Search Results (580)

The header platform of a combine harvester is subjected to severe abrasive and corrosive wear from rice stalks and environmental factors, which significantly limits its service life and operational efficiency. Accurately predicting the complex distribution of this wear over time and across the platform’s surface, however, remains a significant challenge. This paper, for the first time, systematically establishes a quantitative mapping relationship from “material motion trajectory” to “component wear profile” and introduces a novel method for time-sequence wear validation based on corrosion color gradients, providing a complete research paradigm to address this challenge. To this end, an analytical model based on rigid-body dynamics was first developed to predict the motion trajectory of a single rice stalk. Subsequently, a full-scale Discrete Element (DEM) model of the header platform–flexible rice stalk system was constructed. This model simulated the complex flow process of the rice population with high fidelity and was used to analyze the influence of key operating parameters (spiral auger rotational speed, cutting width) on wear distribution. Finally, real-world wear data were obtained through in situ mapping of a header platform after long-term service (1300 h) and multi-period (0–1600 h) image analysis. Through a three-way quantitative comparison among the theoretical trajectory, simulated trajectory, and the actual wear profile, the results indicate that the simulated and theoretical trajectories are in good agreement in terms of their macroscopic trends (Mean Squared Error, MSE, ranging from 0.4 to 6.2); the simulated and actual wear profiles exhibit an extremely high degree of geometric similarity, with the simulated wear area showing a 95.1% match to the actual measured area (Edit Distance: 0.14; Hamming Distance: 1). This research not only confirms that the flow trajectory of rice is the determining factor for the wear distribution on the header platform but, more importantly, the developed analytical and numerical methods offer a robust theoretical basis and effective predictive tools for optimizing the wear resistance and predicting the service life of the header platform, thereby demonstrating significant engineering value. Full article

Background/Objectives: Acanthamoeba keratitis (AK) is a rare but sight-threatening corneal infection, often associated with contact lens wear and resistant to conventional therapies. Preventive strategies capable of reducing Acanthamoeba adhesion to corneal epithelium may represent an important tool for infection control. This study aimed to evaluate the amebicidal and preventive activity of CORNEIAL MED eye drops against Acanthamoeba castellanii adhesion and early adhesion layer on human corneal epithelium (HCE). Methods: Reconstructed HCE models were exposed to A. castellanii under four experimental conditions: negative control (HCE only), positive control (HCE + A. castellanii), co-incubation with CORNEIAL MED and A. castellanii (Study 1), and treatment with CORNEIAL MED after initial A. castellanii adhesion (Study 2). Adherent amoebae were quantified using EDTA detachment and Neubauer chamber counting. The early adhesion layer was characterized by scanning electron microscopy (SEM). Statistical analysis considered p < 0.05 as significant. Results: In Study 1, simultaneous application of CORNEIAL MED with A. castellanii reduced amoeba adhesion by 33.0 ± 11% compared with controls (p = 0.0529). In Study 2, when the product was applied 3 h after amoeba inoculation, adhesion was significantly reduced by 51.9 ± 6.5% (p < 0.05). SEM confirmed a decrease in amoebic colonization and biofilm density in treated samples. Conclusions: CORNEIAL MED demonstrated a measurable inhibitory effect on A. castellanii adhesion to HCE, particularly when applied after initial pathogen contact. These findings suggest a potential preventive role of CORNEIAL MED in reducing AK risk, although further in vivo studies are warranted. Full article

The machining of highly abrasive sand–polymer composites pose significant challenges due to rapid tool wear directly associated with the tool’s stress–strain state. This study provides a detailed analysis of the stress and strain distribution in a T15K6 carbide turning insert under contact loads generated during the cutting of SPCs with varying quartz content. The approach combines analytically derived contact stress distributions on the rake and flank faces with numerical modeling using the finite element method (ANSYS). This hybrid methodology enables the accurate evaluation of stress localization in critical tool regions without performing full-scale cutting simulations. A parametric series of simulations was carried out by varying the mass fraction of quartz in the composite. The results demonstrate an almost linear increase in the maximum equivalent (von Mises) stress with rising quartz content, described by σ_eq = 9.61·C_quartz + 232.41. For the base composition with 60% quartz, the maximum equivalent stress is approximately 812 MPa, corresponding to about 50% of the flexural strength of the T15K6 alloy and ensuring a safety margin of roughly two. However, as the quartz fraction approaches 85%, the stress level becomes close to the critical limit, indicating an elevated risk of tool failure. The proposed analytical-numerical approach provides an effective framework for assessing the stress–strain state of carbide cutting tools and can support the optimization of machining conditions for highly abrasive composite materials. Full article

