Aiming at the need of high precision and high reliability in the machining process of precision machine tools, a condition monitoring system of precision machine tools is designed and developed. The system builds a data acquisition platform with different hardware modules, and uses LabVIEW as the programming environment for software development to realize data acquisition, real-time storage and signal analysis and processing of temperature, vibration, acoustic emission and rotational speed during machine tool operation. At the same time, a database technology is used to manage a large number of stored data. Field test results show that the system runs stably, all functions are normal, and can meet the requirements of use.

In view of the lack of effective intelligent detection methods due to the complex causes of quality defects, the variety of defect features, and the high detection requirements in the development and production of aviation products, this paper first systematically reviews the research status of the intelligent detection technology of aviation equipments, and summarizes the ideas and implementation methods for the research of intelligent detection methods for this application scenario and specific defect characteristics. Secondly, an improved Mask R-CNN algorithm fused with global feature pyramid network is designed, and a digital radiographic detection defect feature dataset for aviation castings is constructed by using data augmentation techniques such as cutting, flipping, Overlap and Mosaic for aviation castings with complex defect features and high detection requirements. Finally, the improved algorithm and the constructed dataset are used to test and verify three types of defects in aviation castings, including porosity, cracks, and high-density inclusions. The experimental results show that the detection accuracy of the improved algorithm is 93.25% and the recall rate is 96.51%, having a good detection effect.

In order to reduce errors and omissions in manual inspections of civil aviation aircraft before takeoff, enhance inspection quality and efficiency while reducing labor intensity, this paper proposes a deep learning-based augmented reality intelligent inspection method for civil aviation aircraft. Firstly, a data augmentation method based on pre augmentation evaluation was designed, which achieved large-scale automatic augmentation of a small number of civil aviation aircraft damage defect image sample datasets. Subsequently, focusing on the visual characteristics of damage defects, and improved YOLOv8 network is proposed to train the augmented dataset for damage defect detection, forming a damage and defect detection model. Finally, this method is integrated into the augmented reality recognition and display process, utilizing augmented reality glasses to achieve intelligent identification of aircraft damage and defects and augmented reality display and maintenance guidance for the identification results. The proposed method is validated on real-world scenarios, showing effective identification of common defects with a detection rate increased from 89.1% to 95.7%, and a maximum reduction in inspection time of 27.0%, thereby effectively assisting inspection personal in achieving intelligent inspection of civil aviation aircraft.

Breakthrough detection is of significant importance for preventing back striking in electrical discharge machining (EDM) of film cooling holes. To address the challenge of accurately capturing the breakthrough moment caused by electrode wear in EDM drilling, a breakthrough detection method is proposed based on Bayesian online change point detection. By sampling the feed speed of machining spindle as a feature signal, a dynamically updating probabilistic statistical model is established to describe the machining state of small-hole EDM process. The occurrence of breakthrough is detected by identifying changes in the model parameters. Furthermore, by quantifying the probability of abrupt changes in the machining stage, this method reduces the impact of transient instabilities on breakthrough detection during actual processing. Compared to the commonly used sliding window method, the detection robustness is significantly enhanced. Repeated experiments with different exit angles (0° and 45°) validate the effectiveness of the Bayesian online breakthrough detection method.

Addressing the challenges of low efficiency and potential oversight in artificial detection of surface defects on aero-engine blades, this paper introduces a novel lightweight intelligent defect detection model, termed YOLOv5-GA. The model incorporates a GhostConv module and C3Ghost into the backbone network to minimize parameters and computational load, thereby enhancing its lightweight nature. Furthermore, the integration of an asymptotic feature pyramid network (AFPN) into the neck network enhances the model’s capability to detect small targets. Experimental findings demonstrate that in the domain of aircraft engine blade defect recognition, the proposed algorithm not only achieves an mAP of 92.6%, a 4.6-percentage-point enhancement over the baseline network but also reduces the model size to a mere 9 MB, reflecting a 38% reduction compared to the baseline. Additionally, on the NEU-DET dataset, the model achieves an mAP of 77%, outperforming other networks while significantly reducing model size. Thus, the proposed network boasts
lightweight, efficient, and reliable characteristics, facilitating the effective detection of critical defects in aero-engines.

The high hardness and brittleness of engineering ceramic materials pose severe challenges to their highduty surface processing. In the process of micro-abrasive air-jet processing, researchers at home and abroad have been dedicated to studying the influence of process parameters on the surface morphology of ceramic materials. However, there are still a lot of knowledge gaps in the influence of different mechanical properties between abrasives and materials on the surface morphology of ceramics. In this paper, micro-abrasive air-jet machining experiments were carried out on Si₃N₄, Al₂O₃ and yttrium-stabilized zirconia YSZ ceramics by using Al₂O₃ abrasives to investigate the effects of different erosion processing angles and different mechanical properties of abrasive and workpiece on the erosion depth, surface morphology and surface roughness of the three hard and brittle materials. The results show that the erosion depth of the three ceramic materials is positively correlated with their elastic modulus, and the surface roughness is negatively correlated with their fracture toughness. With the increase of the erosion processing angle, the surface roughness continues to increase and the surface quality continues to decrease. Under the four erosion angles, there are micro-cutting marks and impact craters on the surface of the three ceramic materials, but there are no micro-cracks and grain breakage. The results confirm that the relative mechanical properties between the abrasive and the workpiece are an important factor affecting the surface morphology of ceramic materials. Under the lower elastic modulus ratio and higher fracture toughness ratio, higher surface quality can be obtained.

