as evidenced by its lower RMSE value and the execution time.

To improve the disassembly automation of remanufactured products and solve the blindness problem in the manual disassembly process, this paper proposes a method about remanufactured product defect gear invalidation judgement based on the improved YOLOv5 target detection algorithm combined with image processing technology. Aiming at the background interference problem in the recognition process, Squeeze-and-Excitation Networks (SENet) is added to the detection model of You Only Look Once version 5 (YOLOv5) to improve the target detection capability of the model. Then exporting the target detection frame, the redundant background of the target image is eliminated by mask processing, which concentrates subsequent identifications’ attention on gear targets. By means of using convex hull detection to judge gear broken teeth defects, and calculating the actual length of cracks by object image coordinate transformation, the method that combines the improved YOLOv5 algorithm model and gear defect recognition realizes gear invalidation judgement. This paper focuses on two parts: identifying gears and gear defects using the YOLOv5 and image processing algorithms. The experiment shows that this method has a good recognition effect on gear broken teeth, and the crack length recognition error does not exceed 1.38%. The whole research improves the rationality and accuracy of gears in the process of remanufacturing and disassembly and provides an effective way for deep disassembly of remanufactured products.

An accurate prediction of bearing remaining useful life (RUL) has become increasingly important for equipment maintenance with the development of monitoring technology and deep learning (DL). Although Transformers are currently the most commonly used unique learning algorithms for sequential data, concerns about their computational efficiency and cost exist. In this regard, Compact Convolutional Transformers (CCT) have emerged as a promising alternative that employs sequence pooling and replaces patch embedding with convolutional embedding to enhance computational efficiency while maintaining high prediction accuracy with smaller model sizes. This study proposes an RUL prediction modeling approach that utilizes the Continuous Wavelet Transform (CWT) to transform time–frequency domain features into images, subsequently fed into CCT to establish a highly accurate prediction model for the RUL of bearings. This study conducted experiments using the XJTU-SY rolling bearing dataset. The performance was evaluated in terms of root mean square error (RMSE) and maximum absolute error (MAE) by modifying the layer configuration and comparing with other state-of-the-art algorithms.

This paper presented a machine learning method to achieve accurate surface defect detection and classification of remanufactured products. An improved You Only Look Once (YOLO) network was proposed for training and testing an image detection model on a steel surface defect dataset. The results show that the proposed YOLO model has high accuracy in detecting surface defects for the remanufacturing quality detection, and the accuracy of the modified YOLO model was improved by 2.4% when compared to the original model in mAP0.5. Moreover, the improved model reasonably reduced the simulation calculation. The improved YOLO model has less operation, higher precision, and higher speed, which has practical application value in the surface defect detection of remanufactured products.

In the context of the digitized and intellectualized transformation of ship pipe manufacturing enterprises, how to transform the massive multi-source heterogeneous data into knowledge and realize the reuse of manufacturing knowledge and experience in the ship pipe manufacturing workshop are the key to optimizing the allocation of workshop manufacturing resources. In order to solve the aforementioned issues, a knowledge graph-driven manufacturing resource recommendation method for ship pipe manufacturing workshops is proposed. Firstly, the correlation between multi-sources heterogeneous manufacturing data (device resources, manufacturing process of pipe, manufacturing orders) is analyzed and integrated. Then, a knowledge graph of manufacturing resources for the ship pipe manufacturing workshop is constructed. On this basis, a manufacturing resource recommendation method based on the Knowledge Graph Convolution Networks is proposed to recommend the device for orders in the ship pipe manufacturing workshop. Finally, a case study is implemented to verify the feasibility and effectiveness of the proposed method.

As an emerging technology that integrates multi-physics, multi-scale and multi-discipline properties, digital twinning can enable the interaction between the physical and information worlds. Disassembly has been widely used as an important method for manufacturing and green manufacturing. However, the uncertainty of the disassembly shop and the isolation of the disassembly information seriously affect the effective operation of the disassembly shop. In this paper, we propose a disassembly scheduling cloud platform based on digital twinning techniques. Compared with intelligent algorithms, it can effectively address the uncertainty factor in the disassembly shop. First, we build a workflow model graph for the disassembly scheduling cloud platform. Second, the whole process monitoring system of the disassembly scheduling workshop is constructed to monitor the disassembly dynamics in real time. Third, we build a digital twinning-based dynamic perturbation architecture for the disassembly scheduling cloud platform and use big data analysis techniques to predict and diagnose dynamic perturbations from multiple sources. Finally, the effectiveness of the proposed framework is validated by enterprise instances, on which the disassembly scheduling cloud platform is applied.

