5755 results about "Aerospace" patented technology

The invention relates to the technical field of numerical control machining, in particular to a tool cutting path machining error compensation method based on online monitoring, which comprises the following steps: establishing a cutting path data model; constructing a multi-modal sensor assembly, and collecting error data in real time; establishing an error state equation to carry out error calculation; constructing a double-layer compensation mechanism; and executing the compensation instruction by adopting a real-time control system. Through real-time synchronous monitoring of the multi-mode sensor, a mixed error resolving framework and a double-layer intelligent compensation mechanism, the influence of dynamic error sources such as cutter abrasion and machine tool thermal deformation can be effectively overcome, the machining precision and the surface quality are remarkably improved, and the high-precision machining requirements in the fields of aerospace, precise instruments and the like are met.

The invention discloses an aerospace knowledge large model construction method based on progressive knowledge injection and retrieval enhancement generation, which comprises the following steps: collecting and preprocessing multi-source knowledge data, and constructing an aerospace field knowledge base; the method comprises the following steps: based on a DeepSeek-R1-8B model, carrying out continuous pre-training by adopting a three-stage progressive mixed course learning framework; performing supervision fine tuning through instruction data and a knowledge guiding mechanism to complete basic construction of a large model; a retrieval enhancement generation module is constructed, a'retrieval-filtering-generation 'process is formed, and the knowledge coverage and fact consistency of the model are improved through deep fusion of real-time retrieval and generation reasoning; and through a multi-dimensional quantitative index and a dynamic test mechanism, the performance of the model is evaluated, and a complete evaluation ecology from data construction to feedback optimization is formed. According to the method provided by the invention, deep integration and efficient application of knowledge in the aerospace field are realized through multi-stage collaborative optimization.

An aero-engine augmented reality virtual-real fusion method based on depth prior scene reconstruction comprises the following steps: firstly, collecting data of an aero-engine look-around RGB image and a depth image, calculating a camera frame pose by using an SFM algorithm and obtaining a sparse point cloud, and optimizing the camera pose by using an ICP algorithm and the point cloud converted from the depth image to obtain an aligned prior dense point cloud; gaussian points are initialized based on the priori dense point cloud and the sparse point cloud, and random generation of 3D Gaussian is limited by using the boundary of the dense point cloud as geometric constraint; through the three-dimensional reconstruction of the adjacent multi-view enhancement strategy, the spatial and visual consistency is improved, and an aero-engine model is reconstructed; then understanding the reconstructed aero-engine model based on semantic segmentation, identifying and marking core components, and finally performing virtual-real fusion visualization on the reconstructed aero-engine through augmented reality to guide training, assembling and maintenance operations. According to the invention, rapid high-quality three-dimensional reconstruction and augmented reality virtual-real fusion visualization of the aero-engine are realized.

The invention belongs to the technical field of aviation energy design, and particularly provides an aviation energy power device fault intelligent inquiry system and method based on a knowledge graph. The method comprises the following steps: firstly, showing a complex data structure and a knowledge relationship to a user in a visual and dynamic manner through knowledge extraction, knowledge graph database construction, knowledge graph visualization and other methods, and constructing a knowledge graph basic framework; according to the method, the depth information of the target object can be extracted from the images from different angles, the fault characteristics of the equipment can be intuitively displayed, and the fault state of the equipment can be visually represented by extracting the depth information of the target object from the images from different angles. The evolution of the equipment state can be tracked through the change of the three-dimensional space; a conversion algorithm of the knowledge graph and the Bayesian network is realized through the Bayesian network, and rapid reasoning of fault causes is realized in combination with a Bayesian network reasoning algorithm; and finally, based on an intelligent inquiry system of a large model, a deep learning technology and a large-scale training model are utilized, and previous fault data and professional knowledge of the aviation energy power device are combined to provide intelligent auxiliary diagnosis support.

