X22CrMoV12-1 refractory heat-resistant high-strength stainless steel is primarily used in the machining and manufacturing of heavy-duty gas turbine compressor blades. To address the demand for green finishing of blades, this paper conducted cutting experiments on X22CrMoV12-1 steel under dry cutting, minimum quantity lubrication (MQL), cryogenic CO₂, and the Supercritical CO2 based minimum quantity lubrication (CMQL) process. Compared to dry cutting, CMQL demonstrated 52.2

SiCf/SiC composites are key materials for aerospace hot-end components. To enhance its service performance by surface microstructure, micro-etching of SiCf/SiC composites was investigated using femtosecond laser. Through XRD phase analysis, grain analysis and material removal rate calculation, the efficient removal mechanism of SiCf/SiC composites by green femtosecond laser based on two-photon nonlinear absorption effect was clarified, promoting the formation of 6H-SiC with better thermal stability, finer crystallite size and higher crystallinity. Negative defocus enabled larger depth-to-width ratios and more vertical sidewalls for microgrooves. Micro-etching process can be divided into three stages. In the transient non-equilibrium physical effect stage, lattice defects were triggered by multiphoton nonlinear absorption effect. In the thermo-chemical synergy stage, oxidation reaction dominated the micro-etching. In the extreme condition decomposition stage, SiC was decomposed by high temperature, generating plasma shock waves that promoted material removal, which was confirmed by the characteristic peaks of Raman spectrum result.

In this work, the dynamic impact behaviors of fine-grained 304L stainless steel were investigated under different strain rates and temperatures. The fine-grained 304L stainless steel exhibits pronounced strain-rate hardening with a strain-rate sensitivity m = 0.116, accompanied by a low temperature rise of 42.5 degrees C. At room temperature and strain rates between 1050 and 2400 s(-1), deformation is dominated by dislocation motion, stacking fault formation and deformation twinning, while martensitic transformation is suppressed. A higher strain rate facilitates the gamma/twin -> alpha ' martensitic transformation, which originates from local stress concentration induced by dislocation pile-ups. Furthermore, the combined effect of low temperature and moderate strain rate awakes the gamma -> epsilon -> alpha ' transformation pathway by reducing stacking fault energy. The resultant multiplicity of deformation mechanisms enhances strain hardening, yielding excellent impact resistance characterized by low temperature rise, strong rate sensitivity and hardening capacity.

Tool condition monitoring (TCM) in Carbon Fiber Reinforced Polymer (CFRP) milling remains highly challenging due to the pervasive lack of physical interpretability. To address the gap, this study proposes a physically driven multi-modal feature fusion framework. The methodology is rigorously validated on 118 experimental samples derived from four distinct cemented carbide substrates—varying in WC grain size and Co content—under fixed industrial cutting parameters (Vc = 200 m/min, fn = 0.5 mm/rev, ae = 4 mm, and ap = 10 mm). Cutting force and acoustic emission (AE) signal features were dimensionality reduced via Principal Component Analysis (PCA). Tool wear states are subsequently classified using a novel gated multi-modal fusion network reinforced by a hybrid GAN-Mixup data augmentation. Cross-substrate generalization is empirically verified through a leave-one-substrate-out testing protocol, achieving accuracy of 93.10%. Comprehensive ablation studies corroborate the necessity: isolating either force or AE signals degrades accuracy to 62.07%, while the physical gating mechanism independently yields a 10.34% accuracy improvement over standard feature concatenation. Furthermore, SHapley Additive exPlanations (SHAP) analysis is employed to map critical PCA features back to the raw sensor domains, which reveals that the collapse of force spectral energy and the dynamic shift in AE peak frequencies physically encode the transition from fiber shearing to matrix friction during tool wear. Ultimately, this approach provides a robust, physically interpretable pathway for deploying multi-sensor condition monitoring in intelligent composite manufacturing.

