ABSTRACT SiC f /SiC composites are key aerospace structural materials, yet their susceptibility to fiber fracture, matrix spalling, and microcracks during drilling severely limits component integrity. This paper establishes single‐grit kinematic models and derives calculation formulas for maximum undeformed chip thickness ( a gmax ) under both conventional drilling and ultrasonic vibration‐assisted abrasive drilling, and uses them to conduct systematic single‐grit machining tests on 2D woven SiC f /SiC. The results reveal that the critical a gmax values governing the transition from microfracture to macrofracture removal are orientation‐dependent, 0.7–0.9 µm for 0° fibers and 0.8 µm for 90° fibers. Under the tested ultrasonic vibration condition ( A = 5 µm, f = 25 kHz), trajectory elongation reduces the single‐grit chip thickness and alters the stress state at the grit‐workpiece contact zone, redirecting crack propagation from downward penetration toward lateral spreading and promoting microfracture as the dominant removal mode. These effects yield a reduction in average chip length, smaller stepped‐fracture spacing on 0° ≤ θ ≤ 90° fiber surfaces, enlarged extrusion zones for 90° ≤ θ ≤ 180° fibers, and significantly less fiber fragmentation for 90° fibers. Cross‐sectional SEM quantification further confirms that ultrasonic vibration‐assisted machining reduces the maximum subsurface damage layer depth by 12%–50% for the SiC matrix, 13%–38% for 0° fibers, and 12%–55% for 90° fibers within the tested a gmax range of 0.4–1.2 µm. These findings provide a mechanistic foundation for understanding how ultrasonic vibration modulates surface generation and subsurface damage at the single‐grit scale in SiC f /SiC machining.

The accurate prediction of machining deformation in thin-walled components remains a significant challenge due to the complex, nonlinear relationship between the distributed residual stress fields and the overall deformation. Conventional mechanistic models are often reliant on unrealistic uniformity assumptions regarding stress distribution, while purely data-driven approaches lack physical interpretability. The predictive capability for deformation is constrained by these boundary conditions, prior knowledge, and generalization. To address these issues, this paper proposes a novel hybrid mechanism consisting of a data-driven framework for online deformation prediction. The mechanism model ensures a physically accurate conversion of stress to deformation at the discrete element. Subsequently, the newly developed Ures-MLP deep learning model synthesizes these local deformations to predict global deformation, thereby resolving the uniformity assumption that is inherent in conventional mechanistic models. Moreover, the model quantitatively evaluates the influence weight of each local stress state on the overall deformation. The results of validation experiments demonstrated that the proposed framework achieved an average online prediction accuracy of 98.41%. The visualizable influence maps were generated to elucidate the mechanism of the stress impact, offering a quantitative basis for online deformation control through stress field regulation. This work provides a robust, interpretable, and generalizable solution for in-process quality prediction in intelligent manufacturing.

Inconel-718 is a critical superalloy for aerospace applications where wire arc directed energy deposition offers significant manufacturing advantages. However existing computational models primarily address laser-based processes and often struggle to capture the complex microstructural morphology and texture evolution driven by the unique thermal gradients of arc deposition. To address this scientific gap this study establishes a strictly coupled finite element and cellular automaton framework to investigate the competitive grain growth and texture formation mechanisms. In this specific framework the equivalent circle diameter and the fitted ellipse aspect ratio are directly utilized to quantify the microstructural dimensions and morphological anisotropy. The simulation results reveal that microstructural evolution is dictated by a non-monotonic shift in the solidification interface stability. Under excessive energy inputs the competitive grain selection mechanism becomes compromised due to a severe degradation of the morphology factor. Elevating the travel speed accelerates the cooling rate to effectively refine the overall grain dimensions. However, this simultaneously restricts lateral growth space and creates highly slender morphologies. The validated multiscale model functions as an efficient virtual optimization platform to explore process structure relationships thereby providing a reliable theoretical tool for controlling grain dimensions and mitigating mechanical anisotropy in additively manufactured components.

