Search Results (14,186)

The infrared (IR)—visible light (VIS) dual-camera system provides complementary cues for image fusion, but issues such as geometric mismatch caused by different imaging methods, inconsistent resolution/field-of-view, and installation offsets often lead to ghosting and artifacts. This study aims to develop a fast-deployable and repeatable calibration workflow based on cost-effective calibration board. We designed an infrared-visible high-contrast checkerboard plate that can be generated through structural parameterization and efficiently manufactured using Python/OpenSCAD. We also established a corner-based registration pipeline that estimates global homography to align the visible-light images onto the infrared pixel grid for fusion and quantitative evaluation. Experiments conducted in a controlled indoor environment demonstrated stable sub-pixel performance within a range of 1.5–2.5 m, with an average re-projection error of 0.47–0.50 pixels per frame and a 95th percentile lower than 0.51 pixels. The corner position re-projection error test further confirmed stability near image boundaries, with a median value of 0.53–0.63 pixels and a 95th percentile of 0.54–0.64 pixels. Overall, the proposed target design and workflow can achieve practical infrared-visible calibration under typical deployment constraints and have repeatable accuracy, providing geometrically consistent input for subsequent fusion and dataset construction. Full article

Microleakage at the implant–abutment interface represents a potential pathway for bacterial penetration and may contribute to peri-implant inflammation, marginal bone loss, and mechanical complications such as screw loosening. The increasing clinical use of compatible prosthetic abutments as cost-effective alternatives to original components has raised concerns regarding their fit, sealing capacity, and mechanical stability at this interface. The aim of this in vitro study was to evaluate differences in sealing capacity and torque loss between original and non-original abutments in a mixed internal connection implant system and to investigate the applicability of a novel quantitative approach for assessing microleakage based on a hydraulic conductance perfusion system. Nine abutments, including four multi-unit and five screw-retained cementable abutments, were connected to Straumann Bone Level implants at two tightening torques (5 N·cm and 35 N·cm). Microleakage was quantified by measuring fluid transport across the implant–abutment interface using the perfusion system, and removal torque values were recorded after testing. Non-original abutments exhibited significantly greater microleakage than original abutments at both torque levels. Microleakage increased significantly when the installation torque was reduced to 5 N·cm. At the manufacturer-recommended torque, screw-retained cementable abutments demonstrated higher microleakage than multi-unit abutments. Non-original abutments also showed significantly greater torque loss. These findings suggest that original abutments provide improved sealing capacity and mechanical stability at the implant–abutment interface, while the hydraulic conductance perfusion system represents a promising quantitative tool for investigating microleakage. Full article

Background: Fused deposition modeling (FDM) is one of the most well-known and often published methods for 3D-printed drug delivery systems. In early scientific reports, the active pharmaceutical ingredients were added by soaking, but later, a new milestone was established, after researchers started to manufacture their own filaments by hot-melt extrusion (HME). The number of publications covering this method has multiplied in the last decade, a wide range of natural and synthetic polymers have been tested with versatile active pharmaceutical ingredient components, and various printing parameters and their effects have been investigated. Objectives: In this review, we aim to synthesize how the available quality by design approaches and the scientific results established so far can facilitate the creation of a guideline for appropriate quality production of HME-FDM 3D-printed pharmaceuticals. Methods: Based on PRISMA 2020 guidelines, a systematic search of relevant publications from 2015 to 2025 was carried out using the PubMed database. Twenty-six articles were included, based on number of monitored parameters and methodological description. Reporting of important quality processes and material parameters was assessed. Results: HME, the FDM, and analytical testing experiences were compared and collected into three tables from the selected publications. In two different sections, the pharmacopeial dosage-form tests and the involvement of process analytical technologies (PAT) were also analyzed. We found that reporting of influential parameters is heterogenous, and lack of robust reporting schemes limits the development of QbD approaches. Conclusions: Regarding the data, trends were synthetized, and a guideline was created which is limited by inconsistent parameter reporting. Full article

Vertical-cavity surface-emitting lasers (VCSELs) have emerged as essential light sources for atomic-precision measurement, quantum-secure communication, high-speed optical transmission, and laser coherent scanning detection, owing to their low power consumption, high-quality beam characteristics, and ease of two-dimensional integration. However, the fundamental limitation on linewidth narrowing in VCSELs arises from their inherently short resonator, resulting in a natural linewidth on the order of 50–100 MHz. This limitation prevents conventional VCSELs from meeting the stringent requirements of advanced applications, making the ultra-narrow linewidth a key focus in optoelectronics research. This review analyzes representative achievements and application scenarios of narrow-linewidth VCSELs, evaluates the merits and limitations of industrial-grade devices, and envisions future directions in next-generation optoelectronic systems. Distinct from existing reviews, it integrates key single-mode fabrication techniques, quantitative linewidth requirements across applications, silicon photonic integration, and scalable manufacturing trends, establishing a complete mechanism–technology–application–industry analytical framework. Full article

