Search Results (500)

This article presents the wear characteristics of the working surface of WC-Co (Tungsten Carbide–Cobalt) tungsten carbide tools obtained using the innovative U-FAST (Upgraded Field-Assisted Sintering Technology) method for particleboard machining. Three groups of tools with a similar chemical composition but differing WC (Tungsten Carbide) grain sizes were tested. Milling tests were carried out on a CNC (Computer Numerical Control) machine tool with the following cutting parameters: spindle rotation at 15,000 rpm, a feed rate of 0.25 mm per tooth, and a feed rate of 3.75. The experimental results show that tools with submicron WC grit sizes of 0.4 µm and 0.8 µm have the longest tool life. Wear of the cutting edges occurred through the removal of the cobalt bond between the tungsten carbide grains, leading to fracture and mechanical removal of the grains from the cutting edge surface. The similarities in the relative wear characteristics of blades with submicron tungsten carbide grain sizes suggest that micro-abrasion and bond phase extrusion may be the main wear mechanisms under the experimental conditions. Nanometric WC grain size significantly influences tool wear through chipping and cracking. Full article

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks. Full article

The present study aims to investigate the effects of cutting parameters (cutting speed, Vc: 60–90–120 m/min; feed rate, f: 0.055–0.085–0.115 mm/rev), cutting tool coatings (CVD: TiN/TiCN/Al₂O₃ and PVD: TiAlN), and machining conditions (dry, air, and MQL) on cutting force (Fc), specific energy consumption (SEC), surface roughness (Ra), cutting temperature (T), and tool wear (Vb) during the milling of Ti6Al4V alloy. As a result, it was observed that all machining tests conducted with the Al₂O₃-coated cutting tool showed improvements of 4.7%, 10.75%, 3.8%, and 6.3% in Fc, SEC, Ra, and T, respectively, compared to the tests performed with the TiAlN-coated cutting tool. Under dry machining conditions, the average Fc, SEC, Ra, and T values were 302.82 N, 4.88 j/mm³, 0.653 µm, and 241.06 °C, respectively. Compared to dry machining conditions, the air and MQL machining conditions demonstrated improvements in the average Fc by 5.15% and 6.3%, SEC by 10.27% and 17.79%, Ra by 6.23% and 11.17%, and T by 8.9% and 19.68%, respectively. The lowest Fc and Ra values for the Al₂O₃-coated cutting tool were measured at 228.33 N and 0.402 µm, respectively, under the MQL machining condition, at a cutting speed of 120 m/min and a feed rate of 0.055 mm/rev. The lowest SEC value (2.694 J/mm³) was also obtained using the Al₂O₃-coated tool under MQL conditions at a cutting speed of 120 m/min and a feed rate of 0.115 mm/rev. Similarly, the lowest cutting temperature (129 °C) was achieved with the Al₂O₃-coated tool under MQL conditions at a cutting speed of 60 m/min and a feed rate of 0.055 mm/rev. The wear performance of the Al₂O₃-coated cutting tool was observed to be superior to that of the TiAlN-coated tool. Full article

Hydraulic erosion from suspended sediment is a major degradation mechanism in Francis turbines of sediment-laden rivers, especially in Andean hydropower plants. This study presents a Python3.9-based computational tool integrating the empirical Oka erosion model within a Lagrangian particle tracking framework, coupled to single-phase CFD in OpenFOAM 10. The novelty lies in a reduced-domain approach that omits the spiral casing and replicates its particle-induced swirl via a custom algorithm, lowering meshing complexity and computational cost while preserving erosion prediction accuracy. The method was applied to a full-scale Francis turbine at the San Francisco hydropower plant in Ecuador (nominal discharge 62.4 m³/s, rated output 115 MW, rotational speed 34.27 rad/s), operating under volcanic and erosive sediment loads. Maximum erosion rates reached ~1.2 × 10⁻⁴ mm³/kg, concentrated on runner blade trailing edges and guide vane pressure sides. Impact kinematics showed most collisions at near-normal angles (85°–98°, peak at 92°) and 6–9 m/s velocities, with rare 40 m/s impacts causing over 50× more loss than average. The workflow identifies critical wear zones, supports redesign and coating strategies, and offers a transferable, open-source framework for erosion assessment in turbines under diverse sediment-laden conditions. Full article