Ceramic matrix composites (CMCs) are highly promising for the hot components of the aero-engines due to their high-temperature resistance and low density. The fibers of CMCs are woven into different structures to meet the specific requirements of heat and stress load. However, the influence of the woven structure on the precision machining remains unknown. This study investigates the material removal behaviors of SiCf/SiC with different woven structures (0º/90º, 30º/60º, 45º/45º, 60º/30º, 90º/0º) via single diamond scratching experiments. The scratched surface morphologies and scratching forces are carefully analyzed. The finite element method is also adopted to analyze the stress distribution. Results show that woven structures influence the distribution of side scratching damage: 0°/90° and 90°/0° exhibit symmetric damage, while 30°/60°, 45°/45°, and 60°/30° show asymmetric damage. Side scratching damage increases with the scratching depth, with the minimum damage of 94 μm at 30°/60° and the maximum damage of 452.4 μm at 90°/0°. Different stress distributions induced by woven structures lead to smooth, stepped, and broken fiber fractures. When the scratching depth increases from 5 μm to 20 μm, the scratching force increases by 112.9

To enhance surface uniformity and mitigate edge rounding in plasma electrolytic polishing (PEP), this study proposes a vibration-assisted plasma electrolytic polishing (V-PEP) approach. Finite element simulations were employed to elucidate the mechanisms through which workpiece vibration influences bubble evolution and electrolyte flow behavior during the polishing process. Experimental results confirm that vibration assistance significantly improves polishing performance under a range of voltage conditions. The effects of vibration frequency and amplitude on surface quality were systematically investigated, followed by a comprehensive evaluation of surface integrity. Compared with conventional PEP, the V-PEP process increases the surface roughness reduction rate by 25.16%, decreases the edge rounding diameter by 39.89%, and enhances the material removal rate by 47.32%. Moreover, vibration assistance does not compromise surface integrity. The induced vibration promotes the forced detachment and uniform dispersion of bubbles, thereby ensuring homogeneous material removal and effectively suppressing edge rounding. The proposed method was further applied to polish laser powder bed fusion (LPBF) additively manufactured sample with micro-groove structures. These findings contribute to maintaining the geometrical fidelity of workpieces and advancing the application of PEP technology in the precision finishing of complex geometries.

The machining deformation of thin-walled parts is a critical factor affecting quality. Adjusting machining parameters is one of the commonly used deformation control methods. However, conventional offline parameter adjustment techniques struggle to adapt to the time-varying physical conditions during the machining process, which limits the effectiveness of deformation control. To overcome this limitation, this study develops an online deformation control system driven by a novel heuristic deep reinforcement learning model. The soft actor-critic algorithm and heuristic mechanism are integrated to establish an artificial intelligence model. The machining process is formulated as a Markov decision process, where the state-action space is defined based on a derived mathematical model that correlates parameter adjustments with deformation response. An autoencoder is embedded for high-dimensional state feature extraction, and the particle swarm optimization algorithm is utilized to guide the exploration strategy, significantly enhancing learning efficiency and policy stability. A closed-loop online control system is implemented to realize the engineering application, which dynamically adjusts cutting parameters in response to real-time machining states. Experimental validation on thin-walled part milling demonstrates that the proposed system achieves the preset deformation target within 2-3 control iterations, substantially reducing workpiece deformation. Compared with conventional offline strategies and other control approaches, the proposed method exhibits superior control precision and operational robustness, offering a practical and intelligent solution for precision manufacturing.

High-quality and efficient process planning methods are crucial for ensuring product manufacturing quality. However, traditional methods have several drawbacks, namely, they are time-consuming, highly dependent on expert experience, and involve considerable repetitive workloads. To overcome these limitations and enhance the efficiency and intelligence of process planning for complex structured parts, this study proposes a machining step sequence reasoning method based on deep reinforcement learning. First, historical process data are preprocessed to convert the knowledge stored in the process files into structured and vectorized data. Second, the process routes and feature step sets serve as inputs, and a proximal policy optimization algorithm is employed to train the historical process instances. The sequencing patterns discovered during training are then integrated with advanced sorting strategies to efficiently generate the machining step sequences. To evaluate the effectiveness of the proposed method, 50 complex structured parts were tested, with 25 representative parts selected for detailed comparative analysis. The training performance of the proposed algorithm was evaluated against those of the advantage actor-critic and soft actor-critic algorithms. In addition, the reasoning results of various state-of-the-art algorithms were analyzed using these test cases. Experimental results demonstrate that the proposed method is effective and competitive for process planning of complex structural parts. Therefore, this study provides practical guidance for enhancing the efficiency and intelligent automation of process planning of complex structural parts.

