Search Results (1,539)

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency. Full article

The control of crystal form in chiral active pharmaceutical ingredients (APIs) is a critical challenge in pharmaceutical development, as differences in solid-state structure can significantly influence physical properties and manufacturing performance. Troglitazone, a molecule with two chiral centers, exists as four stereoisomers (RR, SS, RS, SR) that crystallize as two enantiomeric pairs: RR/SS and RS/SR. This study aims to elucidate the relationship between solution-state molecular interactions and crystallization behavior of these diastereomeric pairs. Antisolvent crystallization experiments were conducted for both mixed solutions containing all four isomers and solutions of individual pairs. Crystallization kinetics were monitored by HPLC, and the resulting solids were characterized by PXRD, DSC, TG, and microscopic observation. Nucleation induction times were determined over a range of supersaturation levels. To probe intermolecular interactions in solution, NOESY and targeted NOE NMR experiments were performed, and the results were compared with crystallographic data. The RS/SR crystals(H-form) consistently exhibited shorter induction times and faster crystallization rates than the RR/SS crystals (L-form), even under conditions where RR/SS solutions were more supersaturated. In mixed solutions, H-form crystallized preferentially, with L-form either remaining in solution or being incorporated into H-form crystals as a solid solution. NOESY and NOE analyses revealed intermolecular proximities between protons that are distant in the molecular structure, indicating the presence of ordered aggregates in solution. These aggregates were more structurally compatible with the H-form than with the L-form crystal lattice, as supported by crystallographic distance analysis. The results demonstrate that differences in nucleation kinetics between troglitazone diastereomers are closely linked to solution-state molecular arrangements. Understanding these relationships provides a molecular-level basis for the rational design of selective crystallization processes for chiral APIs. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

Filter wash water (FWW) from public swimming pools is a recoverable resource, yet full-scale evidence on safe on-site reuse with documented economics is scarce. We evaluated a full-scale integrated recovery unit (SOWA) installed at an indoor public pool. The SOWA system—sedimentation, granular filtration operated at a hydraulic loading rate (HLR) of 7.5–10 m³ m⁻² h⁻¹, ultrafiltration, and chlorine-dioxide (ClO₂) disinfection—was monitored for physicochemical and microbiological performance. Turbidity decreased from 23.1 nephelometric turbidity units (NTU) to 0.25 NTU; chemical oxygen demand, reported as the permanganate index (COD_Mn), fell from 10.4 to 1.6 mg O₂ L⁻¹; and total microbial count declined from 1.6 × 10⁴ to 30 colony-forming units per millilitre (CFU mL⁻¹). Indicator organisms (Escherichia coli, Intestinal enterococci and Pseudomonas aeruginosa) were not detected, and all quality criteria complied with national standards. At the Olender facility, monthly freshwater use dropped from 1700 to 1000 m³ after 24/7 SOWA operation, while combined chlorine was maintained at 0.12 mg Cl₂/L and no issues with chloroform were observed. The unit recovered 4.7 m³ h⁻¹ of FWW for non-potable uses. According to manufacturer catalogue data, the recovery process can reach up to 96%, enabling annual savings up to ~EUR 9000 and a payback of ~2 years under favourable tariffs and loads. Our outcomes are consistent with independent full-scale reuse trains (e.g., ultrafiltration/reverse osmosis) and with disinfection-by-product control strategies reported in the literature, and they align with international guidance for swimming-pool water reuse. This study provides a rare, end-to-end implementation at full scale, documenting continuous operation, verified microbial safety, regulatory compliance, quantified water and cost savings, and site-specific economics for a compact, multi-barrier FBW recovery system that can be directly transferred to similar facilities. Full article