Titanium alloys (Ti-6Al-4V) are widely used in the aerospace field. However, as a typical difficult-to-machine material, titanium alloys have a low thermal conductivity, a high chemical activity, and a significant adiabatic shear effect. In conventional milling (CM), the temperature in the cutting zone rises sharply, leading to tool adhesion, rapid wear, and damage to the workpiece surface. This article systematically investigated the influence of process parameters on the surface roughness, cutting force, and cutting temperature in the ultrasonic-vibration-assisted milling (UAM) process of titanium alloys, based on which multi-objective optimization process of the milling process parameters was conducted, by utilizing the grey relational analysis method. An orthogonal experiment with four factors and four levels was conducted. The effects of various process parameters on the surface roughness, cutting force, and cutting temperature were systematically analyzed for both UAM and CM. The grey relational analysis method was employed to transform the optimization problem of multiple process target parameters into a single-objective grey relational degree optimization problem. The optimized parameter combination was as follows: an ultrasonic amplitude of 6 μm, a spindle speed of 6000 rpm, a cutting depth of 0.20 mm, and a feed rate of 200 mm/min. The experimental results indicated that the surface roughness Sa was 0.268 μm, the cutting temperature was 255.39 °C, the cutting force in the X direction (FX) was 5.2 N, the cutting force in the Y direction (FY) was 7.9 N, and the cutting force in the Z direction (FZ) was 6.4 N. The optimization scheme significantly improved the machining quality and reduced both the cutting forces and the cutting temperature. Full article

The study is motivated by the application of dry finish milling for post-build processing of additive Ti6Al4V blanks, since the use of neither lubricant nor coolants has been attracting increasing attention due to its environmental benefits, non-toxicity, and the elimination of the need for additional cleaning processes. For end mills, wear patterns were investigated upon finish milling of the SLM Ti6Al4V samples under various machining conditions (by varying the values of radial depth of cut and feed values at a constant level of axial depth of cut and cutting speed). When using all the applied milling modes, the identical tool wear mechanism was revealed. Built-up edges mainly developed on the leading surfaces, increasing the surface roughness on the SLM Ti6Al4V samples but protecting the cutting edges. However, abrasive wear was mainly characteristic of the flank surfaces that accelerated peeling of the protective coatings and increased wear of the end mills. The following milling parameters have been established as being close to rational ones: Vc = 60 m/min, Vf = 400 mm/min, a_p = 4 mm, and a_e = 0.4 mm. They affected the surface roughness of the SLM Ti6Al4V samples in the following way: max cutting thickness—8 μm; built-up edge at rake surface—50 ± 3 μm; max wear of flank surface—15 ± 1 μm; maximum adherence of workpiece. Mode III provided the maximum MRR value and negligible wear of the end mill, but its main disadvantage was the high average surface roughness on the SLM Ti6Al4V sample. Mode II was characterized by both the lowest average surface roughness and the lowest wear of the end mill, as well as an insufficient MRR value. Since these two modes differed only in their feed rates, their values should be optimized in the range from 200 to 400 mm/min. Full article

Background: Self-report physical activity (PA) scales, accelerometry, and heart rate (HR) monitoring are reliable tools for PA measurement for children with cerebral palsy (CP); however, there are limitations for those who are primary wheelchair users. The purpose of our study was to evaluate face and construct validity of the PROMIS^® Pediatric PA parent proxy short form 8a in measuring PA amount and intensity in children with CP who are non-ambulatory. Methods: Face validity: Semi-structured interviews with parents and pediatric physical therapists (PTs) were conducted about the appropriateness of each item on the PROMIS^® Pediatric PA short form. Construct validity: Children with CP who were non-ambulatory participated in a one-week observational study. PA amount and intensity were examined using PA monitors (Actigraph GT9X) and HR monitors (Fitbit Charge 4). Activity counts and time in sedentary and non-sedentary intensity zones were derived and compared to the PROMIS^® T-scaled score. Results: Twenty-two physical therapists (PTs) and fifteen parents participated in the interviews, and ten children completed 1-week PA observation. Eight and seven participants completed sufficient time of uninterrupted PA and HR monitor wear, respectively. Parents and PTs agreed that several questions were not appropriate for children with CP who were non-ambulatory. PA intensity via activity counts derived from wrist worn monitors showed a strong positive correlation with the PROMIS^® PA measure. Conclusions: Construct validity in our small sample was established between PROMIS^® scores and accelerometry activity counts when documenting PA amount and intensity; however, there were some differences on PROMIS^® face validity per parent and PT respondents. Despite some concerns regarding face validity, the PROMIS^® Pediatric PA parent proxy short form 8a shows promise as a valid measure of physical activity amount and intensity in non-ambulatory children with CP, warranting further investigation and refinement. Full article