Stress is the primary factor influencing machining deformation. The accurate measurement of internal stress can improve the quality of machining deformation prediction and control. A longitudinal critical refraction (LCR) wave is a waveform emanated by the refraction of ultrasonic waves, which allows for the internal stress non-destructive measurement of components. However, current depth direction measurement models for LCR waves are predominantly obtained through experimental fitting, which reflects the influence of the ultrasound frequency on the measurement depth. To further explain the attenuation characteristics of ultrasound propagation at different frequencies from a theoretical perspective, and reveal the influence mechanism of ultrasound attenuation on stress measurement depth. This article proposes a stress measurement model for the depth gradient that considers the energy dissipation mechanism at varying frequencies during the transmission of LCR waves. Tensile and bending tests are conducted to calibrate the measurement model. The comparison results demonstrate that the maximum deviation between the experiment and the model is nearly 5.1% for the stress values, while the maximum measurement deviation in the depth direction is 4.7%. The developed method achieves accurate measurement of the stress at a specified depth and rapid selection of matching ultrasound probes by revealing the influence mechanism of ultrasound frequency on the measurement depth. This study offers a technical reference for ultrasound-based stress measurement in the depth direction and holds significant potential for enhancing its practical applications.

ObjectivesThe single-layer brazed diamond core drill generally exhibits poor protrusion height uniformity of grains, making it difficult to control the hole diameter and the accuracy when machining SiCf/SiC composites. Pulsed laser is used to dress the core drill to improve grain height uniformity, thereby enhancing hole accuracy on SiCf/SiC composite.MethodsFirstly, a pulsed laser dressing platform for the single-layer brazed diamond core drill is developed, and the influence of laser dressing parameters on grain height uniformity and morphology is revealed. Then, the aperture accuracy of the core drill before and after dressing is compared and analyzed to verify the benefits of laser dressing in improving hole accuracy. Finally, high-quality processing of SiCf/SiC composite holes is achieved using the dressed drill. During the process, the total cutting depth of the pulsed laser is determined by dressing a standard rod to the target aperture size and then replacing it with the core drill for further dressing. The relative distance between the abrasive grains and the laser beam is adjusted, and the laser beam focus is aligned with the cutting point. The laser beam is reciprocally scanned along the tool axis to remove the protruding diamond grains and improve core drill height uniformity.ResultsThe experiments show that pulsed laser dressing can effectively enhance the height uniformity of the side grains on the single-layer brazed diamond core drill. The discrete coefficient of grain height after dressing is reduced by 64%, from 0.11 to 0.04. Following pulsed laser dressing, the contour lines of side grains on the core drill become smoother, indicating improved height uniformity. The surface of diamond grains after pulsed laser ablation appears black due to a graphitization reaction, forming a thin black metamorphic layer that does not affect diamond grain performance. The laser-dressed single-layer brazed diamond core drill exhibits improved hole-making performance with a smaller variation range in hole diameter (4.00 - 4.02 mm) and higher hole-making accuracy. In contrast, the untrimmed core drill shows a larger variation range in hole diameter (4.06 - 3.98 mm) during the hole-making process. Furthermore, pulsed laser dressing has no negative impact on the grinding ability of the core drill. The average drilling force is 13.72 N before dressing and 12.43 N after dressing, with a difference of 1.29 N consistent with the change trend of the drilling force during the entry stage. The laser-dressed core drill maintains aperture accuracy better throughout its lifespan, with aperture deviation being only 0.02 mm, meeting the requirements on hole accuracy and showing a 75% reduction compared to the undressed condition.ConclusionsThe study applies pulsed laser dressing to enhance the protrusion height uniformity of grains on single-layer brazed diamond core drills. A laser dressing device is constructed, and a method is proposed. Using a graphite rod as the standard rod for determining the laser dressing depth reduces the diameter deviation. Pulsed laser dressing effectively improves grain height uniformity, with the discrete coefficient reduced by 64%. The dressed core drill demonstrates smaller aperture deviation (0.02 mm), meeting accuracy requirements without adversely affecting grinding ability or service life. This verifies the advantage of laser-dressed core drills in improving hole-making accuracy.

