Rechargeable Li-based battery technologies utilising silicon, silicon-based, and Si-derivative anodes coupled with high-capacity/high-voltage insertion-type cathodes have reaped significant interest from both academic and industrial sectors. This stems from their practically achievable energy density, offering a new avenue towards the mass-market adoption of electric vehicles and renewable energy sources. Nevertheless, such high-energy systems are limited by their complex chemistry and intrinsic drawbacks. From this perspective, we present the progress, current status, prevailing challenges and mitigating strategies of Li-based battery systems comprising silicon-containing anodes and insertion-type cathodes. This is accompanied by an assessment of their potential to meet the targets for evolving volume- and weight-sensitive applications such as electro-mobility. Large-scale manufacturing of high-energy Li-ion cells is of paramount importance for developing efficient rechargeable battery systems. Here, the authors report in-depth discussions and evaluations on the use of silicon-containing anodes together with insertion-based cathodes.
Abstract The current manufacturing environment aims at getting an increasing variety of customised, high-quality products in flexible batches. The dynamic market demand, the short product lifecycle and the flexibility need mark the transition from the traditional manufacturing systems to the so-called Next Generation Manufacturing Systems (NGMSs). Reconfigurable Manufacturing Systems (RMSs) are within NGMSs and seem to match to these current market trends. RMSs allow rapid change in structure, hardware and software configuration to adjust, promptly, their production capacity and functionality. This paper presents a structured and updated systematic review of the literature about RMSs, highlighting the application areas as well as the key methodologies and tools. The review further provides a schematic of RMS research, identifying five emerging and promising research streams ranging from conceptual models to empirical applications. Compared to previous reviews, focusing on specific aspects of the RMS design and management, this study covers multiple areas and topics and it links reconfigurable manufacturing to the upcoming Industry 4.0 fourth industrial revolution. Finally, important issues and new trends in the literature are outlined to stimulate researchers and practitioners in developing studies in this field strongly linked to the Industry 4.0 environment.
Abstract Increasing individualization demands in products call for high flexibility in the manufacturing systems to adapt changes. This paper proposes a novel digital twin-driven approach for rapid reconfiguration of automated manufacturing systems. The digital twin comprises two parts, the semi-physical simulation that maps data of the system and provides input data to the second part, which is optimization. The results of the optimization part are fed back to the semi-physical simulation for verification. Open-architecture machine tool (OAMT) is defined and developed as a new class of machine tools comprising a fixed standard platform and various individualized modules that can be added and rapidly swapped. Engineers can flexibly reconfigure the manufacturing system for catering to process planning by integrating personalized modules into its OAMTs. Key enabling techniques, including how to twin cyber and physical system and how to quickly bi-level program the production capacity and functionality of manufacturing systems to adapt rapid changes of products, are detailed. A physical implementation is conducted to verify the effectiveness of the proposed approach to achieving improved system performance while minimizing the overheads of the reconfiguration process by automating and rapidly optimizing it.
Abstract Automotive battery packs used for electromobility applications consist of a large number of individual battery cells that are interconnected. Interconnection of the battery cells creates an electrical and mechanical connection, which can be realised by means of different joining technologies. The adaption of different joining technologies greatly influences the central characteristics of the battery pack in terms of battery performance, capacity and lifetime. Selection of a suitable joining technology, therefore, involves several considerations regarding electrical and mechanical properties and an assessment of production and operational conditions. Particularly, during the operation of an electric vehicle, challenges and mutual dependencies of the electrical and mechanical system emerge. The present work provides an overview of interdisciplinary challenges occurring at joints which are exposed to electrical current with a strong focus on interconnecting batteries for electric cars. It summarizes common quality criteria for the joining technologies and recombines those with criteria deduced from an electrical engineering point of view. Scientific literature concerning different joining technologies in the field of battery manufacturing is discussed based on those criteria. The most common joining techniques are ultrasonic welding, wire bonding, force fitting, soldering, laser beam welding, and resistance welding. Besides those, friction stir welding, tungsten inert gas welding, joining by forming and adhesive bonding are presented.
Josefine A. Olsson, Sabbie A. Miller, M. Alexander
Growing urban populations and deteriorating infrastructure are driving unprecedented demands for concrete, a material for which there is no alternative that can meet its functional capacity. The production of concrete, more particularly the hydraulic cement that glues the material together, is one of the world’s largest sources of greenhouse gas (GHG) emissions. While this is a well-studied source of emissions, the consequences of efficient structural design decisions on mitigating these emissions are not yet well known. Here, we show that a combination of manufacturing and engineering decisions have the potential to reduce over 76% of the GHG emissions from cement and concrete production, equivalent to 3.6 Gt CO2-eq lower emissions in 2100. The studied methods similarly result in more efficient utilization of resources by lowering cement demand by up to 65%, leading to an expected reduction in all other environmental burdens. These findings show that the flexibility within current concrete design approaches can contribute to climate mitigation without requiring heavy capital investment in alternative manufacturing methods or alternative materials.