The efficiency decline of Francis turbines, a key component of hydroelectric power generation, presents a multifaceted challenge influenced by interconnected factors such as water quality, incidence angle, erosion, and runner wear. This paper is structured into two main sections to address these issues. The first section applies the Buckingham π theorem to establish a dimensional analysis (DA) framework, providing insights into the relationships among the operational variables and their impact on turbine wear and efficiency loss. Dimensional analysis offers a theoretical basis for understanding the relationships among operational variables and efficiency within the scope of this study. This understanding, in turn, informs the selection and interpretation of features for machine learning (ML) models aimed at the predictive maintenance of the target variable and important features for the next stage. The second section analyzes an extensive dataset collected from a Francis turbine in Colombia, a country that is heavily reliant on hydroelectric power. The dataset consisted of 60,501 samples recorded over 15 days, offering a robust basis for assessing turbine behavior under real-world operating conditions. An exploratory data analysis (EDA) was conducted by integrating linear regression and a time-series analysis to investigate efficiency dynamics. Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. This study seeks to develop a comprehensive understanding of the factors driving Francis turbine efficiency loss and to propose strategies for mitigating wear-induced performance degradation. The synergy lies in DA’s ability to reduce dimensionality and identify meaningful features, which enhances the ML models’ interpretability, while ML leverages these features to model non-linear and time-dependent patterns that DA alone cannot address. This integrated approach results in a linear regression model with a performance (R²-Test = 0.994) and a time series using ARIMA with a performance (R²-Test = 0.999) that allows for the identification of better generalization, demonstrating the power of combining physical principles with advanced data analysis. The preliminary findings provide valuable insights into the dynamic interplay of operational parameters, contributing to the optimization of turbine operation, efficiency enhancement, and lifespan extension. Ultimately, this study supports the sustainability and economic viability of hydroelectric power generation by advancing tools for predictive maintenance and performance optimization. Full article

Mycelium-bonded composites (MBCs), or myco-composites, represent a novel engineered material that combines natural lignocellulosic substrates with a fungal matrix. As a sustainable alternative to plastics, MBCs are gaining increasing interest; however, their large-scale industrial adoption remains limited, partly due to low social acceptance resulting from their unattractive appearance. Laser engraving provides a promising method for fabricating intricate patterns and functional surfaces on MBCs, minimizing tool wear, material loss, and environmental impact, while enhancing esthetic and engineering properties. This study investigates the influence of CO₂ laser parameters on the material removal rate during the engraving of myco-composites, focusing on the effects of variable laser power, beam defocus, and head feed rate on engraving outcomes. The results demonstrate that laser power and beam focus significantly impact material removal in mycelium-bonded composites. Specifically, increasing the laser power results in greater material removal, which is more pronounced when the beam is focused due to higher energy density. In contrast, a beam defocused by 1 mm produces less intense material removal. These findings highlight the critical role of beam focus—surpassing the influence of power alone—in determining engraving quality, particularly on irregular or uneven surfaces. Moreover, reducing the laser head feed rate at a constant power level increases the material removal rate linearly; however, it also results in excessive charring and localized overheating, revealing the low thermal tolerance of myco-composites. These insights are essential for optimizing laser processing techniques to fully realize the potential of mycelium-bonded composites as sustainable engineering materials, simultaneously maintaining their appearance and functional properties. Full article

The use of aluminum honeycomb structures is fast expanding in advanced sectors such as the aeronautics, aerospace, marine, and automotive industries. However, processing these structures represents a major challenge for producing parts that meet the strict standards. To address this issue, an innovative manufacturing method using longitudinal ultrasonic vibration-assisted cutting, combined with a CDZ10 hybrid cutting tool, was developed to optimize the efficiency of traditional machining processes. To this end, a 3D numerical model was developed using the finite element method and Abaqus/Explicit 2017 software to simulate the complex interactions among the cutting tool and the thin walls of the structures. This model was validated by experimental tests, allowing the study of the influence of milling conditions such as feed rate, cutting angle, and vibration amplitude. The numerical results revealed that the hybrid technology significantly reduces the cutting force components, with a decrease ranging from 10% to 42%. In addition, it improves cutting quality by reducing plastic deformation and cell wall tearing, which prevents the formation of chips clumps on the tool edges, thus avoiding early wear of the tool. These outcomes offer new insights into optimizing industrial processes, particularly in fields with stringent precision and performance demands, like the aerospace sector. Full article

