329 results about "Manufacturing data" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably 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 belongs to the technical field of data quality management, and particularly discloses a power transmission and transformation equipment manufacturing full-process data checking method and system, a terminal and a medium, and the method comprises the steps: collecting original business data of each stage of power transmission and transformation equipment manufacturing, and carrying out the standardization, format normalization and preliminary abnormity elimination; uploading the processed data to a cloud processing module, and executing semantic fusion and field mapping processing; calling a corresponding checking rule set according to the manufacturing state, and executing field-level and record-level quality checking on the structured data; generating an abnormal identifier for the unqualified data, feeding back the abnormal identifier to a client interface, and storing the qualified data to the block chain module to realize credible evidence storage; and finally, generating a data quality checking report and performing structured visual display. According to the method, the problems that existing manufacturing data semantics are not uniform, rules cannot be dynamically adapted, abnormity is difficult to identify with high confidence, and data evidence storage is missing are solved.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention relates to the technical field of chip yield prediction, in particular to a GPU chip yield prediction method and system based on multi-dimensional data fusion, and the method comprises the steps: firstly obtaining multi-dimensional data in a GPU chip manufacturing process; performing time sequence calibration and alignment on the multi-dimensional data based on a process flow, and generating high-order interaction features by using an implicit feature interaction technology; then generating a multi-dimensional abnormal association vector through yield bottleneck projection; then generating a local risk value based on the multi-dimensional abnormal association vector and the manufacturing process node, and constructing a global yield map; and finally, constructing an anomaly propagation and accumulation calculation framework, calculating along manufacturing process nodes to obtain an accumulation risk value, and obtaining a final GPU chip yield prediction value through global damage projection. According to the method, the multi-dimensional manufacturing data is subjected to time sequence fusion and feature crossing, the global yield map is constructed, and abnormal propagation and accumulation risks are calculated, so that GPU chip yield prediction is realized.

Methods and computer systems for methods, computer systems, and computer-readable memory media for determining a warp function. An as-designed watertight spline model of an object is received. A set of sweep points along a sweep trajectory for manufacturing the object are used to determine a sweep surface. Model points are sampled from the as-designed watertight spline model and projected onto nearest as-executed points on the sweep surface. The projected as-executed points are used to construct an as-executed model of the object. A warp function is determined based on a difference between the as-designed watertight spline model and the as-executed model. The warp function is a continuous function approximating differences between the as-designed model and the as-manufactured object. The warp function is stored in a non-transitory computer-readable memory medium.

The invention provides an overlay mark asymmetry compensation method and device for a multi-mode deep learning network, and the method comprises the steps: obtaining manufacturing data of different modes, inputting the manufacturing data into the multi-mode deep learning network for processing, and obtaining an overlay mark asymmetry prediction error; according to the multi-modal deep learning network, feature extraction is performed on manufacturing data of different modals, feature fusion is performed on vector features corresponding to the different modals, and prediction errors with asymmetric overlay marks are obtained through association combination of physical parameters of actual production in the manufacturing process and parameters observed by equipment. Therefore, the actual overlay mark is compensated in actual production. By introducing a multi-modal learning network, various data features are fully mined and utilized, the generalization ability of the model is enhanced, meanwhile, the intelligence of equipment is realized by means of a machine learning technology, and an effective traceability and positioning solution is provided for process problems in wafer production.

The invention discloses a high-precision intelligent quality inspection system for diamond grains, and relates to the technical field of defect detection and contour measurement, and the system comprises a multi-mode sensing device which is used for obtaining a two-dimensional image, a three-dimensional shape and spectral data of the grains; the module is used for receiving manufacturing process parameters and constructing a theoretical three-dimensional model; the interface is used for acquiring actual manufacturing process data; the unit is used for processing the fused measured data and comparing the fused measured data with a theoretical model to generate deviation data; the hierarchical artificial intelligence analysis engine comprises a data analysis and coordination control AI model based on multi-head attention, the AI model is coupled with an attribution analysis layer, and the engine fuses and analyzes measured data, deviation data, process parameters and actual manufacturing process data. And performing deep cross-modal association, trend analysis, manufacturing data association analysis and accurate attribution by using an attention mechanism, and finally generating a quality evaluation result containing application performance prediction and process optimization suggestions with attribution information.