As critical aero-engine components, closed impellers demand precision manufacturing to ensure reliability under extreme conditions. Traditional casting and powder-bed additive manufacturing face challenges in defect control and cost-effectiveness. Wire-arc directed energy deposition offers high material utilization and deposition rates for near-net-shape fabrication, yet its inherent high heat input induces microstructural defects such as Laves phase segregation in Inconel-718 superalloy. This study investigates CMT +P-based wire-arc DED processing of Inconel-718, focusing on energy density effects spanning 360-540 J/mm on thin-wall geometry, microstructure, and mechanical properties. Energy-dispersive X-ray spectroscopy and XRD analysis reveal that increased energy density expands primary dendrite arm spacing from 4.68 to 18.97 mu m and Laves phase area fraction from 3.12 to 8.10 %, correlating with reduced as-deposited tensile strength of 725 +/- 45 MPa. Post-deposition solution-aging heat treatment enhances ultimate tensile strength to 1354 +/- 54 MPa. The mechanical properties of Inconel-718 deposited via CMT + P were compared with those produced by the conventional CMT process. Mechanical property benchmarking against Inconel-718 casting and forging standards provides actionable insights for industrial process optimization.

GH4169 superalloy is widely used in the aerospace industry due to its high specific strength, excellent hightemperature oxidation resistance, good fatigue resistance, and superior creep strength. However, its high strength and low thermal conductivity result in severe tool wear and chip-breaking difficulties during the turning process. This leads to degradation in surface quality and a significant decrease in fatigue resistance. Highpressure cooling lubrication-assisted machining technology can enhance the machinability of this superalloy. In this work, finite element simulation is used to analyze the influence of various high-pressure cooling parameters (injection diameter, angle, and pressure) on chip formation, burr morphology, and tool wear during turning with a polycrystalline cubic boron nitride (PCBN) tool. Comparative experiments are conducted to investigate tool wear, chip morphology, and burr formation under dry and high-pressure cooling conditions, verifying simulation accuracy. The results indicate that dry turning produces long spiral chips and significant tool wear. In contrast, high-pressure cooling changes chip morphology from long to short spirals, reduces burr formation, and decreases tool wear. Optimal parameters include an injection pressure of 50 bar, an angle of 0 degrees, and a diameter of 1.6 mm, which lead to extended tool life, well-formed chips, and reduced burrs.

The magnitude and distribution of residual stress field inside thin-walled parts is a critical factor influencing machining deformation. Traditional methods relying on offline data struggle to rapidly and accurately predict evolutionary state of residual stress fields, due to the time-varying and nonlinear characteristics in machining. This paper presents an online prediction method for residual stress in machining thin-walled parts based on deep learning. The multi-channel vector model is proposed to incorporate geometric, physical, and process information as input for deep learning models. A deep learning framework utilizing the IncepU-net network is developed and trained using both experimental and finite element simulation data. Results indicate a mean error of 6.2 MPa compared to experimental values, with an overall prediction time of 0.17 s. The proposed method can online predict the magnitude and distribution of residual stress field in machining, which offers cost-effectiveness and strong generalization capabilities.