The invention provides a dynamic scheduling management method and system for spaceflight measurement and control task resources, and relates to the technical field of spaceflight measurement and control resource scheduling. Defining a task feature vector according to the time sensitivity, the resource demand intensity and the coupling degree between the tasks of the tasks; satellite health parameters, equipment working conditions and environment data are collected in real time through a satellite-ground Internet of Things terminal, and digital twin bodies are constructed; performing priority ranking, resource matching and conflict resolution mechanism on the tasks according to the task feature vectors, performing global optimization scheduling on the spaceflight measurement and control tasks, and executing cross-domain two-stage scheduling during global optimization scheduling; based on bitmap decision matrix calculation, weighting calculation is carried out in combination with task matching values and conflict depths, the priority of conflict tasks is dynamically adjusted, conflicts generated in the scheduling process are dynamically eliminated, and the method can be applied to resource scheduling and optimization of low-orbit satellites and ground stations.

The invention relates to the technical field of calibration test, and particularly discloses a strapdown inertial navigation automatic calibration test method of a temperature control biaxial rate position turntable. Comprising the following steps: S1, multi-dimensional error modeling and layered calibration architecture: constructing a comprehensive error model containing a sensor scale factor error, an installation non-orthogonal error, a zero offset error, a cross axis coupling error and a temperature related error; s2, a wide temperature range self-adaptive compensation mechanism: carrying out heat preservation at a preset temperature point and executing turntable positioning and rate movement; s3, performing automatic data processing and parameter estimation; and S4, performing real-time error correction and dynamic verification: performing real-time error correction based on the estimated parameters, and evaluating navigation performance indexes such as position, speed and attitude precision. Through technical innovation, high-precision, automatic and wide-temperature-range calibration of the strapdown inertial navigation system is realized, and the method is suitable for aerospace, intelligent equipment and other scenes with strict navigation precision requirements, and has remarkable engineering application value and market competitiveness.

The invention belongs to the technical field of aerospace equipment, and discloses an unfolding mechanism and a spacecraft, and the unfolding mechanism is used for winding and unwinding a curling arm and comprises a bearing part, a rotating shaft and a plurality of guide parts. The rotating shaft is rotationally arranged on the bearing piece, one end of the curling arm is fixed to the rotating shaft, and the rotating shaft is used for winding and unwinding the curling arm. The multiple guiding parts are distributed on the bearing piece along the periphery of the rotating shaft and used for providing guiding in the winding and unwinding process of the winding arm. The curling arm is wound through the rotating shaft, and the curling arm can be unfolded or folded by rotating the rotating shaft, so that the solar wing adopting the unfolding mechanism is simple in overall structure, small in size and capable of meeting the space requirement of a small spacecraft. Comprising the unfolding mechanism is small in size and can meet the requirement of miniaturization design.

The invention discloses an aerospace network hierarchical cooperation routing optimization method and system, and relates to the field of aerospace information network communication, and the method comprises the steps: constructing a system model comprising an unmanned plane, satellite nodes and a communication link, defining link attributes, building a routing optimization objective function, introducing a reputation value-based multi-agent cooperation mechanism, and carrying out the routing optimization of the unmanned plane, the satellite nodes and the communication link. A hierarchical reinforcement learning framework is designed, inter-cluster decision and intra-cluster decision are divided, an upper layer selects an inter-cluster path according to global information, a lower layer plans an unmanned aerial vehicle path and defines a state, an action space and a reward function according to local information, an intelligent agent interacts with an environment to collect empirical data, and optimal routing communication is selected in combination with a real-time reputation value after training. Adjustment can be carried out according to network changes; according to the invention, the cooperative reliability is improved through a reputation value mechanism, the decision complexity is reduced through hierarchical learning, the routing performance is optimized, the network robustness, expandability and adaptability are enhanced, and the cooperative communication efficiency of the unmanned aerial vehicle and the satellite and the system stability are improved.

