With the continuous improvement of service performance requirements of aerospace key components, the difficult-to-cut materials, such as high-performance titanium alloy, nickel-based superalloy, high-strength steel, are widely used in the manufacturing process. Cutting process is the main machining method for these difficult-to-cut materials, whereas the problems should be faced, including the low cutting force, high cutting temperature, severe tool wear, poor machining quality. Recently, researchers have done much work on the prediction and control the cutting force, cutting temperature, tool wear and surface quality in the cutting process. This article systematically reviews the simulation and intelligent control technology of cutting force, cutting temperature, tool wear and surface quality in cutting process of difficult-to-cut materials in aerospace industry, and the problems and challenges are then analyzed. Subsequently, the future development trend of the simulation and control technology on cutting process is prospected.

Precise monitoring of machining status is the premise and guarantee for realizing deformation control of thin-walled parts. The latest research progress of milling machining condition monitoring of aerospace thin-walled parts is reviewed, and the key technologies and methods for establishing machining condition monitoring models are introduced in detail, including information collection and processing, feature extraction and feature fusion. It summarizes the development status and defects of scholars at home and abroad in the process of thin-walled parts processing on the specific state monitoring of tool grinding damage, milling flutter, milling deformation and so on. And based on the digital twin technology, a twin system for monitoring the milling processing status of thin-walled parts is constructed. Finally, according to the current development level of this field, the status monitoring of milling processing of thin-walled parts is prospected.

Due to its weak rigidity, thin-walled parts are prone to chatter and deformation in the milling process, which aggravates tool wear. In order to improve the milling efficiency and surface quality of thin-walled parts, a tool wear state recognition method driven by the fusion of digital twin and support vector machine (SVM) is proposed. The feature vectors are extracted by time-frequency domain analysis and wavelet packet transform. The super parameters are optimized by grid search and cross validation (GSCV). Combined with SVM algorithm, the wear state recognition model of milling tool for thin-walled parts is constructed. The experimental results show that SVM algorithm has obvious advantages in the classification and recognition of high-dimensional and small sample data. The recognition accuracy of different milling cutter wear states reaches 96% and 90.16% respectively, and has good generalization ability. Combined with machine learning algorithm, a high fidelity and lightweight digital twin is constructed and embedded into the milling process monitoring platform of thin-walled parts, so as to solve the problems of real-time signal monitoring and online recognition of tool wear state in the machining process.

Aiming at the chatter problem of milling thin-walled parts, a vibration suppression method based on additional mass and eddy current damping is proposed. Firstly, a dynamic model of milling machining of thin-walled parts is established, and the influence law of additional mass and eddy current damping on the machining stability region is obtained through flutter stability analysis; Then an optimization method for added mass of thin-walled parts is proposed to obtain the optimal added mass layout and mass proportion; Finally, a set of thin-walled parts processing vibration suppression device was designed, and the thin-walled parts milling experiment was carried out. The research results show that when the additional mass combination [15,10,15] and eddy current damping are added at the same time, the thinwalled workpiece machining vibration is significantly suppressed, which verifies the effectiveness of the proposed vibration suppression method.

In order to further optimize the quality of the blade surface model, a blade surface smooth modeling method with uniform parameters is proposed. First, according to the curvature characteristics of the blade, the data points of each layer of section curves are divided into four parts: pressure side of blade, suction side of blade, the leading edge and the trailing edge. The center of the circle is used to generate the induced curve of the camber curve of the blade. Then, a series of inscribed circle centers of the profile of the blade section are obtained by the Newton iteration method, so as to obtain the camber curve, which is smoothed and arc length parameterized. Using the method of camber curve-thickness distribution, combined with the transition curve to generate a smooth cross-sectional contour curve to ensure the continuous curvature. Finally, a smooth blade surface model is generated by lofting and fusion optimization algorithms. This method is used for model construction and processing of data points of a certain engine blade. The result analysis shows that the optimized surface model has better smoothness, which avoids the problem of tool path fluctuation caused by the unsmooth model.

Aeronautical thin-walled parts are characterised by large dimensions, thin walls and high material removal rates, which is highly susceptible to machining deformation problems. Machining deformation seriously affects the quality of the parts machined, therefore, the machining deformation control of aviation thin-walled parts is of great importance. In this paper, a transmission reverse disc is used as the object of study, to optimise the machining position of the part by reconstructing the global residual stress distribution of the blank based on the monitored deformation forces during the machining process, at the same time, combined with the responsive fixture adaptive machining process, which enable effective control of the machining deformation of the part. The actual machining tests control the flatness and parallelism of the part to 0.11 mm and 0.04 mm respectively. The results show that the method can significantly control the machining  deformation of the part.

To solve the problem of high vibration and severe wear when milling superalloy thin-walled workpiece, the influence of different coating systems on milling tools was studied. AlTiN/TiAlN, AlCrN/TiAlN and TiSiN/TiAlN double-layer coatings were deposited on the carbide tools by arc ion plating. The morphology and mechanical properties of three coatings were characterized by scanning electron microscope, nano indentation instrument and micron scratch instrument. Milling tests of coated tools were carried out on a high-performance milling machine. The tool wear condition and cutting forces were collected, and the performance of different coatings during milling was analyzed. The results show that TiSiN/TiAlN coating has the highest hardness and wear resistance factor, and the wear of flank surface is the smallest after 24 m of milling, but the coating is easy to bond with the superalloy. Compared with TiSiN/TiAlN, the hardness and wear resistance factor of AlCrN/TiAlN coating are smaller, and the tool is close to failure after 24 m of milling, but the bond with superalloy is not so serious; AlTiN/TiAlN coating has the smallest hardness and wear resistance factor, and the most severe wear condition. The tool has failed after milling of 12 m, but the bond between the coating and the superalloy is the slightest. Therefore, coating with higher hardness and wear resistance factor can effectively reduce the wear of the tool when milling superalloys and prolong tool life.

