Search Results (11)

In the laser powder bed fusion (LPBF) process, the surface quality of intermediate layers impacts interlayer bonding and part forming quality. Due to the complex dynamic process inherent in LPBF, current monitoring methods struggle to achieve high-quality in situ online monitoring, which limits the in-depth understanding of the evolution mechanisms of the surface morphology of LPBF intermediate layers. This paper employs an optimized coaxial optical imaging method to monitor key LPBF processes and analyzes the intermediate layer surface morphology evolution mechanism considering heat, force, and mass transfer. Results indicate that LPBF intermediate layer surfaces are influenced by energy density, melt pool behavior, and previous layer morphology, forming complex topological structures. At a low energy density, insufficient powder melting causes balling, extended by subsequent melt pools to form a reticulated structure and local large-scale protrusions. Heat accumulation at a high energy density promotes melt pool expansion, reduces melt track overlap, and effectively eliminates defects from previous layers via remelting, with spatter becoming the main defect. Additionally, the melt pool wettability on the part contours captures external powder, forming unique, overhanging contour protrusions. This paper enhances understanding of LPBF intermediate layer surface morphology formation mechanisms and provides a theoretical basis for optimizing surface quality. Full article

Distributed strain sensing is a powerful tool for in situ structural health monitoring for a wide range of critical engineering infrastructures. Strain information from a single sensing device can be captured from multiple locations simultaneously, offering a reduction in hardware, wiring, installation costs, and signal analysis complexity. Fiber optic distributed strain sensors have been the widely adopted approach in this field, but their use is limited to lower strain applications due to the fragile nature of silica fiber. Coaxial cable sensors offer a robust structure that can be adapted into a distributed strain sensor. They can withstand greater strain events and offer greater resilience in harsh environments. This paper presents the developments in methodology for coaxial cable distributed strain sensors. It explores the two main approaches of coaxial cable distributed strain sensing such as time domain reflectometry and frequency domain reflectometry with applications. Furthermore, this paper highlights further areas of research challenges in this field, such as the deconvolution of strain and temperature effects from coaxial cable distributed strain sensor measurements, mitigating the effect of dielectric permittivity on the accuracy of strain measurements, addressing manufacturing challenges with the partial reflectors for a robust coaxial cable sensor, and the adoption of data-driven analysis techniques for interrogating the interferogram to eliminate concomitant measurement effects with respect to temperature, dielectric permittivity, and signal-to-noise ratio, amongst others Full article

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations. Full article

The presence of chlorinated solvents polluting groundwater in urbanized areas poses a significant environmental issue. This paper details a thoughtful approach to remediate a tetrachloroethylene (PCE) plume in a district that is characterized by a complex hydrological context with a limited accessibility. Through a geodatabase-driven and coupled hydrogeochemical approach, two distinct remediation technologies were chosen for the management of a contaminant plume. On one hand, coaxial groundwater circulation (CGC) wells coupled with air sparging (AS) aspire to promote the in-situ transfer of PCE from the contaminated matrices into a gaseous stream that is then treated above ground. On the other hand, reagent injection has the goal of enhancing chemical reduction combined with in situ adsorption, creating contaminant adsorbent zones, and stimulating dechlorinating biological activity. The development of an integrated conceptual site model (CSM) harmonizing geological, hydrochemical, and membrane interface probe (MIP) data captures site-specific hydrogeochemical peculiarities to support decision-making. The hydrochemical monitoring reveals contamination dynamics and decontamination mechanisms in response to treatment, quantifying the performance of the adopted strategies and investigating possible rebound effects. The estimation of masses extracted by the CGC-AS system validates the effectiveness of a new and sustainable technique to abate chlorinated solvents in groundwater. Full article

We present an in situ process monitoring approach for remote fiber laser cutting, which is based on evaluating images from a high-speed camera. A specifically designed image processing algorithm allows the distinction between complete and incomplete cuts by analyzing spectral and geometric information of the melt pool from the captured images of the high-speed camera. The camera-based monitoring system itself is fit to a conventional laser deflection unit for use with high-power fiber lasers, with the optical detection path being coaxially aligned to the incident laser. Without external illumination, the radiation of the melt from the process zone is recorded in the visible spectral range from the top view and spatially and temporally resolved. The melt pool size and emitted sparks are evaluated in dependence of machining parameters such as feed rate, cycles, and focus position during cutting electrical sheets. Full article

Wear debris monitoring of lubricant oil is an important method to determine the health and failure mode of key components such as bearings and gears in rotatory machines. The permittivity of lubricant oil can be changed when the wear debris enters the oil. Capacitive sensing methods showed potential in monitoring debris in lubricant due to the simple structure and good response. In order to improve the detection sensitivity and reliability, this study proposes a new coaxial capacitive sensor network featured with parallel curved electrodes and non-parallel plane electrodes. As a kind of through-flow sensor, the proposed capacitive sensor network can be in situ integrated into the oil pipeline. The theoretical models of sensing mechanisms were established to figure out the relationship between the two types of capacitive sensors in the sensor network. The intensity distributions of the electric field in the coaxial capacitive sensor network are simulated to verify the theoretical analysis, and the effects of different debris sizes and debris numbers on the capacitance values were also simulated. Finally, the theoretical model and simulation results were experimentally validated to verify the feasibility of the proposed sensor network. Full article