A comprehensive theoretical framework is presented for the prediction and compensation for geometric deviations in mirror additive manufacturing (MAM), a process whose unique thermo-mechanical coupling distinguishes its geometric error evolution from conventional automated fiber placement (AFP) and fused deposition modeling (FDM). First, viscoelastic flow in the melting zone and elastoplastic deformation in the cooling zone are analyzed to elucidate the formation of dynamic in-process geometric deviations, which scale positively with tape tension. Second, the release of internally generated stresses during deposition is shown to produce elastic springback that counterbalances tension-induced deformation, thereby reducing final geometric deviation. Building on a multi-source thermo-mechanical coupling analysis, a quantitative mapping model is then derived: deposition tension is treated as a key intermediate variable linking process inputs including layer count, deposition speed and compaction force to geometric deviations. Model validation confirms its ability to forecast geometric errors and to inform compensation design. To that end, an active compensation strategy is proposed in which an additional rotational degree of freedom is introduced to the compaction roller, generating counteracting rollingfriction forces that neutralize excessive tape tension. Numerical simulations further substantiate the approach and reveal that increased layer count enhances stiffness, higher deposition speeds elongate the melting zone and exacerbate deviations, and compaction force exhibits a non-monotonic influence. The integrated prediction-compensation methodology markedly improves MAM geometric accuracy and offers actionable guidelines for its industrial implementation.

Tap spiral grooves govern chip evacuation and cutting stability, and their profile accuracy directly affects tool life and thread quality. In semi-automated production lines, spiral-groove form grinding often relies on repeated trial grinding and manual compensation, which undermines efficiency and repeatability. This paper proposes an inverse modeling method to determine the grinding-wheel dressing profile directly from a designed end-face groove curve. The designed cross-section is first parameterized and extended into a helical groove surface through a roll-lift transformation. The wheel-workpiece contact curve is obtained by numerically solving the enveloping condition. The wheel meridian generatrix is then derived by mapping the contact points into the wheel coordinate system. The computed generatrix is converted into a controller-acceptable parametric form. The fitting residual is evaluated and constrained to the dressing/measurement resolution. Production line trials on CNC grinding machines for multiple tap specifications show that the proposed method can reproduce the designed groove profile in a single pass while meeting dimensional and angular requirements, thereby reducing trial grinding iterations and changeover time. Finally, key practical factors affecting forming stability are identified and discussed, including pendulum-angle sensitivity, dressing completeness near edge/corner regions, tool setting repeatability, and trimming-roller wear, which together provide actionable guidance for robust production line implementation.

In next-generation wide-body aircraft, CFRP/Ti stack structures constitute essential components at critical airframe junctions, including flat-tail and wing-body docking regions. With the increasing size of aircraft, these stacks demand larger and deeper holes to ensure sufficient junction strength, which presents substantial challenges for conventional automated machining. Traditional drilling techniques often fail to satisfy the stringent requirements for tool robustness and hole integrity when processing large-diameter holes in multi-layer CFRP/Ti stacks. To overcome these limitations, this study introduces a novel large-diameter hole machining approach based on a gradual reaming strategy tailored for four-layer Ti/CFRP/CFRP/Ti stacks. Specifically, a series of specialized reaming tools featuring helix-stepped geometries is developed to enhance tool strength. Furthermore, two reaming strategies-distinguished by large and small material removal volumes-are proposed to ensure superior hole quality. Through experiments, the hole formation process, cutting-force fluctuations, and chipfracture behavior are systematically analyzed to elucidate the underlying mechanisms of large-diameter reaming in multilayer CFRP/Ti stacks. The experimental findings reveal that the large removal volume strategy generates excessive cutting forces and torque, thereby compromising hole quality. In contrast, the small removal volume strategy sustains machining efficiency while improving hole quality by 85.6 %, thereby effectively fulfilling industrial requirements.

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.

This study conducted stress peening along with conventional shot peening on Al0.7CoCrFeNi high entropy alloy (HEA), and the microstructures, residual stress, and microhardness in the surface layer were systemically characterized prior to evaluating the resultant wear property. The results showed that both types of peening treatments refined the microstructure, established a high compressive residual stress field, and enhanced surface hardness and wear resistance. In comparison with conventional shot peening, stress peening demonstrated a superior effect in microstructure refinement and wear resistance owing to the combined influence of prestress and surface deformation. Although the compressive residual stress presented an anisotropy between the direction along with and vertical to the preloading axile, its magnitude was still higher than that introduced by conventional shot peening. Additionally, stress peening led to a high density of dislocations and twins, and severe lattice distortion in the surface layer. The synergistic effects of refined microstructure, higher compressive residuals stress, and strain hardening imparted by stress peening increased the surface hardness of HEA by more than 30%, reduced the friction coefficient by 50%, signifying a notable practicability in improving the wear resistance of HEA compared with conventional shot peening.