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

To address the issue of surface glazing that occurs during prolonged polishing with gel tools, this study employs a triethanolamine (TEA)-based polishing fluid system to enhance the self-sharpening capability of the gel polishing disc. The inhibitory mechanism of TEA concentration on disc glazing is systematically analyzed, along with its impact on the gel disc’s frictional wear behaviour. Furthermore, the synergistic effects of process parameters on both surface quality and material removal rate (MRR) of SiC are examined. The results demonstrate that TEA concentration is a critical factor in regulating polishing performance. At an optimal concentration of 4 wt%, an ideal balance between chemical chelation and mechanical wear is achieved, effectively preventing glazing while avoiding excessive tool wear, thereby ensuring sustained self-sharpening capability and process stability. Through orthogonal experiment optimization, the best parameter combination for SiC polishing is determined: 4 wt% TEA concentration, 98 N polishing pressure, and 90 rpm rotational speed. This configuration delivers both superior surface quality and desirable MRR. Experimental data confirm that TEA significantly enhances the self-sharpening performance of gel discs through its unique complex reaction. During the rough polishing stage, the MRR increases by 34.9% to 0.85 μm/h, while the surface roughness S_a is reduced by 51.3% to 6.29 nm. After subsequent CMP fine polishing, an ultra-smooth surface with a final roughness of 2.33 nm is achieved. Full article

This study aims to address the issues of high tool wear rate, severe deterioration of machining accuracy, and surface integrity in thin-walled part cutting processes, which are caused by vibration. To do so, this paper proposes a thin-walled part processing vibration control method based on CFS. With aluminum alloy 6061 planar thin-walled parts as the object of study, in this paper a CFS experimental platform was established, the influence of CFS on the dynamic characteristics of the thin-walled parts was analyzed, the effects of milling force and processing vibration during thin-walled part milling were investigated. The results show that compared with UCFS, CFS can significantly reduce the acceleration response amplitude of thin-walled parts and shorten their vibration decay time. When the spraying point coincides with the hammering point, the optimal vibration suppression effect is achieved at a spraying velocity V of 13 m/s, a spraying area S of 31 mm², and a spraying angle θ of 30°; the acceleration response amplitude decreases by 76.2%, and the vibration attenuation time decreases by 74.7%. This method can provide a certain support force and damping effect for thin-walled parts by CFS, thus reducing the milling force and machining vibration. Full article

Deep hole trepanning of large-diameter titanium alloy rods presents several challenges, including chip breaking, chip clogging, tool wear, and chipping. To address these issues, an optimized multi-tooth trepanning design was developed. An L9 orthogonal experimental array was employed to assess the influence of cutting speed, feed rate, and cutting fluid pressure on the chip volume ratio and to determine optimal process parameters. Results indicate that the impact of process parameters on the chip volume ratio of the first and third cutting teeth follows the order of cutting speed, feed rate, and cutting fluid pressure. Optimal chip morphology for internal chip removal is achieved with a cutting speed of 63.3 m·min⁻¹, a feed rate of 0.18 mm·r⁻¹, and a cutting fluid pressure of 4 MPa. Conversely, improper parameter matching can result in numerous long spiral chips, causing adhesive wear, diffusion wear of the multi-tooth drill, and severe chipping of the cutting edge. Full article

Background and Objectives: Conventional methods for removing cemented fixed prosthetic restorations (FPRs) are unreliable and lead to unsatisfactory outcomes. At their best, they allow the tooth to be saved at the expense of a laborious process that also wears down rotating tools and handpieces and occasionally results in abutment fractures. Restorations are nearly never reusable in any of these situations. Erbium-doped yttrium-aluminum-garnet (Er:YAG) and erbium-chromium yttrium-scandium-gallium-garnet (Er,Cr:YSGG) lasers casafely and effectively remove FPRs, according to scientific studiesre. This study sets out to examine the impact of Er:YAG laser radiation on the debonding of different ceramic restorations, comparing the behavior of various ceramic prosthetic restoration types under laser radiation action and evaluating the integrity of prosthetic restorations and dental surfaces exposed to laser radiation. Materials and Methods: The study included a total of 16 removed teeth, each prepared on opposite surfaces as abutments.y. Based on the previously defined groups, four types of ceramic restorations were included in the study: feldspathic (F), lithium disilicates (LD), layered zirconia (LZ), and monolithic zirconia (MZ). The thickness of the prosthetic restorations was measured at three points, and two different materials were used for cementation. The Er:YAG Fotona StarWalker MaQX laser was used to debond the ceramic FPR at a distance of 10 mm using an R14 sapphire tip with 275 mJ, 20 Hz, 5.5 W, with air cooling (setting 1 of 9) and water. After debonding, the debonded surface was visualized under electron microscopy. Results: A total of 23 ceramic FPRs were debonded, of which 12 were intact and the others fractured into two or three pieces. The electron microscopy images showed that debonding took place without causing any harm to the tooth structure. The various restoration types had the following success rates: 100% for the LZ and F groups, 87% for the LD group, and 0% for the MZ group. In terms of cement type, debonding ceramic FPRs cemented with RELYX was successful 75% of the time, compared to Variolink DC’s 69% success rate. Conclusions: In summary, the majority of ceramic prosthetic restorations can be successfully and conservatively debonded with Er:YAG radiation. Full article