With the advancement of the “dual carbon” goals, the electric vehicle market has experienced explosive growth, and user review mining has become key data support for industrial quality improvement and low-carbon transportation transition. Addressing the limitations of existing sentiment classification methods in long-distance feature capture, cross-sentence semantic association, and emotional feature focus, this study proposes a BERT-Bi-xLSTM-Attention fusion model: BERT pre-trained semantic representation extracts deep contextual information, Bi-xLSTM models long-range dependency relationships, and the Attention mechanism locates sentiment-critical markers. Based on multi-platform review data from Chinese Autohome, Yiche, and China Quality Inspection Network, experiments show that the model achieves Accuracy, Recall, Precision, and F1 values of 0.9323, 0.9326, 0.9321, and 0.9328, significantly outperforming baseline models. A “sentiment-topic” fusion analysis framework is constructed, identifying five positive themes and four negative themes, revealing the dual emotional characteristics of range, driving experience, and smart features. Temporal analysis finds that negative attention to intelligent system reliability has continued to rise from 2021 to 2024, becoming an emerging user pain point. Combined with the above findings, it is recommended that consumers comprehensively evaluate multi-attribute experiences when purchasing; manufacturers prioritize optimizing user-concerned attributes; and policymakers improve industrial standards and regulatory mechanisms. This promotes high-quality development of electric vehicles, contributes to the realization of carbon neutrality goals in the transportation sector, and facilitates sustainable transportation development. Full article

With the progressive decline of tailpipe emissions, non-exhaust sources such as brake wear are becoming an increasingly important contributor to traffic-related particulate matter in urban environments. In this context, improving real-world characterization of brake wear particles is essential for air-pollution assessment, source apportionment, and the development of cleaner and more sustainable road transport systems. Here, we investigated the emissions levels, particle size distribution and elemental composition of particles released during harsh real-world braking events by a single light-duty vehicle braking system equipped with an original manufacturer (OEM) non-asbestos organic (NAO) pad formulation. Using a direct on-vehicle sampling system combined with real-time particle sizing and high-resolution microscopy, we observed that particle emissions remained close to background levels at speeds up to 100 km/h, but rose sharply at 120 km/h, reaching 3.7 × 10⁷ #/cm³ in the 8–10 nm size range. This increase suggests that higher speeds are associated with elevated particle emissions, likely due to the higher braking temperatures reached at increased vehicle speeds. The emitted particles were mainly spherical agglomerates rich in iron, titanium, barium, zirconium, and sulphur, consistent with NAO pad formulations. Our results show that the investigated NAO pad system can deteriorate under thermal stress, potentially leading to higher levels of nanoparticle emissions compared to low-metallic or semi-metallic pads investigated under similar conditions. These findings provide real-world evidence relevant to urban air quality research, support the refinement of non-exhaust emissions inventories, and highlight the importance of thermally resilient friction-material formulations for mitigating residual particulate emissions in increasingly cleaner transport systems. Full article

Publicly available electricity datasets from operational industrial facilities remain limited due to instrumentation cost, retrofit complexity, and data governance constraints. This paper presents an openly accessible dataset of asset-level electrical energy measurements collected from a medium-scale industrial manufacturing facility over an approximately one-year observation window, with staged commissioning resulting in heterogeneous temporal coverage. The dataset includes time-series measurements from production machinery, auxiliary systems, and distribution-level assets instrumented using a heterogeneous fleet of Ethernet and RS-485 energy meters integrated via industrial gateways and programmable logic controllers. Measurements were acquired via a SCADA-based logging infrastructure and exported from an operational SQL historian. The publicly released dataset comprises fixed 15 min aggregated energy and power metrics derived from high-frequency SCADA telemetry. In its released ALL-phase representation, the dataset comprises measurements from 43 monitored assets and 1,039,873 15 min windows, corresponding to 2.96 GWh of measured electrical energy. Mean window-level data coverage is 99.99%, and 97.72% of ALL-phase windows satisfy the dataset’s reliability criterion. Interval records include energy consumption, demand, data coverage metrics, and reliability indicators. The dataset reflects real-world industrial monitoring conditions, including mixed communication pathways and irregular sampling behaviour, and is intended to support research in industrial energy analytics, data quality assessment, load profiling, and operational energy modelling. Full article

