Search Results (549)

Preparing a highly wear-resistant AlTiN coating on a powder metallurgy (PM) M42 high-speed steel substrate is a key strategy to enhance tool performance and meet the demands of efficient machining. This study adopted a process route comprising substrate preparation, heat treatment regulation, and arc-PVD deposition of AlTiN coatings to systematically investigate the influence of sintering temperature (1130, 1160, and 1190 °C) and austenitizing time (1150 °C for 0, 15, 60, and 120 min) on the microstructure and mechanical properties of the substrate, as well as on the tribological performance of the AlTiN coatings. The results indicate that elevating the sintering temperature promotes densification of the matrix, with Vickers hardness increasing from 366 HV to 462 HV and bending strength (σ) increasing from 1064 MPa to 1310 MPa. The predominant carbide phases identified are MC, M₂C, and M₆C. During austenitizing, microstructural changes consistent with a progressive transformation from M₂C to MC and M₆C carbides were indicated by SEM and XRD analyses. Precipitation strengthening was most evident after 60 min, with hardness reaching 868 HV. In contrast, bending strength (σ) exhibited a progressive decline with increasing austenitizing time, decreasing from 1310 MPa to 1015 MPa after 120 min, illustrating a clear trade-off between hardness and toughness. The wear behavior of the coating is governed synergistically by substrate hardness, bending strength (σ), coating–substrate interfacial adhesion strength (L_C), and carbide phase transformation. Elevated substrate hardness enhances anti-wear performance; bending strength influences crack propagation and spallation tendency; and L_C determines the efficiency of interfacial load transfer. The carbide phase evolution appears to modulate the coating’s wear behavior by regulating both the microstructure and mechanical properties of the substrate. Among the six sample conditions evaluated, the A3 sample (sintered at 1190 °C and austenitized for 120 min) exhibited the lowest wear rate (2.38 × 10⁻⁶ mm³·N⁻¹·m⁻¹), demonstrating superior wear resistance. These findings provide a reference for process optimization and rational design of M42/AlTiN composite coating systems. Full article

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

TC4DT (ELI) is a damage-tolerant titanium alloy characterized by high fracture toughness and slow crack propagation rates, and is, therefore, considered one of the standard materials for model fasteners in modern equipment. However, its high yield strength leads to excessive tool wear and forming defects. This paper presents a complete FE simulation framework to investigate the thread-rolling process of TC4DT(ELI) bolts M16 × 1.5. Using the actual geometries of the workpiece and rollers, an elasto-plastic three-dimensional finite element model was built in ABAQUS/Explicit to perform verification simulations, with the theoretical blank diameter and forming force as the reference benchmarks. The simulation results agreed well with the actual industrial data. This study carried out single-factor analyses of the effect of three important process parameters—the roll speed, friction coefficient, and initial temperature—on the resulting stress–strain distribution, forming force, and thread formation depth. A modal analysis was performed in ANSYS Workbench to check the structural integrity and avoid resonance while operating. According to the results, the optimized parameters decreased the maximum forming force by 14.8% and improved thread filling. Compared with experimental data, the simulation error in the blank diameter was controlled within 1.2%. The present work, a reliable numerical underpinning for replacing expensive and time-consuming trial-and-error processes, forms a basis for high-performance titanium alloy fasteners and assists in the wider application of such fasteners in modern equipment and any advanced manufacturing industries. Full article

