99 results about "Intelligent manufacturing system" patented technology

The invention discloses an intelligent manufacturing system and method based on an industrial robot, and relates to the technical field of industrial robot control, and the method comprises the steps: constructing a workpiece posture-space structure topological graph according to a cross-scale visual recognition result, carrying out the space comparison with an original trajectory planning graph, outputting a space deviation mapping relation, and carrying out the calculation of a spatial deviation mapping relation; performing spatial correlation analysis and path reachability evaluation on the workpiece attitude-spatial structure topological graph by using a graph neural network to obtain an end execution path instruction; driving the industrial robot to perform intelligent manufacturing operation through the tail end execution path instruction, and obtaining an actual operation result image; and performing feature alignment and difference comparison processing on the actual operation result image and the process reference image to obtain an operation deviation feature mapping relation, performing operation quality evaluation, and outputting an operation result visual detection label. According to the method, the adaptability and the manufacturing precision of the operation path of the industrial robot are effectively improved.

The invention relates to a packaging method for modeling and production unit combination of digital twin virtual equipment, which realizes modeling of the virtual equipment through an abstract process from top to bottom, and ensures high cohesion packaging of key attributes, states and behaviors of the virtual equipment in combination with an information hiding and uniform interface mechanism. A production unit-level combination and hierarchical packaging framework is further constructed based on the equipment model, and recursive combination and interoperation of multiple layers of modules are realized through unified station-level, production line-level and workshop-level interfaces and scheduling logic; according to the method, the modeling and packaging process of the digital twin virtual equipment is defined, the high cohesion packaging and interface uniformity of the model are highlighted, and cross-level combination and scheduling logic design are supported, so that the consistency and the implementability of the model in the system integration, evolution extension and module multiplexing processes are remarkably improved, and the modeling efficiency of the digital twin virtual equipment is improved. And universal and normalized technical support is provided for hierarchical construction and efficient deployment of virtual equipment in an intelligent manufacturing system.

The invention discloses an intelligent manufacturing system deadlock prediction method based on Petri-Net-GCN. The method comprises the following steps: dividing an intelligent manufacturing system into a production equipment module, a production process module and a production resource module; carrying out formalized modeling on the module on the basis of a Petri network; a Petri-Net-GCN deadlock prediction model is constructed, and the Petri-Net-GCN deadlock prediction model comprises a house-to-house transition message transmission module and a transition-to-house message transmission module; performing bidirectional aggregation updating on features among nodes in the Petri net graph by using a graph neural network structure; collecting initial Tokenn distribution, an incidence matrix and a deadlock label of the intelligent manufacturing system to construct a data set, and inputting the data set into a model for training; predicting a deadlock risk in an unknown system state by using the trained model; according to the method, rapid prediction and judgment are carried out in the current state of the intelligent manufacturing system, and prior early warning information is provided for production scheduling and resource configuration.

The invention discloses an intelligent manufacturing system and process for a corrugated steel cylinder fiber reinforced pipe structure, and relates to the technical field of industrial automatic control, the system comprises a real-time monitoring module used for monitoring the machining quality of a plurality of procedures, a central processing module used for carrying out instantaneous analysis on data of the real-time monitoring module and generating a regulation and control instruction, and a processing module used for processing the regulation and control instruction. On the basis of data collected by the real-time monitoring module and output of the central processing module, closed-loop control over the whole process from data collection and analysis to equipment adjustment is achieved, it is ensured that the quality parameters of all procedures are stabilized within the qualified range, and therefore the precision, high efficiency and intelligentization of the pipe manufacturing process are achieved; the problems that in the production process, manual control is dominant, all manufacturing procedures are relatively independent, and effective integration and collaboration are lacked are effectively solved.

The invention relates to the technical field of intelligent manufacturing and reinforcement learning, and discloses a causal-driven intelligent manufacturing strategy optimization method and system, and a medium. Comprising the following steps: collecting state information of an intelligent agent and a production environment, and constructing an environment data set and a zero action data set; constructing a causal decoupling dynamic model comprising a causal intervention module and an environmental evolution module; mixing the environment data set and the zero action data set, and training a causal decoupling dynamic model by using a first mixed data set; performing intervention simulation on the environment data set by using the trained causal decoupling dynamic model to obtain a model data set; mixing the environment data set and the model data set, and training an unbiased strategy optimizer by using a second mixed data set; and deploying the trained unbiased strategy optimizer into a production system, and driving an intelligent agent to execute production operation according to the state information. Through causal-driven modeling and unbiased strategy optimization, high-precision and high-reliability decision control of the intelligent manufacturing system in a complex dynamic environment is realized.

