Search Results (21,790)

Optical frames are used worldwide to correct visual impairments, protect from UV damage, or simply for fashion purposes. Optical frames are often made of poorly biodegradable and fossil-based materials, with designs not targeted to everyone’s tastes and requirements. Additive manufacturing processes allow personalisation of optical frames and the use of new sustainable biomaterials to replace fossil-based ones. This comprehensive review combines an extensive survey of the scientific literature, market trends, and information from other relevant sources, analysing the biomaterials currently used in additive manufacturing and identifying biomaterials (biopolymers, natural fibres, and natural additives) with the potential to be developed into biocomposites for printing optical frames. Requirements for optical devices were carefully considered, such as standards, regulations, and demands for manufacturing materials. By comparing with fossil-based analogues and by discussing the chemical, physical, and mechanical properties of each biomaterial, it was found that combining various materials in biocomposites is promising for achieving the desirable properties for printing optical frames. The advantages of the various techniques of this cutting-edge technology were also analysed and discussed for optical industry applications. This study aims to answer the central research question: which biopolymers and biocomposite constituents (natural fibres, plasticisers, and additives) have the ideal mechanical, thermal, physical, and chemical properties for combining into a biomaterial suitable for producing sustainable, customisable, and inclusive optical frames on demand, using additive manufacturing techniques. Full article

The European forest-based sector faces a perfect storm of demographic, geopolitical, climatic, and policy-driven challenges. These multipronged, oftentimes interlinked factors are particularly consequential for export-oriented, forest-rich economies like Sweden. This study provides a qualitative scenario analysis to assess potential futures for the Swedish forest sector towards 2050, focusing on the impacts of key drivers: geopolitical alignment, European Union (EU) policy implementation, economic and demographic trends, technological progress, and climate change. Two critical uncertainties—Europe’s geopolitical positioning and the policy balance between wood use and forest conservation—form the axes for four contrasting scenarios. Results indicate that, across all futures, volume-based manufacturing in Sweden is expected to stagnate or decline due to high costs and weak EU demand, with bulk production shifting to the Global South. Long-term viability hinges on a strategic shift to high-value segments (e.g., specialty packaging solutions, biochemicals, construction components) and the adoption of advanced technologies. Concurrently, the sector must adapt to increased forest disturbances and diversify tree species, despite industry processes being optimized for current conifers. The study concludes that without a decisive transition from commodity production to innovative, value-added strategies, the Swedish forest sector’s competitiveness and resilience are at serious risk. Full article

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

The inner spacer module, which profoundly affects the final performance of a device, is a critical component in GAA NSFET (Gate-all-around Nanosheet Field Effect Transistor) manufacturing and necessitates systematic optimization and fundamental innovation. This work aims to develop an advanced SiGe etching process with high selectivity, uniformity and low damage to achieve an ideal inner spacer structure for logic GAA NSFETs. For three distinct dry etching technologies, ICP (Inductively Coupled Plasma Technology), RPS (Remote Plasma Source) and Gas Etching, we evaluated their potential and comparative advantages for inner spacer cavity etching under the same experimental conditions. The experimental results demonstrated that Gas Etching technology possesses the uniquely high selectivity of the SiGe sacrificial layer, making it the most suitable approach for inner spacer cavity etching to reduce Si nanosheet damage. Based on the results, in the stacked structures, the SiGe/Si selectivity ratio exhibited in Gas Etching is ~9 times higher than ICP and ~2 times higher than RPS. Through systematic optimization of pre-clean conditions, temperature and chamber pressure control, we successfully achieved a remarkable performance target of cavity etching: the average SiGe/Si etching selectivity is ~56, the inner spacer shape index is 0.92 and the local etching distance variation is only 0.65 nm across different layers. These findings provide valuable guidance for equipment selection in highly selective SiGe etching and offer critical insights into key process module development for GAA NSFETs. Full article

Workers with upper-limb disabilities face difficulties in performing manufacturing tasks requiring fine manipulation, stable handling, and multistep procedural understanding. To address these limitations, this paper presents an integrated collaborative workcell designed to support disability-inclusive manufacturing. The system comprises four core modules: a JSON-based collaboration database that structures manufacturing processes into robot–human cooperative units; a projection-based augmented reality (AR) interface that provides spatially aligned task guidance and virtual interaction elements; a multimodal interaction channel combining gesture tracking with speech and language-based communication; and a personalization mechanism that enables users to adjust robot behaviors—such as delivery poses and user-driven task role switching—which are then stored for future operations. The system is implemented using ROS-style modular nodes with an external WPF-based projection module and evaluated through scenario-based experiments involving workers with upper-limb impairments. The experimental scenarios illustrate that the proposed workcell is capable of supporting step transitions, part handover, contextual feedback, and user-preference adaptation within a unified system framework, suggesting its feasibility as an integrated foundation for disability-inclusive human–robot collaboration in manufacturing environments. Full article