This study investigates the failure mechanisms and machining performance of 3D-printed H13 tool steel end mills driven by the creation of a Finite Element Analysis (FEA)-based digital twin. The primary objective is to assess the process capability of these tools by integrating CAD and FEA with product design simulation-based data acquisition within a digital manufacturing framework, thereby validating a physical model. This research begins by redesigning a 20 mm end mill into a 6 mm, four-flute configuration, and then FEA simulating H13 tool steel and tungsten carbide (WC) tools. This is carried out to machine Al-6082-T6 under spindle speeds of 5500 rpm and 1500 rpm, with a constant feed rate of 0.5 mm/tooth over 100,000 cycles. The process is integrated with the Siemens Insights hub via Node-RED to replicate the simulation to correlate the CPU signal spikes and enhanced processing capacity, especially in relation to CAD/CAE kernel activities. Based on the simulation insights, two H13 end mills are fabricated using Fused Filament Fabrication (FFF). The first tool, tested at 5500 rpm and a 1100 mm/min feed rate, fractured after 70 mm of cutting. The second trial, using a diamond-coated solid carbide tool at 1500 rpm and 300 mm/min, achieved successful machining with graphene-enhanced coolant. The cutting forces ranged from 300 to 500 N for 3D-printed tools, compared with 150–180 N for the carbide tool. The surface roughness varied between 0.6–1 µm and 4–6 µm for the printed tools, aligning closely with traditional tools (0.5–1 µm). Post-machining analysis using SEM and EDX confirmed tool wear and material changes. This work adopted a methodology to capture and monitor CPU signal spikes via the digital twin platform, enabling real-time comparison with failure thresholds. The results demonstrate the feasibility of using 3D-printed H13 tools for sustainable, customizable machining, offering a pathway for industries to adopt in-house tool design and manufacturing with integrated digital validation. Full article

Predictive maintenance plays a key role in digitalization initiatives; however, in real settings, issues related to failure prediction occur when failure instances are rare compared to normal instances, leading to class imbalance. In this study, we systematically compare five machine learning (ML) models—random forest, XGBoost, support vector machine, k-nearest neighbors, and multinomial logistic regression (MLR)—to detect multiclass rare failures using four imbalance-handling approaches (i.e., no handling, manual oversampling, selective manual oversampling, and class weighting), forming 20 configurations. Using the AI4I 2020 predictive maintenance dataset, which contains five failure types, we determined that XGBoost with no handling achieved the highest macro-averaged F1 (macro-F1) score (0.842) but obtained 0% recall for tool wear failure (TWF). MLR with selective manual oversampling achieved approximately 50% TWF recall with lower overall performance (0.636 macro-F1) than top-performing models such as XGBoost. We also found that very rare classes remain difficult to detect. Even high-performing models fail to consistently detect all five failure types. Overall, no single strategy can achieve a high detection rate across all performance measures. Full article

Virtual reality (VR) sickness induced by wearing a head-mounted display and viewing 360° videos has primarily been studied using subjective questionnaires administered before and after content viewing. However, this approach is limited to identifying the onset of sickness during content viewing. This study quantitatively addresses the association between objective measures (gaze direction, head pose, electrocardiogram, and optical flow) and VR sickness, adopting an exploratory approach. Real-time sickness during 360° video viewing was measured using the fast motion sickness scale, and overall sickness susceptibility was evaluated using the simulator sickness questionnaire. The results indicated that a higher VR sickness severity was associated with reduced gaze entropy and an increase in the magnitude and entropy of optical flow, suggesting its potential as an objective measure for real-time VR sickness assessment. Furthermore, in the comparison between susceptibility groups, the high-susceptibility group had a nominally significantly lower heart rate variability than the low-susceptibility group, indicating that physiological signals may serve as auxiliary tools for sensing the baseline of VR sickness. The optical flow reflects the visual stimuli of VR content independent of personal susceptibility, suggesting its potential as a content-driven indicator of VR sickness. Full article

The present study investigates the surface integrity and flank wear of uncoated cermet inserts during dry turning of AISI 304 stainless steel. Three-dimensional metrology techniques were employed to assess both surface roughness and cutting-tool flank wear. Cutting speed and feed rate were the process parameters varied in the experiments. Both parameters exhibited a significant influence on the final surface quality. Specifically, increasing the cutting speed resulted in a deterioration of the surface finish under the evaluated conditions. Considering an average flank wear (VBB) of 0.1 mm as the tool life criterion, tool lives of 15 min and 9 min were achieved at cutting speeds of 120 m/min (lowest level) and 150 m/min (highest level), respectively. At lower cutting speeds, abrasive wear and adhesion were the predominant wear mechanisms, whereas chipping and diffusion became more pronounced at the higher cutting speed. The dry turning of AISI 304 stainless steel with uncoated cermet inserts proved viable in terms of sustainability and surface integrity; however, effective chip evacuation remains a critical concern. The use of compressed air or minimum quantity lubrication (MQL) may help mitigate this issue. Full article