Foodborne diseases (FBDs) represent significant public health concerns as they are conditions associated with deficient manufacturing practices. They comprise important diseases with acute or chronic courses, frequently occurring in outbreak form and associated with significant gastrointestinal disorders. FBDs are related to infrastructure and organizational issues in urban centers, such that contamination in food processing facilities, lack of access to basic sanitation, and social and financial vulnerability are some of the factors that favor their occurrence and the demand for health services. Among the agents associated with FBDs is Listeria sp., especially Listeria monocytogenes (L. monocytogenes). The objective of this article is to characterize L. monocytogenes and its potential impact on One Health, given its importance as a significant foodborne pathogen. A thorough scientific literature search was conducted to obtain information on the subject, aiming to assist in the verification and presentation of evidence. L. monocytogenes is a pathogen with specific characteristics that ensure its adhesion, adaptation, growth, and survival on various surfaces, such as biofilm formation ability and thermotolerance. Several diagnostic methods are available for detection of the agent, including enrichment media, molecular techniques, and subtyping evaluation. Its control represents a significant challenge, with critical implications due to bacterial perpetuation characteristics and the implementation/monitoring of sanitization programs and commercialization of animal-derived products (POAO). Thus, vulnerable and susceptible populations are more exposed to foodborne pathogens due to health-related determinants, such as inadequate sanitation, poor food safety control, and insufficient personal hygiene. The pathogen’s persistence and difficulty of control represent a significant public One Health threat. Full article

An AA6061-7075 composite with a heterogeneous structure was fabricated via the additive friction stir deposition (AFSD) method, and in situ processing data were monitored during the manufacturing process. The results show that the cross-section of the composite subjected to AFSD exhibits a lower degree of plastic deformation behavior compared to the surface and side of the composite, owing to serious heat accumulation during the layer-by-layer stacking process. The denser, heterogeneous structure, consisting of finer (softer) and coarser (harder) grains, which correspond to AA6061 and AA7075, was formed according to transmission electron microscopy (TEM) analysis. Furthermore, the obtained composite subjected to AFSD in this work presents outstanding mechanical properties compared to other as-fabricated AA6061/AA7075 depositions acquired by other additive manufacturing methods along the horizontal building direction, with the ultimate tensile strength (266 MPa) being 89% of that of AA6061-T6 and the elongation 1.1 times that of AA7075-T6. The findings provide useful guidelines for the in situ preparation of Al-based composites and offer ideas for manufacturing high-strength heterostructures for large-scale practical engineering applications. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

CFRP is extensively utilized in the manufacturing of aerospace equipment owing to its distinctive properties, and hole-making processing continues to be the predominant processing method for this material. However, due to the anisotropy of CFRP, in its processing process, processing damage appears easily, such as stratification, fiber tearing, burrs, etc. These damages will seriously affect the performance of CFRP components in the service process. This work employs acoustic emission (AE) and infrared thermography (IT) techniques to analyze the characteristics of AE signals and temperature signals generated during the CFRP drilling process. Fast Fourier transform (FFT) and short-time Fourier transform (STFT) are used to process the collected AE signals. And in combination with the actual damage morphology, the material removal behavior during the drilling process and the AE signal characteristics corresponding to processing defects are studied. The results show that the time-frequency graph and root mean square (RMS) curve of the AE signal can accurately distinguish the different stages of the drilling process. Through the analysis of the frequency domain characteristics of the AE signal, the specific frequency range of the damage mode of the CFRP composite material during drilling is determined. This paper aims to demonstrate the feasibility of real-time monitoring of the drilling process. By analyzing the relationship between the RMS values of acoustic emission signals and hole surface topography under different drilling parameters, it provides a new approach for the research on online monitoring of CFRP drilling damage and improvement of CFRP machining quality. Full article

The term “image captioning” refers to the process of converting an image into text through computer vision and natural language processing algorithms. Image captioning is still considered an open-ended topic despite the fact that visual data, most of which pertains to images, is readily available in today’s world. This is despite the fact that recent developments in computer vision, such as Vision Transformers (ViT) and language models using BERT and GPT, have opened up new possibilities for the field. The purpose of this review paper is to provide an overview of the present status of the field, with a specific emphasis on the use of facial expression recognition and object detection for the purpose of image captioning, particularly in the context of fault-aware systems and Prognostics and Health Management (PHM) applications within Industry 4.0 environments. However, to the best of our knowledge, no review study has focused on the significance of facial expressions in relation to image captioning, especially in industrial settings where operator facial expressions can provide valuable insights for fault detection and system health monitoring. This is something that has been overlooked in the existing body of research on image captioning, which is the primary reason why this study was conducted. During this paper, we will talk about the most important approaches and procedures that have been utilized for this task, including fault-aware methodologies that leverage visual data for PHM in smart manufacturing contexts, and we will highlight the advantages and disadvantages of each strategy. The purpose of this review is to present a comprehensive assessment of the current state of the field and to recommend topics for future research that will lead to machine-translated captions that are more detailed and accurate, particularly for Industry 4.0 applications where visual monitoring plays a crucial role in system diagnostics and maintenance. Full article

