Search Results (492)

Tool wear remains a critical limiting factor in machining performance, particularly in dry cutting conditions where friction and tribological interactions dominate. This study investigates the influence of a 5–8 μm PVD-deposited TiN coating on the wear behavior of high-speed steel (HSS) end mills during milling of three representative wood species (oak, beech, and fir). A spatially resolved wear evaluation methodology was employed, based on ten measurement points distributed along a 20 mm active cutting edge, enabling simultaneous assessment of mean wear and maximum localized wear (Umax). A factorial experimental design combining material type and feed rate (1500–2500 mm/min) was analyzed using two-way ANOVA with effect size quantification (η²). The results reveal a statistically significant reduction in mean wear for TiN-coated tools (F = 7.46, p = 0.0195, η² = 0.34), corresponding to an average decrease of approximately 46% compared to uncoated tools. Maximum wear was influenced by both coating (F = 14.73, p = 0.0028, η² = 0.399) and material (F = 4.37, p = 0.040, η² = 0.237). The experimental findings are interpreted through a tribological framework, indicating a transition from abrasion- and micro-chipping-dominated degradation in uncoated tools to a controlled wear regime in TiN-coated tools, characterized by reduced asperity penetration, delayed crack initiation, and limited tribochemical interactions. These results demonstrate that coating effects dominate global wear evolution, while material properties influence localized degradation. The proposed combined experimental–statistical–mechanistic approach provides a robust framework for understanding and optimizing tool performance in dry machining environments. Full article

Machining Nomex honeycomb structures presents a major challenge due to their thin-walled architecture, orthotropic behavior, and sensitivity to adhesion and delamination. This study develops a three-dimensional numerical model using Abaqus/Explicit to analyze ultrasonic vibration-assisted milling in longitudinal and longitudinal-torsional modes. The model incorporates orthotropic behavior with progressive damage based on Tsai-Wu and experimental friction calibration to accurately reproduce tribological conditions. A parametric analysis examines the effect of vibration mode, amplitude (5–25 µm), frequency (21–22.5 kHz), cutting width, and tool geometry on stresses, bond wear, and material buildup. An optimal coefficient of friction ensures excellent simulation–experiment agreement. Compared to conventional milling, the longitudinal-torsional configuration reduces cutting forces by up to 50%, while frequency optimization allows for gains of 40 to 60%. Hybrid vibration coupling establishes intermittent contact and oscillatory micro-shearing, limiting adhesion and build-up. Thus, longitudinal-torsional assistance improves tribological stability, tool life and wall integrity, offering a validated digital strategy to optimize ultrasonic milling of composite honeycomb structures. Full article

This study systematically investigates the influence of steel fibers on the mechanical properties of cement concrete. End-hook, shear, and milling type steel fibers were selected, with comparisons made to copper-plated and corroded steel fibers. The effects of fiber type, aspect ratio (40–60), and volume content (0.5–1.5%) on the compressive, flexural, and splitting tensile properties of concrete were analyzed. A multi-objective mechanical performance prediction model was established using a combined macro- and micro-scale testing approach, integrated with response surface methodology (RSM) and I-optimal design. The results indicate that steel fibers can significantly enhance the overall mechanical properties of concrete. Among the types tested, the end-hook fiber exhibited the best performance in compressive and splitting tensile strength, and the 28-day compressive strength increased by 41% compared with plain concrete, while the milling fiber showed the greatest improvement in flexural strength, and the value reached up to 72%. Furthermore, the failure mode observations indicated that steel fiber incorporation fundamentally altered the fracture behavior of concrete, transitioning it from brittle fracture to quasi-ductile behavior with post-crack load-carrying capacity, particularly for end-hook and milling fiber types. An optimal parameter window for the fiber reinforcement effect was identified, with the best comprehensive performance achieved at an aspect ratio of 50–60 and a fiber content of 0.5–1.0%. The enhancement effect of copper-plated and corroded steel fibers was limited due to reduced interfacial bonding performance. The developed model demonstrates high prediction accuracy, providing a theoretical and experimental basis for the engineering application of fiber-reinforced concrete. Full article