The texture distribution along the generatrix of the inner and outer surfaces of aluminum alloy shaft pipes contributes to enhancing their fatigue resistance. To investigate the surface texture generation mechanism and effectively control the texture characteristics, this paper performs a numerical simulation of the material removal process during abrasive belt grinding, based on the kinematic and dynamic analysis of the grinding process and the distribution  haracteristics of abrasive grains on the belt surface. Simulations were conducted on the surface morphology of the inner and outer surfaces of the shaft pipe under different abrasive belt mesh numbers, belt speeds, and workpiece rotational speeds. Experimental validation was carried out, and the three-dimensional surface morphology was measured using a laser confocal microscope and a scanning electron microscope. The effects of abrasive belt mesh number, belt speed, and workpiece rotational speed on surface morphology were analyzed. The results show that abrasive belt grinding is suitable for machining the generatrix texture on the inner and outer surfaces of aluminum alloy shaft pipes, with plastic scratches being the primary surface feature and a shallow subsurface deformation depth. Within the range of experimental parameters, effective control of surface roughness and texture characteristics of the inner and outer surfaces of the shaft pipe was achieved by adjusting the abrasive belt mesh number, belt speed, and workpiece rotational speed.

A100 ultra-high-strength steel is the main material of aircraft landing gear and a typical difficult-tomachine aerospace material. In order to solve the problems of low machining efficiency and difficult to control surface quality in its dry high-speed milling processing, and to balance the machining efficiency and surface quality at the same time, the multi-objective optimization experimental research on milling process parameters is carried out with the material removal rate, surface roughness and its signal-to-noise ratio as the evaluation indexes. Firstly, orthogonal experiments were designed to analyze the influence law of milling process parameters on the evaluation indexes based on the experimental results, and establish the optimization target prediction model. Then, the fast non-dominated sorting genetic algorithm (NSGA-II) was used to solve the problem and get the non-dominated solution set of the process parameters. Finally, after considering the machining efficiency, the obtained non-dominated solution set is screened twice by TOPSIS comprehensive evaluation method to obtain the optimal process parameter combinations. After comparison, it is found that the optimized surface roughness is reduced by 34.52% compared to the optimal value before optimization, while the signal-to-noise ratio is improved by 7.14%, which provides a reference basis for the reasonable selection of dry highspeed milling parameters.

When traditional nozzles are used for workpiece finishing, the jet leaves the nozzle, concentrating its energy at the central position, and the one-time processing area is small. As a result, although the overall surface roughness of the workpiece after finishing is effectively reduced, it tends to cause local deformation of the workpiece and the problem of poor uniformity. In order to further improve the surface quality and uniformity of thin-walled and complex workpieces processed by abrasive jet finishing, and to improve the efficiency of the finishing process. By changing the shape of the nozzle outlet, and then change the structure of the jet, so that the jet energy distribution is more uniform, to improve the effect and feasibility of abrasive jet finishing. Fluent software was used to analyse the jet structure of the nozzle, abrasive trajectory, erosion and shear effect, to explore the advantages of the in-line nozzle finishing, to verify the finishing effect of the in-line nozzle through the test, and to analyse the influence of the various influencing factors on the effect of the finishing, and finally to establish the BP neural network prediction model and particle swarm parameter optimisation, to find the optimal parameters of the finishing. The in-line nozzle can effectively improve the surface quality and uniformity of the abrasive jet finishing process, improve the efficiency of the abrasive jet finishing process, and in the case of the same mass flow rate, the in-line nozzle has less influence on the deformation of the workpiece. Finally, simulation analysis and experiments show that the slotted nozzle can effectively improve the surface quality and reduce the surface roughness of abrasive jet finishing, improve the efficiency of abrasive jet finishing, and reduce the influence of jet processing on the workpiece. Through the prediction model constructed by BP neural network and the optimization of particle swarm parameters, when the processing time is 15 min, the abrasive particle size is 20 μm, the target distance is 12 mm, and the pressure is 0.08 MPa, the surface roughness of the aluminum alloy after finishing is reduced from R_a 0.513 μm to R_a 0.219 μm, and the surface roughness is basically the same as that measured in the vertical direction. Experiments verify that the BP neural network prediction model has high accuracy.