The corrosion resistance and microstructural characteristics of 17-4 PH stainless steel fabricated through Metal Binder Jetting (MBJ) were investigated through Cyclic Potentiodynamic Polarization (CPP), Open Circuit Potential (OCP) monitoring, SEM-EDX, optical microscopy, XRD, and chemical etching. Electrochemical tests revealed that as-sintered samples exhibited isotropic corrosion performance across different build-up orientations and directions. The CPP tests indicated the formation of a passive film with limited stability, while the monitoring of the OCP showed initial instability, followed by stabilization over time. Microstructural analysis indicated the presence of microporosities and a structure consisting of martensitic and ferritic grains in the as-sintered 17-4 PH, alongside copper and niobium segregations at grain boundaries, which may deeply influence localized corrosion susceptibility. These findings suggest that the as-sintered 17-4 PH fabricated through MBJ exhibits comparable corrosion behavior to 17-4 PH additive-manufactured through other techniques in which the sintering process is involved. The study highlights the influence of microstructure on electrochemical performance and underscores the need for post processing treatments to enhance corrosion resistance. Full article

In response to the growing demand for automation in aerospace harness manufacturing, this study proposes a digital twin-based intelligent monitoring system for robotic wiring operations. The system integrates a seven-degree-of-freedom robotic platform with an adaptive servo gripper and employs a five-dimensional digital twin framework to synchronize physical and virtual entities. Key innovations include a coordinated motion model for minimizing joint displacement, a particle-swarm-optimized backpropagation neural network (PSO-BPNN) for adaptive gripping based on wire characteristics, and a virtual–physical closed-loop interaction strategy covering the entire wiring process. Methodologically, the system enables motion planning, quality prediction, and remote monitoring through Unity3D visualization, SQL-driven data processing, and real-time mapping. The experimental results demonstrate that the system can stably and efficiently complete complex wiring tasks with 1:1 trajectory reproduction. Moreover, the PSO-BPNN model significantly reduces prediction error compared to standard BPNN methods. The results confirm the system’s capability to ensure precise wire placement, enhance operational efficiency, and reduce error risks. This work offers a practical and intelligent solution for aerospace harness production and shows strong potential for extension to multi-robot collaboration and full production line scheduling. Full article

Nucleic acid aptamers are single-stranded DNA or RNA molecules that can bind to a target with high specificity and affinity, as screened by the Systematic Evolution of Ligands by Exponential Enrichment (SELEX). In recent years, SELEX technologies have been significantly advanced for the screening of aptamers for a variety of target molecules, cells, and even bacteria and viruses. By integrating recent advances of emerging technologies with SELEX, novel screening technologies for nucleic acid aptamers have emerged with improved screening efficiency, reduced production costs and enhanced aptamer performance for a wide range of applications in medical diagnostics, drug delivery, and environmental monitoring. Aptasensors utilize aptamers to detect a wide range of analytes, allowing for the accurate identification and determination of small molecules, proteins, and even whole cells with remarkable specificity and sensitivity. Further optimization of the aptasensor can be achieved by aptamer truncation, which not only maintains the high specificity and affinity of the aptamer binding with the target analytes, but also reduces the manufacturing cost. Predictive models also demonstrate the powerful capability of determination of the minimal functional sequences by simulation of aptamer–target interaction processes, thus effectively shortening the aptamer screening procedure and reducing the production costs. This paper summarizes the research progress of protein-targeted aptamer screening in recent years, introduces several typical aptasensors at present, discusses the optimization methods of aptasensors by combining efficient SELEX with advanced predictive algorithms or post-SELEX processes, as well as the challenges and opportunities faced by aptasensors. Full article