SignificanceCutting tools are indispensable key instruments in the manufacturing industry, whose performance status directly affects machining quality, production efficiency, and equipment safety. Accurate prediction of the remaining useful life (RUL) of tools not only enables the intelligent transition from "scheduled replacement" to "condition-based replacement" but also significantly reduces resource waste caused by premature tool changes, workpiece scrapping and even equipment damage risks due to delayed replacement. With the deep integration of industrial automation, digitalization, and smart manufacturing, tool RUL prediction has become one of the core technologies in intelligent manufacturing and predictive maintenance, holding substantial engineering application value and theoretical research significance for enhancing the overall competitiveness of the manufacturing industry.ProgressThis paper systematically reviews the research progress in methods for predicting the remaining useful life of cutting tools. Based on their prediction principles, these methods are categorized into four main types, and their modeling ideas, applicable scenarios, advantages, and disadvantages are analyzed in depth. (1) Physics-based model prediction methods: These methods start from the physical mechanisms of tool wear, constructing mathematical models to describe the wear process, such as wear mechanism models, cutting force coefficient models, and finite element models. Their advantage lies in having clear physical significance and strong interpretability, making them particularly suitable for stable machining processes with well-understood mechanisms. However, these methods rely on accurate modeling of multiple physical fields in complex machining environments, face difficulties in parameter identification, and exhibit weak adaptability to dynamically changing working conditions. (2) Data-driven statistical model prediction methods: These methods do not rely on physical mechanisms but instead analyze historical monitoring data to build RUL prediction models using statistical laws. They mainly include empirical wear models (e.g., Taylor's formula and its extended forms) and stochastic process models (e.g., Wiener process, Gamma process, inverse Gaussian process). Such methods demonstrate good fitting capability when data is sufficient and can quantify prediction uncertainty, but their performance is limited by data quality and quantity, and their generalization ability is usually weak. (3) Artificial intelligence-based prediction methods: With the advancement of big data and computing power, artificial intelligence methods represented by machine learning and deep learning show great potential in tool RUL prediction. Machine learning models (e.g., SVM, RVM, AR, HMM) are adept at handling small-sample and nonlinear problems; deep learning models (e.g., RNN, LSTM, CNN, DBN) can automatically extract deep features from raw sensor data and possess stronger capabilities for temporal modeling and pattern recognition. Although AI methods offer high prediction accuracy and strong adaptability, their "black-box" nature leads to poor interpretability, and they require large volumes of high-quality labeled data. (4) Hybrid model prediction methods: To compensate for the limitations of single-method approaches, researchers in recent years tend to construct hybrid models that integrate the advantages of physics-based knowledge, data statistics, and artificial intelligence. For example, combining physical models with data-driven methods, or introducing stochastic modeling of the degradation process into the AI framework, to balance prediction accuracy and model reliability. Through multi-source information fusion and complementarity, hybrid models significantly enhance RUL prediction capability under complex working conditions, representing a current hot research direction.Conclusions and ProspectsThrough a systematic review of existing research, it can be concluded that tool RUL prediction methods evolve from single models to multi-method fusion, and from offline analysis to online intelligent diagnosis. However, this field still faces the following major challenges. (1) Reliability of machining signal acquisition and processing: Industrial field data is often plagued by noise interference and incomplete sampling. There is an urgent need to develop more robust feature extraction and signal denoising methods, and to explore real-time data acquisition technologies based on new sensing methods such as intelligent tool holders. (2) Effective fusion of multi-sensor data: Effectively integrating multi-source heterogeneous information (e.g., force, vibration, acoustic emission) and extracting common features strongly correlated with tool degradation from them are key to enhancing model robustness. (3) Balancing model accuracy and generalization ability: Most current models perform well under specific conditions but are prone to performance degradation in application scenarios with varying tool materials and machining parameters. Future research needs to explore cross-condition, adaptive, and lightweight model architectures. (4) Improving the interpretability of hybrid models: Although hybrid models have advantages in accuracy, their decision-making processes often lack transparency. Enhancing model interpretability so that their predictions can be understood and trusted by engineers is a crucial link in promoting technology implementation.