The invention provides an optical proximity correction method and system based on mask error model optimization, and a terminal, and the method comprises the steps: building a data sample set containing a plurality of process conditions through obtaining a large amount of mask manufacturing data under a specific photoetching process; and based on the sample set, constructing a physical optical sub-model and a machine learning sub-model to form a mask error model for outputting a final mask key size value. And then, according to a plurality of preset alternative weight coefficient combinations, reconstructing an objective function which introduces a mask error term, calculating corresponding objective function values, and correcting the photoetching pattern by using an OPC algorithm corresponding to each objective function value to obtain performance index data so as to determine an optimal weight coefficient combination. And finally, optimizing the OPC algorithm by using the target function of the optimal combination, thereby realizing the accurate correction of the photoetching pattern, and improving the precision and yield of semiconductor manufacturing. According to the method, the mask error model comprehensively considering physical optics and machine learning is established, and the OPC process is introduced, so that the influence of the mask error can be predicted and compensated more accurately, the critical dimension deviation and the like are reduced, and the device performance and the yield are improved. The method can adapt to different process conditions, and parameters and weight coefficients can be dynamically adjusted. And moreover, rework can be reduced, automatic adjustment is realized through closed-loop feedback control, the production stability and efficiency are improved, and the cost is reduced.

A method that includes obtaining, from one or more stations, embedding vectors that embed features of the stations, obtaining measurement vectors and associated measurement names, generating a text array of the measurement names utilizing a language model, concatenating the text array and the measurement vector at one or more cross-attention modules configured to encode one or more measurement arrays to one or more latent embedding vectors, generating one or more latent embedding vectors associated with the measurement vector and corresponding measurement names via the cross-attention module and a fixed-size station embedding vector, outputting the latent embeddings; generating a query vector; generating key vectors value vectors utilizing a latent embedding vector; decoding the latent vectors utilizing the key vector and value vector; utilizing the cross attention modules and query vectors, decoding the latent embedding vectors; and output a predication.

A computer-implemented method and apparatus for managing physical and digital products' customization using container format are disclosed. In some embodiments, the method comprises receiving a container file with serialized objects, deserializing these objects to form a hierarchical object structure, and interacting with a database to obtain real-time configuration data. Custom attributes of the products are defined by binding attribute / value pairs to the hierarchical structure. A visualization of the customized product is generated based on these pairs, and manufacturing instructions are determined considering the attribute / value pairs and manufacturing constraints. The apparatus comprises a memory and a processor to execute instructions for deserializing the container file, retrieving configuration data, organizing objects into a hierarchical tree, negotiating product customizations, and outputting manufacturing data. This system enables efficient and accurate production of customized products by managing data, rendering graphics, and incorporating manufacturing constraints.

The invention discloses a block chain and digital twinborn integrated production whole process credible tracing method, and belongs to the technical field of intelligent manufacturing and digital tracing. The system comprises a digital twin construction module, a block chain evidence storage module, a tracing verification module and a data acquisition module. The method comprises the following steps: creating a digital twinborn body for each product instance, and mapping material, process, equipment and quality data of the whole manufacturing process in real time; recording the twinborn key state hash value to a block chain in real time through an intelligent contract; credible tracing verification is provided based on block chain evidence storage and digital twin data; industrial Internet of Things equipment is used for collecting manufacturing data in real time to drive twin updating. According to the method, millisecond-level slice tracing in the manufacturing process is realized, the deep description capability of digital twinning and credible guarantee of the block chain are fused, the problems of insufficient data depth and low credibility of a traditional tracing system are solved, and judicial-level evidence support is provided for quality disputes.