A Comprehensive Energy-Saving and Carbon-Negative system (CESCN) includes a Carbon-negative Emissions Device (CED) based on Carbon Capture, Utilization and Storage (CCUS). CED can obtain a carbon emission reduction target from a current Industry 4.2-Green intelligent Manufacturing (14.2-GiM) system, and then collect the material consumption and equipment operation energy consumption data of the CCUS process of CED through a Cyber Physical Agent (CPA) which is the Internet of Things (IoT) component. An intelligent method is used to identify the execution progress of CCUS and improve the automation and intelligent operation of CCUS. The CESCN can effectively reduce the manpower requirements of implementing CCUS and the overall energy consumption of the production line. In addition, it can calculate the amount of the carbon reduction, the amount of the carbon offset and amount of feasible carbon trading according to the amount of carbon fixation.

The invention relates to the technical field of intelligent manufacturing control, and discloses a dynamic continuous polymerization and online block regulation and control method and system for a polyurethane hot melt adhesive. The invention aims to solve the technical problems of long formula development period, black box in the process and poor batch stability in the traditional polyurethane reaction extrusion process. According to the method, a closed-loop adaptive control system of virtual simulation guidance and physical data correction is constructed, so that intelligent optimization from target performance to an optimal process is realized. The core of the method is that space-time alignment of full-cycle data is realized by utilizing a material retention time distribution function, and internal parameters of an aggregation kinetic model are reversely corrected and a formula knowledge base is updated based on actually measured evolution data and theoretical prediction deviation characteristics. By constructing an intelligent manufacturing system with transparent perception and accurate regulation and control, the formula development efficiency and the product quality consistency are remarkably improved, and the self-adaptive regulation capability in the production process is greatly enhanced.

The invention discloses a multi-algorithm collaborative optimization water turbine key component additive manufacturing method, and relates to the technical field of additive manufacturing. According to the method disclosed by the invention, a complete intelligent manufacturing system is constructed: in a data acquisition stage, a high-precision laser scanning technology (the precision reaches 0.01 mm) is adopted to realize accurate three-dimensional reconstruction of a damaged area; in the modeling analysis link, thermodynamic behaviors in the restoration process are accurately predicted through finite element simulation of multi-physics field coupling; in the aspect of process optimization, an adaptive differential evolution algorithm, an improved NSGA-II multi-objective optimization algorithm and deep reinforcement learning (DRL) are innovatively and organically combined to form a full-process intelligent decision-making system for coverage deviation analysis, path planning and parameter optimization. Particularly, due to introduction of a deep reinforcement learning algorithm, millisecond-level dynamic adjustment of key process parameters such as wire and powder feeding speed and laser power is achieved, and the stability and the material utilization rate of the manufacturing process are greatly improved.

The invention discloses an intelligent manufacturing production line practical training system based on digital twinning and a virtual-real cooperative control method. The system comprises a physical execution layer which comprises a stereoscopic warehouse used for workpiece storage and positioning, an annular conveying line used for workpiece circulation, a visual inspection table used for workpiece identification and a robot used for workpiece transfer; the control layer comprises a PLC main control unit and a man-machine interaction unit and is used for realizing equipment cooperative control and operation monitoring; and the digital twin layer comprises three-dimensional virtual models corresponding to the devices in the physical execution layer, realizes bidirectional data interaction with the physical execution layer through a communication protocol, and supports virtual-real cooperative control and fault simulation. According to the invention, a practical training environment is constructed in a virtuality and reality combination mode, and the cognition, programming and maintenance capabilities of students on the intelligent manufacturing system are cultivated.

