9198 results about "Manufacturing technology" patented technology

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent production scheduling method and system for a manufacturing system, and the method comprises the following steps: collecting an equipment state, a material neat rate and an order emergency degree in real time based on dynamic production data, and calculating an equipment availability coefficient, a material guarantee index and an order priority score through feature analysis; and forming a dynamic production feature set. The system constructs a production scheduling optimization model through work order-equipment matching degree calculation and process priority optimization, and performs multi-objective optimization solution by adopting an NSGA-III algorithm to realize the optimal combination of equipment utilization rate, order delivery rate and inventory balance. Meanwhile, an arbitration mechanism is introduced to coordinate process priority conflicts, and the scheme is verified through time, space and dynamic adaptability, so that feasibility and stability of production scheduling execution are ensured. According to the invention, the production scheduling efficiency of the manufacturing system can be improved, the equipment utilization rate can be improved, the order delivery delay rate can be reduced, and the adaptive ability to the change of the production environment can be enhanced.

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention relates to the technical field of semiconductor manufacturing, and discloses a semiconductor wafer etching method with a detection function. The method comprises the following steps: acquiring initial image data of a wafer surface through an optical detection device, and extracting geometric features and defect distribution information; processing through a multi-scale feature fusion algorithm, identifying an abnormal region and classifying defect types; dynamically adjusting process parameters of etching equipment according to defect conditions, and planning a self-adaptive etching path; during etching, a real-time monitoring module is used for collecting data, and etching parameters are corrected through a feedback control mechanism; and after etching, the etching precision is secondarily detected and verified by means of a multispectral imaging technology. According to the method, defects can be accurately positioned, the etching process is optimized, the etching precision and quality are effectively improved, the rejection rate is reduced, and the efficiency and reliability of semiconductor wafer manufacturing are improved.

The invention provides a robot cable manufacturing process optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting cable manufacturing data, constructing a digital twin model, and achieving the process flow simulation through a graph network; building a deep reinforcement learning environment by taking the manufacturing data and the simulation data as state input; analyzing a causal dependency relationship of process parameter adjustment; constructing an industrial knowledge graph expert system to generate an optimization strategy; and a process optimization closed loop is formed. According to the invention, self-adaptive optimization of the cable manufacturing process is realized, and the manufacturing efficiency and quality are improved.

The invention discloses an intelligent defect detection system and a self-repairing method for a flexible base material RFID tag, and belongs to the technical field of Internet of Things electronic device manufacturing. According to the system, a three-dimensional dynamic scanning system is constructed by integrating a high-resolution image acquisition module, a multispectral sensor array and a mechanical arm motion platform, and surface and internal structure characteristics of a flexible substrate are captured in real time. A defect identification algorithm based on deep learning is combined with a multi-scale convolutional neural network and a transfer learning technology, precise classification of 12 types of defects such as microcracks, conductive layer fractures and base material deformation is realized, and the detection precision reaches 99.2%. A dual-mode self-repairing mechanism is put forward, specifically, nano-silver conductive colloid is injected through a microfluid channel for the defects of the conductive layer, and 3D structure reconstruction is achieved through a controllable temperature field; for substrate damage, a photoresponse shape memory polymer patch is adopted, and molecular-level bonding repair is achieved after ultraviolet light activation. According to the scheme, the detection efficiency is improved by more than 5 times, and the radio frequency performance of the tag is recovered to 98.7% of the initial value after self-repairing.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

The invention relates to the technical field of process optimization, in particular to a knowledge graph-driven manufacturing process optimization system, which comprises a graph node generation module, a dependency relationship construction module, a semantic entity matching module, a process path screening module and a trend-driven early warning module. According to the method, semantic implicit relation recognition is achieved through sequential alignment and numerical difference comparison, quantitative screening between the machining size and the tolerance grade is introduced in path construction, and it is ensured that a path connection structure is optimized under the condition that the size precision constraint is met; in combination with the time sequence trend splitting and continuous fluctuation consistency discrimination mechanism of the equipment operation state, the dynamic marking and early warning annotation set generation of the process deviation trend can be completed at the node path level, and the closed-loop processing from knowledge modeling, path screening to trend pre-judgment is realized. And the process path adaptability, the parameter configuration precision and the abnormality identification advancement are effectively improved.