Due to large working space and strong flexibility, serial industrial robots are widely used in the machining of large structural parts such as aircraft skin and aviation transparent part. However, the low stiffness of industrial robots and large differences in the spatial distribution of dynamic characteristics lead to low limits of their milling stability, significant variations in milling performance in different machining regions, and narrow windows of available process parameters. It is important to study the dynamic characteristics of the robot milling system during machining and to establish a positional correlation modal prediction model to improve the robot machining performance. In this paper, a modal prediction method based on deep neural network is proposed for an ABB robotic machining system. Firstly, the modal experiment of the robot processing system is carried out by using the Doppler vibrometer, and the spatial variation of each order modal is analyzed. Then, according to the actual working space of the robot, an experiment is designed to obtain the frequency response function set related to the pose, and the related modal parameters are accurately identified by the rational polynomial method. On this basis, the hyperparamter optimization method is used to establish a deep neural network prediction model, which can accurately predict the pose-dependent modal parameters in the robot workspace. Finally, the experimental results show that the prediction accuracy of this method can reach more than 80%.

Excessive clamping force of temporary fasteners will cause damage to the composite holes and seriously affect the static load-bearing and dynamic fatigue performance of the structure. The clamping force of the temporary fastners needs to be controlled at the aviation assembly site. Therefore, in view of the fluctuation characteristics of the output torque of the temporary fasteners pneumatic installation tool, a test and verification device for the installation of temporary fasteners for composite structures was designed to explore the relationship between the installation process and the clamping force. The input air pressure of the pneumatic installation tool is controlled by an electric proportional valve, and the torque and clamping force during the installation of temporary fasteners are monitored by a torque sensor and a clamping force sensor. The measurement system of the built device is analyzed, the repeatability is verified by the Pearson correlation coefficient formula, and the uncertainty is calculated by the Bessel formula. The results show that the designed device can be used to test and verify the installation process of temporary fasteners for composite components. This will provide a method and basis for the formulation of temporary fastening technology at the assembly site of aeronautical composite materials.

Aiming at the problem that a large number of non-instantiated standard parts in aircraft product have not yet achieved consumable process design, the idea of two-level splitting and consumable allocation of non-instantiated standard parts according to assembly requirements is proposed. Combined with the business structure process, the secondary development of CATIA CAA was used to carry out the position-level split of the non-instantiated standard parts R model, and their consumable allocation and management technology of non-instantiated standard parts in the assembly process design process were systematically studied. System testing and application verification have been carried out on the some products, AO creation has been completed, the consumable assembly process design of all participating parts has been realized, the consistency and accuracy of manufacturing data and engineering data have been ensured, and good application results have been achieved.

To improve the speed of NC machining process planning for corner box parts, the automatic recognition process of machining feature was researched, and a feature recognition method based on single feature sequence graph matching in recognition direction was proposed. According to the processing process of corner box parts, the machining feature was divided into four types of front machining feature, back machining feature, front and back machining feature, and side machining feature. The recognition process of machining feature was introduced, which included the key steps such as the recognition and suppression of hole and transition feature, the generation and decomposition of attributed adjacency graph, and the discrimination of machining feature types. The prototype system of the machining feature recognition of corner box parts was established. A corner box typical part was used to verify the validity and correctness of the proposed method.

Using T800 grade carbon fiber/epoxy resin unidirectional tape automatic laying prepreg, test laminates with different thinkness were manufactured by automatic fiber placement process. The effects of curing pressure, laminate thinkness and size, automatic fiber placement pressure and other parameters on porosity and porosity distribution were investigated. Composites porosity intervals were characterized using ultrasonic C-scan. The results show that when the curing pressure drops to a certain critical point, the porosity increases rapidly first and then slows down gradually; Thicker laminates have higher porosity than thinner laminates, and there are more pores near the middle and bottom of the ply; For laminaters with a larger size, the areas with high porosity are relatively concentrated near the middle of the laminates, and the porosity interval is large; When the curing pressure is low, the laminates laid with a small laying pressures will form a higher porosity; For large and complex composites structures, the influence of different automatic fiber placement parameters on porosity also has the above trend.

A space electron gun for welding was designed, in which LaB₆ is chosen as cathode material and the cathode is heated by indirect cathode. The initial values of the electron gun are obtained by using the comprehensive iterative method of Vaughan. According to these initial values, the three-pole structure of the electron gun is designed, and then the double magnetic lens is designed. The results show that the parameters of the three electrodes and the lens of the electron gun are adjusted to make the simulation results meet the requirements of the actual design. At the same time, the regularity of the lens debugging process is analyzed. The simulation results show that the design meets the target parameters of 60 mA total emission beam, 0.3 mm cross spot diameter and 0.4 mm beam spot at 300 mm working distance. This design method and the whole design process can provide some reference for the design of electron gun.