(1) Purpose: To compare the fabrication accuracy, internal fit, and marginal adaptation of three-unit definitive resin fixed dental prostheses (FDPs) produced by subtractive milling and additive manufacturing. (2) Materials and Methods: A typodont mandible was prepared for a three-unit FDP, with full crown preparations on teeth mandibular left canine and mandibular left second premolar featuring 1 mm chamfer finish lines. The FDP was designed with a 16 mm² connector and a 100 µm cement gap. Two milling materials (Ambarino High-Class, IPS e.max CAD) and two experimental 3D printing hybrid resins (3D-1, 3D-2) were used. All restorations were scanned using an intraoral scanner and compared to the original STL using reverse engineering software for surface trueness and deviation analysis. The internal fit was evaluated using the triple-scan method, while marginal fit was assessed via micro-CT imaging. Statistical analysis was conducted using one-way ANOVA and Kruskal–Wallis tests (α = 0.05). (3) Results: Milled groups demonstrated a lower prevalence of external, marginal, and overall surface deviations (p < 0.001), while 3D-1 exhibited comparable deviations within the internal region with M-E (p = 0.067). Milled groups had average gap values that were similar to 3D-1 (p > 0.08), but significantly lower than 3D-2 (p < 0.002). In marginal adaptation evaluated by micro-CT, the M-A and M-E groups provided significantly lower gaps, while the 3D-1 and 3D-2 groups exhibited wider marginal and axial gaps. (4) Conclusions: These results indicate that while milling remains a more reliable manufacturing method for achieving external and marginal precision, position 3D-1 is a compelling, chairside alternative to milling. Full article

Micro heat exchangers (MHXs) have emerged as a critical technology for advanced thermal management in the food and pharmaceutical industries due to their high surface area-to-volume ratios, compact design, and precise temperature control. This review provides a systematic and integrated analysis of MHX technology, covering their fundamental principles, classification, design methodologies, performance enhancement techniques, and industrial applications. Unlike existing reviews, the present work establishes a unified framework that links microscale heat transfer mechanisms, such as Brownian motion, surface corrugation effects, and non-dimensional parameters, with practical design choices, manufacturing routes, and the process requirements specific to food and pharmaceutical systems. The subsequent sections explore the key performance-influencing factors, including channel geometry, surface enhancement strategies, nanofluid utilization, and governing non-dimensional numbers (e.g., Nusselt, Reynolds, and Knudsen numbers), which are systematically compared across different operating regimes. Recent advances in materials and fabrication techniques, such as laser ablation, lithography, micro-milling, embossing, and additive manufacturing, are analyzed with respect to their scalability, thermal–hydraulic performance, and industrial feasibility. Furthermore, the review highlights the emerging trends in micro heat exchanger (MHX) optimization, including computational fluid dynamics (CFD)-driven design, smart monitoring systems, and energy-efficient integration within processing lines. Finally, the paper also identifies the key challenges and limitations of micro heat exchangers, including pressure drop, fouling, scaling, manufacturing complexity, and cost constraints. These are critically discussed along with future research directions aimed at improving reliability and sustainability. By consolidating the dispersed research outcomes into a coherent, design-oriented perspective, this review offers new insights and practical guidance for researchers, engineers, and industry practitioners seeking to advance the deployment of MHXs in food and pharmaceutical processing. Full article

In order to provide clinically significant evidence on the long-term functional performance of CAD/CAM provisional materials, especially 3D-printed and milled resins, accurate tribologically in vitro wear tests that integrate wear parameters and surface topography analysis are necessary. The goal of the study was to assess the wear resistance of several CAM-obtained dental crown materials and the relationship between wear and the manufacturing process, distinctive postprocessing, microhardness, microroughness, and surface topography. A standardized ball-on-flat tribological protocol was applied to (n = 70) CAD/CAM-fabricated PMMA specimens (four 3D-printed groups with distinct post-processing protocols (Optiprint) and three milled materials (TelioCAD, Shaded PMMA, Copra Temp Symphony)) to quantify wear parameters micro- and nanoroughness (Ra, Rz, Sa, Sy), and Vickers microhardness, followed by comprehensive statistical analysis (t-tests, Pearson correlations) to elucidate material- and process-dependent differences in wear behaviour. Nanoroughness was carried using atomic force microscopy evaluation. Wear testing showed that most materials, particularly the 3D-printed groups, developed limited wear, whereas the milled materials evolved toward groove-dominated wear topographies. Wear statistics showed that the printed resins consistently had an advantage, meaning that the degree and rate of wear are significantly influenced by the manufacturing process. Hardness has a central role in governing the wear performance of interim resin materials, while nanoroughness acts as a secondary factor. Optimised post-processing of printed materials, particularly a prolonged post-curing period, yields a beneficial combination of low wear and specific topography, thereby providing a significant clinical advantage. Full article