The invention relates to the technical field of industrial automation and intelligent manufacturing, in particular to a rubber tube production line monitoring method and system.The method comprises the steps that multi-mode production data of a production line is collected and preprocessed, a virtual production line model is generated through a dynamic twin model and compared with actual data, and virtual-real difference data is generated; performing anomaly detection on the virtual-real difference data based on a classification algorithm and a rule matching structure, and optimizing parameters of an anomaly detection model; generating abnormal trend prediction data by using the optimized model, and proposing a system correction suggestion according to the prediction data; and dynamically adjusting the operation parameters of the equipment through a closed-loop feedback mechanism. According to the invention, through innovative dynamic twin modeling, deep learning optimization and closed-loop control technologies, real-time monitoring and intelligent adjustment of the production line are realized, and the production efficiency and the product quality are effectively improved.

The invention discloses an industrial equipment intelligent operation and maintenance management system and method based on 5G-MOM, and belongs to the technical field of industrial internet and intelligent manufacturing. The system comprises a multi-source heterogeneous data acquisition layer deployed in industrial equipment, an edge computing node cluster based on 5G, a cloud intelligent analysis platform and a man-machine collaborative operation and maintenance terminal. The method comprises the following steps of collecting equipment vibration, temperature and current multi-dimensional working condition data in real time through a 5G network; performing data cleaning and feature extraction by using edge computing nodes, and constructing an equipment operation digital twin model; a cloud deep neural network is adopted to carry out fusion analysis on the multi-dimensional time series data, and self-adaptive diagnosis and residual life prediction of a fault mode are realized; a dynamic maintenance strategy is generated based on an MOM system, and field personnel are guided to execute precise maintenance through an AR terminal. According to the invention, 5G ultra-low time delay communication and an industrial mechanism model are creatively combined, and real-time visual management and predictive maintenance decision optimization of the equipment health state are realized.

The invention relates to a liquid medicine concentration automatic control correction method and system in the technical field of semiconductor manufacturing, and the method comprises the steps: if the concentration deviation exceeds a preset threshold value, comparing real-time monitoring data with a chemical equilibrium model, obtaining the offset of a reaction rate, and judging the specific parameters of the liquid supplementing amount and the adjustment time; a proportional-integral control algorithm is combined with a trigger condition and a liquid supplementing parameter, the liquid medicine concentration is dynamically adjusted, a compensation factor is introduced for temperature fluctuation, and an adjusted concentration control instruction is obtained; liquid level distribution data in the cleaning tank are synchronously collected through a liquid level detection device, and whether the uniformity of the liquid medicine in the tank reaches a preset standard or not is judged in combination with the adjusted concentration control instruction; and continuously acquiring a stable distribution result, comparing the stable distribution result with an initial data set, judging the optimization degree of the control precision and the response speed, and obtaining a concentration fluctuation range and a uniformity index after dynamic adjustment.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent defect detecting and repairing method and system for a vacuum isothermal forging based on multi-mode sensing data. The method comprises the following steps: respectively acquiring image data, heat distribution data and stress monitoring data of the vacuum isothermal forging through a visual sensor, a thermal sensor and a stress sensor, and preprocessing the data to generate a time-space aligned multi-modal data set; performing fusion processing on the multi-modal data set, extracting multi-dimensional features of defects through a deep learning network, and generating a defect feature sequence; optimizing a repair path planning model based on the defect feature sequence in combination with the stress distribution features, and generating a repair task sequence; and inputting the repairing task sequence into the intelligent repairing equipment to execute repairing operation. According to the method, the multi-mode sensing technology and the deep learning algorithm are combined, high-precision detection, classification and repair of the defects of the vacuum isothermal forging are achieved, the production efficiency and the product quality are remarkably improved, and the defective rate and the repair cost are reduced.

