604939 results about "Manufacturing engineering" patented technology

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

The present invention discloses a digital production operation management method for data quality assessment and analysis, including: step 1: constructing a multi-level distributed data acquisition system; step 2: designing a multimodal data fusion framework, combining graph neural network and time series data processing technology to realize data fusion and multidimensional analysis; step 3: constructing an intelligent scheduling engine to realize dynamic adaptive scheduling strategy to cope with real-time changes in the production environment; step 4: designing a decentralized self-organizing production network to realize flexible adjustment and optimization of the production process; step 5: realizing the coordinated optimization of product design and equipment maintenance through topology optimization, multi-objective optimization and machine learning algorithms; step 6: constructing a virtual production network to realize real-time monitoring, dynamic prediction and optimization decision support of the production system. Through the present invention, enterprises can break the limitations of traditional production models, improve production efficiency, flexibly respond to changes in market demand, and optimize resource allocation.

The invention relates to the technical field of digital factory production management, and discloses a digital factory-oriented multi-target production plan optimization method and system, and the method comprises the steps: decomposing an enterprise order into a plurality of subtasks through a production task decomposition module, and extracting related information; the resource dynamic evaluation module collects data in real time to generate a resource state matrix; the multi-objective optimization algorithm module constructs a model and adopts an improved non-dominated sorting genetic algorithm to solve; the dynamic priority distribution module adjusts the priorities of the sub-tasks according to the real-time data; and the conflict resolution unit is used for solving resource allocation conflicts. The method can accurately decompose tasks, evaluate resources in real time, collaboratively optimize multiple targets, flexibly deal with abnormities, efficiently resolve conflicts, comprehensively improve the production efficiency and benefits of the digital factory, and effectively solve the complex problems in the production plan making and optimizing process of the digital factory.

The invention provides a wafer-level test yield prediction and process optimization method and system based on big data, and relates to the technical field of wafer-level tests.The wafer-level test yield prediction and process optimization method comprises the steps that wafer manufacturing process data is obtained, a time-sequence-sensitive multi-modal data fusion network is constructed, a LSTM and GNN combined mixed architecture is adopted, and a multi-modal data fusion network is constructed; self-adaptive weight distribution is carried out through an attention mechanism to predict the yield; and when the predicted yield is lower than a threshold value, triggering a layered progressive process optimization system, and adjusting process parameters through an equipment parameter optimization layer and a process level optimization layer. According to the invention, the accurate prediction of the wafer test yield and the automatic optimization of the process parameters are realized, the production efficiency is improved, and the manufacturing cost is reduced.

The invention relates to the technical field of machining control, and discloses a forging part machining process control method and system based on digital twinning. The method comprises the following steps: acquiring temperature field and stress field data streams of large oil press equipment and forgings at multiple points in different areas; performing organization-defect association mode recognition on the data stream to generate a forging process database; a constitutive unit nesting method is adopted to construct a digital twinborn model, and a microstructure evolution unit is embedded in a macroscopic unit; performing nonlinear progressive simulation to obtain a critical deformation condition and a defect threshold value; and carrying out reverse solution on the process window to obtain an optimal process parameter combination. According to the method, the forging digital twin model is established through the constitutive unit nesting method, bidirectional coupling of macroscopic deformation and microstructure evolution is achieved, a stable process parameter combination is determined through the process window reverse solution technology, and therefore the quality stability and production efficiency of forged parts are improved.

