Search Results (3,672)

Additive Manufacturing (AM) Direct Laser Fabrication (DLF) of Ti-5Al-5V-5Mo-3Cr (Ti5553) is being developed as a method for producing aircraft components. The additive manufacturing process can produce flaws near the surface, such as porosity and material voids, which act as stress raisers, leading to potential component failure. Eddy current testing was investigated to detect flaws on or near the surface of DLF Ti5553 bar samples. For this application, the objective was to develop an eddy current probe capable of detecting flaws 500 µm in diameter, located 1 mm below the component’s surface. Two initial sets of coil parameters were chosen: The first, based on successful experiments that demonstrated detection of a near surface flaw in Ti5553 using a transmit-receive array probe, and the second, derived from simulation by Finite Element Method (FEM). An optimized transmit receive coil design, based on the FEM simulations, was constructed. The probe was evaluated on Ti5553 samples containing sub-surface voids of the target size, as well as samples with side-drilled holes and samples with holes drilled from the opposing inspection surface. The probe was able to effectively detect 80% of the sub-surface voids. Limitations included the probe’s inability to detect sub-surface voids near sample edges and a sensitivity to surface roughness, which produces local changes in lift-off. Multifrequency mixing improved signal-to-noise ratio when surface roughness was present on average by 22%. A probe based on that described in this paper could benefit quality assurance of additively manufactured aircraft components. Full article

Orthodontic miniscrews (MSs) are used for enhancing orthodontic anchorage either by supporting the teeth of the reactive unit or by obviating the need for the reactive unit altogether. Despite MSs’ popularity, their clinical application is not lacking in complications. The limited space of the insertion site (inter-radicular space), temporary use (limiting osseointegration) and the necessity to minimize the biological cost of insertion (bone incision) required the size of this auxiliary to be reduced, making it susceptible to mechanical failure. This review aimed to investigate factors influencing MS plastic deformation and fracture. The search applied five engines: PubMed, PMC, Web of Science, Scopus, Embase, and Ebsco. Quality assessment was performed according to the QUIN tool. After a thorough search process, 22 articles were included in this review. The most important factor influencing miniscrews’ plastic deformation and fracture was the screw diameter. The MS length and metal alloy did not influence its plastic deformation or fracture. The cylindrical design of the screw is preferable. If the cortical bone thickness in the insertion site exceeds 3 mm, pre-drilling upon insertion is recommended. Orthodontic MSs should not be reused. There is a need for high-quality clinical studies on the subject of MS deformation and fracture. The PROSPERO number is CRD42024509895. Full article

Accurately predicting the drilling efficiency of rotary drilling is the key to achieving intelligent construction. The current types of principle analysis (based on traditional interactive experimental methods) and efficiency prediction (based on simulation models) cannot meet the requirements needed for the efficient, real-time, and accurate drilling efficiency predictions of rotary drilling rigs. Therefore, we adopted a method based on machine learning to predict drilling efficiency. The extremely complex rock fragmentation process in drilling conditions also brings challenges to predicting drilling efficiency. Therefore, this article went through a combination of mechanism and data analysis to conduct correlation analysis and to clarify the drilling characteristic parameters that are highly correlated with drilling efficiency, and it then used them as inputs for machine learning models. We propose a rotary drilling rig drilling efficiency prediction model based on the GA-BP neural network to construct an accurate and efficient drilling efficiency prediction model. Compared with traditional BP neural networks, it utilizes the global optimization ability of a genetic algorithm to obtain the initial weights and thresholds of a BP neural network in order to avoid the defect of ordinary BP neural networks, i.e., that they easily fall into local optimal solutions during the training process. The average prediction accuracy of the GA-BP neural network is 93.6%, which is 3.1% higher than the traditional BP neural network. Full article