The invention provides a permanent magnet motor and a scroll inverter compressor, and relates to the technical field of scroll compressor design and manufacturing, and the permanent magnet motor comprises a stator iron core and a rotor iron core; the stator core comprises stator teeth and a stator yoke which are uniformly distributed along the circumference, fluid through grooves are formed in the outer side of the stator yoke at the tops of the stator teeth, stator grooves are formed in two adjacent stator teeth and the stator yoke between the two stator teeth, and a plurality of strands of conductive circular enameled wires are embedded in each stator groove; the outer side of the rotor core is composed of linear segments and arcs, air grooves which are evenly distributed are formed in the rotor core, non-uniform magnetic isolation bridges are formed between the air grooves and the linear segments on the outer side of the rotor core, a certain distance is formed between every two adjacent air grooves, parallel magnetic conductive ribs are formed, and permanent magnets with the magnetic field directions changing alternately are embedded in the air grooves. A rivet hole, a lightening hole and a shaft hole are arranged close to the inner side of the rotor core. Therefore, the defined proportionality coefficient Ks is kept in a reasonable range, the power density of the motor can be increased, the cost is reduced, and the efficiency is improved.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention provides an abnormity early warning method and system used in a fan manufacturing process. The abnormity early warning method comprises the steps that a multi-source process data stream composed of part forming process data, structure assembling process data and performance testing process data in the fan manufacturing process is acquired; performing process feature analysis processing on the multi-source process data stream to obtain a time sequence evolution feature process coupling feature; performing combined anomaly identification processing on the time sequence evolution features and the process coupling features through a pre-deployed manufacturing anomaly diagnosis model to generate a process anomaly identification result; based on the front-back sequence relation of all the procedures in the fan manufacturing process and the abnormal probability value and the possibility level in the procedure abnormal identification result, the abnormal target procedure node and the associated procedure range of the abnormal influence are positioned; and generating an early warning instruction according to the target process node and the associated process range, and pushing the early warning instruction to a manufacturing execution system. Abnormity real-time monitoring and early warning in the fan manufacturing process are achieved, and the fan manufacturing quality and production efficiency are improved.

The invention provides a pre-buried brazing flux aluminum alloy brazing filler metal plate blank and a preparation method thereof, a pre-buried brazing flux composite aluminum alloy brazing plate strip and a preparation process and application thereof. After the mixed powder of the aluminum-silicon alloy and the brazing flux is pressed into the cylinder, the cylinder is extruded into the aluminum alloy brazing filler metal plate blank with the pre-embedded brazing flux through the semi-continuous extruding machine, the production efficiency is high, the yield is high, and the prepared aluminum alloy brazing filler metal plate blank with the pre-embedded brazing flux is uniform in component and good in extensibility. The prepared pre-buried brazing flux aluminum alloy brazing filler metal plate blank and an aluminum alloy core material are easy to roll and compound, the obtained pre-buried brazing flux composite aluminum alloy brazing plate strip has a good compound interface, does not have obvious defects such as slag inclusion, bubbles, layering and peeling, has the mechanical property meeting the requirements, has excellent ductility and good brazing performance, and is suitable for large-scale production. And the brazing effect with other aluminum components is good, the surface of a workpiece is bright, and soldering flux traces are not left. The technology can greatly simplify the manufacturing process of the aluminum heat exchanger, and can be used for large-scale industrial production.