The growing demand for direct reduced iron (DRI) in green steel production requires high-quality fired pellets as the burden for the gas-based shaft furnace direct reduction process. However, the properties of magnetite concentrate as pellet feed present a significant impact on the quality of fired pellets, especially the metallurgical performance. A systematic study of the effect of pretreating the magnetite concentrate on the properties of fired pellets was conducted using two pretreatment technologies, i.e., damp-milling and high-pressure roll grinding (HPRG). Green balls were made from pretreated magnetite concentrates and fired under optimal conditions. Their performance was then evaluated in a laboratory-scale setup simulating the HYL shaft furnace environment. Key metrics included cold compressive strength (CCS), reducibility index (RI), reduction swelling index (RSI), and dynamic low-temperature reduction degradation (LTD). The pretreatment of magnetite concentrates with HPRG twice showed significant benefits. The fired pellets not only have a CCS of 2500 N/p at a roasting temperature 150 °C lower, but also achieve an RI of 3.37 and an RSI of 3.15%, respectively. Furthermore, the reduction degradation tendency was markedly reduced; the +6.3 mm fraction reached 94.72% with a whole pellet ratio of 75.49%. Conversely, while damp-milling improved the LTD, it required a 100 °C increase in preheating temperature and yielded a whole pellet ratio of only 49.15%, failing to meet industrial requirements. The improvement in metallurgical performance is attributed to the intense micro-cracking induced by the two-pass HPRG process, which optimizes the particle size distribution, specific surface areas and improves the microstructure and pore properties of the fired pellets. Full article

Humidification conditioning has been increasingly applied in brown rice milling to improve processing performance. However, the underlying mechanisms by which humidification alters the mechanical behavior and microstructure of the bran layer remain insufficiently understood. In this study, the effects of humidification on the mechanical properties and surface microstructure of the brown rice bran layer were investigated, and the optimal conditioning parameters were further determined based on milling performance. Brown rice samples were conditioned to different moisture levels, and the corresponding changes in bran layer tensile strength, surface roughness, and microstructural features were analyzed using tensile testing, three-dimensional surface profilometry, and scanning electron microscopy. The results show that humidification significantly disrupts the continuity of the fibrous matrix in the bran layer, leading to reduced tensile strength and wear resistance. Moderate humidification (around 16% moisture content) promotes the formation of micro-pores and weakens structural integrity, facilitating bran removal during milling and improving head rice yield (HRY), whereas excessive humidification results in over-softening and increased kernel breakage. On this basis, a quadratic orthogonal rotatable composite design was employed to optimize the combined effects of moisture content, humidification time, and equilibration time on HRY and specific energy consumption. The optimal conditioning parameters were identified as 16% moisture content, 30 s humidification time, and 36 min equilibration time. This study provides the mechanistic insights into the humidification-induced structural and mechanical evolution of the brown rice bran layer, through experimental optimization of humidification operating parameters, offering practical guidance for improving milling quality and energy efficiency. Full article