The invention relates to a high-strength complex-phase hot-rolled wire rod for a 2500MPa-grade bridge cable and a manufacturing method of the high-strength complex-phase hot-rolled wire rod, after the high-carbon component of Nb-V-Mo is rolled and spun into a wire rod, the wire rod is subjected to online molten salt tail end quenching toughening treatment, so that the wire rod passes through front-section molten salt and is cooled at a cooling speed of greater than or equal to 38 DEG C / s, a part of austenite structure is transformed into quenched martensite, and a part of austenite structure is transformed into quenched martensite; the method comprises the following steps of: heating to a sorbite phase region temperature through a rear-section molten salt, controlling unconverted austenite to be converted into sorbite, performing isothermal tempering, controlling precipitation and growth of carbides, and finally performing slow cooling through a roller way to prepare a hot-rolled wire rod of which a microscopic structure comprises a complex-phase structure consisting of tempered martensite and tempered sorbite, wherein the tensile strength of the hot-rolled wire rod is 1710-1750MPa; the production method has the advantages that the material cost is controlled, the production efficiency is improved, and the production method is suitable for 2500MPa bridge cable production and is beneficial to reducing the risk of wire breakage in downstream cold drawing and subsequent processing processes.

The invention relates to the technical field of production debugging control, and discloses a production debugging control method and system for a plastic container. The method comprises the following steps: arranging a plurality of different sensors in plastic container blow molding equipment, and simultaneously collecting blow molding process parameter data; performing wavelet threshold denoising and anomaly identification on the blow molding process parameter data to obtain a process parameter anomaly identification result; establishing a transfer function matrix between the process parameters and quality indexes according to the process parameter anomaly identification result, and obtaining a target process parameter set through multi-target optimization; and inputting the target process parameter set into a double-integral enhanced recurrent neural network for parameter regulation and control calculation to obtain a process parameter adjustment amount. According to the method, early detection and accurate positioning of process abnormity in the blow molding process are realized, the quality fluctuation risk and the defective product rate are greatly reduced, and the technical problem that different response characteristic parameters are difficult to coordinate is solved.

The invention relates to the technical field of processing temperature control, and discloses a temperature control method and system for a hot processing process and a storage medium. The method comprises the following steps: collecting hot working whole process temperature data through multiple sensors, calculating a temperature change rate, an extreme point and a uniformity index, and obtaining key characteristic parameters; establishing a quantitative relation matrix of the process parameters and the temperature field by using an orthogonal test; calculating an optimal process parameter combination based on the matrix by using a rolling time domain optimization algorithm to generate a control instruction; and performing execution opportunity and duration prediction and correction on the instruction to form a precise target control instruction. The temperature control precision and stability in the hot working process of the large alloy steel component are improved, so that the product quality is improved, the energy consumption is reduced, and the production period is shortened.

The invention provides a rapid welding storage battery busbar structure and a manufacturing process, and relates to the field of lead-acid storage batteries, the rapid welding storage battery busbar structure comprises a plate group, a non-lead metal strip, a non-lead metal positive terminal and a non-lead metal negative terminal, the non-lead metal strip and the non-lead metal positive terminal are made of metal or alloy with the conductivity higher than that of lead, and the non-lead metal strip is electrified to be rapidly heated and is welded with the cluster group tab and the non-lead metal positive and negative terminals at the same time, and after being firmly welded, the non-lead metal strip is cut to form a busbar and is sealed by glue. According to the lead-acid storage battery, metal with the conductivity higher than that of lead is adopted to replace lead and lead-based alloy to serve as the busbar of the lead-acid storage battery, the gravimetric specific energy of the lead-acid storage battery is improved, the material cost of the lead-acid storage battery is reduced, the mode of welding by heating the metal strip is adopted to replace the technology of manufacturing the busbar through hot melting of lead, the production efficiency is improved, and the energy consumption is reduced; meanwhile, clean production is achieved, and lead pollution in the traditional production process is avoided.

The invention relates to the technical field of flexible circuit board production, in particular to a process optimization method and system for a flexible circuit board production line. The method comprises the following steps: acquiring characteristic data of a flexible circuit board material; executing a circuit board thermocuring action according to the material characteristic data of the flexible circuit board, performing thermal stress intensity evaluation, and generating stress hot spot distribution data; performing real-time curing temperature control feedback adjustment according to the stress hotspot distribution data to obtain circuit board curing effect evaluation data; detecting a real-time surface wettability index of the to-be-processed flexible circuit board in front of the laminating station; and fitting process parameter compensation is carried out based on the circuit board curing effect evaluation data and the real-time surface wettability index so as to realize curing-fitting process optimization of the flexible circuit board production line. According to the invention, intelligent optimization of the curing-laminating process of the flexible circuit board production line is realized, and the quality problems of uneven stress and overlarge deformation in the thermocuring-laminating process are effectively solved.