The invention relates to the technical field of digital manufacturing, and discloses a digital twin middle station and self-healing decision system for oil and gas equipment management, which comprises a multi-source sensing terminal, a twin optimization module, a fault topology diagnosis module and an early warning execution terminal, a multi-modal synchronous fusion mechanism is constructed, and when the state of oil and gas equipment is monitored, a heterogeneous data alignment standard is formulated, and a dynamic frequency normalization rule is set for different equipment types, so that the definiteness of multi-source sensing data fusion is ensured; meanwhile, vibration, temperature, pressure and sound wave data are synchronously collected in real time, millisecond-level detection can be achieved, the data time difference problem can be eliminated, the accuracy of equipment state feature extraction is guaranteed, and the feature fusion error is further reduced; and generating a self-adaptive correction coefficient in real time by calculating the dynamic deviation of the physical model and the data model.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention provides a continuous casting quality control method based on a meta-cognitive coordination architecture agent cluster, and relates to the technical field of ferrous metallurgy intelligent manufacturing. The continuous casting quality control method comprises the steps of data input and standardization, center coordination and intelligent agent cluster operation and maintenance. In the central coordination process, the meta-cognitive coordination agent serves as a core to coordinate six kinds of functional agents including a semantic analysis agent, a data perception agent, a defect prediction agent, a root cause analysis agent, a process optimization agent and a digital twinborn agent, and task scheduling and state monitoring of the whole system are achieved. Three key functions of task decomposition, data scheduling and closed-loop management and control are completed; in the closed-loop management and control step, risk early warning, defect prediction, root cause analysis, process optimization and digital twinborn verification and feedback are carried out for continuous casting quality. Compared with a traditional scheme, optimization is carried out in the aspects of whole process, multiple modes, intelligence, collaboration and the like, the management efficiency is improved, and economic benefits can also be increased.

The invention relates to the technical field of additive manufacturing, in particular to an air field system with uniform airflow distribution for an additive manufacturing large-breadth forming platform, which comprises an air inlet three-way pipe unit and a negative pressure air draft unit, the air inlet three-way pipe unit comprises a main air supply pipe, an upper air outlet pipe and a lower air outlet pipe; the upper air outlet pipe and the lower air outlet pipe are each internally provided with a flow dividing grating assembly, air outlets of the upper air outlet pipe and the lower air outlet pipe are divided into a plurality of sub air outlets with the same opening size through the flow dividing grating assemblies, and flow guide pipes are arranged at the air outlets of the upper air outlet pipe and the lower air outlet pipe. An airflow dispersion unit is arranged on the side, close to the sub air outlets, in the flow guide pipe, a linear air outlet hole is formed in the other side of the flow guide pipe, and a conical flow channel with a conical section is arranged between the airflow dispersion unit and the linear air outlet hole. By means of the multi-stage flow dividing and guiding design, the effect that airflow in all areas of the large-breadth forming platform is evenly distributed under the condition of low air volume is achieved.

The invention relates to the technical field of intelligent manufacturing, in particular to an HTCC ceramic defect automatic identification method based on industrial model assistance, which comprises the following steps: step 1, collecting sintering temperature gradient, lamination pressure and green body water content process parameters in the HTCC production process in real time; step 2, performing non-contact dielectric property scanning on the sintered HTCC substrate on a production line conveyor belt; 3, based on the coordinates of the dielectric abnormal region, controlling an annular polarization light source to inhibit the incident angle reflected by the metallization layer from irradiating the target region; 4, inputting the dielectric anomaly frequency band feature vector and the defect image texture feature in the step 3 into a cross-modal attention network to generate a fusion feature map; and 5, establishing an association mapping library of the defect type and the process deviation type. Through accurate real-time monitoring, intelligent defect identification and classification, automatic process adjustment and a continuously optimized feedback mechanism, the efficiency, quality and intelligent level of the ceramic production process are greatly improved.