GH4169D superalloy exhibits exceptional service performance, enhancing the capabilities of aeroengines. However, it also poses challenges to component machining. Ultrasonic vibration-assisted machining has demonstrated advantages in enhancing material machinability. However, comprehensive analyses that pertain to the tool cutting edge path, material removal mechanism, and surface texture in longitudinal ultrasonic vibration-assisted side-milling (LUVM) are rare. In this study, GH4169D superalloy was subjected to LUVM and conventional milling (CM) to investigate the material removal mechanism and surface texture generation. Furthermore, a noncutting time ratio model was proposed to predict the reduction in maximum milling force achieved by LUVM. Results indicated that compared with that of CM, the machining of LUVM was divided into milling and noncutting. The inclusion of noncutting contributed to a reduction in the maximum milling force during LUVM. However, as milling speed increased, noncutting time ratio decreased and subsequently diminished the advantage of LUVM. The chip morphology formed using LUVM exhibited a greater degree of curliness compared with that obtained using CM, facilitating chip breaking. The utilization of LUVM resulted in the formation of a thinner lamellar structure on the free surface of chips compared with the use of CM. The machined surface exhibited a distinct ultrasonic vibration texture in LUVM, which was characterized by a physics formula. The utilization of LUVM demonstrated a reduction in machined surface roughness Ra compared with the use of CM at a low milling speed. The findings of this study contribute to the prediction of the effects of LUVM on reducing maximum milling force and achieving control over chip morphologies and machined surface texture.

Ceramic matrix composites (CMCs) are highly promising for the hot components of the high thrust-to-weight ratio aeroengines because of their excellent high-temperature resistance and lightweight. Submillimeter cooling holes are necessary cooling structures for the extremely high working temperature of a CMC hot component. However, CMCs are hard, brittle, and poorly conductive. Current machining is trapped in severe tool wear, poor hole quality, and low efficiency in machining such small size holes. This paper proposes high-frequency ultrasonic vibration-assisted drilling (UAD) to machine submillimeter holes of CMCs. It reveals that increasing vibration frequency during machining can enlarge the cutting edge's effective rake angle and reduce drilling forces. Correspondingly, the tool life and hole quality are improved. The machinability of high-frequency UAD is rationalized by both theoretical analysis and experiments. Results show that compared with low-frequency UAD and Conventional drilling (CD), the drilling force in high-frequency UAD is reduced by 47% and 81%, the tool life is more than three times, and the hole quality is improved by 12% and 35%, respectively. Therefore, the study paved the way for the machining of submillimeter cooling holes in CMCs, which is important to the industrial application of CMCs.

The Ti2AlNb intermetallic alloy, belonging to the titanium aluminum (TiAl) family, effectively fulfills the requirement of weight reduction in aero-engine key components applications due to its lightweight and high-temperature resistant properties. However, the milling process of Ti2AlNb material involves substantial cutting forces and heat generation, posing challenges to ensure machined surface integrity. The microstructure of the machined subsurface significantly influences the machined surface integrity and mechanical properties of the key components. Therefore, this study primarily investigates the characterization of microstructure evolution during side milling of Ti2AlNb material using both coated and uncoated carbide tools. Specifically, Energy dispersive spectrometer (EDS) and Electron Back Scatter Diffraction (EBSD) technology were employed. The milling force, metallographic structure, orientation image microscopy (OIM) maps along with corresponding average grain diameter (AGD), kernel average misorientation (KAM) maps, Schmid factor (SF) maps, low angle grain boundaries (LAGBs) and high angle grain boundaries (HAGBs), microhardness, and residual stress at different milling times were investigated. The results indicate that with the increase in milling time, the depth of the deformation layer machined by both two carbide tools exhibits an upward trend, which aligns with the variation pattern of milling force during the milling process. Grain refinement is observed on the machined subsurface under both milling conditions, with a coated tool and an uncoated tool, accompanied by phenomena of grain deformation and breakage. Furthermore, milling-induced plastic deformation promotes the formation of LAGBs. During the entire milling process of the two cutters, a progressive increase in compressive residual stress is observed on the machined surface. Additionally, a similar growth trend is noted in the microhardness of the machined surfaces, indicating surface strengthening.

