7784 results about "Manufacturing technology" patented technology

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 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 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 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 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.

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 resistor chips, in particular to a method and a system for optimizing a manufacturing process of a thermistor chip. According to the method, multi-scale process entropy analysis is carried out on a process parameter dynamic flow, the coupling strength between key processes is analyzed, and a process parameter cluster with strong non-monotonic correlation and a process system entropy increase path of the process parameter cluster are identified, so that a collaborative stability domain boundary model is constructed; according to transient fluctuation characteristics extracted from a process parameter dynamic flow obtained in real time, in combination with the collaborative stability domain boundary model, determining a parameter subset exceeding a collaborative stability operation interval as a drift parameter cluster, executing an optimization instruction set to perform trial production, synchronously collecting collaborative trajectory data of a process parameter cluster, and performing collaborative trajectory prediction; and the collaborative stability is verified by calculating the entropy change rate of the process system and is fed back to the collaborative stability domain boundary model for boundary toughness strengthening, dynamic collaborative convergence of relevant parameters such as the sintering gradient and the electrode pressure is achieved, and the product consistency and the yield stability are improved.

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 relates to the technical field of additive manufacturing, and particularly provides a heat treatment method of a nickel-based superalloy for additive manufacturing. The heat treatment method comprises the following steps: S1, preparing a nickel-based high-temperature alloy forming part by adopting a selective laser melting process; s2, the formed part is subjected to homogenizing annealing treatment at the temperature ranging from 1000 DEG C to 1200 DEG C; s3, the formed part subjected to homogenizing annealing treatment is subjected to water quenching treatment and cooled to the room temperature; and S4, the formed part subjected to water quenching treatment is subjected to primary aging treatment at the temperature of 700-800 DEG C, then cooled to 600-700 DEG C, subjected to secondary aging treatment and then air-cooled to the room temperature. According to the method, the effect of simultaneously regulating and controlling the grain structure and the precipitated phase of the SLM-GH4169 alloy is achieved by increasing the homogenizing annealing heat treatment time, combining a water quenching cooling mode and cooperating with two-stage aging, and the strength and plasticity of the alloy are successfully and synergistically improved. According to the prepared SLM-GH4169 alloy, not only can the tensile strength reach 1400 MPa, but also the elongation at break can reach 22% or above.

The invention discloses a fuse wire additive manufacturing method for regulating and controlling the strength of titanium alloy by adding rare earth oxide, which comprises the following steps of: 1, ultrasonically stirring and mixing rare earth oxide nano powder and ethanol solution to obtain turbid liquid; 2, polishing the surface of the titanium or titanium alloy substrate until the roughness is not greater than Ra1.6; thirdly, the surface of a substrate is evenly coated with the turbid liquid, after drying, fuse wire additive manufacturing of titanium or titanium alloy wires is carried out under inert atmosphere protection, and the titanium alloy with the improved strength is obtained; the fuse wire additive manufacturing process comprises the steps that layer-by-layer deposition is conducted, and after each layer is deposited and completely solidified and cooled, the surface of the deposited layer is coated with turbid liquid and dried. According to the method, coating of the turbid liquid containing the rare earth oxide and the fuse wire additive manufacturing process are combined, the titanium or titanium alloy containing the rare earth oxide and uniform in component is prepared, metallurgical bonding of the rare earth oxide and the titanium or titanium alloy wire is achieved, and the method is suitable for the technical field of metal additive manufacturing.

The embodiment of the invention discloses a flexible job shop scheduling method based on preference-driven graph reinforcement learning, and relates to the field of shop dynamic scheduling in an intelligent manufacturing technology. According to the method, by constructing a multi-objective optimization model, multiple objectives of the job shop can be optimized at the same time, including the minimum completion time, the total delay and the total cost. Wherein an imperfect maintenance model is constructed, and the maintenance demand and the maintenance opportunity of each machine are dynamically determined. And capturing a complex relationship between the operation and the machine by using an improved graph neural network. In combination with a preference-driven mechanism, a maintenance plan and workshop scheduling are adjusted in real time through a graph reinforcement learning method, and efficient priority scheduling rules under different preferences are learned, so that an integrated decision of machine allocation, an operation sequence and maintenance arrangement in dynamic scheduling is realized.