Remanufacturing, aimed at restoring end-of-life products to like-new condition, has become an essential value-recovery strategy to guarantee sustainable industrial development. Despite its innumerable social, economic and environmental associated benefits, the high uncertainty on the condition of end-of-life products represents a major barrier to the automation of disassembly tasks. Therefore, the effective adoption of remanufacturing in industries is limited these days. In this context, worldwide consolidation of the Internet of Things has led to the recent creation of massive data ecosystems, the potential of which is yet to be exploited in a remanufacturing domain. Data analytics and predictive planning built on top of the Digital Twin concept can significantly improve decision-making through the modelling of “what-if” scenarios leveraging real-time information collected and made available by Internet of Things network infrastructures. However, the remote monitoring of end-of-life products and the integration of information from multiple sources are not trivial tasks, which respectively deal with large-scale wireless network deployments and heterogeneous spatio-temporal patterns. To shed light on this matter, this work explores one of the recent advances of Digital Twin infrastructures towards remanufacturing, as well as open challenges and research directions in the field of data-driven decision-making. Second, relevant sources of information are discussed and reviewed, which are then classified into three hierarchical categories: (i) product-condition information, (ii) context information, and (iii) spatio-temporal features. Finally, a conceptual Digital Twin model leveraging multi-source information integration is proposed, which motivates a case study for data-driven decision-making in end-of-life product recovery considering context information. This work represents a step further in the adoption of the Internet of Things in the remanufacturing domain by addressing both data collection and exploitation dimensions towards Digital Twin modelling.

Pipe manufacturing is an essential phase in ship construction. Aiming at the problems of insufficient dynamic response-ability, human-oriented workshop control, and unbalanced equipment load in ship pipe manufacturing workshops, a knowledge-driven scheduling method for flexible ship pipe manufacturing workshops is proposed to comprehensively improve the efficiency of ship pipe manufacturing. A digital twin-enabled scheduling framework for flexible ship pipe manufacturing workshops is designed. On this basis, an ontology model of ship pipe manufacturing is described by analyzing the data of the ship pipe manufacturing workshop. And according to the low-volume and multi-variety manufacturing demand, a knowledge-driven multi-objective optimal shop scheduling model is constructed based on the equipment selection rules and scheduling optimization rules produced by ontology knowledge inference. Then, an improved Multi-objective Evolutionary Algorithm Based on Decomposition (IMOEA/D) is proposed. Finally, a case study in a pipe machining production line is studied to validate the proposed approach and the simulations show that the proposed approach is superior to other scheduling algorithms and decision makers can obtain more effective production execution plans.

In the realm of remanufacturing, the strategic choice of globalization stands at a crossroads, promising technological leaps, enhanced value creation, and broader opportunities. This study unveils a sophisticated model that scrutinizes the impact of incentive policies, with a focus on outsourcing subsidies, on the financial outcomes of manufacturers and third-party remanufacturers within the circular economy. Incorporating variables such as pricing strategies, remanufacturing capacity, and warranty costs, the model sets forth distinct objectives and constraints for both parties. The findings highlight the profound influence of these incentives on profitability dynamics. While manufacturers have the potential to amplify both the remanufacturing market and their profit margins through judicious use of these incentives, the success of such endeavors hinges on factors like market demand, astute pricing, and cost optimization. The research delineates three plausible scenarios: (1) manufacturers facing downturns while third-party remanufacturers prosper, (2) a win–win profitability scenario, or (3) a challenging landscape with losses for both. To navigate these outcomes and ensure effective policy implementation, a deep understanding of market intricacies and strategic cost management is paramount. In essence, this study accentuates the importance of meticulously designed policies and decision-making tools in fostering a resilient circular economy and sustainable progression, serving as a beacon for industry stakeholders.