The increasing application of lithium-ion batteries in manufacturing and energy storage systems necessitates high-precision screening of abnormal cells during manufacturing, so as to ensure safety and performance. Existing methods struggle to break down the barrier between prior knowledge and data, suffering from limitations such as insufficient detection accuracy and poor interpretability. This becomes even more prominent when facing distributional shifts in data. In this study, we propose a knowledge-enhanced anomaly detection framework for cell screening. This framework integrates domain knowledge, such as electrochemical principles, expert heuristic rules, and manufacturing constraints, into data-driven models. By combining features extracted from charging/discharging curves with rule-based prior knowledge, the proposed framework not only improves detection accuracy but also enables a traceable reasoning process behind anomaly identification. Experiments based on real-world battery production data demonstrate that the proposed framework outperforms baseline models in both precision and recall, making it a promising preferred solution for quality control in intelligent battery manufacturing. Full article

Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article

Critical mineral supply chains underpin electric mobility, power electronics, clean hydrogen, and advanced manufacturing. Drawing on the resource-based view (RBV), the relational view, and dynamic capabilities, we conceptualize advantage not as ownership of ore bodies but as orchestration of multi-tier resource systems: upstream access, midstream processing know-how, standards and permits, and durable inter-organizational ties. In a world of high concentration at key stages (refining, separation, engineered materials), full “decoupling” is economically costly and technologically constraining. We argue for structured cooperation among the United States, European Union, China, and other producers and consumers, combined with selective domestic capability building for bona fide security needs. Methodologically, we conduct a structured conceptual synthesis integrating RBV, relational view, dynamic capabilities, and network-of-network research, combined with a structured comparative policy analysis of U.S./EU/Chinese instruments anchored in official documents. We operationalize the argument via technology–material dependency maps that identify midstream bottlenecks and the policy/standard levers most likely to expand qualified, compliant capacity. Full article

Faced with the contradiction between the increasingly growing demand and labor-intensive manufacturing modes, in the current era of rapid development of informatization and artificial intelligence, improving manufacturing efficiency by means of automated manufacturing equipment has become a recognized development direction for most shipyards. This trend is particularly evident in the manufacturing of sub-assemblies, which are the smallest composite units of the hull. Taking an automated sub-assembly welding line in a shipyard as the research object, this paper constructs a mathematical model aimed at optimizing production efficiency based on the analysis of its operational processes and characteristics and proposes an improved two-layer fruit fly optimization algorithm (ITLFOA) for solving the automated sub-assembly welding line scheduling problem (ASWLSP). The proposed ITLFOA features a two-layer nested algorithm structure, with several key improvements proposed for both optimization layers, such as heuristic rules for spatial layout, improved neighborhood operators, an added disturbance mechanism, and an added population diversity restoration mechanism. Finally, the performance of ITLFOA is validated through a comparative analysis against the initial two-layer fruit fly optimization algorithm (initial TLFOA), the well-established Variable Neighborhood Search (VNS) algorithm and the actual manual operation results on a specific case of a shipyard. Full article

Additive manufacturing has been adopted in several industries including the medical field to develop new personalised medical implants including tissue engineering scaffolds. Custom patient-specific scaffolds can be additively manufactured to speed up the wound healing process. The aim of this study was to design, fabricate, and evaluate a range of materials and scaffold architectures for 3D-printed wound dressings intended for soft tissue applications, such as skin repair. Multiple biocompatible polymers, including polylactic acid (PLA), polyvinyl alcohol (PVA), butenediol vinyl alcohol copolymer (BVOH), and polycaprolactone (PCL), were fabricated using a material extrusion additive manufacturing technique. Eight scaffolds, five with circular designs (knee meniscus angled (KMA), knee meniscus stacked (KMS), circle dense centre (CDC), circle dense edge (CDE), and circle no gradient (CNG)), and three square scaffolds (square dense centre (SDC), square dense edge (SDE), and square no gradient (SNG), with varying pore widths and gradient distributions) were designed using an open-source custom toolpath generator to enable precise control over scaffold architecture. An in vitro degradation study in phosphate-buffered saline demonstrated that PLA exhibited the greatest material stability, indicating minimal degradation under the tested conditions. In comparison, PVA showed improved performance relative to BVOH, as it was capable of absorbing a greater volume of exudate fluid and remained structurally intact for a longer duration, requiring up to 60 min to fully dissolve. Tensile testing of PLA scaffolds further revealed that designs with increased porosity towards the centre exhibited superior mechanical performance. The strongest scaffold design exhibited a Young’s modulus of 1060.67 ± 16.22 MPa and withstood a maximum tensile stress of 21.89 ± 0.81 MPa before fracture, while maintaining a porosity of approximately 52.37%. This demonstrates a favourable balance between mechanical strength and porosity that mimics key properties of engineered tissues such as the meniscus. Overall, these findings highlight the potential of 3D-printed, patient-specific scaffolds to enhance the effectiveness and customisation of tissue engineering treatments, such as meniscus repair, offering a promising approach for next-generation regenerative applications. Full article