Deep hole structures are widely used in the fields of aerospace, engineering machinery, marine, etc. During the deep hole machining processes, especially for boring procedures, the vibration phenomenon caused by the large aspect ratio of boring tools seriously restricts the machining accuracy and production efficiency. Therefore, extensive research has been devoted to the design and development of damped boring tools with different structures to suppress machining vibration. According to varied vibration reduction technologies, the damped boring tools can be divided into active and passive categories. This paper systematically reviews the advancements of vibration reduction principles, structure design, and practical applications of typical active and passive damped boring tools. Active damped boring tools rely on the synergistic action of sensors, actuators, and control systems, which can monitor vibration signals in real-time during the machining process and achieve dynamic vibration suppression through feedback adjustment. Their advantages include strong adaptability and wide adjustment capability for different machining conditions, including precision machining scenarios. Comparatively, vibration-absorbing units, such as mass dampers and viscoelastic materials, are integrated into the boring bars for passive damped tools, while an energy dissipation mechanism is utilized with the aid of boring tool structures to suppress vibration. Their advantages include simple structure, low manufacturing cost, and independence from an external energy supply. Furthermore, the potential development directions of vibration damped boring bars are discussed. With the development of intelligent manufacturing technologies, the multifunctional integration of damped boring tools has become a research hotspot. Future research will focus more on the development of an intelligent boring tool system to further improve the processing efficiency of deep hole structures with difficult-to-machine materials. Full article

Electrochemical energy storage technology, primarily lithium-ion batteries, has been widely applied in large-scale energy storage systems. However, differences in assembly structures, manufacturing processes, and operating environments introduce parameter inconsistencies among cells within a pack, producing complex, high-volume datasets with redundant and fragmented charge–discharge records that hinder efficient and accurate system monitoring. To address this challenge, we propose a hybrid ASW-UKF-TRF framework for the classification and compression of battery data collected from energy storage power stations. First, an adaptive sliding-window Unscented Kalman Filter (ASW-UKF) performs online data cleaning, imputation, and smoothing to ensure temporal consistency and recover missing/corrupted samples. Second, a temporally aware TRF segments the time series and applies an importance-weighted, multi-level compression that formally prioritizes diagnostically relevant features while compressing low-information segments. The novelty of this work lies in combining deployment-oriented engineering robustness with methodological innovation: the ASW-UKF provides context-aware, online consistency restoration, while the TRF compression formalizes diagnostic value in its retention objective. This hybrid design preserves transient fault signatures that are frequently removed by conventional smoothing or generic compressors, while also bounding computational overhead to enable online deployment. Experiments on real operational station data demonstrate classification accuracy above 95% and an overall data volume reduction in more than 60%, indicating that the proposed pipeline achieves substantial gains in monitoring reliability and storage efficiency compared to standard denoising-plus-generic-compression baselines. The result is a practical, scalable workflow that bridges algorithmic advances and engineering requirements for large-scale battery energy storage monitoring. Full article

Artificial intelligence (AI), particularly machine learning (ML), is equipping laser micro/nano processing with significant intelligent capabilities, demonstrating exceptional performance in areas such as manufacturing process modeling, process parameter optimization, and real-time anomaly detection. This transformative potential is driving the development of next-generation laser micro/nano processing technologies. The key challenges confronting traditional laser manufacturing stem from the complexity of laser–matter interactions, resulting in difficult-to-control processing outcomes and the accumulation of micro/nano defects across multi-step processes, ultimately triggering catastrophic process failures. This review provides an in-depth exploration of how machine learning effectively addresses these challenges through the integration of data-driven modeling with physics-driven modeling, coupled with intelligent in situ monitoring and adaptive control techniques. Systematically, we summarize current representative breakthroughs and frontier advances at the intersection of machine learning and laser micro/nano processing research. Furthermore, we outline potential future research directions and promising application prospects within this interdisciplinary field. Full article