In the coming decades, a primary challenge for the global economy lies in reconciling economic growth with environmental sustainability and the responsible consumption of natural resources. The transition from a linear economy to a circular one has gained recognition as a pivotal strategy for achieving net-zero greenhouse gas emissions. Industrial Symbiosis (IS) emerges as a crucial tool in this transition, fostering a circular economy at both national and global levels. IS represents an innovative approach to minimize raw material extraction and optimize resource utilization. Despite recognized environmental, economic, and social benefits, various barriers hinder IS implementation, particularly in manufacturing environments and few detailed case studies in scientific literature have been reported. This study aims to bridge this knowledge gap by systematically analyzing 16 selected articles on IS in manufacturing systems. The analysis categorizes case studies by various parameters, including publication year, manufacturing sector, country, entities involved, IS network characteristics, and exchange methods. It also highlights key trends, barriers, and drivers for IS implementation. The study reveals an increasing interest in IS since 2011, emphasizing its potential to address sustainability challenges while fostering economic growth. It underscores the importance of clear legislation, economic incentives, and knowledge dissemination to facilitate IS adoption. In conclusion, industrial symbiosis and the circular economy are vital strategies to achieve sustainable resource management and economic resilience. Overcoming barriers and promoting IS practices are essential steps towards a more sustainable future.

In the context of increasing resource scarcity, the Circular Economy has gained traction among researchers and managers as a potential solution to achieve a more sustainable system through efficient use of resources. Despite the importance of implementing circular practises in supply chains, the operational impact of circularity remains largely unexplored. Therefore, motivated by the existing interest in this topic, the aim of this work is to contribute to the current research gap and explore the impact of circularity on the dynamics of supply chains. Specifically, this study examines the performance of supply chains that incorporate remanufacturing through industrial symbiosis, i.e. which refers to the exchange of waste or by-products between different processes or firms, known as symbiotic supply chains. Using a modelling and simulation approach, this study evaluates the impact of operational factors on supply chain dynamics and identifies the most influential. Furthermore, this study offers practical guidance for managers seeking to integrate industrial symbiosis into their supply chains. Among our findings, we observe that, depending on the specific objectives of the supply chain, some circular strategies can have more impact on the dynamic performance of the supply chains than others.

The circular economy introduces a novel approach to sustainable production and consumption, emphasizing collaboration to develop innovative business models. Material innovation is a key element in aligning with this circular economy, transcending traditional disciplinary boundaries. This article focuses on bacterial cellulose, a material naturally produced by bacteria. Due to its mechanical strength and eco-friendly qualities, it offers a sustainable alternative to plastic materials. We start with a theoretical-technical overview and then explore practical Do It Yourself (DIY) applications. Our emphasis is on producing bacterial cellulose from local organic waste, in line with circular economy principles. We conduct a field study to assess local producers’ readiness to contribute organic waste for bacterial cellulose production. The waste collected is used to create practical, disposable, and compostable solutions, which are returned to local stakeholders, closing the production and consumption loop. These solutions also serve as raw materials for further cellulose synthesis. In summary, this article investigates bacterial cellulose as a sustainable material and implements a circular economy approach by utilizing local organic waste for material production and reuse. This contributes to a more sustainable, closed-loop system, fostering innovation and collaboration.

Offshore wind energy is considered as one of the fastest growing sustainable energy sources in the world. Offshore wind turbines (OWTs) are the most common technology used to harness wind power at sea for conversion to electrical energy. OWTs are often designed for a lifespan of 25 to 30 years, with a possible extension of up to 5 years. Most of the OWTs that were installed in the 1990s and early 2000s have reached, or are approaching, the end of their service lives. This has resulted in increasing concerns about how the large quantities of materials extracted from end-of-life OWTs can be best utilized. “Circular economy” is a new area of research that has recently attracted considerable interest from the offshore wind energy community. This paper aims to present an analysis of the technological, economic, environmental, regulatory, policymaking, cultural, and social challenges confronting the offshore wind energy industry with respect to end-of-life management, including reusing, retrofitting, recycling, remanufacturing, and repowering of OWTs, to support the transition toward circular economy. We then outline some of the business opportunities that Industry 4.0/5.0 and digital technologies can provide for building a more circular and sustainable wind power industry in the future.

