Search Results (143)

Laser-based directed energy deposition for metallic materials (DED-LB/M) is a versatile additive manufacturing (AM) technique that facilitates the deposition of advanced protective coatings, the refurbishment of degraded components, and the fabrication of intricate metallic structures. Despite the technological advancements and potential, the presence of process-induced defects poses significant challenges to the repeatability and stability of the DED-LB/M process, limiting its widespread application, particularly in industries requiring high-quality products. In-situ process monitoring stands out as a key technological intervention, offering the possibility of real-time defect detection to mitigate these challenges. Focusing on the DED-LB/M process, this review provides a comparative analysis of various in-situ monitoring techniques and their effectiveness in identifying process-induced defects. The review categorises different sensing methods based on their sensor data format, utilised data processing techniques, and their ability to detect both surface and internal defects within the fabricated structures. Furthermore, it compares the capabilities of these techniques and offers a critical analysis of their limitations in defect detection. This review concludes by discussing the major challenges that remain in implementing in-situ defect detection in industrial practice and outlines key future directions necessary to overcome them. Full article

Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing. Full article

This paper introduces an acoustic-based monitoring system for high-speed CNC drilling, aimed at optimizing processes and enabling real-time machine state detection. High-fidelity acoustic sensors capture sound signals during drilling operations, allowing the identification of critical events such as tool engagement, material breakthrough, and tool withdrawal. Advanced signal processing techniques, including spectrogram analysis and Fast Fourier Transform, extract dominant frequencies and acoustic patterns, while machine learning algorithms like DBSCAN clustering classify operational states such as cutting, breakthrough, and returning. Experimental studies on materials including acrylic, PTFE, and hardwood reveal distinct acoustic profiles influenced by material properties and drilling conditions. Smoother sound patterns and lower dominant frequencies characterize PTFE drilling, whereas hardwood produces higher frequencies and rougher patterns due to its density and resistance. These findings demonstrate the correlation between acoustic emissions and machining dynamics, enabling non-invasive real-time monitoring and predictive maintenance. As AI power increases, it is expected to extract in-situ process information and achieve higher resolution, enhancing precision in data interpretation and decision-making. A key contribution of this project is the creation of an open sound library for drilling processes, fostering collaboration and innovation in intelligent manufacturing. By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. This AI-ready hardware enhances the accuracy and efficiency of drilling operations, improving quality, reducing tool wear, and minimizing downtime. The research establishes acoustic monitoring as a transformative approach to advancing CNC drilling processes and intelligent manufacturing systems. Full article

Lead (Pb) is one of the most relevant contaminants due to its high toxicity, even at low concentrations. The growing need for research about real-time Pb analysis in the field has driven advancements in portable, sensitive, and automated analytical methodologies. These innovations are crucial for taking proactive measures against the impacts of Pb pollution on ecosystems and public health. Flow analysis techniques have proven to be very effective in automating procedures for isolating and preconcentrating Pb in surface water and biological samples. Such automation boosts sample throughput and reduces processing time and reagent consumption, aligning with the green chemistry principles by lowering costs and minimizing waste. This review covers 31 recent automated analytical methodologies employing flow analysis techniques such as FIA, SIA, MSFIA, and LOV, emphasizing the trend toward portability and miniaturization, which facilitates in-situ analysis. Additionally, this review examines the pretreatment methods and detection systems used, highlighting the analytical parameters of each technique. The methodologies discussed demonstrate the capability to process up to 55 samples per hour accurately. Limits of quantification as low as 0.014 µg L⁻¹ are reported, enabling environmental monitoring that effectively detects Pb concentrations below the WHO and EPA drinking water reference values of 10 µg L⁻¹ and 15 µg L⁻¹, respectively. Full article