The invention relates to a flexible intelligent manufacturing system based on AI intelligent control. The flexible intelligent manufacturing system comprises a rack, a first working module, a second working module, a position detection unit and a control unit. The rack is provided with a standard interface group; the first working module is provided with a module interface group, the standard interface group is matched with the module interface group to enable the first working module to be detachably installed on the rack, and the module interface group and the standard interface group have the same matching size and connection form to achieve interchange of working modules of the same type or different types. The control unit is configured to obtain an offset change vector in a first unit time according to the real-time position data, and predict a predicted offset of a first time node according to the offset change vector; and if the predicted offset exceeds a first preset threshold value, performing prevention operation on each working module. The working module is adjusted in advance according to the predicted offset, and the problems of continuous production interruption and safety caused by position offset of the working module are effectively solved.

The invention relates to the technical field of intelligent manufacturing system design, and discloses an intelligent manufacturing production line automatic design method and system based on a large model, and the method comprises the steps: building a semantic understanding engine through a pre-training large language model, and converting user unstructured text input into a standardized design element set; based on the process knowledge graph and the product manufacturing feature library, performing process path reasoning and task level disassembly by using a graph neural network, and disassembling the standardized design element set into a plurality of sub-tasks; constructing an equipment capability database, realizing automatic matching of a plurality of subtasks and equipment through vectorization similarity retrieval and a multi-objective optimization model, and outputting a recommended equipment combination list; based on the recommended equipment combination list and the spatial constraint condition, a digital workshop sketch is established by adopting a 3D spatial modeling module, the equipment layout and the logistics path are optimized by applying a genetic algorithm, and an optimal layout scheme set is output; according to the invention, intelligent design and optimization support of the whole life cycle are provided.

The utility model discloses an industrial control board based on STM32F103 singlechip, it includes main control chip, RS 485 level conversion circuit, digital I / O circuit and LED pilot lamp, and RS 485 circuit realizes efficient level conversion through three stage anti -interference framework magnetic pearl filtering, photo -coupler isolation, TVS protection, and digital I / O circuit adopts photo -coupler and isolation amplifier to enhance signal stability, and is equipped with LED pilot lamp feedback state, and the application effectively reduces the code rate less than 10 ‑7 , reduces signal dithering, and the temperature drift attenuation is only 2.1%, is suitable for industrial automation and intelligent manufacturing system, has simple structure, low in cost, stable performance and the advantages such as.

1. The name of the design product: programmable controller (VG700). 2. The use of the design product: used for switch logic control, analog control, motion control, process control, data processing and networking communication of intelligent manufacturing system equipment in automated production. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention relates to the technical field of collision sensing devices, and discloses a collision sensing device for an industrial robot capable of being conveniently installed, the collision sensing device comprises at least one collision sensing module, at least one data processing module, a shell and a quick release type adhesion fixing mechanism, the collision sensing module is used for detecting collision and outputting capacitance information; the data processing module is used for collecting and processing capacitance information output by the collision sensing module; the shell comprises a sensing end shell, a sensing and processing end shell and a processing end shell, collision sensing modules are installed in the sensing end shell and the sensing and processing end shell, and data processing modules are installed in the sensing and processing end shell and the processing end shell; the quick-release adhesion fixing mechanism is used for fixing the sensing end shell, the sensing and processing end shell and the processing end shell to the surface of the industrial robot, real-time and accurate sensing of the collision behavior of the industrial robot is achieved, and the safety and reliability of the intelligent manufacturing system are improved.

This invention discloses a method for processing multimodal data acquisition in intelligent manufacturing systems, relating to the field of industrial data governance technology. The method includes: acquiring multimodal data streams; segmenting raw data blocks and standardizing and encapsulating them to generate metadata records; establishing time indexes, batch indexes, and process indexes; hashing the metadata records to generate content addresses and writing them to object storage; recalculating the hash and comparing the content addresses to complete consistency verification and writing the results to a commit log table; generating operator records for successfully committed data blocks and hashing them to obtain operator addresses; registering lineage edges to form directed acyclic lineage relationships; generating leaf lists according to a preset range and aggregating them to obtain root digests; verifying content addresses object by object based on the leaf lists and root digests; replaying and recalculating to generate replay-derived data blocks; comparing them to generate audit records and storing them. This invention ensures continuous monitoring of data quality during big data acquisition through the replay and recalculation processing of directed acyclic lineage relationships.