Smart hydrogel systems with stimuli-responsive properties are increasingly being investigated in combination with advanced additive manufacturing techniques for targeted drug delivery and wound healing in regenerative medicine; however, their clinical translation remains limited by challenges related to material performance, design complexity, and manufacturing scalability. This review analyzes recent developments in smart hydrogel design and 4D-printed scaffolds, with emphasis on programmable and stimuli-responsive architectures. The literature is selectively evaluated based on relevance to (i) hydrogel structure–property relationships, (ii) 3D/4D printing strategies, and (iii) demonstrated performance in drug delivery and wound healing applications. The analysis highlights design approaches enabling spatiotemporal control of drug release and dynamic scaffold behavior, while also examining how fabrication methods influence functional outcomes. Major limitations are critically assessed, including issues of reproducibility, mechanical stability, long-term performance, and the gap between experimental studies and clinical application. Challenges in defining and implementing 4D printing in biomedical contexts are discussed as well. Overall, this review identifies current design trade-offs, outlines priorities for improving reliability and translational potential, and synthesizes emerging trends in 3D and 4D printed hydrogel scaffolds for precision drug delivery and regenerative wound therapy. Full article

Additive manufacturing (AM) enables the fabrication of complex three-dimensional components with embedded internal flow channels, but the as-built inner surfaces often exhibit high roughness and poor surface-quality uniformity, particularly at non-coplanar corner regions such as sharp bends and junctions. Conventional abrasive flow machining (AFM) can improve the overall surface finish of such channels; however, corner regions commonly remain weak-removal zones because of local flow stagnation and insufficient abrasive action. To address this limitation, this study proposes a six-degree-of-freedom (6-DOF) robotic-arm-assisted liquid metal-driven abrasive flow (LM-AF) polishing strategy in which robotic pose regulation is used to guide the liquid metal droplet to designated corner regions while preserving its responsiveness to the electric field. Numerical simulations and conventional AFM experiments on S-shaped and M-shaped spatial channels were first conducted to identify the corner regions as the primary sources of polishing non-uniformity. A robotic posture-control framework was then established through manipulator kinematics, point-cloud-based flow-direction identification, and Rodrigues-matrix-based pose transformation. On this basis, localized secondary polishing was experimentally performed on an S-shaped channel using an AC electric-field-driven liquid-metal abrasive system. The results show that corner-region roughness was significantly reduced and approached the straight-channel benchmark after secondary polishing, demonstrating a marked improvement in inner-surface uniformity. This study provides a practical route for targeted compensation polishing in complex three-dimensional internal channels and offers a new framework for robotic-assisted post-processing of AM-fabricated flow paths. Full article

Fueled by the push for “More than Moore”, three-dimensional integrated circuits (3D ICs) have become a backbone of next-generation electronics. Their complex architectures place unprecedented demands on etching technologies, which must now deliver atomic precision, stringent high-aspect-ratio (HAR) control, and virtually damage-free profiles. Meeting these challenges requires metrology capable of true 3D, quantitative analysis at the nanoscale. Atomic force microscopy (AFM) has proven essential in this regard, offering non-destructive, sub-nanometer characterization that other techniques cannot provide. This review systematically examines AFM’s pivotal role in advancing key etching processes for 3D ICs, including deep reactive ion etching of through-silicon vias (TSVs), atomic layer etching (ALE), and cryogenic plasma etching. We detail AFM’s unique contributions to quantifying sidewall roughness, verifying etch-per-cycle rates, and assessing surface damage. We also discuss how recent innovations, such as tilting-AFM, HAR probes, and automated inline systems, are overcoming traditional barriers in throughput and access to sidewalls and deep trenches. Looking forward, the integration of AFM with optical metrology, machine learning, and multi-scale modeling opens a path toward truly autonomous process control and optimization. As such, AFM stands as an indispensable tool for developing and refining the etching processes that underpin next-generation 3D semiconductor manufacturing. Full article

The textile industry faces machinery maintenance challenges due to reactive practices, lack of real-time monitoring, and absent integrated management systems, resulting in unplanned downtime, elevated costs, and quality variability. This study addresses these limitations by proposing a hybrid predictive–prescriptive framework integrating XGBoost 3.2.0 and LSTM models with a multi-objective optimization engine to generate data-driven maintenance recommendations. The framework was validated on four critical components, needles, hooks, needle guides, and thread tensioners, using operational data from a textile plant (November 2024–January 2026). Plant-wide Mean Time Between Failures increased by 38% (15–21 to 24–28 h), while Mean Time To Repair decreased by 15% (5.31 to 4.6 h). These improvements yielded 5.5% lower maintenance costs, 9% less fabric waste, and reduced cost per operating hour from $25 to $23.5. The prescriptive module transformed imperfect predictions into robust decisions by evaluating interventions against production constraints, spare parts availability, and risk criteria. Beyond quantitative gains, the framework enabled sustainable practices including data-driven spare parts policies and condition-based inspections. This work demonstrates that integrating prediction with prescription effectively overcomes structural maintenance challenges in textile manufacturing, providing a replicable methodology for broader industrial adoption. Full article