Coastal zones, particularly sandy beaches, are highly dynamic environments subject to a variety of natural and anthropogenic forcings. Instantaneous shoreline is a widely used indicator of beach changes in image-based applications, and it can display undulations at different spatial and temporal scales. Megacusps, periodic seaward and landward shoreline perturbations, are an example of such undulations that can significantly modify beach width and impact its usability. Traditionally, the study of these phenomena relied on video monitoring systems, which provide high-frequency imagery but limited spatial coverage. Instead, this study explored the potential of employing multispectral satellite-derived shorelines, specifically from Sentinel-2 (S2) and PlanetScope (PLN) platforms, for characterizing and monitoring megacusps’ formation and their dynamics over time. First, a tool was developed and validated to guarantee accurate shoreline detection, based on a combination of spectral indices, along with both thresholding and unsupervised clustering techniques. Validation of this shoreline detection phase was performed on three micro-tidal Mediterranean beaches, comparing with high-resolution orthomosaics and in-situ GNSS data, obtaining a good subpixel accuracy (with a mean absolute deviation of 1.5–5.5 m depending on the satellite type). Second, a tool for megacusp characterization was implemented and subsequent validation with reference data proved that satellite-derived shorelines could be used to robustly and accurately describe megacusps. The methodology could not only capture their amplitude and wavelength (of the order of 10 and 100 m, respectively) but also monitor their weekly–daily evolution using different potential metrics, thanks to combining S2 and PLN imagery. Our findings demonstrate that multispectral satellite imagery provides a viable and scalable solution for monitoring shoreline megacusp undulations, enhancing our understanding and offering an interesting option for coastal management. Full article

The presence of potentially toxic pollutants, such as pesticides and metal ions, even at low concentrations, can significantly impact aquatic environmental health. This pollution is a globally widespread problem and requires fast and reliable analysis, especially for in-situ identification/quantification. Atomic absorption spectrometry and plasma-based spectrometry techniques have been considered the most analytical tools used to monitor potentially toxic metal ions in aquatic media and other related matrices. The dynamics of global climate change and its correlation with pollution, especially from anthropogenic sources, have encouraged the development of other faster analytical tools for monitoring these pollutants. A noteworthy alternative for determining potentially toxic pollutants is using algae-based biosensors, resulting in a cost reduction and simplification of environmental analysis, enabling a more reliable comprehension of the role of humans in climate change. These biosensors, which may not have the highest sensitivity in quantification, have demonstrated remarkable potential in the identification of potentially toxic pollutants and several field applications. Biosensors can be an excellent biotechnology solution for monitoring global environmental changes. Thus, this review highlights the main advances in developing and comparing algae-based biosensors and other analytical possibilities for the identification of potentially toxic pollutants and their possible applications in environmental analysis. Full article

A continuous flow with reagent injection method on a novel inlaid microfluidic platform for nitrite determination has been successfully developed. The significance of the high-frequency monitoring of nutrient fluctuations in marine environments is crucial for understanding our impacts on the ecosystem. Many in-situ systems face limitations in high-frequency data collection and have restricted deployment times due to high reagent consumption. The proposed microfluidic device employs automatic colorimetric absorbance spectrophotometry, using the Griess assay for nitrite determination, with minimal reagent usage. The sensor incorporates 10 solenoid valves, four syringes, two LEDs, four photodiodes, and an inlaid microfluidic technique to facilitate optical measurements of fluid volumes. In this flow system, Taylor–Aris dispersion was simulated for different injection volumes at a constant flow rate, and the results have been experimentally confirmed using red food dye injection into a carrier stream. A series of tests were conducted to determine a suitable injection frequency for the reagent. Following the initial system characterization, seven different standard concentrations ranging from 0.125 to 10 µM nitrite were run through the microfluidic device to acquire a calibration curve. Three different calibrations were performed to optimize plug length, with reagent injection volumes of 4, 20, and 50 µL. A straightforward signal processing method was implemented to mitigate the Schlieren effect caused by differences in refractive indexes between the reagent and standards. The results demonstrate that a sampling frequency of at least 10 samples per hour is achievable using this system. The obtained attenuation coefficients exhibited good agreement with the literature, while the reagent consumption was significantly reduced. The limit of detection for a 20 µL injection volume was determined to be 94 nM from the sample intake, and the limit of quantification was 312 nM. Going forward, the demonstrated system will be packaged in a submersible enclosure to facilitate in-situ colorimetric measurements in marine environments. Full article