Intelligent manufacturing systems for the optimized curing of adhesives for battery assemblies, methods for manufacturing / using such systems, and stored instructions for automating the operation of such systems are presented. One method for assembling a battery module involves aligning a stack of battery cells on a workpiece carrier and compressing the cell stack to a predefined length. Module housing side plates are positioned on opposite lateral sides of the compressed cell stack, and a thermosetting structural adhesive (SAM) is injected between the side plates and the lateral sides of the cell stack. A conductive heating system heats the module housing side plates to cure the SAM. While the side plates are heating, a cell thermal conditioning system cools a selected surface of the compressed cell stack.After determining that a monitored curing time has reached a predefined curing time, the system interrupts the heating of the side plates and the cooling of the battery cells.

The application discloses an industrial full-link through implementation method based on unique identification and double contract anchoring, and belongs to the technical field of intelligent manufacturing informatization. The method takes a plan number as a unique and unchangeable identification throughout the full life cycle from raw material warehousing to finished product warehousing, takes a contract hash value as a cross-table association anchor point, adopts a step-by-step anchoring mechanism of sales contract priority binding and procurement contract subsequent binding, and combines batch processing, transaction safety control and full-chain backtracking synchronization technology to realize bidirectional synchronous updating of procurement and sales contract information in full life cycle data tables such as raw material inventory tables and production process quality inspection record tables. The application breaks the strong coupling of the procurement and production processes, supports cost information post-recording, solves the pain points of data disconnection, untraceability, low efficiency and maintenance difficulty of traditional industrial software, has a simple architecture, high performance and stable operation, and is suitable for various intelligent manufacturing systems such as MES and ERP. The application has been applied in actual industrial scenes and has industrialization popularization capability.

The invention relates to the technical field of additive and subtractive integrated intelligent manufacturing, and discloses an intelligent manufacturing system and method for coaxial feeding, stirring and friction solid-phase additive-subtractive machining integrated equipment. And efficient additive deposition and precise subtractive machining can be achieved on one equipment platform. A metal material is fed into a friction stir head through a coaxial feeding device, surface finishing and surface layer removal are carried out through a material reducing module while additive manufacturing is carried out through solid heating and plastic deformation technologies, surface, interlayer and internal structure performance regulation and control are carried out in combination with needle-free friction stir treatment and synchronous induction heating rolling, and the mechanical property of the metal material is improved. The problems of precision, surface quality and production efficiency which cannot be independently achieved through single additive manufacturing or subtractive machining are solved, efficient utilization of materials can be achieved in manufacturing of high-performance and large complex structural parts through the additive and subtractive composite technology, and meanwhile the forming precision and the mechanical property are guaranteed.

The invention relates to the field of intelligent manufacturing systems of aluminum alloy casting workshops, in particular to a molten aluminum supply production line for aluminum alloy low-pressure casting and a control method of the molten aluminum supply production line for aluminum alloy low-pressure casting. And the optimization model is solved in combination with the operation process and logic of the molten aluminum supply production line, so that the optimal solution of the operation parameters of the multiple devices is obtained. Besides, when the optimization model is solved, a pause probability model is introduced, and the optimal solution of the operation parameters under the target of achieving the minimum cost in the operation process of the molten aluminum supply process is obtained under the condition that pause is introduced. According to the aluminum liquid supply process proactive optimization method provided by the invention, the optimization model and the solving method based on the multi-agent simulation and meta-heuristic algorithm are constructed, so that the efficient optimization of the operation parameters is realized, and the purposes of reducing cost and improving efficiency are remarkably achieved.

This invention discloses an intelligent manufacturing system for large, complex, thin-walled parts, including a process database, an intelligent control system, a machining system, a measurement feedback system, and a monitoring system. This invention provides an automated and intelligent intelligent manufacturing system for the entire machining process of large, complex, thin-walled parts.