Additively manufactured (AM) aluminum (Al) alloys are very useful in sectors like automotive, manufacturing, and aerospace because they have unique mechanical properties, such as their light weight, etc. AlSi10Mg made by laser powder bed fusion (LPBF) is one of the most promising materials because it has a high strength-to-weight ratio, good thermal resistance, and good corrosion resistance. But machining AlSi10Mg parts is still hard because they have unique microstructural properties from the way they were produced. This research investigates the machining efficacy of the AM-AlSi10Mg alloy in distinct cutting conditions (dry, flood, chilled air, and minimal quantity lubrication with castor oil). The study assesses how different cooling conditions affect important performance metrics such as cutting temperature, surface roughness, and tool wear. Due to castor oil’s superior lubricating and film-forming properties, MQL (Minimal Quantity Lubrication) reduces heat generation between 80 °C and 98 °C for the distinct speed–feed combinations. The Multi-Objective Optimization by Ratio Analysis (MOORA) approach is used to determine the ideal cooling and machining conditions (MQL, Vc of 90 m/min, and fr of 0.05 mm/rev). The relative closeness values derived from the MOORA approach were used to predict machining results using machine learning (ML) models (MLP, GPR, and RF). The MLP showed the strongest relationship between the measured and predicted values, with R values of 0.9995 in training and 0.9993 in testing. Full article

This paper presents a novel numerical–experimental workflow to evaluate the sealability of casing connections in geothermal and underground gas storage wells, where cyclic thermal and pressure loads challenge conventional qualification methods. The approach combines experimental make-up and cyclic loading tests with finite element analysis by explicitly modeling the connection geometry and the contact conditions. Validation against experimental data shows good agreement in seal ovality, roughness, and wear, confirming the predictive reliability of the model. Results indicate that initial geothermal discharge and seasonal storage cycles generate the highest von Mises stresses, expressed as a percentage of the material’s yield strength (%VMS), mainly under combined tensile and internal pressure loading. After the first make-up, subsequent cycles reduced seal contact pressure and length, increasing leakage risk; however, repeated loading improved tribological behavior, enhancing sealability despite occasional galling. The proposed framework enables accurate prediction of connection integrity under extreme cyclic conditions, offering a novel tool to optimize design and streamline qualification testing. Full article

Precision dicing with diamond wheels is a key technology in semiconductor dicing, integrated circuit manufacturing, aerospace, and other fields, owing to its high precision, high efficiency, and broad material applicability. As a critical processing stage, a comprehensive analysis of dicing technologies is essential for improving the machining quality of hard-and-brittle optoelectronic materials. This paper reviews the core principles of precision diamond wheel dicing, including dicing processes and blade preparation methods. Specifically, it examines the dicing mechanisms of composite and multi-mode dicing processes, demonstrating their efficacy in reducing defects inherent to single-mode approaches. The review also examines diverse preparation methods for dicing blades, such as metal binder sintering and roll forming. Furthermore, the roles of machine vision and servo control systems are detailed, illustrating how advanced algorithms facilitate precise feature recognition and scribe line control. A systematic analysis of key components in grinding wheel dicer is also conducted to reduce dicing deviation. Additionally, the review introduces models for tool wear detection and discusses material removal mechanisms. The influence of critical process parameters—such as spindle speed, feed rate, and dicing depth—on dicing quality and kerf width is also analyzed. Finally, the paper outlines future prospects and provides recommendations for advancing key technologies in precision dicing, offering a valuable reference for subsequent research. Full article