Microfluidics provides precise control of microscale fluid transport and has become central to biomedical, pharmaceutical, and industrial technologies. However, conventional fabrication methods such as photolithography and soft lithography require cleanroom facilities, use costly materials, and offer limited capability for constructing complex or multi-material architectures. This review highlights emerging manufacturing strategies, focusing on polymer-based micro-milling as an accessible and cost-effective alternative for microfluidic device production. Advances in micro-milling now enable the fabrication of microchannels and functional features with improved dimensional accuracy and surface quality, while additive manufacturing offers complementary rapid prototyping and design flexibility. Micro-milling is particularly promising for rapid prototyping of polymeric biosensor chips designed for point-of-care diagnostics. The technique supports diverse materials and eliminates reliance on cleanroom processing. Critical parameters, including tool geometry, spindle speed, and feeding rate, strongly influence fidelity and surface roughness, which directly affect biosensor sensitivity. Despite its advantages, challenges such as tool wear, burr formation, and limits on minimum feature size continue to hinder reproducibility. Recent progress in toolpath optimization, hybrid additive–subtractive methods, and real-time process monitoring shows the potential to overcome these barriers. Overall, micro-milling offers a scalable and economical route for fabricating accessible microfluidic and biosensing platforms, with future work needed to standardize processes and improve integration with surface functionalization methods. Full article

Severe fluid loss in fractured, depleted reservoirs usually defeat conventional water-based drilling fluids (WBDFs), and rigid lost-circulation materials (LCMs) struggle to form durable, conformal seals. We report an eco-oriented colloidal gas aphron (CGA) fluid built from a nanostructured corn biopolymer (NCBP) and a biodegradable peanut-oil-derived surfactant, benchmarked against a reference fluid (RF) and aphron-only baselines (aphron based fluid, ABF). NCBP, produced by ball milling, was confirmed nanostructured by x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), electron and atomic microscopies. Performance was evaluated from 25 to 90 °C for rheology, aphron stability and filtration at low temperature and low pressure (LTLP) of 100 psi and 25 °C, with post-test mud cake imaging. The optimized formulation, NCBP-2, showed stronger shear-thinning and higher gel strengths with heat, sustained stable and uniform aphrons for at least 120 min with foam persistence beyond 24 h, and delivered 3.0 mL filtrate with a 0.8 mm mud cake. These outcomes correspond to 60% less filtrate and approximately 73% thinner mud cakes than RF (7.5 mL; 3.0 mm), and about 14% and 33% improvements over the best ABF (3.5 mL; 1.2 mm). Micrographs revealed denser, finer-pored mud cakes, consistent with a mechanism in which deformable aphrons bridge micro-fractures while nano-scale polymeric fillers tighten the mud cake network. The results demonstrate decisive loss-control gains with temperature-tolerant rheology, supporting bio-based CGA fluids for depleted and fractured formations. Full article

Titanium alloys face challenges such as high temperatures, high forces, and tool wear during turning, milling, drilling, and grinding operations. Cryogenic minimum quantity lubrication (CMQL) technology, which combines cryogenic cooling with micro-lubrication, offers an effective solution to these challenges through its synergistic mechanism of heat suppression via cooling and friction reduction via lubrication. This paper first elucidates the cooling and lubrication principles of various CMQL technologies and their adaptability process. It then reviews CMQL applications across four titanium alloy machining processes, systematically analyzing their effects on cutting forces, temperatures, tool wear, surface integrity, and chip morphology. Research indicates that CMQL technology demonstrates universal advantages over minimum quantity lubrication (MQL) across diverse titanium alloy machining processes. Furthermore, incorporating nanofluids or integrating ultrasonic vibration to form enhanced composite processes can further improve medium permeability, reduce machining loads, and enhance surface quality. Future developments in this field will advance toward intelligent and sustainable directions, providing critical technological support for high-performance green manufacturing of titanium alloys. Full article

The milled surface topography of NiTi SMA critically affects its frictional behavior, corrosion resistance, and biocompatibility, which are essential for biomedical and aerospace applications. This study combines simulation and single-factor experiments to investigate the coupling behavior among surface topography evolution, work hardening, plastic deformation, and residual stress evolution. Results showed that increasing feed per tooth led to a significant rise in surface residual height and an improvement in surface isotropy. With the increase in feed per tooth, the error between the experimental and simulated heights gradually decreased from 105.6% to 30.9%, indicating that both material properties and feed per tooth strongly affect residual profile formation in the feed direction. In addition, larger feed per tooth intensifies work hardening and plastic deformation but reduces surface residual stress, thereby increasing microhardness. These effects can mitigate material rebound and improve surface profile accuracy. The results provide a direct basis for controlling the surface integrity of NiTi SMA components through machining parameter optimization, enabling precise tailoring of functional surface characteristics, such as wear performance, chemical stability, and biological response, which is of critical importance for high-end biomedical implants and aerospace systems. Full article