The use of tuning forks to measure fluid density and viscosity is widely employed in fields such as food, medicine, textiles, automobiles, petrochemicals, and deep drilling. The explicit analytical model based on the Euler–Bernoulli cantilever-beam theory for the relationship between tuning-fork resonance characteristics and the density and viscosity of fluid is only applicable to the situation where the fluid viscous effect is very small. In this paper, the finite element method is used to simulate the influence of large variations in fluid density and viscosity on the resonance characteristic parameters (resonant frequency and quality factor) of the tuning fork. The numerical simulation results are compared with the analytical analysis results and experimental measurement results. Then, the sensitivity of tuning-fork resonance characteristic parameters to fluid density and viscosity is studied. The results show that compared with the analytical results, the numerical simulation results have a higher degree of agreement with the experimental measurement results. The relative difference in resonant frequency is less than 2%, and the relative difference in quality factor is less than 4%. This indicates that the finite element method includes the influence of fluid viscosity on tuning-fork resonance parameters, which is more in line with the actual conditions than the analytical model. Simulating and analyzing the sensitivity of the tuning fork to fluid density and viscosity by the finite element method, it is possible to consider the situation where fluid density and viscosity vary over a large range. Compared with experimental measurements, this method has higher efficiency and can significantly save time and economic costs. This study can overcome the limitation of existing explicit analytical models, which are only applicable when the viscous effects of the fluid are very small. It enables a more accurate simulation of the coupling vibration between tuning forks and fluids, thereby providing theoretical references for further optimizing tuning-fork structural parameters to enhance the accuracy of measuring fluid characteristic parameters. Full article

Military personnel need physical fitness to effectively carry out operational military activities within their specific field of operation. This research investigates the effects of a 34-week training program on Angolan cadets’ body composition, muscle strength, and cardiorespiratory fitness. Seventy-four volunteer recruits, aged 18 to 26 years, were monitored during their eight-month military service, following an exercise program protocol comprising 12 weeks of strength training followed by 24 weeks of endurance training. Anthropometric variables, including body mass, body mass index, and fat mass, were assessed, along with cardiorespiratory fitness (VO₂max), sprint performance, countermovement jump (CMJ), medicine ball throw, push-ups, and curl-ups. The physical training protocol encompassed running sessions, strength exercises, agility drills, and flexibility exercises. The initial assessment revealed gender differences in various parameters such as body mass, body fat percentage, VO₂max, sprinting, countermovement jump (CMJ), medicine ball throw, and push-ups. Following the training program, changes were observed in all variables (effect size between 0.48 and 2.33, p < 0.01) for the participants. Significant interactions (sex × time) were found in body mass (F = 5.18, p = 0.03, ηp² = 0.06), body fat percentage (F = 5.31, p < 0.01, ηp² = 0.14), and medicine ball throw (F = 10.84, p < 0.01, ηp² = 0.13). Specifically, males exhibited a greater reduction in body mass (females: 2.70%, males: 3.47%, p < 0.05) and a substantial improvement in ball throwing performance (females: 7.74%, males: 11.47%, p < 0.01), while females experienced a greater reduction in fat mass (females: 5.34%, males: 3.15%, p < 0.01). The physical training regimen effectively influenced body composition, particularly in enhancing strength performance among males. The integration of exercise programs with military service led to a notable reduction in fat tissue and an increase in lean tissue. Hence, the development of tailored training protocols is imperative to enhance the physical capacity and overall health of military recruits, considering sex-specific characteristics and physical attributes. Full article

Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the “digitalrocksportal” database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters. Full article

The uplift–depression framework controls the source–reservoir assemblage. However, the exploration breakthrough is restricted by an insufficient understanding of the uplift–depression differentiation framework in the Early Cambrian Keping–Wensu area. In this paper, based on field outcrops evaluations, thin section analysis, logging data, drilling data, and 3D seismic data, Wensu low paleo-uplift was discovered in the northern Tarim Basin, and the planar distribution was demonstrated in detail, generally shown as a SW–NE trending nose structure, extending roughly 114 km in length to the southwest, about 35 km in width to the northeast, and with the overall characteristic of being high in the west and low in the east. During the Early Cambrian, the Tabei paleo-uplift evolved into the Wensu low paleo-uplift and largely died out by the Middle Cambrian, with the development of ramps and rimmed carbonate platforms. The tectonic-sedimentary evolution of the uplift–depression framework controlled the development of a set of main source rocks and two sets of large-scale effective reservoir rocks in the Lower Cambrian, constituting two sets of effective hydrocarbon accumulation in the upper and lower stratigraphic parts of the basin. Among them, the upper assemblage holds more potential for hydrocarbon exploration, and is expected to be a next strategic target area for hydrocarbon exploration of Cambrian subsalt in the Keping–Wensu area. Full article