Superelastic degradation (SED), a progressive loss of functionality in NiTi shape memory alloys under cyclic loading, remains challenging to characterize precisely, thereby constraining their engineering applications and broader adoption. In this study, an interpretable learning framework was proposed to predict the SED of NiTi alloys and uncover the degradation mechanisms using interpretability analysis methods. The framework incorporates multi-source microstructure and loading conditions through a multi-branch architecture that effectively decouples and integrates heterogeneous features, achieving an R2 of 0.981. The competition between slip and transformation was identified: at high amplitudes, SED is dominated by transformation regions with high Schmid factors, whereas at low amplitudes, dislocation slip on the {011}〈001〉 and {011}〈111〉 systems prevails. Subsequently, the influence of Ni4Ti3 precipitates was quantified, revealing a loading-dependent and non-uniformly beneficial role. The results highlight the potential of interpretable machine learning in exploring the cyclic deformation process and pave the way for AI-driven research on smart materials. This manuscript highlights an interpretable learning framework to explore superelastic degradation in NiTi alloys, revealing mechanisms linked to grain orientation and Ni4Ti3 precipitates, and promoting data-driven design of shape memory alloys.

To improve the quality of cavity preparation in alveolar bone is crucial for enhancing higher primary implant stability. Therefore, compared to conventional drilling, a densification drilling method for alveolar hole preparation was proposed to form dense layer within machined hole. To elucidate the mechanism of material densification formation, a series of basic experiments were performed with polyurethane foam composites. Then, the processed holes were scanned using X-ray microscopy, and the slices were processed via image algorithm. In addition, the two- and three-dimensional quantitative models were established to explore the mapping relationship between machining parameters and the degree of densification. Experimental results demonstrate that this method maximizes the retention of raw materials, thereby enhancing the initial stability of the implant. The phenomenon is attributable to unique machining mechanism of densification drilling, where material removal is primarily governed by continuous extrusion and friction rather than shear action. And, based on the quantitative model of densification, the optimal parameter combination of 1500 rpm, 24 mm/min can be obtained. The results of the study on the formation mechanism and evaluation system of densification drilling can provide theoretical support for clinical procedures.

Tungsten (W) facing plasma environments suffers from severe microstructural degradation induced by synergistic thermal, mechanical, and irradiation loads. To address this, potassium (K) doped W has been developed, in which K is sequestered into pressurized bubbles/void-K complexes during high-temperature processing and cooling. However, their dislocation-pinning mechanisms and their dependence on temperature, size, and dynamic rupture remain unclear. Here, molecular dynamics (MD) simulations with a developed W-K neuroevolution potential (NEP-WK) were employed to quantify dislocation pinning by K-bubbles as a function of temperature, bubble size, and dynamic rupture. A size-dependent mechanistic transition is identified in which cut-through dominates for 1-2 nm bubbles, whereas at similar to 3 nm intrabubble loop nucleation activates Orowan bypass, producing a permanently bowed dislocation line and a sustained flow-stress plateau above the Peierls-stress level. Furthermore, we demonstrate that the dynamic rupture of a parent bubble into a daughter cluster provides higher maximum pinning forces and produce a characteristic two-stage stress drop during depinning. The peak pinning stress scales approximately as 1/L with the inter-bubble spacing, indicating Orowan-type dependence and cooperative pinning. These atomistic insights establish design bounds for exploiting soft, deformable second phases (K-bubbles) to co-optimize strength, toughness, and thermal-shock/irradiation tolerance in fusion-relevant W alloys.

Drilling multidirectional (MD) carbon fiber reinforced polymers (CFRPs) has posed tremendous challenges for the modern manufacturing industry due to their unique properties including anisotropy and heterogeneity. Critical defects such as delamination, burrs and tearing are important issues that raise serious concerns in the manufacturing sectors. To deal with these issues, the current study analyzes the damage formation and surface integrity for MD-CFRP drilling via both numerical and experimental approaches. The key novelty of this research lies in accounting for the interfacial interaction between individual plies and the effect of ply directionality on drilling responses. Based on the developed macroscopic CFRP models, drilling simulations have been conducted along with cutting experiments. The current work offers a comprehensive understanding of how variations in drilling parameters influence the machinability aspects and cutting-induced damages for MD-CFRP laminates. The dynamic delamination formation, which was not fully addressed in traditional experiments, is revealed in the study. The burrs are mainly formed in the exit side of cut CFRP holes and are primarily influenced by the feed rate. Increasing the feed rate significantly exacerbates the tearing damage by 0.18 mm2, while elevating the spindle speed from 1326 to 5305 rpm slightly reduces tearing by 0.03 mm2. To minimize surface damage, low feed rates and moderate spindle speeds are recommended for use from the industrial point of view. The results obtained provide technical guidance and practical implications to realize damage-free drilling of MD-CFRPs for industrial applications.