Vincent van Vliet, Menno van den Hout, Eduward Tangdiongga
et al.
Free-space optical communication links can enable high-capacity wireless connectivity in urban areas. We discuss the feasibility, challenges, and recent developments for high-capacity urban free-space optical links at kilometer scale.
Çiğdem Şemsettin, Yildiz Bülent, Meidute-Kavaliauskiene Ieva
et al.
As industrial organizations use more technology, systems’ data production capacities improve. Thus, big data analytics (BDA) is becoming more critical. BDA and supply chain integration (SCI) require IoT technology to gather, transmit, and process enormous amounts of real-time data from multiple sources. Because IoT devices are equipped with sensors and actuators that collect and share data from the physical world, these devices generate large amounts of data regarding product movement within the supply chain, environmental conditions, equipment status, etc. These data serve as a valuable source of information for BDA. Additionally, IoT devices facilitate communication and collaboration between different supply chain components. Suppliers, manufacturers, distributors, and retailers can share real-time data to coordinate operations and respond quickly and efficiently to changes. This affects SCI positively. In this context, this study investigated the impact of manufacturing companies’ use of IoT technology on their BDA capacity and SCI. As a result of the structural equation model analysis, it was found that the use of IoT technology positively affects BDA capacity and SCI. It has been determined that BDA capacity has a significant positive effect on SCI. According to the research results, suggestions were made to companies and researchers.
Chiluba Mwila, Anna Mia Ekström, Beate Kampmann
et al.
The COVID-19 pandemic underscored Africa’s urgent need for vaccine security and self-reliance. In response, the African Union and Africa Centers for Disease Control and Prevention (CDC) established the Framework for Action (FFA) through the Platform for Harmonized African Health Products Manufacturing (PHAHM), with a goal of 60% local vaccine production by 2040. During 2024, Africa CDC, Karolinska Institutet, and Charité Universitätsmedizin Berlin organized a seminar series to discuss advancing this agenda, including a multidisciplinary international expert panel. The series concluded that achieving this requires a comprehensive approach to addressing gaps in the ecosystem, including research and development (R&D), workforce development, technology transfer, regulatory systems, demand creation, and coordination. Strengthening R&D entails investment, capacity building, and equitable academic partnerships. A skilled workforce is essential, necessitating a coordinated approach through Regional Capability and Capacity Networks (RCCNs), training of vaccine manufacturing personnel, and academic programmes for sustainable workforce development. Technology transfer requires building trust between technology holders and recipients, alongside a supportive environment for knowledge exchange. Robust regulatory frameworks, including regional harmonization and strengthened National Regulatory Authorities (NRAs), are crucial for vaccine quality and safety, with the Africa Medicines Agency (AMA) providing oversight. Necessary market shaping through demand creation is achieved by advocating for procurement of locally produced vaccines, enhancing outreach for public trust, and operationalizing the African Pooled Procurement Mechanism (APPM). Coordination mechanisms are needed to optimize resource allocation, promote information sharing, and avoid redundancy. Strategic investments and policy support will be instrumental in achieving Africa’s vaccine manufacturing aspirations and long-term health security.
In manufacturing, capacity planning is the process of allocating production resources in accordance with variable demand. The current industry practice in semiconductor manufacturing typically applies heuristic rules to prioritize actions, such as future change lists that account for incoming machine and recipe dedications. However, while offering interpretability, heuristics cannot easily account for the complex interactions along the process flow that can gradually lead to the formation of bottlenecks. Here, we present a neural network-based model for capacity planning on the level of individual machines, trained using deep reinforcement learning. By representing the policy using a heterogeneous graph neural network, the model directly captures the diverse relationships among machines and processing steps, allowing for proactive decision-making. We describe several measures taken to achieve sufficient scalability to tackle the vast space of possible machine-level actions. Our evaluation results cover Intel's small-scale Minifab model and preliminary experiments using the popular SMT2020 testbed. In the largest tested scenario, our trained policy increases throughput and decreases cycle time by about 1.8% each.
Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini
The increasing integration of distributed energy resources (DERs) into power systems presents opportunities and challenges for ancillary services (AS) provision. Technical requirements of existing AS (i.e., duration, reliability, ramp rate, and lead time) have been designed for traditional generating units, making their provision by DER aggregates particularly challenging. This paper proposes a method to design the duration of reserve capacity AS products considering the operational constraints of DERs and the temporal dynamics of system imbalances. The optimal product duration is determined by maximizing product availability and aligning the supply profile with the system's balancing needs. We apply the methodology to a realistic Swiss low-voltage network with a diverse DER portfolio. The results reveal that (i) shorter product durations maximize average availability and (ii) long product durations improve the alignment with system balancing needs. This paper offers valuable insights for system operators to design AS products tailored for DER participation.
This paper explores the integration of Additive Manufacturing (or 3D printing) into decentralized supply chains, focusing on the strategic decisions manufacturers and retailers make when facing capacity constraints. Using a Stackelberg game framework, we analyze how AM impacts traditional wholesale pricing across two scenarios. First, a manufacturer producing two distinct products and second, a case involving multiple products, each affected by AM's capacity limitations. For the first scenario, we derive sufficient conditions to find the equilibrium under a generic demand distribution, and for a uniform distribution, we fully derive the equilibrium and identify the critical cost threshold below which AM adoption is preferable. For the second scenario, we determine the conditions for the equilibrium, offering insights into the feasibility of AM relative to traditional manufacturing. Furthermore, we find that, contrary to some literature, even when AM incurs higher per-unit costs than traditional methods, it remains viable for some supply chains. However, limited AM capacity, particularly under high demand, may restrict its adoption, highlighting capacity constraints as a pivotal factor in AM decision-making. This study extends current research by examining multi-product and capacity-driven scenarios, providing valuable guidance for supply chain managers weighing the benefits of traditional manufacturing against those of AM.
First of all, the smelting equipment is the most important component of a foundry’s main production process and therefore requires constant reproduction. This is ensured by timely and high-quality maintenance and repair, the cost of which is 8–12% of the total costs. The technical and economic conditions of the enterprise itself depend on this, as the productivity of workers during production is directly related to the technical condition of the equipment and its downtime for repairs. An important factor in ensuring a melting furnace’s reproduction is a replacement of the worn lining, which leads to downtime of the smelting furnace and reduces the efficiency of its operation. The amount of torque required depends directly on the compound used. The quality of the manufacturing and sintering process of the lining, which provides the necessary durability, is affected by the heat capacity of the materials used when they are affected by the melting temperature of the alloys. In the present work, using the BRUKER D8 ADVANCE diffractometer, the Shimadzu XRF-1800 spectrometer and the STA 449 F1 Jupiter synchronous thermal analyzer, we probed the changes in the heat capacity of quartzite and PKMVI-3 under the action of temperatures of 200–1550 °C. This technology allows the manufacture of a lining that maintains high stability during operations at 1550–1600 °C melting modes.
While Industry 4.0 improves human productivity, it also raises sustainability and social challenges. Industry 5.0, as a supplement and logical continuation of the Industry 4.0 paradigm, focuses on the development of a human-centric, sustainable, and resilient manufacturing system. This paper reviews the existing literature. First, it discusses the definition and implementation framework for Industry 5.0. Then, it expounds the application status of Industry 5.0 in the field of intelligent manufacturing from four perspectives: digital manufacturing and intelligence, human-centric intelligent manufacturing and production process management, decentralized and resilient production, and sustainable production. It summarizes the role of Industry 5.0 technology in various intelligent manufacturing scenarios, as well as the challenges it faces, and concludes by analyzing Industry 5.0’s potential development direction and future technologies. This paper believes that the application of Industry 5.0 technology will effectively improve the production capacity of intelligent manufacturing systems and promote the development of intelligent manufacturing systems in a safe, efficient, sustainable and resilient direction; the development of Industry 5.0 will focus on giving full play to human creativity, avoiding repetitive labor through human-robot collaboration, and thereby realizing human value.