The invention discloses a new energy lithium battery surface defect visual detection method based on a YOLOv8 model, and belongs to the field of new energy lithium battery manufacturing and detection.The new energy lithium battery surface defect visual detection method comprises the steps that S1, a stable data acquisition platform is built; s2, optimizing optical parameters; s3, surface defect images of a plurality of lithium ion batteries are collected for data preprocessing, and a standardized data set is constructed; s4, adopting an improved network architecture deep learning detection engine based on YOLOv8; and S5, inputting the manufactured data set into an improved YOLOv8 model for model training. According to the intelligent detection system based on machine vision and the YOLOv8 model provided by the invention, the precision and robustness of defect identification are remarkably improved while efficient detection is ensured.

The invention relates to a manufacturing process quality tracing and root cause analysis system based on a data graph, and belongs to the technical field of manufacturing process quality control. The system performs cleaning, duplicate removal and standardization processing on multi-source heterogeneous data in the whole manufacturing process through a data processing module to generate structured data; the data graph construction module fuses the manufacturing domain knowledge ontology, constructs a manufacturing process digital twin semantic model and a multi-dimensional manufacturing data graph, introduces a dynamic association weight updating mechanism and mines node potential implicit association; the quality tracing module positions map nodes based on query semantic vectors, and reconstructs an entity state change track by using a digital twin semantic model to generate a tracing link; and the root cause analysis module screens candidate abnormal factors, locates a root cause in combination with dynamic weight and multi-scene simulation verification, and generates a quality optimization scheme. According to the invention, full-link accurate tracing and root cause efficient positioning of the manufacturing process quality are realized, and the intelligence and refinement level of manufacturing quality management and control is improved.

The invention relates to the technical field of knowledge maps, in particular to a steel manufacturing knowledge map construction method based on multi-source heterogeneous data, which comprises the following steps: converting message data into a standardized semantic triple through a semantic mapping rule, and complementing missing attributes by using physical and chemical component balance logic to form a standardized entity; meanwhile, a semantic processor is used for extracting unstructured report text logic, cross-modal coupling is carried out on the unstructured report text logic and SEM image features, and a failure mechanism enhanced entity is generated. Then, full-process dynamic parameters are collected and regularized into equal-interval time sequence parameters; and finally, taking the normalized entity as an index feature and the mechanism enhanced entity as an association constraint, executing semantic fusion by utilizing a topological feature mapping engine, and constructing a dynamic weight graph structure by combining with a dynamic probability distribution correction operator iteration parameter weight. According to the method, efficient association and semantic organization of manufacturing whole-process multi-modal data are realized, and the retrieval precision and knowledge discovery capability of steel manufacturing data are greatly improved.

The invention discloses a manufacturing industry processing equipment information digitization system comprising a data acquisition layer which acquires data through a sensor which is connected to a data acquisition terminal in a wired or wireless mode; the data acquisition terminal is used for receiving the data acquired by the data acquisition layer, respectively marking the acquired data as key monitoring parameters and non-key monitoring parameters, and then transmitting the key monitoring parameters and the non-key monitoring parameters to the server; the invention relates to the technical field of manufacturing industry data processing, and can realize equipment fault point judgment when equipment fails, can more comprehensively capture subtle changes of equipment operation states in fault prediction, greatly improves the fault feature identification degree, and improves the production continuity by predicting the fault occurrence time and type. And the risks of capacity loss and sharp rise of maintenance cost caused by faults are reduced. Impact of faults on equipment is reduced, and the service life of the equipment is prolonged.