The invention discloses a laser cutting method for cutting complex shapes, and particularly relates to the field of laser cutting. A three-dimensional curved surface model of a to-be-cut workpiece is obtained, and a surface normal tensor field is constructed; extracting a driving point set based on the curvature derivative and generating a sliding path network; constructing a laser power density mapping model in combination with the thermal diffusion response model and the thermophysical parameters of the material; thermal feedback images are collected in the cutting process, an energy offset function is constructed, laser focus displacement and the scanning rate are dynamically adjusted through fuzzy control, and closed-loop control over power, paths and thermal feedback is achieved; according to the method, self-adaptive adjustment of laser power distribution, curved surface adaptation of a cutting path and thermal field real-time compensation in the machining process can be achieved under a complex geometric structure, the cutting precision, stability and energy efficiency are effectively improved, and the method is suitable for laser machining application of high-requirement complex structures such as aerospace, mold manufacturing and medical instruments.

The invention relates to the technical field of satellites, and discloses an artificial intelligence aerospace infrastructure agent system, which comprises a satellite agent, and the satellite agent comprises an energy management module, a calculation module, a task execution module and a communication module. The artificial intelligence aerospace infrastructure agent method comprises the following steps: acquiring real-time states of a plurality of satellite agents, performing task scheduling and resource allocation by using an optimal control theory, optimizing a task allocation strategy in combination with a game theory model, and optimizing a communication path according to a channel capacity theorem, so as to obtain an artificial intelligence space infrastructure agent; and the task sequence and the resource allocation strategy are adjusted in real time in the task execution process, so that efficient collaboration and task completion are ensured. According to the method, task scheduling is optimized by combining the optimal control theory and the game theory, a communication path is optimized by adopting the channel capacity theorem, the task execution sequence is adjusted in real time, adaptive resource allocation is performed, efficient cooperation between satellite agents is ensured, and the task execution efficiency, the resource utilization rate and the system stability are effectively improved.

The invention relates to the technical field of intelligent manufacturing of aeronautical parts, and discloses an aeronautical part technological procedure reasoning method and system based on a template, and the method comprises the steps: firstly obtaining and storing technological characteristics and geometric structure characteristics of a to-be-machined part; secondly, encoding the feature data of the to-be-processed part, the feature data of the historical processing part, the technological procedure template data and the technological knowledge data which need to be used in a One-hot mode; then, according to the process characteristics and geometric structure characteristics of the to-be-machined part, a matched process procedure template is obtained through a characteristic-based template matching algorithm, and typical process parameters related to part process procedure compilation based on the process procedure template are obtained through a typical process parameter reasoning decision algorithm; and the matched technological procedure template and the typical technological parameters obtained through calculation are fused, and a part technological procedure is formed and output. According to the method, the required part technological procedure can be quickly and intelligently generated, and the design efficiency of the technological design link in the production process is greatly improved.

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

The invention discloses a multifunctional aircraft skin bionic heterostructure and a preparation method thereof, belongs to the technical field of aerospace, and aims to solve the problem that a traditional aircraft skin structure is difficult to synergistically realize impact resistance, sound absorption, noise reduction and electromagnetic shielding, and a multifunctional composite skin protection structure is designed in a breakthrough manner. Through collaborative innovation of a bionic framework and a functional material, triple optimization of mechanical-acoustic-electromagnetic characteristics is realized. A bionic skeleton structure with outstanding mechanical properties is obtained through a layered annular structure of a glass sponge skeleton and a hierarchical structure design of a thighbone, and the bionic skeleton structure is combined with modified polyurethane foam with excellent sound absorption performance and electromagnetic shielding performance to obtain a multifunctional heterostructure with impact resistance, noise reduction and electromagnetic shielding.

The invention discloses a rod type six-component high-precision trace rolling moment wind tunnel balance and a measurement method, and belongs to the technical field of aerospace force measurement test power measurement. According to the invention, the problems of large processing difficulty, long processing period and incapability of meeting aviation tests in research and development of micro rolling moment balances at present are solved. A longitudinal column beam, a transverse column beam and a roll-on *-shaped beam are connected between a front cone and a combined element and between a rear cone and the combined element respectively, a resistance beam is arranged on the combined element, the thickness range of sheet beams of a multi-sheet structure of the longitudinal column beam is 0.5 mm-0. 8mm, the gap range of the sheet beams is 0.5 mm-1mm, the thickness range of sheet beams of a multi-sheet structure of the transverse column beam is 0.5 mm-0. 8mm, and the gap range of the sheet beams is 0.5 mm-1mm. According to the rod type six-component high-precision trace rolling moment wind tunnel balance and the measurement method, rolling output is effectively increased, and matching output of small rolling moment and loads in other directions is achieved.