This study addresses the effects of densification at varied pelletization temperatures on the novel Malaysian <i>Khaya senegalensis</i> wood-derived pellets biomass fuel characteristics. The lack of comprehensive understanding regarding the biomass fuel characteristics of this species prompted the research. By addressing this knowledge gap, this study explores the impact of temperature variations on key fuel properties, contributing to the optimization of sustainable biomass fuel production in manufacturing and materials processing. <i>Khaya senegalensis</i> wood, grown and harvested in Malaysia, was pelletized at different temperatures to analyze the calorific value, volatile matter content, ash content, fixed carbon, bulk density, and moisture contents of the pellets. The experimental data revealed a significant relationship between temperature and these fuel properties. Pelletizing at 75 °C produced the highest calorific value of 19.47 MJ/kg and the maximum fixed carbon content of 10.04%. A low ash level of 4.26% was achieved via pelletizing at 75 °C. According to the results, 75 °C produced the best thermophysical properties. These findings provide valuable understanding of how pelletization temperature influences fuel pellet thermophysical properties, a critical aspect in optimizing fuel pellet production, storage, advancing renewable energy resource utilization, and, finally, promoting a cleaner and more sustainable energy future.
The technology’s integration into factories has accelerated automation’s growth, creating autonomous working conditions and cutting-edge capacity for production. Modern and smart factories provide consumers with time-saving solutions and reliable outcomes. The present paper presents the concept of Event-Dependent Process Planning (EDPP), which seeks to improve the time-effectiveness of smart factories. The suggested approach automatically arranges planned and queued activities according to previous results, matching them with customer demands. Before process planning, essential data are provided by intelligent measuring equipment in the factories. Recurrent learning ensures the integrated process planning is successful and aligned with customers’ needs. The efficiency with which the planning method exceeded customer expectations in earlier years is used to instruct this learning process. Applications of the technique are made to the manufacturing automation process’s delivery and production layers. Essential metrics like processing time, response ratio, delivery delay, and backlogs are evaluated in an experimental analysis to validate the suggested process strategy. The proposed EDPP achieves 11.38% less processing time, 5.43% high response ratio, 10.18% less delivery delay, and 3.8% less backlog rate.
Thaise Regina Matos de Morais, Aloísio de Castro Gomes Júnior, Lasara Fabricia Rodrigues
O tempo de resposta de um Sistema Médico de Emergência (SME) é uma métrica preponderante de eficiência, visto que prestar atendimento rápido as vítimas de urgência, determina a minimização de sequelas permanentes ao mesmo tempo que maximiza a taxa de sobrevida do paciente. Neste artigo, propomos um modelo de simulação via otimização, desenvolvido em linguagem Python, capaz de avaliar o desempenho de SME’s. Aplicamos ao método proposto, os dados reais de um SME brasileiro e verificamos, a partir dos resultados obtidos, que configurações estratégicas resultariam na redução de aproximadamente 10% no tempo de resposta médio. Além disso, foi verificada a importância de se considerar outras variáveis de forma conjunta ao número de habitantes, na determinação do número de ambulâncias necessário para se atender as demandas de emergência no serviço pré-hospitalar.
Production management. Operations management, Production capacity. Manufacturing capacity
Reza Shahabi-Shahmiri, Reza Tavakkoli-Moghaddam, Zdenek Hanzalek
et al.
Identifying appropriate manufacturing systems for products can be considered a pivotal manufacturing task contributing to the optimization of operational and planning activities. It has gained importance in the food industry due to the distinct constraints and considerations posed by perishable and non-perishable items in this problem. Hence, this study proposes a new mathematical model according to knowledge discovery as well as an assignment model to optimize manufacturing systems for perishable, non-perishable, and hybrid products tailored to meet their unique characteristics. In the presented model, three objective functions are taken into account: (1) minimizing production costs by assigning the products to the right set of manufacturing systems, (2) maximizing the product quality by assigning the products to the systems, and (3) minimizing total CO2 emissions of the machines. A numerical example is utilized to evaluate the performance of AUGMECON2VIKOR compared to AUGMECON2. The results show that AUGMECON2VIKOR obtains superior Pareto solutions across all objective functions. Furthermore, the sensitivity analysis explores the positive green impacts, influencing both cost and quality.
The increasing popularity of Deep Learning (DL) based Object Detection (OD) methods and their real-world applications have opened new venues in smart manufacturing. Traditional industries struck by capacity constraints after Coronavirus Disease (COVID-19) require non-invasive methods for in-depth operations' analysis to optimize and increase their revenue. In this study, we have initially developed a Convolutional Neural Network (CNN) based OD model to tackle this issue. This model is trained to accurately identify the presence of chairs and individuals on the production floor. The identified objects are then passed to the CNN based tracker, which tracks them throughout their life cycle in the workstation. The extracted meta-data is further processed through a novel framework for the capacity constraint analysis. We identified that the Station C is only 70.6% productive through 6 months. Additionally, the time spent at each station is recorded and aggregated for each object. This data proves helpful in conducting annual audits and effectively managing labor and material over time.