The invention relates to the technical field of finished automobile manufacturing, and discloses a finished automobile assembly state intelligent detection method and system based on multi-source data fusion. The method comprises the following steps: deploying a multi-source data acquisition unit, and fusing vision, force sensing and radio frequency identification data to determine a component assembly state and a process parameter deviation; a multi-dimensional verification rule is generated through the virtual assembly verification model for assembly consistency judgment; constructing a hierarchical decision-making system, and dynamically generating a node detection strategy according to the real-time data quality and the environmental interference level of the M detection analysis nodes; comparing the assembly states of the components and then outputting a finished automobile assembly quality evaluation result; and an encrypted data transmission channel is established, and node verification and intelligent detection are carried out on an evaluation result. The method integrates multi-source data, dynamically adjusts the detection strategy, guarantees the data safety, improves the precision and adaptability of the whole vehicle assembly state detection, and is suitable for a complex whole vehicle assembly scene.

The invention discloses a glass fiber particle assembly line-based magnetic suction quick-release vibration disc system and a detection method, and relates to the technical field of production and processing, the system comprises a data acquisition module for acquiring data of a vibration light-emitting chassis, a magnetic suction column, an assembly line and particles by means of a distributed sensor network, and transmitting the data to an intelligent self-adaptive control module after signal processing; the intelligent self-adaptive control module realizes intelligent dynamic regulation and control of the parameters of the magnetic suction quick-release vibration disc, optimizes the production process of the glass fiber particles, and automatically generates an accurate control instruction based on a data analysis result by integrating multi-source information and performing deep fusion and analysis, so that the production efficiency of the glass fiber particles is improved. Parameters such as the vibration frequency and amplitude of the vibration light-emitting chassis and the magnetic attraction strength of the magnetic attraction column are adjusted to meet the requirements in different production states, so that the system can quickly respond to changes in the production process, defective particles are reduced, and the production efficiency is improved.

The invention provides a communication and interaction method of a digital twinning system in a compressor blisk machining process, and relates to the technical field of digital twinning and aeronautical manufacturing, and the method comprises the steps: collecting vibration, temperature and acoustic emission data in the compressor blisk machining process in real time, analyzing machine tool machining data, carrying out the alignment and noise reduction of the collected data, and obtaining a digital twinning result; then time-frequency domain mixed feature extraction is carried out, and time-frequency domain mixed feature data are optimized and uploaded in a graded mode; a deterministic transmission network is constructed, data transmission priorities of 5G communication and LoRa dual channels are dynamically adjusted according to a network load, and intelligent switching between 5G communication and a LoRa protocol is realized based on a switching decision model; analyzing normal processing data of the whole stage of the processing process, and establishing a processing data thread; and predicting the abnormal processing risk in the processing process, and updating the model parameters. According to the method, the communication and interaction efficiency of the digital twin system in the machining process of the compressor blisk in the aero-engine can be improved.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention discloses a light-weight manufacturing method and system for a PET bottle for food packaging, particularly relates to the technical field of PET bottle blank manufacturing, and aims to solve the problem that the subsequent blow molding quality is affected due to the fact that complex internal stress is generated in a bottle blank due to a rapid cooling process after injection molding in the prior art. Image information and temperature distribution data of a bottle blank after injection molding are obtained, geometric features and cooling rate differences are analyzed and recognized based on multi-physics coupling, a stress cause weight evaluation model is established to generate a stress distribution map, and then structural entropy increase factors under different temperature adjustment paths are simulated and calculated according to thermodynamic parameters, so that the temperature adjustment precision is improved. And carrying out partitioned temperature adjustment treatment on the bottle blank, and finally, carrying out constant-temperature and constant-humidity standing to sufficiently relax the stress. The internal stress distribution of the bottle blank can be effectively controlled, and the wall thickness uniformity and the mechanical property consistency of the light-weight PET bottle are improved.