Objectives: Prevention of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease COVID-19 is a public health priority. The efficacy of non-pharmaceutical interventions such as wearing face masks to prevent SARS-CoV-2 infection has been well established in controlled settings. However, evidence for the effectiveness of face masks in preventing SARS-CoV-2 transmission within real-world settings is limited and mixed. The present systematic review evaluated the effectiveness of face mask policies and mask wearing to prevent SARS-CoV-2 transmission and COVID-19 in real-world settings. Methods: Following PRISMA guidelines, scientific databases, and gray literature, were searched through June 2023. Inclusion criteria were as follows: (1) studies/reports written in or translated to English; (2) prospectively assessed incidence of SARS-CoV-2 or COVID-19; (3) assessed the behavior and/or policy of mask-wearing; and (4) conducted in community/public settings (i.e., not laboratory). Studies were excluded if they did not parse out data specific to the effect of mask wearing (behavior and/or policy) and subsequent SARS-CoV-2 transmission or COVID-19 disease or if they relied solely on statistical models to estimate the effects of mask wearing on transmission. A total of 2616 studies were initially identified, and 470 met inclusion and exclusion criteria for full-text review. The vote counting method was used to evaluate effectiveness, and risk of bias was assessed using JBI critical appraisal tools. Results: A total of 79 unique studies met the final inclusion criteria, and their data were abstracted and evaluated. Study settings included community/neighborhood settings (n = 34, 43%), healthcare settings (n = 30, 38%), and school/universities (n = 15, 19%). A majority of studies (n = 61, 77%) provided evidence to support the effectiveness of wearing face masks and/or face mask policies to reduce the transmission of SARS-CoV-2 and/or prevention of COVID-19. Effectiveness of mask wearing did not vary substantially by study design (67–100%), type of mask (77–100%), or setting (80–91%), while 85% of masking policies specifically reported a benefit. Conclusions: This systematic literature review supports public health recommendations and policies that encourage the public to wear face masks to reduce the risk of SARS-CoV-2 infection and COVID-19 in multiple real-world settings. Effective communication strategies are needed to encourage and support the use of face masks by the general public, particularly during peak infection cycles. Full article

Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, we propose a machine-learning (ML)-based tool condition monitoring system based on multi-sensor data to preempt excessive tool wear during drilling in robotic surgery. Real-time data is acquired from the six-component force sensor of a collaborative arm along with the data from the temperature and multi-axis vibration sensor mounted on the bone specimen being drilled upon. Raw data from the sensors may have noises and outliers. Signal processing in the time- and frequency-domain are used for denoising as well as to obtain additional features to be derived from the raw sensory data. This paper addresses the challenging problem of identification of the most suitable ML algorithm and the most suitable features to be used as inputs to the algorithm. While dozens of features and innumerable machine learning and deep learning models are available, this paper addresses the problem of selecting the most relevant features, the most relevant AI models, and the optimal hyperparameters to be used in the AI model to provide accurate prediction on the tool condition. A unique framework is proposed for classifying tool wear that combines machine learning-based modeling with multi-sensor data. From the raw sensory data that contains only a handful of features, a number of additional features are derived using frequency-domain techniques and statistical measures. Using feature engineering, we arrived at a total of 60 features from time-domain, frequency-domain, and interaction-based metrics. Such additional features help in improving its predictive capabilities but make the training and prediction complicated and time-consuming. Using a sequence of techniques such as variance thresholding, correlation filtering, ANOVA F-test, and SHAP analysis, the number of features was reduced from 60 to the 4 features that will be most effective in real-time tool condition prediction. In contrast to previous studies that only examine a small number of machine learning models, our approach systematically evaluates a wide range of machine learning and deep learning architectures. The performances of 47 classical ML models and 6 deep learning (DL) architectures were analyzed using the set of the four features identified as most suitable. The Extra Trees Classifier (an ML model) and the one-dimensional Convolutional Neural Network (1D CNN) exhibited the best prediction accuracy among the models studied. Using real-time data, these models monitored the drilling tool condition in real-time to classify the tool wear into three categories of slight, moderate, and severe. Full article

The turbine mortise is a critical structural feature of turbine disks, and its manufacturing quality directly determines the performance and service life of aircraft engines. With the increasing application of advanced nickel-based superalloys, severe tool wear in conventional mechanical broaching of turbine mortises has emerged as a key limitation, substantially elevating production costs. Electrochemical broaching (ECB), which removes material through anodic dissolution reactions, eliminates tool wear and thus offers low cost and efficiency advantages, making it a promising method for turbine mortise fabrication. In this study, COMSOL Multiphysics 6.2 was employed to simulate the multiphysics field comprising the electric field, flow field, temperature field, bubble ratio, and dynamic mesh and elucidate the evolution of the electric field during the ECB process. ECB experiments of specimens on Inconel 718 were conducted under different feed speeds. On this basis, optimal processing parameters were identified. The results of the mid-position ECB experiments revealed five distinct dissolution states: pre-processing, pre-transition, stable dissolution, post-transition, and post-processing stages. A material dissolution mechanism model for the ECB process was established. Finally, fir-tree turbine mortises were successfully manufactured on Inconel 718 using a self-developed specialized electrochemical machining system at a feed speed of 70 mm/min. The mortise profile demonstrated dimensional deviations of (+16 to −21) μm, with working surface variations maintained within ±5 μm. The machined surfaces exhibited uniform and dense morphology with a surface roughness of Ra 0.275 μm. Three sets of mortise specimens processed under identical parameters showed excellent consistency, presenting a maximum deviation in profile removal thickness of +4.1 μm. The tool cathode was repeatedly reused without any detectable wear. Full article