The invention relates to the technical field of digital factories, in particular to a full-process collaboration method and system for a digital factory. The method comprises the following steps: obtaining production decision data, and carrying out process parameter analysis according to the production decision data to obtain process parameter data; according to the process parameter data, building a factory resource agent through a preset digital factory model to obtain factory resource agent data; performing multi-agent negotiation according to the factory resource agent data to obtain digital workshop task allocation data; and carrying out industrial Internet of Things monitoring according to the digital workshop task allocation data to obtain industrial Internet of Things real-time data so as to carry out digital factory whole-process collaborative auxiliary operation. By constructing a task allocation mechanism based on semantic comprehension and multi-agent cooperation, intelligent matching and flexible scheduling among various resources in a digital factory are realized, and the task execution efficiency and the resource utilization rate of the system are effectively improved.

The invention discloses an assembling method and an assembling tool for a bottom cold shield side plate of a fusion reactor, the bottom cold shield side plate is constructed into a ring shape defined by a plurality of side plates, and the assembling method for the bottom cold shield side plate of the fusion reactor comprises the following steps: the plurality of side plates are assembled into a sector; the plurality of sectors are assembled into a ring at the Dewar base of the host hall. According to the assembly method of the bottom cold shield side plate of the fusion reactor, the plurality of side plates are assembled into the sector section, and then the plurality of sector sections are assembled into the ring at the Dewar base of the host hall, so that the bottom cold shield side plate can well meet the assembly requirement in a narrow space when being assembled; and the installation difficulty and the assembly cost of the bottom cold shield side plate are well reduced.

The invention relates to the technical field of EPP (foamed polypropylene) production and manufacturing, and discloses an EPP foaming process parameter optimization method which comprises the following steps: monitoring temperature field and pressure changes in real time, and judging whether a thermodynamic state deviates from a preset window or not; during deviation, constructing a multi-physics field coupling model to evaluate a bubble growth dynamic unbalance risk, and identifying micro-pore non-uniform distribution characteristics through multi-source sensing data fusion and frequency domain analysis; and judging whether the technological parameters are globally coordinated and regulated based on risks and characteristics, analyzing the heat transfer and heat radiation influence of the temperature control unit, quantifying the parameter regulation sensitivity and determining the regulation priority. The system comprises a thermodynamic monitoring module, a dynamic coupling analysis module and the like. According to the invention, real-time monitoring, accurate analysis and intelligent regulation and control of the EPP foaming process are realized, the process stability and the product quality are improved, and the method is suitable for the field of EPP production and manufacturing.

The invention relates to the technical field of medical related data processing, in particular to a medical fabric cleaning and disinfecting inspection method and equipment. VMF, LeNet-5 and an edge learning framework are combined, spectral analysis and chip identification technologies are combined, and the method is used for automatic inspection and sorting of medical fabric cleaning and disinfection. Results show that the method is obviously superior to the existing method in the aspects of detection accuracy, speed and efficiency, and a new technical approach is provided for standardized supervision and intelligent management of medical fabric cleaning and disinfection quality.

Provided is a high-strength steel sheet having excellent member strength, ductility, stretch flangeability, bendability of a shear end surface, and low-temperature toughness. A high-strength steel sheet having a prescribed component composition and a steel structure in which the area ratio of martensite is 10%-80%, the area ratio of bainite is 2%-70%, the area ratio of ferrite is 80% or less, and the area ratio of retained austenite is 15% or less at a position at 1 / 4 of the sheet thickness. The ratio of the number of martensite blocks in which metastable carbides are present relative to the number of martensite blocks is 2% or more, and the standard deviation [sigma] n of the nano-hardness relative to the average value [Hn] ave of the nano-hardness is 0.60 x [Hn] ave or less when the nano-hardness at 225 points or more is measured at a position of 1 / 4 of the sheet thickness.