The invention relates to the technical field of semiconductor manufacturing, and discloses an edge breakage defect prediction and compensation method in a wafer cutting process, and the method comprises the steps: obtaining a wafer cutting initial scheme and historical cutting data, extracting edge breakage key parameters, defining an edge vulnerability function, and building a high-risk region space mapping model; according to the real-time cutting position, multi-dimensional data are collected through a three-level monitoring mode, the abnormal deviation state of the track is judged, and an edge breakage risk assessment model is constructed and early warning is carried out; a compensation scheme is formulated after early warning is triggered, cutting is implemented, and the effect is verified; according to the method, multi-dimensional data are fused, the risk area is dynamically delimited, self-adaptive adjustment of the cutting parameters is achieved, and the wafer cutting yield is remarkably improved.

The invention discloses a wind power generation ring forging three-dimensional design and quality management system based on 5G communication, and belongs to the technical field of wind power equipment manufacturing. The system comprises a modeling module used for constructing a parameterized three-dimensional design model and outputting design parameters used for quality evaluation and defect identification; the process data acquisition module acquires process data such as temperature, pressure and vibration of the key structure area in real time; the defect identification module fuses the image and the signal data, and outputs defect types, positions and sensitivity influences thereof; the quality evaluation module performs quantitative scoring on the product quality based on the scoring function and the disturbance response parameters; the feedback regulation and control module identifies the dominant deviation and generates a regulation and control instruction when the score is abnormal; and the 5G communication module realizes high-speed data interaction between the modules. The method realizes closed-loop control of design, manufacturing, detection and evaluation, improves the manufacturing quality and efficiency of ring forgings, and is suitable for manufacturing scenes of large-size and high-reliability wind power structural members.

Additive manufacturing devices with subtractive tools are provided. In one aspect, an additive manufacturing device includes a shoulder configured to rotate about a central axis, the shoulder including a central channel extending from a first end of the shoulder to a second end of the shoulder, the central channel configured to allow a filler material to pass through the shoulder from the first end towards the second end. The additive manufacturing device also includes a subtractive tool assembly including a subtractive tool and an insert sleeve. At least a portion of one or more cutting surfaces or edges of the subtractive tool extend outside the central channel of the shoulder when the subtractive tool is removably received in the insert sleeve and the insert sleeve is received in the central channel of the shoulder.

The invention relates to the technical field of semiconductor manufacturing, in particular to an automatic loading and unloading control system for semiconductor material plates. A clamping force dynamic control module; the path planning and obstacle avoidance module is used for dividing safety levels of loading and unloading areas on the basis of material plate stacking parameters, and generating an obstacle avoidance track by adopting an improved path search algorithm; optimizing an acceleration curve of the path according to the joint load data of the mechanical arm; the closed-loop verification module is used for carrying out deviation comparison on the actual pose of the tail end of the mechanical arm and the target coordinates, and if the deviation exceeds the limit, the visual positioning and morphology detection module is triggered to cooperatively reset to the path planning and obstacle avoidance module; the visual positioning and morphology detection module and the closed loop verification module are executed in sequence, and output parameters of the previous module serve as input parameters of the next module. Through close cooperation of the modules, efficient and accurate automatic loading and unloading control is achieved, the automation level is improved, and operation safety and accuracy are guaranteed.

The invention discloses a knowledge graph multi-hop reasoning-based complex manufacturing process intelligent planning decision method, which comprises the following steps of: 1, constructing a manufacturing process knowledge graph to obtain a triple set, generating a query-path pair sequence, and mapping entities and relationships in the query-path pair sequence into low-dimensional vectors through an embedded layer; 2, performing expansion causal convolution calculation on the query-path pair sequence through a shallow feed-forward convolution network module, extracting key process features and potential correlation between processes, and generating a feature vector fusing the potential process correlation; 3, performing rotation position coding and causal mask processing on the feature vector output in the step 2 by adopting a parallel multi-query attention module, and realizing cross-process dependency relationship modeling through a multi-head attention mechanism; 4, generating a global optimal process scheme through beam search; and on the basis of a rule-guided dynamic aggregation iteration strategy, mixing a process path generated by the model with an original training set, and updating parameters.