Remanufacturing has emerged as a pivotal practice in the pursuit of sustainable manufacturing but it is facing challenges, including decision complexity, ergonomic concerns, and assembly and disassembly plan efficiency. This paper introduces a framework for advancing remanufacturing processes emphasizing Industry 4.0 (I4.0) concepts with special focus on Virtual Reality (VR) as exemplified by three applications. The first one leverages an armband motion sensor for intuitive programming of a robot, demonstrating potential enhancements in programming robotic tasks involved. The second one introduces VR for automated ergonomic assessment in remanufacturing tasks involving the human, using standard protocols. The third one explores VR-based evaluation and guidance of collaborative assembly plans by human and robot.

In recent years, a circular economy model has been established to promote sustainability. The model supports a service-oriented approach where leasing-based strategies can be applied to part and component use (multiple use) and recovery and rearrangement. Product leasing can recreate value in each cycle for both the lessor (i.e., remanufacturer) and the lessee (i.e., consumer). Nevertheless, there is not a clear vision/perspective about the effectiveness in sustainability of a leasing-based model in terms of a product’s environmental impact. Furthermore, the benefits generated by the leasing contract should be balanced between the involved agents. In this work, the authors introduce the concept of remanu-leasing, proposed as the optimal configuration of a leasing contract aimed at balancing and maximising lessor and lessee interests while minimising the product’s environmental impact.

Life cycle costing (LCC) is a method of assessing the total cost of ownership of a durable item and has been used around the world for the last few decades. This paper investigates the issue of LCC of food technologies, and in particular of a milling plant. The main cost components of this plant are initial acquisition costs, operating costs, maintenance costs and end-of-life costs. An analytic model is developed under Microsoft Excel™ to quantify these cost components. The application of the model is then detailed for one component (i.e., the operating cost) taken as an example, while for the remaining components, we present the main results obtained from the LCC application but omit the detailed computational procedure, for brevity. Overall, outcomes show that the highest economic impact is due to the operating costs, while the remaining costs contributes to the LCC to a limited extent.

Remanufacturing plays an important role to contribute for circular economy by extending the life cycle of used products. Remanufacturing is a recovery process of worn-out product through multi-process remanufacturing to achieve quality, specifications, and performance “as good as new” condition. It Remanufacturing processes focuses on disassembling, cleaning, repairing, and reassembling used products or components to restore them to like-new or even better-than-new condition. The basic problem in a remanufacturing process planning is how to appropriately select the manufacturing/remanufacturing process (technology) to have in a remanufacturing company that has the capability to ensure “like new” quality of remanufactured product. This paper presents a mathematical model for optimizing remanufacturing processes planning by considering quality loss and carbon emissions. The model was formulated using integer linear programming to obtain optimal solution. We study a remanufacturing system with four stage processes under varying quality condition of incoming core. This study also considered multiple remanufacturing processes that can be selected for each stage of the processes, then the processes may lead to different process characteristics, i.e. quality tolerance and carbon emission. A numerical example was used to validate the proposed model for a case of remanufactured hydraulic cylinder process planning. The model consistently shows an optimal solution for the case.

Nowadays, most industrial processes require increased attention to the rational use of resources in line with economic, environmental, and social principles. In this context, products in a state of deterioration present valid solutions for reintegration into the lifecycle through the use of recovery, restoration, and reuse techniques. The so-called remanufacturing process is a facilitator of sustainability and circular economy practices through the design of closed-loop supply chains. However, transitioning from linear to closed-loop supply chains through the introduction of remanufacturing is not straightforward. For this reason, the aim of this paper is to provide a tool for assessing the readiness level for introducing remanufacturing from a circular economy perspective. A multi-methodological design approach, including a systematic literature review on readiness models and a systematic literature network analysis for remanufacturing adoption, is employed to identify gaps and propose a novel readiness model. The results propose R4CE, a remanufacturing readiness model for circular economy adoption in closed-loop supply chains. The model assesses the readiness on five levels and the three dimensions of collaboration mechanisms, closed-loop supply chain decisions, and policies and regulations. The research findings may be of interest to practitioners and researchers for readiness assessment.