A tool-health digital twin for CNC predictive maintenance is developed and operationalised as a fusion-and-state-estimation core that produces a latent tool-health trajectory (wear level and wear-rate dynamics) from multi-rate sensor streams for diagnosis and remaining useful life (RUL) forecasting under strict edge latency constraints. The scope is tool-health–informed maintenance decisions (condition-based tool replacement/scheduling), rather than a comprehensive maintenance twin for all CNC subsystems. Multi-rate vibration, spindle-current, and temperature signals are synchronized and windowed, and a linear state-space model with Kalman filtering and innovation-guided adaptive noise estimation stabilizes the latent health state across operating-regime changes. The fused state is then used by compact sequence learners, an LSTM for edge feasibility, and a compact Transformer as a higher-accuracy comparison, to output fault categories and RUL estimates. Predictive uncertainty is quantified via a Monte Carlo dropout and linked to reliability-aware actions through a simple alarm/defer/schedule policy, while SHAP provides feature-level interpretability. On a CNC testbed, fusion improves fault F1 from 0.811 to 0.892 and PR-AUC from 0.867 to 0.918 while reducing RUL RMSE from 10.4 to 8.1 cycles; the compact Transformer reaches 0.903 F1 and 7.9-cycle RMSE at higher inference time. The end-to-end pipeline remains within a ≤100 ms breakdown, maintains in-band innovation statistics, supports rehearsal-based updates under drift, and is additionally evaluated on external tool-wear and turbofan datasets. Full article

The proper selection of surface topography (ST) parameters is crucial for ensuring the effective performance of machine components, including their wear and corrosion resistance. In the literature, research on the ST of hardened stainless steels (SSs) after finish turning using cubic boron nitride (CBN) inserts, as well as comparisons with cemented carbide (CC) inserts depending on cutting parameters, is still limited. In this study, the ST of X20Cr13 martensitic hardened SS under dry finish turning with various cutting speeds and feed rates was investigated. Experiments were conducted using a CNC lathe with CBN and CC inserts. A Sensofar S Neox 3D optical profilometer was employed to characterize the ST features, including height surface roughness (SR) parameters, SR profiles, and 2D and 3D surface images. The Parameter Space Investigation method was used to design the experimental plan. For both CBN and CC inserts, the feed rate was the dominant factor influencing the overall SR, described by the Sa and Sq parameters. The extreme parameters Sp, Sv, and Sz were determined by the relationship between feed rate and cutting speed. With appropriately selected turning parameters, it is possible to obtain low Sa values (0.4–0.6 µm), which can eliminate the need for grinding operations. CBN inserts ensured a more regular shape of the ST, while CC inserts contributed to a wavy surface characteristic, associated with more intense plastic deformation. However, low Sa values may be accompanied by isolated peaks, indicating that this parameter does not always fully reflect the presence of extreme micro-irregularities. On the machined surfaces, adhesive bonds of chips and cutting tool material were observed. In addition, micro-scratches were registered for CBN inserts, and a side flow phenomenon for CC inserts. The results confirm that dry turning of hardened SSs can be effectively performed using both CC and CBN inserts. Full article

Polycrystalline diamond (PCD) compacts are extensively applied in downhole drilling tools owing to their exceptional hardness and wear resistance. However, their tribological performance is strongly influenced by the thermal and chemical characteristics of drilling fluids. In this study, the coupled effects of temperature (25–125 °C) and oil–water ratio on the tribological behavior of PCD were systematically investigated. The results indicate that under relatively high oil–water ratios (50:50, 80:20, and 100:0), both the friction coefficient and wear rate increase monotonically with temperature, which is associated with intensified interfacial thermal stress and suppressed formation of protective carbon-based transfer films. In contrast, at low oil–water ratios (0:100 and 20:80), the friction coefficient exhibits a non-monotonic dependence on temperature, decreasing initially and then increasing with a transition near 100 °C. This behavior is attributed to temperature-activated surface passivation through C-OH bond formation in water-rich environments, followed by the deterioration of passivation due to water evaporation at elevated temperatures. These findings provide insight into temperature-dependent lubrication regime transitions and tribo-chemical evolution of PCD in complex drilling fluid environments. Full article