Additively manufactured Ti6Al4V (TC4) alloys exhibit broad application potential in aerospace, marine and biomedical fields. This study systematically investigated the microstructure, residual stress, and mechanical properties-including hardness, tensile and tribological performances-of electron beam powder bed fusion fabricated TC4 alloy with different build orientations. The as-build microstructure primarily consisted of columnar prior-R grains epitaxially grown along the build direction (BD), which contained acicular alpha ' martensite and finely dispersed R phases. Microtextured regions with distinct crystallographic orientations and texture preferences were identified within the prior-R grains. The XY plane (perpendicular to BD) exhibited an equiaxedlike grain morphology with finer grains and higher grain boundary density, resulting in superior strength-ductility synergy. Its yield strength (similar to 871 MPa) and elongation (similar to 14.1 %) were significantly higher than those of the YZ plane (parallel to the build direction). The presence of compressive residual stress in the XY plane also contributed to the improved static mechanical properties. In contrast, the YZ plane displayed superior wear resistance due to the alignment of columnar grains nearly perpendicular to the sliding direction, which reduced interfacial cracking and material removal.

A reliable tool condition monitoring (TCM) system is essential for intelligent machining and cost-efficient manufacturing. Most deep learning-based tool wear prediction algorithms rely on multi-sensor fusion, which complicates deployment and increases implementation costs in industrial environments. To overcome these limitations, this study proposes a Dual-Band Interactive Convolution and Frequency-Prior Adaptive Channel Attention Network (DBFA-Net) for accurate tool wear prediction using single-channel vibration signals acquired from an intelligent tool holder. The model first applies multi-scale convolutional kernels to decompose the input signal into components of high-frequency details and low-frequency trends. A dual-band interactive convolution module then enables deep fusion by exchanging information across frequency bands through convolutional kernels with distinct receptive fields. To further enhance feature representation, a frequency-prior adaptive channel attention mechanism is introduced, which assigns attention weights based on spectral characteristics. A gradient-frozen branch and a Sigmoid-based soft mask are designed to stabilize attention modeling. The proposed model is evaluated via milling experiments on TC4 using a commercial intelligent tool holder. The DBFA-Net consistently outperforms existing models in prediction accuracy and generalization. Ablation studies confirm the effectiveness of each module in improving prediction accuracy and validate the reliability of frequency-separated modeling and frequency-prior extraction.

Thin-walled ring gears are prone to hobbing-induced distortion because material removal redistributes body residual stress (BRS) and introduces machining-induced residual stress (MIRS). This study distinguishes BRS and MIRS and establishes a coupled finite element method (FEM) workflow for normalized 18CrNiMo7-6 ring gears by combining AdvantEdge cutting simulation with ANSYS material-removal analysis. Initial BRS was evaluated using strain energy density, and residual-stress evolution during hobbing was examined under different hobbing-cutter rotational speeds and axial feed rates. Cutting power was used to assess the cutting-load input, whereas post-hobbing deformation measurements were used to evaluate the endpoint prediction of the coupled model. The results show that residual stress is concentrated near the tooth root and that a moderate increase in rotational speed or axial feed rate can reduce tensile surface residual stress within the tested parameter range. The through-thickness machining position also strongly affects radial deformation. These findings provide a basis for controlling hobbing distortion in thin-walled ring gears through process-parameter selection and blank-position planning.

Carbon fiber reinforced composites (CFRP) are widely used structural materials in advanced aircraft, and CFRP is often bolted to titanium alloys to form CFRP/Ti joints for important load-bearing parts of aircraft. Due to the complex alternating loads borne by such components, bolts are prone to bending, leading to severe stress concentration and fatigue failure at the CFRP hole, which seriously affects the long-term service performance of the aircraft. To address the above problems, this work proposes a CFRP hole expansion (HE) process, investigates the effect of the process on the stress state and fatigue performance of CFRP/Ti single-bolt joints holes through experiments and simulations, and reveals the gain mechanism of the fatigue life. The results show that the HE process can reduce the compressive strain of CFRP by 67 % during loading, delay the onset of initial damage under load, and improve the sustained load-bearing capacity of the specimen. More importantly, the bushing installed during the HE process can reduce bolt bending by 64 % through plastic deformation, improve stress concentration at the CFRP hole, and prevent premature compressive failure of the hole. Consequently, the fatigue life of the CFRP/Ti single-lap joint is extended to 2.6 times that of the untreated joint.