The invention discloses a thin-wall structure lightweight design method based on additive manufacturing, and belongs to the technical field of additive manufacturing, and the thin-wall structure lightweight design method comprises the following steps: S100, designing an initial structure based on function and performance mapping; s200, an additive manufacturing process knowledge base is constructed, and the adaptability of the additive manufacturing process is evaluated; s300, carrying out process and structure bidirectional coupling finite element analysis; s400, collaborative optimization of a process and a structure driven by a multi-objective optimization algorithm; s500, feature part manufacturing verification and design and process scheme iterative optimization are carried out; and S600, generating a design and process integrated scheme, wherein the integrated scheme comprises design parameters and manufacturing parameters. The technical problem that in the prior art, the structure optimization target and the manufacturing adaptive capacity are lack of collaboration can be solved.

The invention relates to the technical field of non-electrical variable control, in particular to a digital visual control system and method for a glass bottle production line. The method comprises the following steps: acquiring digital image data of molten glass; molten glass bubble distribution is identified based on the molten glass digital image data, the quality state of the molten glass is evaluated, and micro-defect distribution map data is generated; collecting the internal temperature of the smelting furnace through a temperature sensor array, and constructing a three-dimensional temperature field distribution model of the smelting furnace; and predictively optimizing and adjusting the heating control parameters of the smelting furnace based on the three-dimensional temperature field distribution model of the smelting furnace, and generating a regional heating power distribution strategy. According to the invention, through fusion of a plurality of advanced technologies, accurate sensing and whole-process linkage control of the molten glass state are realized, intelligence, quality uniformity and process visualization level of glass bottle production are significantly improved, and glass manufacturing is promoted to develop towards the intelligent and efficient direction.

The invention discloses an intelligent regulation and control method applied to cement grinding particle gradation, and relates to the technical field of cement grinding control, and the method comprises the following steps: S1, collecting grinding system operation parameters in real time, including mill current, powder concentrator rotating speed, feeding amount, grinding sound signals and online particle size detection data; s2, calculating core characteristic parameters based on the operation parameters, wherein the core characteristic parameters comprise an effective crushing power index, a dynamic fine powder retention rate and an in-mill material filling state index; s3, inputting the core characteristic parameters into a grading prediction model, and outputting predicted particle size distribution; s4, generating an adjustment instruction through a hierarchical control strategy based on the predicted particle size distribution; and S5, outputting the adjustment instruction to an execution mechanism. The particle size distribution is accurately predicted by collecting and analyzing the operation parameters of the grinding system in real time and combining core characteristic parameters, closed-loop intelligent regulation and control are achieved, the grinding efficiency, stability and product quality are improved, and meanwhile the control precision is optimized.

The invention relates to the technical field of welding quality evaluation, in particular to a carbon steel pipe welding quality evaluation system which comprises a welding area three-dimensional scanning module, a welding seam dynamic grid generation module, a welding form change analysis module, a welding seam structure diagram construction module, a welding seam quality diagram convolutional network module and a welding defect intelligent recognition module. According to the invention, through real-time adjustment of the dynamic grid generation module, tiny changes of the geometric shape of the welding seam can be captured, new dimensions are provided for welding quality evaluation through generation of welding form characteristic data and construction of the welding seam structure diagram, and the introduction of the welding seam quality diagram convolution network module can improve the accuracy of welding quality evaluation. Node relations and structural features in a weld joint diagram structure are analyzed through a deep learning technology, an intelligent means is provided for welding quality evaluation, an intelligent welding defect identification module optimizes an identification process, various welding defects can be accurately classified, and a powerful technical support is provided for guarantee of welding quality.