Inland water level and its dynamics are key components in the global water cycle and land surface hydrology, significantly influencing climate variability and water resource management. Satellite observations, in particular altimetry missions, provide inland water level time series for nearly three decades. Space-based remote sensing is regarded as a cost-effective technique that provides measurements of global coverage and homogeneous accuracy in contrast to in-situ sensors. The advent of Open-Loop Tracking Command (OLTC), and Synthetic Aperture Radar (SAR) mode strengthened the use of altimetry missions for inland water level monitoring. However, it is still very challenging to obtain accurate measurements of water level over narrow rivers and small lakes. This scoping systematic literature review summarizes and disseminates the research findings, highlights major results, and presents the limitations regarding inland water level monitoring from satellite observations between 2018 and 2022. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and through a double screening process, 48 scientific publications were selected meeting the eligibility criteria. To summarize the achievements of the previous 5 years, we present fundamental statistical results of the publications, such as the annual number of publications, scientific journals, keywords, and study regions per continent and type of inland water body. Also, publications associated with specific satellite missions were analyzed. The findings show that Sentinel-3 is the dominant satellite mission, while the ICESat-2 laser altimetry mission has exhibited a high growth trend. Furthermore, publications including radar altimetry missions were charted based on the retracking algorithms, presenting the novel and improved methods of the last five years. Moreover, this review confirms that there is a lack of research on the collaboration of altimetry data with machine learning techniques. Full article

Bridges are designed and built to be safe against failure and perform satisfactorily over their service life. Bridge structural health monitoring (BSHM) systems are therefore essential to ensure the safety and serviceability of such critical transportation infrastructure. Identification of structural damage at the earliest time possible is a major goal of BSHM processes. Among many developed damage identification techniques (DITs), vibration-based techniques have shown great potential to be implemented in BSHM systems. In a vibration-based DIT, the response of a bridge is measured and analyzed in either time or space domain for the purpose of detecting damage-induced changes in the extracted dynamic properties of the bridge. This approach usually requires a comparison between two structural states of the bridge—the current state and a reference (intact/undamaged) state. In most in-situ cases, however, data on the bridge structural response in the reference state are not available. Therefore, researchers have been recently working on the development of DITs that eliminate the need for a prior knowledge of the reference state. This paper thoroughly explains why and how the reference state can be excluded from the damage identification process. It then reviews the state-of-the-art reference-free vibration-based DITs and summarizes their merits and shortcomings to give guidance on their applicability to BSHM systems. Finally, some recommendations are given for further research. Full article

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets. Full article

With the growing interest in metal additive manufacturing using laser powder bed fusion (LPBF), there is a need for advanced in-situ nondestructive evaluation (NDE) methods that can dynamically monitor manufacturing process-related variations, that can be used as a feedback mechanism to further improve the manufacturing process, leading to parts with improved microstructural properties and mechanical properties. Current NDE techniques either lack sensitivity beyond build layer, are costly or time-consuming, or are not compatible for in-situ integration. In this research, we develop an electrical resistance diagnostic for in-situ monitoring of powder fused regions during laser powder bed fusion printing. The technique relies on injecting current into the build plate and detecting voltage differences from conductive variations during printing using a simple, cheap four-point electrode array directly connected to the build plate. A computational model will be utilized to determine sensitivities of the approach, and preliminary experiments will be performed during the printing process to test the overall approach. Full article

Atmospheric radon measurements assist in many aspects of climate and meteorological research, notably as an airmass tracer and for modelling boundary layer development, mixing heights and stability. Daughter products from radon decay are sometimes incorporated into the particle pollution measurements of commercially available beta-attenuation monitors (BAM). BAMs incorporating radon measurements are used in air quality monitoring networks and can supplement traditional radon measurements. Here we compare in-situ radon measurements from Thermo Fisher Scientific (Franklin, MA, USA) BAM instruments (Thermo Scientific 5014i, Thermo Scientific 5030 SHARP, Thermo Anderson FH62C14) at two air quality monitoring stations in New South Wales, Australia. Between systems we find strong correlations for hourly measurements (r = 0.97–0.99); daily means (r = 0.97–0.99); hour of the day (r = 0.84–0.98); and month (r = 0.82–0.98). The regression analysis for radon measurements between systems showed strong linear responses, although there are some variations in the slopes of the regressions. This implies that with correction BAM measurements can be comparable to standard measurement techniques, for example, from the Australian Nuclear Science and Technology Organisation (ANSTO) dual flow loop monitors. Our findings imply that BAM derived radon measurements are precise, although their accuracy varies. BAM radon measurements can support studies on boundary layer development or where radon is used as an atmospheric transport tracer. Full article