The invention relates to the technical field of intelligent manufacturing and dynamic personnel scheduling optimization, and discloses a dynamic personnel scheduling optimization method based on DDQN. According to the method, a DDQN intelligent decision framework oriented to the industrial scheduling problem is constructed, and the problem of Q value over-estimation in a traditional DQN algorithm is effectively solved by designing a multi-dimensional state space containing a process state, a team and a parallel mark and directly mapping an action space of an actual scheduling decision; and process states and worker behaviors are modeled through a deep reinforcement learning framework, and a task allocation and resource scheduling scheme is dynamically adjusted in combination with a real-time worker behavior prediction module, so that the total completion time is minimized and the resource utilization rate is maximized in a multi-process and multi-constraint scene, and the working efficiency is improved. The method is suitable for complex industrial environments such as chemical workshops and flexible manufacturing systems, scheduling challenges caused by process dependence, worker heterogeneity and dynamic task requirements can be effectively handled, and the production efficiency and the response capability of the intelligent manufacturing system are improved.

The invention belongs to the technical field of intelligent manufacturing data protection, and discloses an intelligent manufacturing system and method for an explosion-proof motor. The system comprises a formulating module which is used for determining a flow link of intelligent manufacturing of the explosion-proof motor, and uploading and storing the flow link to a cloud server; wherein the process links correspond to equipment and equipment control parameters; the setting module is used for configuring a sub-data bin corresponding to one process link, the sub-data bin is correspondingly configured with a data port, and the data port is externally deployed with a docking information area; according to the method and the device, an illegal user can be effectively prevented from being connected with the sub-data bins through imaginary data, the cracking time of requests which do not conform to a preset condition can be prolonged, request behaviors which do not conform to the preset condition can be confused, and a time window corresponding to copy and destroy skin replacement is won for the sub-data bins; the method and the device can avoid request positioning sub-data bins which do not accord with preset conditions so as to facilitate safety protection of data of each flow link in an intelligent manufacturing system of the explosion-proof motor.

The invention discloses an industrial control board based on an STM32F103 single chip microcomputer. The circuit comprises a main control chip, an RS-485 level conversion circuit, a digital I / O circuit and an LED indicating lamp. The RS-485 circuit realizes high-efficiency level conversion through three-stage anti-interference framework magnetic bead filtering, optical coupler isolation and TVS (Transient Voltage Suppressor) protection; the digital I / O circuit adopts an optical coupler and an isolation amplifier to enhance the signal stability, and is equipped with an LED indicating lamp to feed back the state; according to the invention, the bit error rate is effectively reduced to be less than 10 <-7 >, signal jitter is reduced, the temperature drift attenuation is only 2.1%, and the device is suitable for industrial automation and intelligent manufacturing systems, and has the advantages of simple structure, low cost, stable performance and the like.

The invention provides an automatic ultrasonic detection device and method for a differential mechanism welding assembly, and the device comprises an ultrathin sheet-shaped ultrasonic probe which is installed below a welding seam of the differential mechanism welding assembly in a height-adjustable manner; the active jet flow coupling system forms a stable sound wave conduction medium layer between the ultrathin sheet-shaped ultrasonic probe and the welding seam; the ultrasonic analyzer and control unit is connected with the ultrathin sheet-shaped ultrasonic probe and is used for collecting ultrasonic echo signals and analyzing, identifying and quantifying weld defects in real time by adopting a perimeter algorithm, and when defect echoes exceeding a preset threshold value are found, perimeter equivalent calculation is immediately started and all the defects are automatically graded; outputting a final judgment conclusion of qualification or not; and the mechanical scanning mechanism is used for clamping and driving the differential mechanism welding assembly to rotate. According to the invention, efficient, accurate and reliable automatic detection which is deeply fused with an intelligent manufacturing system can be realized.

The invention discloses an intelligent manufacturing data security protection method based on an electronic information encryption technology. The method comprises the following steps: S1, preprocessing original sensor data generated in an intelligent manufacturing process; s2, performing wavelet decomposition on the preprocessed original sensor to obtain a multi-scale coefficient; s3, scrambling the multi-scale coefficient obtained by wavelet decomposition through chaotic mapping; s4, performing fusion encryption on the scrambled multi-scale coefficient and an encryption key; and S5, outputting the fused and encrypted data. Therefore, by combining the multi-scale characteristic of signal processing and the unpredictability of chaotic encryption, the industrial sensor data in the intelligent manufacturing process is converted into the high-security encrypted data, the method has extremely high differential attack resistance and processing rate, the high real-time requirement of an intelligent manufacturing scene for data encryption can be met, and the method is suitable for popularization and application. The method is suitable for safety protection of industrial sensor data in an intelligent manufacturing system.