This work addresses the relevant issue of objectively evaluating the useful life of mechanical components through life cycle and cost analysis, when destinated to remanufacturing in agricultural machinery field. The goal is to identify the most efficient strategic choices for predicting and managing the end of life of a component/product to maximize profit and minimize consumption of economic and natural resources. The approach of Design for Remanufacturing and Design for Extension of Life is presented as effective solutions to extend the useful life of structural mechanical components, making them suitable for multiple remanufacturing process, and emphasizing the importance of considering long-term implications on the product and entire system life cycle. Life cycle analysis (LCA) and costing (LCC) are described as the proper tools to evaluate alternatives and identify which design scheme can ensure maximum profit and minimum consumption of material and energy. Specifically, the article proposes a methodology to compare between producing more components with the current design that will undergo fewer rework cycles or producing fewer components with the updated design that will undergo more regeneration cycles. The main questions that the article seeks to answer are: to what extent is it profitable, from an economic and natural resource perspective, to extend the useful life of a structural mechanical component in prevision of remanufacturing processes? What is the design scheme that can ensure maximum profit and minimum consumption of material and energy? Objective evaluation of process alternatives, considering design for remanufacturing and life extension, can provide a multi-objective optimal solution that maximizes product value and minimizes environmental impact.

With the drive to fight climate change, governments world-wide have signed up to achieving net zero. Remanufacturing is a strategy within the circular economy that can be harnessed to achieve net zero. Remanufacturing is the process whereby a product is reworked at the end of life and transformed into a product with same or higher warranty and quality specification of a brand-new product. In this paper, using a qualitative literature review methodology, it is argued that ‘design for remanufacture’ is very important in achieving net zero and has to be taken into consideration by original manufacturers along with circular business models and reverse logistics strategies to conserve energy, enable zero waste and reduce Green House Gas emissions. It is suggested that through ‘design for remanufacture’ products can be sustainably designed ‘ab initio’ in such a way that it easy for disassembly and remanufacture of products reducing pressure on landfills, energy utilisation and environmental pollution.

Remanufacturing is a strategy for processing a product at its end of life. Remanufacturing aims to return a used product or a product that has reached its end of life to the performance and condition of a new product and is equipped with the same new product warranty. Product design is an essential part of a product that increases the remanufacturability of the product. In this study, we discuss how to improve the product design of refrigerators. Refrigerators are white goods products almost every household has. If a refrigerator is not designed to be remanufactured, it will generate a lot of e-waste. This study proposes design improvement of refrigerator products by using the guidelines from design for remanufacturing based on life-cycle thinking. The proposed design improvement considers the product's overall life cycle. With the proposed revision of the refrigerator product design, the refrigerators are expected to be more remanufacturable and produce a more negligible environmental impact.

This paper focuses on developing a Discrete Event Simulation (DES) model using WITNESS for the process of remanufacturing End of Life (EoL) batteries. This study shows how the DES model could be used to understand the dynamics of the process flow for identifying the bottlenecks and finding feasible solutions based on Fit Remanufacturing principles in order to improve the performance of the whole remanufacturing process. The DES model was run using an iterative process to reduce idle times, increase utlisation times and minimize blockages and waiting times for the processes involved leading to an increase in the throughput of remanufactured battery packs/day from 10 to 30 after four weeks of continuous operation. Further, the blockage times were eliminated and reduced to less than 4% in all but two machines used in the remanufacturing of EoL batteries.

The disassembly process plays a key role in remanufacturing. The paper presents a systematic evaluation of the various contact states that can occur during the extraction a dual-peg from a dual-hole (ie. the dual peg-hole disassembly problem), with a particular emphasis on the problem of jamming that arises in two-point contact states. This paper shows the geometrical and quasi-static analyses, simulations, and boundary conditions of the two-point contact states.