The invention relates to a tire character recognition method, in particular to a tire character recognition method based on three-dimensional point cloud, and the method comprises the steps: calibrating an industrial 3D camera, managing and controlling the connection and communication of the camera through a tire point cloud data acquisition module, and visualizing and storing an acquisition result; performing combined filtering on the tire point cloud data by using voxel downsampling and a statistical filtering method, and then making a tire data set; putting the manufactured data set into a PointNet + + network added with a self-attention mechanism for training, and generating a training weight; and visualizing a tire identification result and storing a prediction result file. The invention relates to the technical field of three-dimensional recognition, geometric characteristics of the surface of a tire are more accurately captured by using point cloud, the influence of factors such as light and tread stains is reduced, hierarchical feature extraction of PointNet + + is combined with a Transform self-attention mechanism, detail features are more sufficiently extracted by using the relationship between different hierarchical features, and the recognition accuracy is improved. And the robustness of tire character recognition in a complex environment is improved.

Methods and systems for executing tracking and monitoring manufacturing data of a battery are disclosed. One method includes: receiving, by a server system, sensing data of the battery from a sensing system; generating, by the server system, mapping data based on the sensing data; generating, by the server system, identification data of the battery based on the sensing data; generating, by the server system, monitoring data of the battery based on the sensing data, the identification data, and the mapping data; and generating, by the server system, display data for displaying a simulated electrode of the battery on a graphical user interface based on the monitoring data of the battery.

Provided are a method for predicting a product strain and a system thereof. A method for predicting a product strain according to one embodiment of the present disclosure is a method performed by a computing device, the method comprising the steps of: acquiring a three-dimensional shape model of a first product; preprocessing the three-dimensional shape model and generating graph data including nodes and edges; inputting an image dataset to a measurement model for predicting shape characteristics and physical property characteristics of an object, the image dataset being obtained by imaging, from multiple angles, the object manufactured on the basis of the three-dimensional shape model, and generating manufacturing data of the first product; comparing graph data and the manufacturing data for each node unit, and generating label data including strain data for each node unit; and fine-tuning a graph data-based graph node classification model by using a strain data set composed of pairs of the graph data and the label data. The step of generating the graph data may include the steps of: generating predictive precision reference information for each unit mesh included in the three-dimensional shape model by using the three-dimensional shape model of the first product and the manufacturing data of the first product; and according to the predictive precision reference information for each unit mesh, applying a first resolution to a first unit mesh that requires first predictive precision, and a second resolution to a second unit mesh that requires second predictive precision.

The invention discloses a chef machine intelligent control method and system based on the Internet of Things, and relates to the technical field of chef machines, and the method comprises the steps: building a two-dimensional thermophysical property distribution diagram of a pot bottom based on a cook machine pot structure and manufacturing data, loading a heat conduction finite element simulation model, simulating the thermal diffusion behavior of a pot body in an embedded edge calculation module, and calculating the thermal diffusion behavior of the pot body; and spatial registration and difference operation are performed by combining a temperature distribution diagram acquired by the thermal infrared imager in real time, so that online self-calibration of thermal field simulation is realized. Then dividing the pan bottom into asymmetric thermosensitive cells through a region growing algorithm and a weighted Voronoi strategy, and extracting multi-dimensional thermal sensing data including a temperature value, thermal capacity density, image disturbance characteristics and the like by taking the highest temperature point in each cell as a thermal sensing acquisition point; the mechanism not only improves the modeling precision of the pot bottom heterogeneity thermal response, but also effectively improves the hot spot identification and interference elimination capability.

The invention discloses a full-life-cycle-based product carbon footprint intelligent accounting system and method, relates to the technical field of resource management, and solves the problems that data is difficult to accurately acquire and the carbon emission condition of a target product cannot be accurately reflected during the current full-life-cycle product carbon footprint intelligent accounting. Comprising a data processing module, a data division module and a carbon footprint calculation module, the data processing module is used for processing primary data and secondary data of a target material corresponding to a target product in a full life cycle, and the data division module is used for dividing the primary data and the secondary data of the target material corresponding to the target product; the carbon footprint calculation module is used for calculating carbon emission of a target product at different stages, and dividing the target product into raw material data, production and manufacturing data, distribution and transportation data, use consumption data and recovery processing data; according to the invention, accurate calculation of the carbon emission condition corresponding to the target product is realized while the carbon emission data of the target product is comprehensively obtained.