The flexible micro-structured surface found in biological skins exhibits remarkable drag reduction properties, inspiring applications in the aerospace industry, underwater exploration, and pipeline transportation. To address the challenge of efficiently replicating such structures, this study presents a composite flexible polymer film with a bio-inspired micro-dimple array, fabricated via an integrated process of precision milling, polishing, and micro-injection molding using thermoplastic polyurethane (TPU). We systematically investigated the influence of key injection parameters on the shape accuracy and surface quality of the film. The experimental results show that polishing technology can significantly reduce mold core surface roughness, thereby enhancing film replication accuracy. Among the parameters, melt temperature and holding time exerted the most significant effects on shape precision PV and bottom roughness R_a, while injection speed showed the least influence. Under optimized conditions of a melt temperature of 180 °C, injection speed of 60 mm/s, holding pressure of 7 MPa, and holding time of 13 s, the film achieved a micro-structure shape accuracy of 13.502 μm and bottom roughness of 0.033 μm. Numerical simulation predicted a maximum drag reduction rate of 10.26%, attributable to vortex cushion effects within the dimples. This performance was experimentally validated in a flow velocity range of 0.6–2 m/s, with the discrepancy between simulated and measured drag reduction kept within 5%, demonstrating the efficacy of the proposed manufacturing route for flexible bio-inspired drag reduction film. Full article

Semi-autogenous (SAG) milling represents one of the most energy-intensive and variable stages of copper mineral processing. Traditional deterministic models often fail to capture the nonlinear dependencies and uncertainty inherent in industrial operations such as granulometry, solids percentage in the feeding or hardness. This work develops and validates a stochastic model based on Discrete Bayesian networks (BNs) to represent the causal relationships governing SAG Production and SAG Power under uncertainty or partial knowledge of explanatory variables. Discretization is adopted for methodological reasons as well as for operational relevance, since SAG plant decisions are typically made using threshold-based categories. Using operational data from a Chilean mining operation, the model fitted integrates expert-guided structure learning (Hill-Climbing with BDeu/BIC scores) and Bayesian parameter estimation with Dirichlet priors. Although validation indicators show high predictive performance (R² ≈ 0.85—0.90, RMSE < 0.5 bin, and micro-AUC ≈ 0.98), the primary purpose of the BN is not exact regression but explainable causal inference and probabilistic scenario evaluation. Sensitivity analysis identified water feed and solids percentage as key drivers of throughput (SAG Production), while rotational speed and pressure governed SAG Power behavior. The BN framework effectively balances accuracy and interpretability, offering an explainable probabilistic representation of SAG dynamics. These results demonstrate the potential of stochastic modeling to enhance process control and support uncertainty-aware decision making. Full article

The intrinsically low stiffness of titanium alloy thin-walled components causes residual stresses to readily accumulate during high-speed micro-milling, leading to deformation and hindering machining precision. To clarify the residual-stress formation mechanism and enable deformation control, this study first proposes a surface residual stress characterization model based on an exponentially decaying sinusoidal function, with model parameters efficiently identified via an improved particle swarm optimization algorithm, allowing rapid characterization of stress distributions under different process conditions. A response surface model constructed using a central composite design is then employed to reveal the coupled effects of machining parameters on residual stress and top-surface deformation. On this basis, a GA-BP neural network–based prediction framework is developed to improve the accuracy of residual stress and deformation prediction, while the AGE-MOEA2 multi-objective evolutionary algorithm is used to optimize micro-milling parameters for the simultaneous minimization of residual stress and deformation via Pareto-optimal solutions. Validation experiments on thin-wall micro-milling confirm that the optimized parameters significantly reduce peak residual stress and suppress top-surface deformation. The proposed modeling and optimization strategy provides an effective reference for high-precision machining of titanium alloy thin-walled components. Full article