Dry machining of medium-carbon steels plays an important role in sustainable manufacturing; however, high tool wear and thermal instability pose challenges. The study aims to evaluate the kinematic–tribological performance of EN8 steel during dry milling and compare up-milling and down-milling to trade-off tool life and surface finish. The experiments were conducted using a central composite design (CCD) as part of response surface methodology (RSM), with 36 runs to evaluate interactions among spindle speed, feed rate, and depth of cut. Down-milling outperformed up-milling, achieving 12.4% less tool wear, 45.9% better surface finish, and a 47 °C lower peak temperature from cutting. The above benefits are attributed to the unique kinematics of chip formation during down-milling, which offers lower friction at entry and better heat dissipation, contrasting with the high-friction ploughing phase of up-milling. Grey relational analysis (GRA) found that down-milling with a mid-range cutting speed (22.31 m/min) and a low feed rate (25 mm/min) provided a multi-objective optimum. The findings support the existence of a kinematic–tribological coupling, providing a solid single approach to optimising the dry machining of harder materials. Full article

With the advancement of high-end manufacturing, the application of difficult-to-machine materials such as titanium alloys and superalloys is becoming increasingly widespread. Their inherent material properties pose challenges during machining, including high cutting temperatures, rapid tool wear, and difficulty in controlling surface quality. Nanofluid minimum quantity lubrication (NFMQL) technology, as an advanced lubrication and cooling method, enhances the thermal conductivity and lubricating properties of fluids by uniformly dispersing nanoparticles in the base oil. This paper reviews the preparation methods, advanced atomization techniques, and core mechanisms of NFMQL technology. It focuses on analyzing the effectiveness of this technology in four major machining processes, turning, milling, grinding, and drilling, for typical materials such as titanium alloys, steel, and superalloys. Compared to dry cutting, conventional MQL, and poured cooling, NFMQL reduces cutting forces/torque, cutting temperatures, tool wear, and surface roughness while improving material removal rates, machining accuracy, and surface integrity. This paper concludes by summarizing the technology’s advantages, current challenges, and future research directions. 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

Advances in medical care now enable significant functional recovery after traumatic limb injuries. The return-to-duty decision-making process is highly variable and dependent on multiple factors. To retain service members (SM) post-injury, there needs to be a robust method to inform the decision-making process. The collection of outcome data and decision tree analysis has the potential to assist in the development of an efficient decision support tool. Data were combined from two previous research studies on 31 injured SMs (26 with limb salvage wearing custom dynamic ankle–foot orthoses and 5 with varying levels of lower limb amputation wearing prostheses). Forty-two factors across military, demographic, injury, and outcome measures were used to develop categorical tree models to classify return to duty after injury. The feasibility of the final pruned model was evaluated using a 10-fold cross-validation to calculate sensitivity, specificity, and misclassification rate. The overall misclassification rate for the final pruned model was 29% (9/31). The model classified participants into successful return to duty: (1) Post Concussion Symptom Scale < 20 and (2) age at time of assessment ≥34. These preliminary results suggest that decision tree modeling could be an effective approach to augmenting the return-to-duty decision-making process. Full article

This study presents a systematic investigation of laser surface hardening of 9CrSi tool steel with the aim of establishing the relationships between processing parameters, microstructural evolution, and resulting mechanical and tribological properties under the applied laser conditions. The influence of laser power, modulation frequency, and scanning speed on the hardened layer depth, microstructure, and surface properties was analyzed. Laser treatment produced a martensitic surface layer with varying fractions of retained austenite, while the transition zone consisted of martensite, granular pearlite, and carbide particles. X-ray diffraction identified the presence of α′-Fe, γ-Fe, and Fe₃C phases, with peak broadening associated with increased lattice microstrain induced by rapid self-quenching. The surface microhardness increased from approximately 220 HV_0.1 in the untreated state to 950–1000 HV_0.1 after laser hardening, with hardened layer thicknesses ranging from about 500 to 750 µm depending on the processing regime. Instrumented indentation showed higher elastic modulus values for all hardened conditions. Tribological tests under dry sliding conditions revealed reduced coefficients of friction and more than an order-of-magnitude decrease in wear rate compared with untreated steel. The results provide a parameter–microstructure–performance map for laser-hardened 9CrSi steel, demonstrating how variations in laser processing conditions affect hardened layer characteristics and functional performance. Full article