The invention discloses a numerical control machining process generation method based on a PVFNN neural network, and the method mainly comprises the steps: 1) data preprocessing: collecting and processing the 3D shape, material and quality information of a part; 2) constructing and segmenting a data set to form a training, verification and test set; 3) constructing a PVFNN neural network model, integrating a 3D convolutional neural network, a point cloud neural network and a full connection network, and realizing feature fusion through a feature fusion layer and an MLP; 4) performing model training and verification, using a training set to train the model, and using a test set to evaluate performance; 5) process generation: performing model processing on new part information to generate a processing process; the method is used for automatically learning the capability of the discrete manufacturing process from design and manufacturing data, the accuracy and efficiency of numerical control machining process generation are improved, and the problem that in the prior art, due to the influence of manufacturing process capability knowledge and manufacturing experience, a user often cannot select the optimal manufacturing process is solved.

The invention relates to the technical field of data processing, and discloses a digital twinning-based automobile manufacturing industrial data multi-stage processing method and a digital twinning-based automobile manufacturing industrial data multi-stage processing system. The method comprises the following steps: performing grading processing on automobile manufacturing data through a five-grade grading identification mechanism to obtain a grading data set; establishing a process-equipment-material ternary coupling relation matrix according to the hierarchical data set; carrying out dynamic weight distribution on the coupling relation matrix to obtain multi-stage processing weights; dynamically reconstructing a digital twin structure according to the change of the coupling relation matrix; and carrying out collaborative decision-making on the reconstruction structure and the processing weight to generate a multi-level control instruction. According to the invention, the problem that the prior art lacks dynamic sensing and self-adaptive reconstruction capabilities for the ternary coupling relationship change of process parameters, equipment states and material attributes is solved, and the accuracy and real-time performance of tracking and responding to the complex multi-element collaborative change in the automobile manufacturing process by the digital twin system are improved.

The invention discloses a micro spring washer manufacturing process optimization method using a collaborative filtering algorithm, and the method comprises the following steps: S1, collecting and preprocessing historical manufacturing data, and constructing a tensor structure comprising manufacturing batches, process parameters and product quality indexes; s2, converting the tensor structure into a scoring matrix, and predicting an initial process parameter combination of a target batch by adopting a collaborative filtering algorithm; s3, constructing a weighted graph, introducing a label propagation network to complement scores, and determining a recommendation parameter combination; s4, constructing a multi-objective optimization model with size consistency, residual stress balance and unit energy consumption as objectives based on the recommended combination; s5, solving by adopting an improved cat swarm optimization algorithm, and outputting an optimal solution; and S6, applying the optimal solution to the manufacturing process, collecting quality result feedback, updating the scoring matrix and the graph model, and realizing a manufacturing optimization closed loop. According to the method, the tag propagation network and the improved cat swarm optimization algorithm are fused, and the manufacturing process of the miniature spring washer is optimized.

A method of overhaul is provided. During this overhaul method, a substrate is scanned using structured light to provide substrate scan data. The substrate is from a component previously installed within an engine. The substrate scan data is compared to substrate reference data to provide additive manufacturing data. Material is deposited with the substrate using an additive manufacturing device based on the substrate scan data to provide a first object. The first object is scanned using the structured light to provide first object scan data. The first object scan data is compared to first object reference data to provide machining data. The first object is machined using the machining data.