The invention provides an unmanned aerial vehicle inspection accurate positioning method and device integrating vision and satellite navigation, and relates to the technical field of aerospace information. According to the method, satellite signal quality features reflecting aerospace information reliability and multi-dimensional features such as visual geometric textures reflecting ground environment features are jointly extracted, and the environment state is deeply perceived; based on real-time dynamic generation of a fusion strategy adaptive to the current environment, a data weight and an algorithm model are intelligently adjusted, and the limitation of a traditional fixed parameter fusion mode is broken through; a specific inspection stage and a target object where the unmanned aerial vehicle is located are understood through visual identification, and a corresponding priori task constraint model is loaded, so that the positioning process closely meets the precision and safety requirements of actual operation. And finally, fusion calculation is carried out under the common guidance of a dynamic strategy and task constraints, so that the problem of inaccurate positioning of the unmanned aerial vehicle in a complex environment is solved, and the environmental adaptability, robustness and task guidance accuracy of a positioning system are improved.

The invention discloses an airspace engine 3D printing thermal stress distribution control method which comprises the following steps: S1, importing a three-dimensional CAD model of an airspace engine structure, and generating a printing layer set; s2, constructing a three-dimensional node graph structure of a printing layer; s3, inputting the thermodynamic prediction input tensor into the heterogeneous attention map thermodynamic network to obtain a node-level thermal stress tensor map; s4, analyzing the node-level thermal stress tensor diagram, and extracting an optimized region; s5, calling an improved chameleon search algorithm in the optimization region; s6, optimal printing parameters of the current printing layer are determined; s7, the 3D printing device is controlled based on the optimized printing parameters, and real-time thermal inductance data are collected; and S8, mapping the thermal inductance feedback set of the printing layer to the corresponding node, and repeatedly executing the steps S2 to S7 until the whole airspace engine structure is printed. According to the method, the heterogeneous graph network and the improved chameleon search algorithm are fused, and precise control over the printing thermal stress of the airspace engine is achieved.

The invention discloses a multi-mode sensing electromechanical actuator fault prediction system and a control method thereof, and relates to the field of intelligent maintenance of electromechanical integrated equipment. Comprising the following steps: S1, multi-mode sensor deployment and signal acquisition: cooperatively deploying a vibration sensor, a current sensor and a temperature sensor at a bearing, a motor winding and a driver of an electromechanical actuator; and S2, bionic pulse coding and time synchronization: converting the three-mode signal into a sparse pulse stream with time synchronization through a self-adaptive integral distribution model with dynamic threshold adjustment. Through bionic multi-mode sensing, brain-like edge calculation and dynamic fault-tolerant control, real-time health management of the high-precision electromechanical system is realized, the equipment reliability is remarkably improved, and the operation and maintenance cost is reduced. The method is suitable for industrial application with precise assembly requirements such as automobile welding lines, aerospace machining and metal 3D printing.

The invention discloses an aero-engine missing value filling method based on multi-scale time sequence and attribute feature extraction. Firstly, for data collected by each sensor of an aero-engine, a temporary replacement value of a missing value is generated by adopting linear interpolation; extracting a plurality of significant cycles of the aero-engine sequence data as a time scale, decomposing the sequence into trend terms and season terms according to the significant scale, and splicing all the trend terms and season terms in the time dimension; then, in-scale and inter-scale time sequence dependence is captured in a time dimension by using cross-scale graph convolution so as to carry out missing value filling in time sequence. In the attribute dimension, local neighborhood information is accumulated by adopting a cross-attribute graph volume, global attribute features are calculated by utilizing a relational network, node features are updated, and attribute association is supplemented on the basis of a time sequence filling result. And finally, aggregating the node features of a specific scale through a maximum pooling layer to represent the features, and calculating by adopting two linear layers and an activation function to obtain a final filling result.