The invention discloses a processing control method and system for a carbon fiber composite transmission shaft, and relates to the technical field of intelligent transmission shaft manufacturing, and the method comprises the steps: dynamically adjusting the control current of a magneto-rheological damper through a PID controller based on a process control parameter set, and recording tension fluctuation and temperature gradient data; inputting tension fluctuation and temperature gradient data into an ANSYS coupling simulation engine, establishing a three-dimensional temperature field model, calculating a resin flow compensation coefficient, generating a temperature-pressure cooperative control curve through an NSGA-III multi-objective optimization algorithm based on the resin flow compensation coefficient, and calculating the resin flow compensation coefficient and the temperature-pressure cooperative control curve according to the resin flow compensation coefficient and the temperature-pressure cooperative control curve. And integrating through a time sequence control engine to generate a solidification control instruction. According to the invention, a multi-source data fusion algorithm is combined with the industrial Internet of Things edge computing gateway, so that accurate monitoring and synchronous processing of the process control parameter set are realized, and the dynamic behavior of the carbon fiber composite material in the processing process is comprehensively reflected.

A method for performing an automated additive manufacturing (AM) process comprising obtaining a set of first AM factors associated with performing a first AM process and obtaining a set of first characteristics of a first AM output, wherein the first AM output is generated by performing the first AM process using the set of first AM factors. A predictor is trained using the set of first AM factors and the set of first characteristics, which is a machine learning model, and a mathematical model is generated based on extracting internal parameters of the predictor. The mathematical model comprising represents an estimated relationship between first AM factors and first characteristics. A set of target characteristics is obtained and, using the mathematical model, a set of second AM factors is identified. The set of second AM factors are then used to perform a second AM process, generating a second AM output.

The invention discloses an intelligent sealing test method and system for a wafer in a semiconductor chip, and relates to the technical field of semiconductor manufacturing, and the method comprises the steps: initializing a magneto-electric field cooperation parameter, optimizing the magneto-electric field cooperation parameter through a multi-target genetic algorithm, and driving core-shell nanoparticles to carry out the metallization filling of a high-precision through hole wafer, generating a wafer with a metalized through hole structure; based on the metalized through hole structure wafer, bonding parameters are generated through a laser Doppler frequency vibration spectrum analysis method, flip interconnection is achieved through a thermal ultrasonic virtual process, and a low-defect bonding wafer is generated; a finite element inversion algorithm is adopted, a stress distribution map of a bonding interface is established, an anti-thermal-stress packaging layer is constructed through an intelligent stress matching algorithm, and the low-defect bonding wafer is packaged; and through a multi-target genetic algorithm, magnetoelectric field cooperation parameters are optimized, core-shell nano-particles are driven to directionally deposit, and accurate control over the metallization filling process is achieved.

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 relates to an in-situ alloying method for an electric arc additive ultrahigh-strength aluminum alloy, in particular to an in-situ alloying method for a double-wire feeding electric arc additive ultrahigh-strength aluminum alloy. The invention aims to solve the problems that the microscopic uniformity of the material is poorer and the ideal mechanical property is difficult to realize due to the fact that the aluminum alloy is easy to form a thick organization structure in a deposition state, and the compactness and the quality stability of the material are seriously influenced due to the fact that solidification shrinkage cavities are easy to appear in the deposition process. By adjusting the wire feeding speed and angle of the two wires, it is ensured that the two wires are molten in the same molten pool, and in-situ alloying of ultrahigh-strength aluminum alloy additive manufacturing is achieved. By optimizing the components and the organization structure, the method effectively improves the component uniformity, the forming stability and the mechanical property of the aluminum alloy, and the high-performance aluminum alloy additive component with high strength, excellent toughness and durability is prepared. The invention belongs to the technical field of additive manufacturing.