The invention discloses a flexible intelligent manufacturing system based on AI intelligent control, and belongs to the technical field of industrial intelligent manufacturing. The system realizes sensing, transmission, analysis and control closed loop of the whole manufacturing process through an industrial Internet of Things architecture, and breaks through the bottleneck of traditional flexible manufacturing in combination with an AI algorithm; the system covers an industrial Internet of Things sensing layer, an edge transmission layer, an AI intelligent control layer, a flexible execution layer and a cloud collaboration layer. The sensing layer uses multi-type sensors to collect data precisely in a global manner. The edge transmission layer realizes low-delay and high-reliability transmission and preprocessing based on a specific protocol and a 5G gateway; the AI intelligent control layer analyzes data in real time and performs fault prediction and the like; the flexible execution layer responds to the instruction to realize flexible cooperation; and the cloud collaboration layer gathers data and iterates the model. According to the method, multiple technical problems of traditional flexible manufacturing are solved, the system flexibility, the intelligent level and the production efficiency are improved, the cost is reduced, and the method is suitable for multi-field flexible production scenes.

This invention discloses a flexible control method for digital twin manufacturing systems addressing uncertainties in the production process, belonging to the field of intelligent manufacturing system technology. The method includes: statistically analyzing the time of the four stages of perception, analysis, decision-making, and execution; determining the advance prediction time by combining system complexity and simulation speed; constructing an scalable system state transition matrix; performing accelerated simulation based on real-time states and injecting uncertain events according to trigger probabilities to obtain multiple future states; selecting the top n high-probability states in descending order of occurrence probability for parallel decision-making; binding decision instructions with corresponding states and issuing them in advance; and executing the corresponding instructions through state matching when the physical system reaches the predicted time, achieving flexible control. This invention can improve the consistency and robustness of "virtual control of the real" in digital twin systems under uncertain conditions and is applicable to multi-level digital twin systems such as machine tools, robots, production lines, and workshops.

The application relates to the technical field of industrial communication and intelligent manufacturing system integration, and discloses an enterprise-based intelligent manufacturing digital integration analysis method, which comprises the following steps: collecting real-time operation data of production equipment, forming an integrated data pool after cleaning, standardization and space-time alignment, constructing an enterprise intelligent manufacturing knowledge graph based on the business logic of the intelligent manufacturing field and calculating key performance indicators, combining the integrated data pool and the knowledge graph, applying the integrated data pool and the knowledge graph to constructing a digital twin model, predicting production bottlenecks and evaluating different production scheduling plans, and finally generating a comprehensive analysis report based on the index calculation results and the simulation results. The application realizes full-process automation from data perception, integration and application to decision output, and significantly improves the transparency, analysis accuracy and decision intelligence level of the manufacturing management process.

1. The name of the design product: programmable controller. 2. The use of the design product: used in the centralized control of frequency converters, servo drives and other equipment in automatic production and intelligent manufacturing systems. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

The invention relates to an intelligent manufacturing system maintenance method considering machine-raw material-environment coupling degradation in the technical field of intelligent manufacturing, and the method comprises the steps: constructing an intelligent manufacturing system with a multi-process route flexible manufacturing network as a core into an intelligent manufacturing system architecture under the background of the Internet of Things, abstracting an intelligent manufacturing system architecture into a complex manufacturing dynamic network composed of heterogeneous machines and heterogeneous product process routes; constructing a machine-raw material-environment coupled intelligent manufacturing system degradation model, and determining the real-time reliability of the machine according to the intelligent manufacturing system degradation model; according to the average fault time interval of the machine and the performance efficiency loss caused by machine shutdown, the maintenance importance degree of the machine in the intelligent manufacturing system is determined; and when the real-time reliability and the maintenance importance of the machine are determined, a comprehensive maintenance strategy is adopted to maintain the machine in the intelligent manufacturing system. According to the method, the system maintenance cost is reduced, and reliable, efficient and low-cost system operation is finally realized.