The invention relates to the technical field of TFT (Thin Film Transistor) material display manufacturing data processing, and provides a TFT material display manufacturing process method which is improved aiming at the problem that point defect detection is not timely in the prior art. And training a TFT material display manufacturing process control model and a point defect detection model by using the neural network model. In the production process, operation data are collected in real time and input into a point defect detection model, and when a point defect is detected, a result is analyzed immediately and process parameters are adjusted. Meanwhile, the control model is optimized through a genetic algorithm, and real-time adjustment and feedback of process parameters are achieved. The production efficiency and the quality of finished products are effectively improved, automatic defect detection and treatment are achieved, and remarkable beneficial effects are achieved.

The present disclosure presents systems and methods for creating and recording digital cyber-physical passports during a manufacturing process. One such system or method is adapted to configure monitoring devices or a manufacturing machine to supply manufacturing data for a particular physical part instance being manufactured by the manufacturing machine; track a progress of the manufactured part instance and store manufacturing data supplied by the one or more monitoring devices and / or the manufacturing machine in a data store as the part instance progresses during individual phases of a manufacturing process; generate digital cyber-physical passports for each completed phase of manufacturing for the particular part instance; and record individual cyber-physical passports on a cyber-physical passport-linked ledger on a distributed ledger technology platform during the manufacturing process of the part instance.

The invention belongs to the technical field of auxiliary precision manufacturing, and discloses a multi-modal auxiliary precision manufacturing database interaction method based on a large model, and the method comprises the steps: obtaining a monitoring video and user query of a precision manufacturing production scene; rewriting the user query into a new query; according to the new query, performing schema retrieval in a precision manufacturing database to obtain a schema set; performing feature extraction on the monitoring video and the new query, and performing multi-modal fusion on the production scene video feature vector, the production scene audio feature vector and the user query text feature vector to obtain a fused feature vector; retrieving an external vector database by using the fusion feature vector to obtain a retrieval result document set; according to the new query and retrieval result document set, constructing SQL statements to generate cue words; sQL statement generation cue words are input into the large model, and SQL statements are generated; and putting the SQL statement into a precision manufacturing database for operation, and outputting a final retrieval result.

The invention discloses a metro vehicle supervision manufacturing multi-station data traceability control system, and relates to the technical field of industrial data traceability, and the system comprises a data collection module which obtains the original manufacturing data of each station of metro vehicle manufacturing; the feature extraction module is used for processing the original manufacturing data and generating behavior fingerprint vectors and station features; the identity analysis module is used for carrying out feature matching calculation and outputting a candidate component identity set and matching similarity; the logic verification module is used for carrying out logic consistency verification and determining the identity of the target component; and the traceability control module establishes a mapping relation with the original manufacturing data based on the identity of the target component, generates a unique traceability node, and writes the traceability node into a multi-station traceability data chain. According to the method, the problems of identification failure, data link breakage and affiliation errors caused by station switching or equipment sharing are solved, supervision data continuity and identity recognition accuracy are improved, and reliable support is provided for vehicle full-life-cycle data tracing.

The invention relates to the technical field of manufacturing data acquisition, and discloses a distributed manufacturing data acquisition system based on NB-IoT. The system comprises a heterogeneous node sensing layer, a narrowband signaling scheduling layer, an edge fusion processing layer, a cloud collaborative decision-making layer and a closed-loop optimization feedback layer. The heterogeneous node sensing layer obtains multi-source data through a sensor array, generates an equipment operation state thermodynamic diagram and divides region priorities; the narrowband signaling scheduling layer deploys NB-IoT nodes, transmits time division multiplexing signaling and generates a node load distribution diagram; the edge fusion processing layer synchronizes the clock, fuses the multi-source data and the load distribution map, and generates fusion confidence; the cloud collaborative decision-making layer converts the fusion confidence into an executable instruction and issues the executable instruction; and the closed-loop optimization feedback layer monitors the load change of the system, generates an optimization index and dynamically optimizes an acquisition strategy. The system can penetrate through network congestion, improves the real-time performance and accuracy of data acquisition, dynamically adapts to the change of a manufacturing environment, and is suitable for a distributed manufacturing scene.