The invention relates to the technical field of process parameter design, and discloses a silver electrolysis process parameter design method and system based on multi-objective optimization. The method comprises the following steps: performing differential time sequence parameter decomposition on operation data of the silver electrolysis process to obtain a sensitive parameter set and a reference parameter set; performing reweighted periodic feature extraction on the sensitive parameter set and the reference parameter set, and establishing a dynamic mathematical model of the silver electrolysis process parameters; based on the dynamic mathematical model of the silver electrolysis process parameters, physical and chemical process numerical simulation is conducted through COMSOLMultiphysics software, and a response curved surface model is obtained; and according to the response curved surface model, a process parameter combination evaluation value sequence is generated, silver electrolysis process parameter multi-objective optimization is carried out based on the process parameter combination evaluation value sequence, and a silver electrolysis process parameter optimization scheme is output. According to the application, multi-physical-field collaborative simulation of electrode reaction, ion transmission, heat conduction and the like is realized, and performance index responses under different process parameters are accurately predicted.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

Process condition management facilitates the combination of dry development and etching into a single process chamber; eliminating the necessity for a post-dry development bake step during semiconductor manufacturing. Methods and apparatuses for rapidly instituting a large drop in process chamber pressure allow thermal dry development and an O2 flash treatment or thermal dry development and plasma hardmask open operations to take place without wafer transfer.

The invention discloses an intelligent production method of a corrosion-resistant pressing rivet nut in the technical field of information, which comprises the following steps: carrying out surface treatment on the pressing rivet nut subjected to heat treatment, selecting a zinc plating or nickel plating process according to the corrosion resistance requirement of a product, and obtaining a uniform and compact plating layer by controlling the parameters of the temperature and the concentration of a plating solution and the electroplating time, the plating layer quality is detected by adopting a plating layer thickness meter and an adhesive force tester, the pre-plating treatment and post-plating treatment processes are optimized, and the protection performance and the appearance quality of the plating layer are improved; integrating a surface defect detection result with other performance indexes to serve as a comprehensive evaluation basis of product quality, establishing a correlation model of pressure riveting nut performance detection data and material attributes, a heat treatment process and a surface treatment process, mining an influence rule of each element on the product performance by adopting a correlation rule mining and time sequence prediction method, and determining the quality of the product. And predicting product performance distribution under different production parameter combinations.

The invention belongs to the technical field of steel and iron material design, and discloses a stainless steel strength and toughness collaborative optimization method and system based on heterogeneous integration, and the method comprises the steps: constructing a database of stainless steel components, process parameters and mechanical properties; according to the database, establishing a stacked heterogeneous integration model; quantizing contribution weights of input variables of the stacked heterogeneous integrated model to mechanical properties based on an SHAP method, identifying key factors, and setting a multi-target strength and toughness collaborative optimization index to guide an optimization direction; optimal components and processes are screened according to SHAP analysis, a sample is prepared through vacuum melting, hot rolling and heat treatment, the performance is verified according to the ASTM standard, and a final optimization scheme is determined; and a cross-scale digital twinborn verification system is constructed, and collaborative optimization of alloy components, process and macroscopic performance is realized. According to the method, the problems of low efficiency and insufficient generalization ability of a single model of a traditional trial and error method are solved, and an efficient and explainable intelligent optimization scheme is provided for development of high-performance stainless steel.

Energy is locally supplied to a cutting surface that is formed in an outer circumferential region of a wafer in a trimming step, before a grinding step of grinding the wafer. This can remove or repair at least part of a damage layer formed in the outer circumferential region of the wafer due to the trimming step. As a result, breakage of the wafer that originates from the outer circumferential region in the grinding of the wafer which has been subjected to the edge trimming and generation of dust in a step after this grinding can be suppressed.

Methods and systems for converting a renewable fuel intermediate composition to finished transportation fuel are provided herein. In some examples, a renewable fuel intermediate composition is flowed over a first catalyst in a first reaction zone to generate a saturated, hydrodeoxygenated product. A liquid portion of the renewable fuel intermediate composition may be characterized as having more than about 70 wt % of the oxygen being within ketone groups. The saturated, hydrodeoxygenated product may be flowed over a second fixed-bed catalyst in a second reaction zone to generate a product including the finished transportation fuel.