A spherical weathering body, also called a boulder, is an element of complex geological strata and presents a significant challenge to tunnelling by a tunnel-boring machine (TBM). In this study, ground-based drilling and blasting were used to precondition boulders. To determine the specific charge needed for preconditioning blasting, model blasts were conducted, and the relationships among the specific charge, fragment size, and overburden depth in the model blasts were investigated. The determined specific charge from the model blasts was then modified by considering the overburden depth and used to precondition the boulders for practical TBM tunnelling. The results indicate the following: (1) model blasts were effective in determining the correct specific charge and predicting boulder fragment sizes resulting from blasting; (2) the boulders met in practical TBM tunnelling were successfully preconditioned by using the determined specific charge; (3) the determined specific charge was 3.26 kg/m³, corresponding to an average fragment size 4.5 cm with an overburden of zero and increased with increasing overburden; (4) fragment sizes were dependent on both the specific charge and overburden depth. Our conclusions can be used to accurately determine the specific charge instead of empirical formulas. Full article

To ensure the prevention of thermal trauma and tissue necrosis during bone drilling in surgical procedures, it is crucial to maintain temperatures below the time- and temperature-dependent threshold of 50 °C for 30 s. However, the absence of a current standard for assessing temperatures attained during bone drilling poses a challenge when comparing findings across different studies. This article aims to address this issue by introducing a standardized testing method for acquiring thermal data during experimental bone drilling. The method requires the use of three controlled variables: infrared thermography, standard bone blocks, and a regulated drilling procedure involving a drill press with irrigation that simulates a surgeon. By utilizing this setup, we can obtain temperature data that can be effectively applied in the evaluation of other variables, such as surgical techniques or drill bit design, and translate the data into bone damage/clinical outcomes. Two surgical drill bits (2.0 mm-diameter twist drill bit and 3.3 mm-diameter multi-step drill bit) are compared using this experimental protocol. The results show the 2.0 mm bit reached significantly higher temperatures compared to the 3.3 mm bit when preparing an osteotomy (p < 0.05). The 2.0 mm drill bit reached temperatures over 100 °C while the 3.3 mm drill bit did not exceed 50 °C. Full article

Titanium alloy is widely used as oil drill pipe material because of its light weight, high strength, good toughness, corrosion resistance, fatigue resistance, and good process performance. However, due to its low hardness, poor wear resistance, serious oxidation at high temperature (700 °C), and difficulty in lubrication, in oil and gas field exploration and development drilling, especially in deep wells, high displacement wells, horizontal wells, and highly deviated wells, wear and tear are prone to occur. The application and development of titanium alloys are greatly limited. This paper introduces the research status of the common surface modification technologies of titanium alloys, such as laser cladding, magnetron sputtering, plasma spraying, micro arc oxidation, etc. It points out the improvement effect of various modification technologies on the wear resistance and high-temperature oxidation resistance of titanium alloys and discusses the advantages and disadvantages of various modification technologies. A proposed method for enhancing the wear resistance and high-temperature oxidation resistance of titanium alloys was finally introduced, and its potential for future development was investigated. Full article