The invention discloses an additive manufacturing intelligent repairing and surface optimizing method for a complex special-shaped curved surface, and particularly relates to the technical field of additive manufacturing. A three-dimensional digital model is obtained, and a complex special-shaped curved surface defect area is recognized; extracting a local morphology parameter set; constructing a repair path generation model based on a graph neural network, and performing collaborative optimization of forming precision, residual stress and deposition stability on the path through a multi-objective optimization algorithm; additive manufacturing equipment is controlled to perform material deposition according to the optimized path; carrying out online error detection by adopting a structured light system, judging repair quality through a fuzzy decision tree model, and triggering path reconstruction if the repair quality exceeds a threshold value; after repairing is completed, surface micro texture factors are extracted, and functional texture regulation and control are conducted through the laser micromachining technology; according to the method, high-precision intelligent repair of the complex curved surface area and collaborative improvement of surface function performance are achieved, and the method is suitable for the fields of aerospace, energy, high-end equipment manufacturing and the like.

The invention belongs to the technical field of fastener machining equipment, and discloses bolt tail integrated machining equipment which is characterized in that a material conveying rail comprises a first rail and a second rail, a material containing gap is formed between the first rail and the second rail, a workpiece moves in the material containing gap, the material conveying rail is provided with a station rail section, and the station rail section is connected with the first rail and the second rail. The station rail section and the machining station are correspondingly arranged, and in the station rail section, the position, relative to the horizontal plane, of the first rail is higher than the position, relative to the horizontal plane, of the second rail; when the workpiece moves to a machining station, the movable positioning tool acts to clamp the workpiece between the first positioning groove and the second positioning groove, so that the machining mechanism machines the workpiece; the action direction of the jacking mechanism is parallel to the rail surface of the conveying rail, and the machining axis of the machining mechanism is perpendicular to the action direction of the jacking mechanism. Aiming at tail machining of aerospace fasteners, the machining precision can be guaranteed, the process time can be shortened in a single process or a continuous process, and batch production is achieved in an assembly line mode.

The computer fault diagnosis method based on the causal reasoning and mapping knowledge domain hybrid architecture comprises the steps of obtaining and processing multi-source heterogeneous data of an embedded computer, constructing a system mapping knowledge domain integrating the multi-source data of the embedded computer, and forming a knowledge base containing components, functions, fault phenomena and association relationships thereof; the method is characterized in that a causal reasoning engine is designed, domain knowledge constraints are utilized, a real cross-level fault propagation causal chain is mined from atlas association, and root causes are verified through anti-fact reasoning. And the engine dynamically feeds back the mined causal knowledge to the atlas, so that the causal knowledge is continuously optimized. According to the hybrid architecture, accurate and rapid tracing with causal explanation from a fault phenomenon to a root cause is realized, and the diagnosis capability in high-reliability fields such as aerospace and industrial control is remarkably improved.

The invention discloses a mechanical property testing method for an inflatable wing, and belongs to the technical field of aerospace testing. The method comprises the following steps: S1, wing installation: clamping an inflatable wing on a test board, and enabling a chord plane of the inflatable wing to be perpendicular to a loading direction; s2, applying a load: driving a loading mechanism to apply a vertical load to the inflatable wing through an external universal tester; s3, data acquisition: synchronously acquiring a deflection curve and stress distribution data by using a displacement measurement unit and a strain detection unit; and S4, failure judgment: when monitoring that the shape of the deflection curve suddenly changes or the strain data exceeds a threshold value, judging that the inflatable wing is subjected to buckling failure, stopping loading and recording the maximum load. The test bench serves as an expansion platform of a universal tester, can be adapted to a large-size inflatable wing, does not need special equipment, reduces the cost, realizes cooperative monitoring of displacement and strain data, realizes closed-loop control of inflation air pressure-deformation-load, and remarkably improves the test precision and the data reliability.