This paper describes some explorations on the concept of disassemblability as an important circularity indicator for products because of its severe impact on reuse value. Although usefulness of the concept for determining disassembly strategies and for improving circular product design clearly shows in earlier studies, the link with Industry 4.0 (I4.0)-related process innovation is still underexposed. For further technical development of the field of remanufacturing, research is needed on tools & training for operators, diagnostics, disassembly/repair instructions and forms of operator support. This includes the use of IoT and cobots in remanufacturing lines for automatic disassembly, sorting and recognition methods; providing guidance for operators and reduction of change-over times. A prototype for a disassembly work cell for a mobile phone has been developed together with researchers and students. This includes the removal of screws by means of a cobot using both vision & the available info in the product’s Bill-Of-Materials, the removal of covers, opening of snap fits and replacement of modules. This prototyping demonstrates that it is relatively easy to automate disassembly operations for an undamaged product, that has been designed with repairability in mind and for which product data and models are available. Process innovations like robotisation influence the disassemblability in a positive way, but current indicators like a Disassembly Index (DI) can’t reflect this properly. This study therefore concludes with suggestions for an evaluation of disassemblability by looking at the interaction between product, process and resources in a coherent way.

In remanufacturing, Reverse Engineering (RE) tools and techniques could become essential in the rapid creation of digital 3D models of existing components, especially when drawings are no longer available. These models can be used to investigate potential remanufacturable parts, identify damages or defects and assist in the decision-making process for as-is rebuilding or redesigning of the part to improve performance. In this context, Additive Manufacturing (AM) technology is particularly well suited due to its capability to rapidly produce parts with minimal geometry constraints and at relatively low cost. This study proposes a methodological procedure based on the combined use of RE-AM to facilitate the remanufacturing decision-making process for better part performance and strategy. Moreover, through the use of Machine Learning (ML) algorithms, damages can be automatically and objectively identified and quantified. An application case is used to demonstrate the effectiveness of the proposed approach.

Remanufacturing is an industrial process which aims to restore a component to at least its original performance, and it is considered a key processes to support the transition to circular economy. For restoring a metal component, additive manufacturing processes based on Direct Energy Deposition (DED) techniques are the most widely used, because they can process a damaged part with a complex geometry. Among these, Wire Arc Additive Manufacturing (WAAM) has several advantages including a high deposition rate, lower operative, material, and equipment costs. Nevertheless, it is also characterized by low accuracy and a high risk of defects if the process is not tuned correctly. It is therefore crucial to develop effective deposition strategies to ensure defect-free deposition and high efficiency. This study focuses on the repair of edges of steel made components, and specific toolpaths has been designed and tested for repairing this geometrical features, both concave and convex, coupled with the selection of proper welding parameters and torch tilting angle.

Ultrasonic guided wave-based (UGW) structural health monitoring (SHM) is a sophisticated and invaluable process that employs an array of sensors and measurement devices to meticulously assess the ongoing performance and condition of various structures. Over time, accumulated data can facilitate the structural diagnosis, enabling informed maintenance decisions, and supporting remanufacturing efforts. SHM can play a key role in regeneration by providing valuable information on the condition of structures and components. This encompasses its capacity to ascertain the feasibility of remanufacturing, guiding determinations regarding the salvaging of reusable parts as opposed to the replacement of those that no longer meet operational standards. Moreover, SHM excels in its ability to pinpoint damage within structures and components, meticulously monitor the performance of remanufactured components, and ensure their alignment with predefined performance criteria. In this work, a finite element (FE) model of UGW-based SHM system on a aluminum plate, in pristine, damaged and remanufactured conditions, is presented. Notably, the wealth of SHM data gathered throughout the remanufacturing process can be strategically leveraged to refine the design of components and structures in subsequent iterations, thereby perpetually advancing the overall quality and efficiency of remanufactured products.

This research aims to optimize asset performance by integrating spare parts replacement, remanufactured parts, 3D printed parts, and repair, corrective, and preventive maintenance. The study reviews the current asset maintenance practices and proposes a framework for integrating various maintenance and spare parts strategies to achieve maximum asset performance. The proposed framework considers the asset's criticality, the availability of spare parts, and the effectiveness of each maintenance strategy in influencing the reliability and useful life of the spare parts utilized. The research also explores the potential of using 3D printing technology to produce spare parts and remanufacturing as a sustainable alternative to buying new parts. A case study is conducted to demonstrate the effectiveness of the proposed framework in optimizing asset performance. The results show that integrating the various maintenance strategies can save significant maintenance time and improve asset performance. The study concludes that a comprehensive approach to asset maintenance that considers all available maintenance and spare parts strategies is essential for achieving maximum asset performance, reducing downtime, and improving the overall efficiency of asset management.