Subsurface damage (SSD) is one of critical problems in machining ceramic matrix composite (CMC). SSD distribution in CMC is too complicated to control. A suitable method for evaluating SSD is important to control the damage during machining CMC. However, current methods provide limited information of SSD. To recover more comprehensive information of SSD distribution in CMC, this study proposes a novel evaluation method. The method takes the damage type into consideration. Based on the damage type, a new concept of damage degree is used as evaluation index. The proposed method is experimentally demonstrated effective to evaluate SSD distribution in CMC. It could provide both damage depth and damage degree. Finally, the SSD model of CMC is established. The newly proposed method can provide more comprehensive understanding of SSD mechanism in machining CMC, which is important to high efficiency and low damage machining of CMC.

Machining deformation of thin-walled components is critical for ensuring their dimensional accuracy and fatigue life. Accurate online prediction of deformation is essential for effective control. However, existing deformation prediction models have limitations in online detectability and prediction accuracy. To address these challenges, this paper proposes an online detection and prediction method for machining deformation based on the IncepRes-Informer two-stage deep learning model. The input of the model is collected multi-source sensor data. The IncepRes-net model is established using residual and Iception structures, which replaces the convolutional kernels in the U-net network. This design enhances the deformation detection accuracy at the current moment. The Informer model is established for predicting deformation at subsequent moments. Then the milling experiment was conducted to optimize the hyperparameters and validate the effectiveness. The experimental results show that the maximum prediction error is 1.95 x 10-3 mm, with a prediction accuracy of 98.51 %. The proposed model demonstrates superior accuracy and generalization compared with other commonly used machine learning models. Finally, this model is used to predict deformation throughout the entire machining process. This provides a robust tool for deformation online monitoring technology through current moment detection and subsequent prediction.

Microwave modules, as the core elements of modern electronic systems, are evolving toward lead-free and highperformance designs. However, the employment of leaded solders, step soldering processes, and rework operations inevitably leads to hybrid solder joints of SnPbAg and SnAgCu (SAC), whose performance on Au/Ni/MoCu substrates remains underexplored. In this research, SnPbAg-xSAC (x = 25 wt %, 50 wt %, 75 wt %) hybrid solders were fabricated via a melting-casting method to assess the performance of hybrid solder joints comprehensively. Experimental results indicated that SnPbAg-xSAC hybrid solders significantly suppressed gold embrittlement compared to the SnPbAg solder. The shear strength and toughness of hybrid solder joints were improved via the grain refinement strengthening mechanism. Particularly, the SnPbAg-75 %SAC solder joints achieve the highest shear strength, reaching 43.4 +/- 1 MPa, under the synergistic effects of fine-grain strengthening and solid-solution strengthening. Furthermore, with the incorporation of 75 wt % SAC, the fracture mode of the solder joints transitioned from dominant intermetallic compound cleavage fracture to controlled ductile fracture. This research provides a theoretical basis for evaluating the performance of hybrid solder joints in microwave modules and puts forward new ideas for the development of solder materials in electronic packaging.

Ceramic matrix composites (CMCs) are promising for manufacturing the high thrust-to-weight ratio aero engines. For the sake of assembly and cooling, it is necessary to drill holes in a CMC part. However, CMC is hard, brittle, and heterogeneous. The hole machining of CMCs is accompanied by low efficiency, uncontrolled drilling damage, and severe tool wear. Among the hole machining methods, ultrasonic vibration-assisted drilling (UAD) represents a huge potential for damage control and increased efficiency. However, most works on UAD use a fixed vibration frequency. This study tries to use high-frequency ultrasonic vibration-assisted drilling (HFUAD) to drill CMC holes. The drilling performance of HFUAD is compared with conventional machining (CD) and UAD by both experimental study and theoretical analysis. Results show that HFUAD reduces hole cylindricity errors by 65 % and 29 % compared with CD and UAD, respectively. By increasing ultrasonic vibration frequency, the high-stress area during drilling is greatly reduced, leading to dramatic reductions in both drilling damage and drilling force. Moreover, HFUAD extends the drilling tool life more than 3.3 and 2.5 times compared with CD and UAD, respectively. HFUAD is demonstrated effectively in realizing high accuracy and low damage machining of CMC holes. Therefore, this study provides a new way to high-performance machining of CMCs.