Achieving optimal lubrication during machining processes, particularly turning of stainless steel, remains a significant challenge due to dynamic variations in cutting conditions that affect tool life, surface quality, and environmental impact. Conventional Minimum Quantity Lubrication (MQL) systems provide fixed flow rates and often fail to adapt to changing process parameters, limiting their effectiveness under fluctuating thermal and mechanical loads. To address these limitations, this study proposes an ambient-aware adaptive Auto-Tuned MQL (ATM) system that intelligently controls both nanofluid concentration and lubricant flow rate in real time. The system employs embedded sensors to monitor cutting zone temperature, surface roughness, and ambient conditions, linked through a feedback-driven control algorithm designed to optimize lubrication delivery dynamically. A Taguchi L9 design was used for experimental validation on AISI 304 stainless steel turning, investigating feed rate, cutting speed, and nanofluid concentration. Results demonstrate that the ATM system substantially improves machining outcomes, reducing surface roughness by more than 50% and cutting force by approximately 20% compared to conventional MQL. Regression models achieved high predictive accuracy, with R-squared values exceeding 99%, and surface analyses confirmed reduced adhesion and wear under adaptive lubrication. The proposed system offers a robust approach to enhancing machining performance and sustainability through intelligent, real-time lubrication control. Full article

In recent years, the rapid development of underground engineering projects has driven a significant increase in the variety and quantity of excavation equipment. The wear of excavation tools significantly increases construction costs and reduces construction efficiency. The wear rate of excavation tools is closely related to the abrasiveness of the rock. The Cerchar abrasivity index (CAI) is the most widely used index for estimating rock abrasiveness. The primary objective of this paper is to develop a novel prediction model for CAI, which is established based on the mechanical properties and petrographic parameters of rocks. These parameters include uniaxial compressive strength, Brazilian splitting strength, quartz content, equivalent quartz content, average quartz size, brittleness indices, rock abrasive index, and Schimazek’s F-abrasiveness. Correlation analysis was used to conduct a preliminary analysis between CAI and single-influence parameters. The results indicated that a single factor is not suitable for directly predicting CAI. In addition, multiple linear regression (MLR) and a non-linear algorithm, gene expression programming (GEP), were used to establish new prediction models for CAI. A statistical comparison was conducted between the prediction accuracy of the GEP-based model and the MLR-based model. In comparison to the MLR-based model, the GEP-based model demonstrates higher accuracy in predicting CAI. Full article

Turn-milling is a relatively new process which combines turning and milling operations, offering a number of advantages such as chip breaking and interrupted cutting, which improves tool life. In addition to providing the capability of producing eccentric forms or shapes, it increases productivity for difficult-to-machine material at lower cost. This study investigates the influence of cutting speed and feed on surface roughness and tool wear in conventional turning and turn-milling of H13 tool steel. The tests were conducted for longitudinal and face machining strategies. It was found that the range of surface roughness in turning is lower than in turn-milling. In longitudinal turning, face-turning, and face turn-milling operations, surface roughness is elevated in the higher feeds. However, the surface roughness in longitudinal turn-milling operations can be reduced by increasing the feed. Although the simultaneous rotation of the tool and workpiece in turn-milling could negatively affect the surface quality, this operation provides the advantage of an interrupted cutting mechanism that produces discontinuous chips. Also, the wear of the endmill in longitudinal and face turn-milling operations is lower than the wear of the inserts used in conventional longitudinal and face turning. Using Response Surface Methodology (RSM), mathematical models were developed for surface roughness and tool wear in each operation. The RSM models developed in this study achieved coefficients of determination (R²) above 90%, with prediction errors below 7% for surface roughness and below 3% for tool wear. The analysis of variance (ANOVA) revealed that the feed and cutting speed are the most influential parameters on the surface roughness and tool wear, respectively, with p-value < 0.05. The experimental results demonstrated that tool wear in turn-milling was reduced by up to 50% compared to conventional turning. Full article