Monitoring turbidity is essential for sustainable coastal management because an increase in turbidity leading to diminishing water clarity has a detrimental ecological impact. Turbidity in coastal waters is strongly dependent on the concentration and physical properties of particles in the water column. In the Belgian part of the North Sea, turbidity and suspended particulate matter (SPM) concentrations have been monitored for decades by satellite remote sensing, but this technique only focuses on the surface layer of the water column. Within the water column, turbidity and SPM concentrations are measured in stations or transects with a suite of optical and acoustic sensors. However, the dynamic nature of SPM variability in coastal areas and the recent construction of offshore windmill parks and dredging and dumping activities justifies the need to monitor natural and human-induced SPM variability in 3D instead. A possible solution lies in modern multibeam echosounders (MBES), which, in addition to seafloor bathymetry data, are also able to deliver acoustic backscatter data from the water column. This study investigates the potential of MBES as a 3D turbidity and SPM monitoring tool. For this purpose, a novel empirical approach is developed, in which 3D MBES water column and in-situ optical sensor datasets were collected during ship transects to yield an empirical relation using linear regression modeling. This relationship was then used to predict SPM volume concentrations from the 3D acoustic measurements, which were further converted to SPM mass concentrations using calculated densities. Our results show that these converted mean mass concentrations at the Kwinte and Westdiep swale areas are within the limits of the reported yearly averages. Moreover, they are in the same order of magnitude as the measured mass concentrations from Niskin water samples during each campaign. While there is still need for further improvement of acquisition and processing workflows, this study presents a promising approach for converting MBES water column data to turbidity and SPM measurements. This opens possibilities for improving future monitoring tools, both in scientific and industrial sectors. Full article

Acrylonitrile butadiene styrene (ABS) is a thermoplastic polymer widely used in several everyday life applications; moreover, it is also one of the most employed plastics in contemporary artworks and design objects. In this study, the chemical and thermal properties of an ABS-based polymer and its photo-degradation process were investigated through a multi-analytical approach based on thermal, mass spectrometric and spectroscopic techniques. LEGO^® building blocks were selected for studying the ABS properties. First, the composition of unaged LEGO^® bricks was determined in terms of polymer composition and thermal stability; then, the bricks were subjected to UV–Vis photo-oxidative-accelerated ageing for evaluation of possible degradation processes. The modifications of the chemical and thermal properties were monitored in time by a multi-technique approach aimed at improving the current knowledge of ABS photodegradation, employing pyrolysis online with gas chromatography and evolved gas analysis, coupled with mass spectrometric detection (Py-GC-MS and EGA-MS), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and corroborated by external reflection FT-IR spectroscopy. The multimodal approach provided new evidence on the two-step degradation pathway proposed for ABS, defining molecular markers for polybutadiene oxidation and styrene-acrylonitrile depolymerization. Moreover, the results highlighted the feasibility of correlating accurate compositional and thermal data acquired by bulk techniques with external reflection FT-IR spectroscopy as a non-invasive portable tool to monitor the state of conservation of plastic museum objects in-situ. Full article

Smartphone-assisted fluorescence and colorimetric methods for the on-site detection of Hg²⁺ and Cl⁻ were established based on the oxidase-like activity of the Au–Hg alloy on the surface of Au/Cu/Ti₃C₂ NSs. The Au nanoparticles (NPs) were constructed via in-situ growth on the surface of Cu/Ti₃C₂ NSs and characterized by different characterization techniques. After the addition of Hg²⁺, the formation of Hg–Au alloys could promote the oxidization of o-phenylenediamine (OPD) to generate a new fluorescence emission peak of 2,3-diaminopenazine (ADP) at 570 nm. Therefore, a turn-on fluorescence method for the detection of Hg²⁺ was established. As the addition of Cl⁻ can influence the fluorescence of ADP, the fluorescence intensity was constantly quenched to achieve the continuous quantitative detection of Cl⁻. Therefore, a turn-off fluorescence method for the detection of Cl⁻ was established. This method had good linear ranges for the detection of Hg²⁺ and Cl⁻ in 8.0–200.0 nM and 5.0–350.0 µM, with a detection limit of 0.8 nM and 27 nM, respectively. Depending on the color change with the detection of Hg²⁺ and Cl⁻, a convenient on-site colorimetric method for an analysis of Hg²⁺ and Cl⁻ was achieved by using digital images combined with smartphones (color recognizers). The digital picture sensor could analyze RGB values in concentrations of Hg²⁺ or Cl⁻ via a smartphone app. In summary, the proposed Au/Cu/Ti₃C₂ NSs-based method provided a novel and more comprehensive application for environmental monitoring. Full article