This work presents a methodology for the generation of continuous fibre trajectories based on principal stress directions in continuous fibre-reinforced additive manufacturing (CFAM). The material system considered consists of continuous carbon fibre (CCF-1.5K) embedded in a CFC-PA thermoplastic matrix. CFAM enables the deposition of fibres along tailored paths, allowing improved alignment with the load direction, compared to traditional composite manufacturing. In this way, the strong anisotropy of composite materials, typically considered a limitation, is exploited as a design opportunity by aligning fibres with the structural load paths. The proposed approach combines finite element analysis with a path generation procedure, including the computation of principal stress directions, the extraction of streamlines of the principal stress field, and a dedicated post-processing stage aimed at obtaining continuous and manufacturable fibre layouts. The effectiveness of the method is assessed through a finite element-based comparison with conventional fibre configurations, showing an increase in global stiffness of approximately 20% with respect to the best-performing unidirectional layout. In addition, the feasibility of the generated trajectories is demonstrated through printing tests performed on a continuous fibre additive manufacturing system. The results confirm that the proposed methodology enables the generation of physically realizable fibre paths while improving structural performance. Full article

The transition toward sustainable, human-centric, and resilient manufacturing systems has accelerated the emergence of Industry 5.0, repositioning quality management as a key enabler of sustainable industrial transformation. Quality 5.0 extends digitally enabled quality practices by explicitly integrating human wellbeing, environmental responsibility, and organizational resilience. However, for manufacturing firms in developing economies, guidance on how to prioritize the critical success factors (CSFs) for effective Quality 5.0 adoption remains limited. This study aims to identify and prioritize sustainability-oriented CSFs for Quality 5.0 adoption in South African manufacturing organisations using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). A systematic literature review informs the development of a hierarchical CSF model, which is subsequently evaluated through expert judgements from industry and academia. Triangular fuzzy numbers and Chang’s extent analysis are employed to address uncertainty and subjectivity in decision-making. Key findings indicate that workforce skills and competence (global weight = 0.134), human-centric leadership (0.122), reliable digital infrastructure (0.118), employee engagement and empowerment (0.109), and environmental sustainability integration (0.096, rank 5) are top enablers. The findings highlight that technological readiness alone is insufficient, and that social and organizational sustainability dimensions play a dominant role in Quality 5.0 adoption within resource-constrained contexts. This study contributes by providing a sustainability-oriented decision-support framework for prioritizing Quality 5.0 adoption initiatives and offers actionable insights for managers and policymakers seeking to advance sustainable manufacturing in developing economies. Full article

Intelligent manufacturing requires strategic performance indicators that link shop-floor performance with productivity and sustainability goals. This study examines Overall Equipment Effectiveness (OEE) as a strategic key performance indicator and applies it to a hydraulic plastic injection-moulding machine producing an automotive component. Production data captured through a PLC-and-SQL-integrated digital monitoring system over 14 months were used to calculate monthly Availability, Performance, Quality, and OEE values and to identify the main sources of efficiency loss. The baseline period showed low OEE, driven mainly by unplanned downtime, minor stoppages, and cycle times above the 45 s target, whereas Quality remained consistently close to 100%. A diagnostic analysis combining production logs, downtime stratification, cycle-time records, and consultations with plant personnel was then used to define improvement actions. The implemented measures included preventive and predictive maintenance, process-parameter optimisation, operator training, and wider use of digital monitoring and analytics. In the post-improvement period, OEE increased markedly, downtime decreased, and cycle-time stability improved, reaching values close to world-class performance. The results confirm that OEE can function as a unifying KPI for intelligent manufacturing, supporting data-driven decision-making, continuous improvement, and more sustainable production. Full article

Manufacturing tolerances inevitably induce cell-to-cell inconsistencies. These inconsistent cells are connected in series and parallel to form battery packs, which will affect the safety and reliability of the battery system. This study presents a novel optimization framework integrating the multi-level physical model with machine learning to improve battery consistency from the manufacturing perspective. The multi-level physical modeling approach is applied to establish the link between the parameter deviations of the calendering process and the battery inconsistency performance. Based on the multi-level physical model, the Monte Carlo method is used to describe parameter deviations and generate datasets of electrochemical properties. The coefficients of variations in battery capacity and resistance are calculated as the consistency evaluation index based on these datasets. The proposed optimization approach applies machine learning to reduce the computational cost of the multi-level physical simulations due to lots of Monte Carlo simulations. Combined with the multi-level physical model and neural network model, the multi-objective particle swarm optimization algorithm is adopted to provide the optimal calendering process parameter deviations by achieving the trade-off between battery consistency performance and manufacturing cost. Results indicate that the battery consistency performance is improved by controlling the precision of the calendering process and manufacturing cost. This approach can effectively give feedback and guidance to the inverse design of the manufacturing process. Full article