Nickel-based alloys, including Inconel 718 and alloy 625, are indispensable in industries such as aerospace, marine, and nuclear energy due to their exceptional mechanical strength, high-temperature performance, and corrosion resistance. However, these very properties pose severe machining challenges, such as accelerated tool wear, poor surface finish, and high cutting forces. Although several studies have investigated coatings, lubrication strategies, and process optimization, a comprehensive and up-to-date integration of these advancements is still lacking. To address this gap, a systematic review was conducted using Web of Science and Scopus databases. The inclusion criteria focused on peer-reviewed journal and conference articles published in the last eleven years (2014–2025), written in English, and directly addressing machining of nickel-based alloys, with particular emphasis on tool coatings, lubrication/cooling technologies, and machinability optimization. Exclusion criteria included duplicate records, non-English documents, papers lacking experimental or modeling results, and studies unrelated to tool life or coating performance. Following this screening process, 101 high-quality articles were selected for detailed analysis. The novelty of this work lies in synthesizing comparative insights across TiAlN, TiSiN, and CrAlSiN coatings, alongside advanced lubrication methods such as HPC, MQL, nano-MQL, and cryogenic cooling. Results highlight that CrAlSiN coatings retain hardness up to 36 ± 2 GPa after exposure to 700 °C and extend tool life by 4.2× compared to TiAlN, while optimized cooling strategies reduce flank wear by over 30% and improve tool longevity by up to 133%. The integration of coating performance, thermal stability, and lubrication effects into a unified framework provides actionable guidelines for machining optimization. The study concludes by proposing future research directions, including hybrid coatings, real-time process monitoring, and sustainable lubrication technologies, to bridge the remaining gaps in machinability and promote industrial adoption. This integrative approach establishes a robust foundation for advancing machining strategies of nickel-based superalloys, ensuring improved productivity, reduced costs, and enhanced component reliability. Full article

Tool wear detection is very important in CNC machine tool cutting. Once the tool is excessively worn, it is not only easy to cause the workpiece to be scrapped, but even to damage the machine. Therefore, common external turning tools of CNC machines are studied. The effect of tool nose wear on machining accuracy was analyzed by a building mathematical model. According to different wear conditions, a linear detection method based on edge images and input features was proposed to detect the main and secondary cutting edges, which helped determine the theoretical center of the tool nose and build a morphological visual model. For different error cases, the axial and radial error compensation strategies were proposed, respectively. By comparing the experimental data of four kinds of workpieces before and after compensation machining, the average errors of them were reduced separately, and the maximum value reached 79.2%, which verified the effectiveness of the compensation strategy. The intelligent compensation strategies will significantly improve the micro-machining accuracy and efficiency of the external turning tools in CNC machines. Full article

The growing demand for precision and consistency in the forging industry has heightened the need for predictive simulation tools. While extensive research has focused on parameters such as flow stress, die wear, billet fracture, and residual stresses, the phenomenon of billet buckling, especially during cold upset forging, remains underexplored. Most existing models address only elastic buckling for slender billets using classical approaches like Euler and Rankine-Gordon formulae, which are not suitable for inelastic deformation in shorter billets. This study presents a numerical model developed to analyze inelastic buckling during cold forging and to determine associated stresses and deflection characteristics. The model was validated through finite element simulations across a range of billet geometries (10–40 mm diameter, 120 mm length), materials (aluminum, stainless steel, and copper), and friction coefficients (µ = 0.12, 0.16, and 0.35). Stress distributions were evaluated against die stroke, with particular emphasis on the influence of strain hardening and geometry. The results showed that billet geometry and strain-hardening exponent significantly affect buckling behavior, whereas friction had a secondary effect, mainly altering overall stress levels. A nonlinear regression approach incorporating material properties, geometric parameters, and friction was used to formulate the numerical model. The developed model effectively estimated buckling stresses across various conditions but could not precisely predict buckling points based on stress differentials. This work contributes a novel framework for integrating material, geometric, and process variables into stress prediction during forging, advancing defect control strategies in industrial metal forming. Full article