This study contributes to the advancement of circular economy practices in polymer manufacturing by applying machine learning algorithms (MLA) to predict the tensile strength of recycled low-density polyethylene (LDPE) building films. As the construction and packaging industries increasingly seek eco-efficient and low-carbon materials, recycled LDPE offers a valuable route toward sustainable resource management. However, ensuring consistent mechanical performance remains a challenge when reusing polymer waste streams. To address this, tensile tests were conducted on LDPE films produced from recycled granules, measuring tensile strength, strain, mass per unit area, thickness, and surface roughness. Three established machine learning algorithms—feed-forward Neural Network (NN), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were implemented, trained, and optimized using the experimental dataset using R statistical software (version 4.4.3). The models achieved high predictive accuracy, with XGBoost providing the most robust performance and the highest level of explainability. Feature importance analysis revealed that mass per unit area and surface roughness have a significant influence on film durability and performance. These insights enable more efficient production planning, reduced raw material usage, and improved quality control, key pillars of sustainable technological innovation. The integration of data-driven methods into polymer recycling workflows demonstrates the potential of artificial intelligence to accelerate circular economy objectives by enhancing process optimization, material performance, and resource efficiency in the plastics sector. Full article

An interior permanent magnet synchronous motor (IPMSM) rotor equipped with a carbon-fiber-reinforced plastic (CFRP) sleeve, based on an electromagnetic–mechanical coupled analysis approach, was designed and analyzed. As the demand for high-speed traction motors increases, addressing the excessive rotor stress caused by higher rotational speeds has become critical. To mitigate this issue, a CFRP sleeve is used to enhance mechanical strength while minimizing the impact on electromagnetic performance. However, the introduction of the CFRP sleeve increases design complexity because of changes in air gap length and structural constraints. The crucial considerations required in the design and manufacturing processes, including tradeoffs between mechanical integrity and magnetic performance, are discussed. The proposed design methodology differentiates the rotor structure from conventional IPMSMs, offering a viable solution for high-speed, high-power-density motor applications. Full article

Despite the immense potential of digital intelligence technologies to enhance corporate profitability, manufacturing enterprises often face the “digital–green paradox”, which indicates that while companies invest in digital and intelligent transformation, their energy consumption increases rather than promoting green transition. To provide reasonable transformation solutions for manufacturers still caught in this paradox, this paper adopts a single-case study approach from a product lifecycle perspective. Focusing on FAW-Volkswagen—a manufacturing enterprise demonstrating outstanding performance in digital-intelligent green transformation—this study conducts an in-depth investigation into the stage characteristics and underlying mechanisms. The results show that the following: (1) The digital-intelligent green transformation of manufacturing enterprises is an iterative process evolving from “green design, low-carbon production, intelligent service to enterprise spiral value-added”, with distinct digital-intelligent empowerment models at each stage. (2) By leveraging digital-intelligent technologies, manufacturing enterprises can build a multi-tiered “internal-external dual circulation” green development system encompassing the “enterprise—industrial chain—full ecosystem,” driving comprehensive green upgrades across the entire industry and ecosystem. This paper reveals the intrinsic mechanisms through which digital-intelligent technologies facilitate manufacturing enterprises’ green transformation. It expands and enriches the research context and theoretical implications of product lifecycle management, offering management insights and strategic references for other enterprises pursuing green transformation and upgrading pathways in the digital-intelligent economy era. Full article

The external color of smoked sausages is a critical indicator of quality and uniformity in processing. Commercial colorimeters are unsuitable for high-throughput sorting due to the challenges posed by the sausage’s curved cylindrical surface and the need for an inline application. This study introduces a novel non-contact sensing module (LEDs at 45°, fiber optic collection at 0°) to acquire spectral data (400–700 nm) and derive CIE LAB. First, a handheld prototype validated the accuracy of the sensing module against a benchtop spectrophotometer. It successfully categorized five color grades (‘Over light’, ‘Light’, ‘Standard’, ‘Dark’, and ‘Over dark’) with a clear distribution on the a*-L* diagram. This established acceptable color boundary conditions (44.2 < L* ≤ 61.3, 14.1 < a* < 23.9). Second, three sensing modules were integrated around a conveyor belt at 120° intervals, forming the core of an automated inline sorting system. Blind field tests (n = 150) achieved high sorting accuracies of 95.3–97.3% with an efficient inspection time of less than 2 s per sausage. This work realizes the standardization, digitalization, and automation of food color inspection, demonstrating strong potential for smart manufacturing in the processed meat industry. Full article