As one of the most promising metal additive manufacturing (AM) technologies, the selective laser melting (SLM) process has high expectations ofr its use in aerospace, medical, and other fields. However, various defects such as spatter, crack, and porosity seriously hinder the applications of the SLM process. In situ monitoring is a vital technique to detect the defects in advance, which is expected to reduce the defects. This work proposed a method that combined acoustic signals with a deep learning algorithm to monitor the spatter behaviors. The acoustic signals were recorded by a microphone and the spatter information was collected by a coaxial high-speed camera simultaneously. The signals were divided into two types according to the number and intensity of spatter during the SLM process with different combinations of processing parameters. Deep learning models, one-dimensional Convolutional Neural Network (1D-CNN), two-dimensional Convolutional Neural Network (2D-CNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were trained to establish the relationships between the acoustic signals and characteristics of spatter. After K-fold verification, the highest classification confidence of models is 85.08%. This work demonstrates that it is feasible to use acoustic signals in monitoring the spatter defect during the SLM process. It is possible to use cheap and simple microphones instead of expensive and complicated high-speed cameras for monitoring spatter behaviors. Full article

During laser metal deposition (LMD) of thin-walled aluminum alloy structures, the deposition height and width is hard to keep stable because of the special properties of aluminum alloys, such as high reflectivity to laser beams, low viscosity, and high thermal conductivity. Monitoring the LMD process allows for a better comprehension and control of this process. To investigate the characteristics of the aluminum alloy LMD process, three real-time coaxial optical sensors sensitive to visible light, infrared light, and back-reflected lasers ere used to monitor the aluminum alloy LMD process. Thin-walled parts were deposited with different laser power, and the characteristics of the three in situ signals are analyzed. The results show that there exists high linear correlation between reflected laser and accumulated deposition height. A laser reflection model was built to explain the correlation. Besides, the infrared light is linearly correlated with deposition width. Overall, the results of this study show that the optical signals are able to reflect the deposition height and width simultaneously. Infrared light signals and reflected laser signals have the potential to serve as the input of online feedback geometry control systems and real-time defect alarm systems of the LMD process. Full article

Chemical reactions with reservoir fluids and geology related in-situ stress changes may cause damages to cement sealing material in plugged and abandoned oil, gas and CO₂ wells. To avoid leakages, a legitimate monitoring technique is needed that could allow for early warning in case such damages occur. In this paper, we test the utility of oil and gas well cement with a conductive filler in sensing stress changes. To this end, we have measured the resistance response of Portland G—oil and gas well cement with carbon nanofibers (CNF) to axial load during uniaxial compressive strength test. Simultaneously, the microseismicity data were collected. The resistance of the nanocomposite was measured using two-point method in the direction of loading. The resistance changes were correlated with acoustic emission events. A total of four different material response regions were distinguished and the resistivity and acoustic emission changes in these regions were described. Our results suggest that the two complementary methods, i.e., acoustic emission and resistance measurements, can be used for sensing stress state in materials including well cement/CNF composites. The results suggest that the well cement/CNF composites can be a good candidate material to be used as a transducer sensing changes in stress state in, e.g., well plugs up to material failure. Full article

The Underground Coal Gasification (UCG) system is a clean technology for obtaining energy from coal. The coaxial UCG system is supposed to be compact and flexible in order to adapt to complicated geological conditions caused by the existence of faults and folds in the ground. In this study, the application of a coaxial UCG system with a horizontal well is discussed, by means of an ex situ model UCG experiment in a large-scale simulated coal seam with dimensions of 550 × 600 × 2740 mm. A horizontal well with a 45-mm diameter and a 2600-mm length was used as an injection/production well. During the experiment, changes in temperature field and product gas compositions were observed when changing the outlet position of the injection pipe. It was found that the UCG reactor is unstable and expands continuously due to fracturing activity caused by coal crack initiation and extension under the influence of thermal stress. Therefore, acoustic emission (AE) is considered an effective tool to monitor fracturing activities and visualize the gasification zone of coal. The results gathered from monitoring of AEs agree with the measured data of temperatures; the source location of AE was detected around the region where temperature increased. The average calorific value of the produced gas was 6.85 MJ/Nm³, and the gasification efficiency, defined as the conversion efficiency of the gasified coal to syngas, was 65.43%, in the whole experimental process. The study results suggest that the recovered coal energy from a coaxial UCG system is comparable to that of a conventional UCG system. Therefore, a coaxial UCG system may be a feasible option to utilize abandoned underground coal resources without mining. Full article

Metal-ceramic coaxial cable Fabry-Pérot interferometric (MCCC-FPI) sensors have been developed using a stainless steel tube and a stainless steel wire as the outer and inner conductors, respectively; a tubular α-alumina insulator; and a pair of air gaps created in the insulator along the cable to serve as weak reflectors for the transmitting microwave (MW) signal. The MCCC-FPI sensors have been demonstrated for high temperature measurements using MW signals in a frequency range of 2–8 GHz. The temperature measurement is achieved by monitoring the frequency shift (Δƒ) of the MW interferogram reflected from the pair of weak reflectors. The MW sensor exhibited excellent linear dependence of Δƒ on temperature; small measurement deviations (±2.7%); and fast response in a tested range of 200–500 °C. The MCCC has the potential for further developing multipoint FPI sensors in a single-cable to achieve in situ and continuous measurement of spatially distributed temperature in harsh environments. Full article