The invention relates to the technical field of intelligent manufacturing, in particular to a nodular cast iron alloy component self-adaptive optimization method and system based on artificial intelligence, and the method comprises the following steps: obtaining furnace temperature and component parameters, generating a stable comparison legend, screening offset abnormal combinations to form an available set, extracting performance curve characteristics to construct a trend chart, and obtaining a nodular cast iron alloy component self-adaptive optimization result. And superposing the structural parameters and the element distribution positioning segregation area, and outputting feeding correction parameters to generate an optimization scheme. According to the method, normalization processing is carried out on furnace temperature, carbon-silicon ratio and other multi-factor offset values, a component offset map is constructed, trend feature extraction and sorting of response curves under multiple working conditions are combined, component combinations with stable performance are screened out, a mapping relation is established through the structural curvature, the wall thickness slope and the injection rate, and a spatial coincidence map is generated through coincidence analysis. And the feeding direction and the injection rate are inversely calculated, dynamic adaptation of components and the structure is achieved, and the structure uniformity and the performance consistency are improved.

The invention discloses a 3D printed part morphology control method and system based on supporting structure morphology compensation, and relates to the technical field of additive manufacturing. Comprising the steps that a to-be-printed target model is processed, and a to-be-printed model considering a supporting structure form and slurry infiltration characteristics is generated; the printing direction of the to-be-printed model and the interface positions of different slurries during multi-material printing are determined, and the interface positions are perpendicular to the printing direction; according to the determined printing direction and the determined interface position, parts corresponding to different kinds of slurry are printed in sequence, and when the slurry is replaced, the datum plane of the printed part is cleaned, and residual materials are removed; and curing the printed model, removing the supporting structure, cleaning and integrally curing to obtain a final finished product. Liquid shaping is achieved only by means of the modified supporting structure, the printing time cost and the mechanical cost are saved, and accurate regulation and control of the liquid resin form are achieved on the premise that no external device is additionally arranged through integrally-formed supporting morphological optimization.

The invention relates to the technical field of intelligent manufacturing, in particular to a fur processing laser cutting machine and an intelligent control system and a cutting method thereof. The fur processing laser cutting machine comprises a rack, a material transferring mechanism, a negative pressure stabilizing device, a laser cutting mechanism and an intelligent control device, the material transferring mechanism is arranged in the feeding direction of the rack and comprises a fur preprocessing part and a net-shaped conveying belt, the negative pressure stabilizing device is arranged below the net-shaped conveying belt, and the laser cutting mechanism is arranged below the negative pressure stabilizing device. The laser cutting mechanism is erected on the machine frame and located above the net-shaped conveying belt, and the intelligent control device cooperates with the control focus depth and the cutting path of a laser beam based on the fur thickness and deformation data. By optimizing cooperative work of the fur feeding system, the fur pretreatment part, the negative pressure stabilizing device and the intelligent control system of the laser cutting machine, the defects of an existing fur processing laser cutting machine in the aspects of cutting precision and wool stability are overcome.

The invention belongs to the technical field of intelligent manufacturing of aeronautical manufacturing enterprises, and relates to an assembly process procedure auxiliary compilation method based on a large model, which comprises the following steps: data preparation and processing: collecting and preprocessing an assembly process procedure text file; carrying out algorithm model selection and fine tuning, using an assembly process procedure text as a training data set and a test data set, and selecting different types of large models to carry out fine tuning; service publishing: deploying a plurality of large models with the best evaluation indexes as services, publishing the services, and externally providing available interface services; constructing an RAG knowledge base, vectorizing the assembly process procedure text, and constructing a vector knowledge base; intelligent question and answer: performing knowledge question and answer based on the RAG knowledge base; and assembly process procedure file generation: constructing the assembly process procedure file through text generation. According to the method, the functions of semantic retrieval intelligent question answering of the assembly technological procedure knowledge base, automatic assembly technological procedure file generation, information recording and the like are achieved.