As a crucial branch of additive manufacturing technology, laser additive manufacturing (LAM) has attracted significant attention in near-net-shape forming of metallic components due to its inherent advantages of high precision and elevated material utilization efficiency, finding extensive applications in advanced equipment manufacturing sectors including aerospace and biomedical industries. However, the inherent characteristics of LAM involving rapid cooling rates and non-equilibrium solidification tend to induce high-magnitude residual tensile stresses, which frequently lead to defect formation such as porosity and cracking, consequently constraining the mechanical performance and practical engineering applications of fabricated components. Laser shock peening (LSP) emerges as an effective surface enhancement technique that utilizes high-energy laser-induced shock waves to generate gradient residual compressive stress fields within material surface layers, thereby improving both microstructural characteristics and mechanical properties. This paper systematically reviews the fundamental principles of LSP technology, with particular focus on its tripartite applications in LAM-processed metallic materials: as post-processing treatment, off-situ processing, and in-situ processing strategy. Recent research advancements are critically analyzed from these three implementation perspectives, followed by prospective discussions on future development directions for LSP applications in laser additive manufacturing systems.

316L stainless steel is widely used in aerospace, ships, automobiles and other fields due to its high strength and toughness, strong corrosion resistance and good processability. In this study, three kinds of triply periodic minimal surface (TPMS) lattice structures were prepared by laser powder bed melting technology. The mechanical properties and deformation behavior of TPMS multi-oriented structures fabricated by additive manufacturing were studied by experimental test methods.The results show that increasing the volume fraction and sandblasting process can improve the Young’s modulus, yield strength and energy absorption capacity of the three structures. The bearing capacity of the DT structure is obviously improved due to the increase of the volume fraction. The bearing capacity of the DF structure is obviously improved due to the sandblasting process. The strain distribution obtained by digital image correlation technique (DIC) shows that the orientation design significantly changes the strain transfer process of TPMS structure under compressive load. This study provides a valuable solution for the manufacture of 316L lightweight lattice structures with more controllable performance.

6061 aluminum alloy is a kind of metal material widely used in aerospace equipment and automobile manufacturing, in the process of selective laser melting (SLM) forming, this material is prone to porosity and crack defects. Due to its poor printability, most current studies have not focused on its thermal dissipation value. This paper thoroughly investigated the effects of different forming processes and aging heat treatment on the densification degree, microstructure, and thermal conductivity of SLM-formed 6061 samples. The relationship of forming processes, defects, and thermal conductivity was revealed, and the influence mechanism of microstructure on thermal conductivity and its anisotropy was analyzed. The results show that the thermal conductivity of SLM-formed 6061 samples can exceed 140 W/(m·K), and the thermal conductivity can be further improved by about 5% – 30% after aging treatment. The thermal conductivity exhibits anisotropy, specifically showing higher thermal conductivity along the forming direction compared to the direction perpendicular to it, which is related to defects and grain morphology. This study provides a theoretical basis for the application of SLM technology in the preparation of high thermal performance aluminum alloy heat sinks.

To address the challenge of real-time monitoring and control of cladding layer dimensions during laser directed energy deposition (LDED), we propose an integrated framework combining an enhanced CondenseNet architecture with gated recurrent units (GRUs) for online measurement and prediction. The framework comprises two key components: A modified CondenseNet algorithm that fuses key process parameters and melt-pool images to achieve realtime measurement of cladding layer dimensions; A temporal modeling module based on GRUs, which utilizes historical dimension sequences to predict future cladding layer height. Experimental results demonstrate an average percentage error of 5.68% for width measurements and 3.72% for height measurements, with an inference time of 17.6 ms per image under computational resource constraints. Leveraging these real-time measurements, the GRU-based predictor further achieves a height prediction error of 3.60%. The proposed framework enables high-precision, real-time monitoring of cladding layer dimensions with limited computational resources, offering a robust solution for closed-loop control in LDED processes.

To explore the optimal process parameters of wire-feed laser cladding for 7034 aluminum alloy and thereby avoid surface defects while enhancing the mechanical properties of the cladding layer, this study conducted cladding experiments using a high-power fiber laser system integrated with a high-precision push-pull wire feeder. The macroscopic morphology, microstructure, phase composition, and mechanical properties of TiC-reinforced 7034 aluminum alloy cladding layers were systematically investigated. The results indicate that among the parameters of laser power, scanning speed, and wire feeding speed, laser power predominantly governs the macroscopic morphology of the cladding layer. With increasing laser power, the average grain size increased due to elevated thermal input and reduced cooling rates during deposition. Furthermore, TiC nanoparticles acted as heterogeneous nucleation sites, effectively impeding grain growth at the solidification front, which refined grain size and promoted the formation of equiaxed grains. The mechanical properties of the samples exhibited inhomogeneity. Under the deposition parameters of laser power 4800 W, scanning speed 1000 mm/min, and wire feeding speed 3.2 m/min, the 7034 aluminum alloy demonstrated optimal performance, achieving a maximum elongation of 8.78% and a tensile strength of 326.39 MPa.