The invention relates to the technical field of industrial production scheduling optimization, and discloses an industrial production process intelligent scheduling and optimization method and system.The industrial production process intelligent scheduling and optimization method comprises the steps that resource competition hotspots are identified by establishing a resource time two-dimensional matrix; according to the method, the resource competition relationship between the emergency order and the existing order can be accurately identified, global re-planning is replaced by local fine tuning, the optimal insertion position is determined by constructing an order interference degree evaluation model, the hotspot intensity is reduced by implementing a resource competition mitigation strategy, and optimal embedding of the emergency order is realized by adopting a progressive order insertion algorithm. The disturbance to an existing production plan is minimized, the emergency order processing efficiency and the system stability are remarkably improved, and the technical problem of intelligent scheduling of emergency orders in industrial production is solved.

The invention discloses an intelligent preparation and coating method for lithium battery electrode slurry in the technical field of information, which comprises the following steps: acquiring particle size distribution, specific surface area and chemical composition attribute parameters of a lithium battery electrode slurry raw material, and establishing a quantitative relation model among the slurry component proportion, rheological property and stability through an autonomous learning algorithm; adjusting the component proportion by adopting a dynamic optimization algorithm to obtain a slurry formula meeting a target attribute; feature extraction and classification are performed on the constructed data set, a decision tree algorithm is adopted to train and generate optimization rules and judgment logic of slurry preparation process parameters, a preparation process knowledge base is formed, and autonomous optimization of the process parameters is realized; aiming at the coating quality problem, slurry preparation process parameters are traced and analyzed, collaborative optimization of a slurry formula, a preparation process and coating parameters is realized through a human-computer interaction interface, and the production efficiency and the yield are improved while the electrode performance is ensured.

The invention relates to the technical field of anode carbon block roasting, and discloses an energy-saving control system of an anode carbon block roasting furnace, which comprises a data acquisition module, an energy-saving analysis module, a digital twin modeling module, a control execution module and a feedback optimization module, when the combustion state of the roasting furnace is monitored in real time, a multi-layer monitoring framework is constructed to dynamically track the change trend of temperature, flue gas oxygen content and pressure parameters, continuous comparison is performed based on a preset energy-saving reference interval, and when the parameters deviate from an optimization interval, a digital twinborn model is immediately triggered to intervene in analysis, so that the real-time monitoring of the combustion state of the roasting furnace is realized. Prejudgment identification of potential risks of combustion efficiency reduction and abnormal heat loss is achieved, hidden energy loss caused by imbalance of the air-fuel ratio is avoided, the non-planned shutdown frequency of equipment is reduced, and the continuous energy efficiency management and control stability of the production process is guaranteed.

The invention discloses an intelligent control method of a zinc ash-containing tail gas purification system, which relates to the technical field of tail gas purification systems, and comprises the following steps of: firstly, continuously monitoring a condensation process of high-temperature zinc-containing steam in a cooler through a monitoring device, and collecting zinc steam condensation conditions and particle generation data of tail gas under different temperature and pressure states in real time; according to the invention, intelligent monitoring, signal analysis, machine learning identification and dynamic regulation and control are combined to realize accurate control of the zinc-ash-containing tail gas purification system. Key features are extracted through real-time monitoring and data preprocessing, intelligent recognition of particle concentration is achieved based on feature vectors and machine learning, the rotating speed of the centrifugal separator is adjusted in a self-adaptive mode, and efficient separation and system stability are ensured by linkage with pulse back-blowing dust removal. Compared with a traditional fixed rotating speed mode, the tail gas purification efficiency is improved, the maintenance cost is reduced, the service life of equipment is prolonged, the automatic operation capacity is enhanced, and the system is kept in the optimal working state during load fluctuation.