The invention belongs to the technical field of bridge manufacturing, and discloses a highway bridge manufacturing method and system based on deep learning, and the method comprises the steps: data collection and preprocessing: collecting bridge design parameters, construction state data and environment feature data through a BIM model, a sensor network and construction logs, and constructing a multi-modal input data set; performing cleaning, normalization and feature coding on the data to generate a structured data matrix; multi-task deep learning model construction: constructing an integrated deep neural network; joint training and optimization: adopting a multi-objective loss function, fusing design compliance, construction efficiency and structural safety indexes, and realizing global optimization of model parameters through a gradient descent method; an adversarial generative network is introduced to perform data enhancement on historical fault samples, and the generalization ability of the model is improved. According to the invention, the deep learning technology is comprehensively applied to the whole process of bridge manufacturing, and intelligentization of design, construction, control and knowledge management is realized.

The invention is a divisional application, and the application number of the original application is 2024117659062. The invention relates to the technical field of automobile spring manufacturing, in particular to a parking system spring bending forming device and a working method thereof. Comprising a clamping seat used for fixing an elastic piece body, the elastic piece body comprises a mounting plate and an extension plate, and a roller is pivoted to the front end of the extension plate; a forming limiting face is arranged below the position, corresponding to the bent position of the elastic piece body, of the clamping base, and the extending plate extends out of the forming limiting face. The material moving driving device is used for driving the clamping seat to move to a group of stations in sequence, and the stations comprise a bending station; and the bending module is right opposite to the bending station, the bending module comprises a bending execution module and an elastic piece performance detection module, the bending execution module comprises a bending driver and a bending punch, and the elastic piece performance detection module comprises a pressure detection probe and a probe driver. And the qualified rate of products can be improved.

The invention discloses aluminum alloy cable production and processing equipment, and relates to the technical field of cable manufacturing, the aluminum alloy cable production and processing equipment comprises a base, and the base is sequentially provided with a lead frame, a lead reel and a stranding part; a driving part is arranged on one side of the lead frame, an output shaft of the driving part is connected with a rotating shaft, and the rotating shaft sequentially penetrates through the lead frame and the lead disc; the driving part is fixed on the base and can be arranged on one side, far away from the lead disc, of the lead frame; a plurality of mounting ports are formed in the lead disc, and two abutting parts are symmetrically arranged in each mounting port; two abutting parts are symmetrically arranged in the mounting opening and located on the inner walls of the sides, close to and away from the rotating shaft, of the mounting opening correspondingly. A lead ball is arranged in the mounting opening in a sliding manner, and a wire passing hole is formed in the lead ball; the technical effects that the bending degree of the cable can be adjusted according to the distance value between the wire leading disc and the wire twisting part, and the probability that the cable is not tightly twisted or the bending position is broken in the cable twisting process is reduced are achieved.

The invention relates to the technical field of photovoltaic module manufacturing, in particular to a lamination process parameter self-adaptive regulation and control system and method. Comprising a multi-source sensing module, an edge calculation module and a dynamic execution module, and temperature, pressure and thickness data of a lamination process are collected in real time through an infrared thermal imaging unit, a distributed pressure sensing unit and an ultrasonic thickness monitoring unit; the edge calculation module adopts a CNN-LSTM hybrid model to perform spatial-temporal feature fusion analysis on the multi-source data, predicts a defect risk level and generates a regulation and control instruction; the dynamic execution module achieves dynamic parameter adjustment through the partition temperature control unit, the hydraulic servo unit and the emergency braking unit, the problems of parameter stiffness, defect detection lag and the like of a traditional lamination process are solved, the AOI spot check defect rate is reduced to 0.6% from 1.3%, the lamination period is shortened by 3 minutes, and the production efficiency and the product quality are remarkably improved.