The invention belongs to the technical field of optical fiber sensing, and discloses a high-temperature-resistant and high-pressure-resistant multi-parameter integrated sensor and system based on optical fiber dislocation welding, and the sensor comprises a packaging main body, and the center of the packaging main body is provided with a center hole and a cavity which are used for accommodating a transmission optical fiber. The tail end of the transmission optical fiber is sequentially connected with a temperature detection device and a pressure / sound detection device which are positioned in the cavity; the temperature detection device comprises a fiber bragg grating; the pressure / sound detection device comprises a single-mode fiber, a multimode fiber, a first hollow capillary fiber and a second hollow capillary fiber which are connected in sequence; the axes of the first hollow capillary optical fiber and the second hollow capillary optical fiber are connected in a staggered manner to form an open structure. The pressure / sound-temperature multi-parameter composite in-situ synchronous measurement device realizes composite in-situ synchronous measurement of multiple parameters of pressure / sound-temperature, has the advantages of high temperature resistance, small size and high test precision, and has wide application prospects in the fields of aerospace, petroleum metallurgy, environmental monitoring and the like.

The invention relates to the field of carbon fiber composite material mechanics, in particular to a fine evaluation method for the strength of a small-size large or full-opening CFRP (carbon fiber reinforced plastic) shell, which comprises the following steps: establishing a microscopic damage model containing material anisotropy, defining damage variables and evolution equations, uniformly quantifying multi-scale damage behaviors such as fiber fracture, matrix cracking and layered expansion, and evaluating the strength of the small-size large or full-opening CFRP shell. The problem that a traditional single-scale model cannot represent the multiphase coupling effect is solved; through cross-scale transmission of damage variables, whole-process prediction from microscopic damage initiation to macroscopic failure is realized, and the precision of strength evaluation of the complex opening structure is significantly improved. The method realizes refinement, reliability and engineering practicability of strength evaluation of the small-size large-opening or full-opening CFRP shell, is particularly suitable for the fields of aerospace and the like with extremely high requirements on structural weight and safety, and has remarkable technical leading and application value.

The invention relates to a preparation method of a low-cost high-quality titanium ingot. The method comprises the following steps that raw materials including 20%-30% of new materials (titanium sponge, aluminum-vanadium intermediate alloy, aluminum shots and aluminum plates) and 70%-80% of recycled materials (TC4 blocky recycled materials and TC4 scrap-shaped recycled materials) are prepared, and the recycled materials are treated; mixing the materials and pressing a titanium alloy lump; material distribution is conducted, specifically, the titanium alloy lumps, the recycled materials and the aluminum plates are evenly stacked in an electron beam cold bed furnace (EB furnace) for drying treatment; eB is smelted to obtain an EB cast ingot; performing end flattening treatment on the EB cast ingot; and the EB cast ingot serves as a consumable electrode and is subjected to secondary smelting in a vacuum consumable electric arc furnace (VAR furnace), and finally the finished TC4 titanium alloy cast ingot with uniform cast ingot components is obtained. The method effectively utilizes the recycled materials, reduces the production cost, improves the ingot casting quality, and is suitable for preparing high-performance titanium alloy materials for aviation, aerospace and the like.

The invention discloses a double-domain artifact correction method based on multi-energy-level data and physical prior fusion, and the method achieves the efficient and precise removal of dispersive metal artifacts through the construction of a complete technical scheme of the combination of multi-energy-level data collection, double-domain cooperative correction and physical model constraint. The system has the beneficial effects that the system covers multiple fields of medical treatment, industry, security and protection, aerospace and the like, and has extremely high universality and adaptability. On the basis of a multi-energy-level slow switching scanning protocol, full-angle scanning of at least two energy levels is completed by dynamically adjusting radiation source parameters, and obtained complete multi-energy-level projection data is converted into a high-dimensional tensor through a channel dimension splicing image fusion method. The constructed multi-channel virtual image completely retains attenuation characteristics and structure information of a target object under each energy, provides a more comprehensive input source with discrimination for a deep learning network, builds a data basis for accurate correction in different fields fundamentally, and adapts to various imaging scenes containing metal targets.