Cutting tool coolant channels play a pivotal role in machining processes, facilitating the efficient supply of cooling agents to high-stress areas and effective heat dissipation. Achieving optimal cooling at the tool’s cutting-edge is essential for enhancing production processes. Experimental investigations into tribological stress analysis can be limited in accessing complex tool–workpiece contact zones, prompting the use of numerical modelling to explore fluid dynamics and tribology. In this study, the coolant flow dynamics and heat dissipation in drilling operations were comprehensively investigated through computational fluid dynamics (CFD) modelling. Four twist drill models with varying coolant channel arrangements were studied: standard model drill, standard model drill with notch, profile model drill, and profile model drill with notch. Two distinct approaches are applied to the coolant inlet to assess the impact of operating conditions on fluid flow and heat dissipation at the cutting-edge. The findings emphasize that cutting-edge zones have insufficient coolant supply, particularly in modified drill models such as the standard model drill with notch and profile model drills with and without notch. Moreover, enhanced coolant supply at the cutting-edge is achieved under high-pressure inlet conditions. The standard model drill with a notch exhibited exceptional performance in reducing thermal load, facilitating efficient coolant escape to the flute for improved heat dissipation at the cutting-edge. Despite challenges like dead zones in profile models, the standard-with-notch model yielded the most promising results. Further analyses under constant pressure conditions at 40 and 60 bar exhibited enhanced fluid flow rates, particularly at the cutting-edge, leading to improved heat dissipation. The temperature distribution along the cutting-edge and outer corner demonstrated a decrease as the pressure increased. This study underscores the critical role of both coolant channel design and inlet pressure in optimizing coolant flow dynamics and heat transfer during drilling operations. The findings provide valuable insights for designing and enhancing coolant systems in machining processes, emphasizing the significance of not only coolant channel geometry but also inlet pressure for effective heat dissipation and enhanced tool performance. Full article

Lost circulation, a common risk during the drilling process, significantly impacts drilling safety and efficiency. The presence of data noise and temporal evolution characteristics pose significant challenges to the accurate monitoring of lost circulation. Traditional supervised intelligent monitoring methods rely on large amounts of labeled data, which often do not consider temporal fluctuations in data, leading to insufficient accuracy and transferability. To address these issues, this paper proposes an unsupervised time series autoencoder (BiLSTM-AE) intelligent monitoring model for lost circulation, aiming to overcome the limitations of supervised algorithms. The BiLSTM-AE model employs BiLSTM for both the encoder and decoder, enabling it to comprehensively capture the temporal features and dynamic changes in the data. It learns the patterns of normal data sequences, thereby automatically identifying anomalous risk data points that deviate from the normal patterns during testing. Results show that the proposed model can efficiently identify and monitor lost circulation risks, achieving an accuracy of 92.51%, a missed alarm rate of 6.87%, and a false alarm rate of 7.71% on the test set. Compared to other models, the BiLSTM-AE model has higher accuracy and better timeliness, which is of great significance for improving drilling efficiency and ensuring drilling safety. Full article

Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM neural network model with an attention mechanism (ATT-LSTM) is introduced. By applying the attention mechanism in each time step, the model can dynamically adjust the degree of attention to different parts of the sequence and better capture the key information in the data, which improve the accuracy of the recognition and the generalization ability of the model. By testing the trained model on field data, the test results show that the method achieves more significant performance improvement on the stuck pipe recognition task, and the prediction accuracy of the intelligent model increases by 21.31% after data enhancement. Full article

The rock classification system was initially applied to drill-and-blast tunnels and subsequently adapted for TBM tunnels; however, the majority of these systems primarily focused on rock stability while neglecting considerations of boreability. Compared with conventional tunnels, TBM tunnels are characterized by their rapid tunneling speed and excellent self-stabilization of the rock mass. Therefore, it is imperative to develop a novel rock mass classification system that considers both the tunneling efficiency of TBMs and the required support measures for tunnel construction. This paper introduces a novel rock classification system for TBM tunnels that accurately predicts the construction rate by evaluating the penetration rate and daily utilization, enabling a more precise assessment of daily advancement in tunneling. Firstly, the penetration rate and construction utilization in different rock strata are analyzed based on comprehensive statistics of existing construction data. Consequently, a discriminant matrix for classifying the boreability of rock is derived. Subsequently, employing the Ensemble Classifier method, a regression prediction model for rock boreability classification is established by incorporating input parameters such as thrust, torque, rotational speed, field penetration index, and the uniaxial compressive strength of rock. The validity of the proposed model is verified by comparing predicted machine performance with actual data sets. The proposed method presents a novel approach for predicting the performance of TBM construction. Full article

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance. Full article