The invention discloses a method for predicting the elastic property of a fiber reinforced composite material, and aims to solve the problems of low precision, low efficiency and difficulty in processing multi-scale correlation of a traditional method. The method comprises the following steps: firstly, generating a representative volume unit by using a random algorithm, and constructing a high-quality training data set by combining Sobol sequence sampling and an SMOGN data enhancement technology; secondly, designing and training a deep neural network embedded with residual blocks and physical constraints, and improving the generalization ability of the model through Bayesian optimization adaptive parameter adjustment; and finally, establishing a plurality of macrostructure models, and realizing end-to-end prediction of the multi-scale elastic performance. The method realizes breakthrough in prediction precision and calculation efficiency: the time consumed by single analysis is shortened from several hours to a minute-second level, and the average prediction error is lower than 5%. The technology can be widely applied to the fields of aerospace, new energy automobiles, wind power and the like, and intelligent support is provided for design and manufacturing of high-performance composite materials.

The all-metal vibration isolation buffering device comprises a bearing base, a center disc is arranged in the center of the bearing base, a buffering pad is arranged between the center disc and the bearing base in a clamped mode, and a plurality of vibration isolation structures are evenly distributed on the periphery of the center disc in a circumferential mode; and the vibration isolation structure is telescopically connected with the central disc. Due to the excellent high and low temperature resistance, fatigue resistance and omni-directional vibration isolation and buffering performance, an efficient and stable vibration isolation and buffering effect can be provided in an extreme environment. The all-metal multidirectional vibration isolation buffer structure can replace a traditional rubber vibration isolator, is applied to the fields of spaceborne, aerospace, precise instruments, ship equipment, high-speed traffic and the like, and is used for reducing the influence of vibration on key equipment, improving the stability of a system, prolonging the service life of the system and meeting the application requirements of high reliability and long service life.

The invention discloses a closed-loop self-adjusting method for online defect identification and real-time feedback in the additive manufacturing process of a titanium alloy component based on intelligent sensing, belongs to the field of laser additive manufacturing, and solves the key problems of insufficient defect identification capability, quality control lag and the like in the current additive manufacturing process. The method is based on a multi-mode sensor architecture, integrates an infrared thermal imager, a molten pool camera and a spectrum sensor, and synchronously monitors a molten pool thermal field, radiation characteristics and surface topography. Traditional image processing and signal analysis are adopted to extract defect features such as air holes and cracks, and calibration is implemented in combination with off-line detection such as industrial CT. The recognition result is analyzed in real time through the central control platform, causes are automatically diagnosed based on defect types and distribution characteristics, parameters such as laser power and scanning paths are dynamically adjusted by matching a preset control strategy, and defect suppression and online repair are achieved. And a closed-loop self-adaptive dynamic adjustment mechanism improves the forming quality and reliability. The method is suitable for aerospace and other scenes with extremely high requirements for the quality of large complex titanium alloy components.

The invention relates to the technical field of ignition systems of gas turbines, and discloses a special ignition system for a gas turbine, which comprises a monitoring control layer, an energy distribution layer and a combustion optimization layer. The monitoring control layer monitors parameters such as combustion chamber pressure fluctuation in real time through multiple types of sensors and executes phase synchronization and injection control. The energy distribution layer is provided with a multi-stage energy release strategy and a self-adaptive time sequence matching mechanism, a stable ignition energy field is constructed, and physical and digital space linkage regulation and control are achieved through a multi-modal energy interface; and the combustion optimization layer establishes a multi-dimensional mapping combustion state control model based on a dynamic combustion algorithm and real-time data, and adopts a collaborative optimization algorithm to calibrate an ignition time sequence, regulate and control a fuel mixing ratio and maintain flame stability. According to the system, through multi-dimensional energy fusion, model parameter dynamic correction and intelligent algorithm application, the accuracy, stability and multi-working-condition adaptability of the gas turbine ignition process are remarkably improved, and the system is suitable for gas turbine efficient combustion control in the fields of aerospace, electric power and the like.