26 results about "Process improvement" patented technology

The invention provides a business workflow dynamic arrangement and optimization method based on a large model agent, and the method comprises the steps: (1) analyzing the features and operation logic of a multi-element business workflow, and defining a business workflow description language; and (2) decomposing a complex business process construction process, and providing a layered process modeling system to guide a large model agent to construct a workflow model. And (3) constructing a task execution queue based on the task dependency relationship, and dynamically executing and monitoring the workflow. And (4) according to the execution log of the business workflow, discovering the bottleneck, redundancy and abnormity of the workflow model by adopting a process mining algorithm, and proposing a process improvement and optimization strategy. And (5) constructing a collaboration framework of a plurality of large model agents, and realizing dynamic arrangement, monitoring and optimization of the business workflow. According to the method, the defects that the current business workflow construction depends on experience of designers, and effectiveness and function requirements are difficult to guarantee are overcome; the defect that the business workflow is difficult to adapt to dynamic demand changes is overcome.

The utility model relates to a process improvement device of a cage type anti-floating anchor, which belongs to the technical field of building construction and comprises a top plate, electric guide rails are fixedly mounted on two sides of the inner wall of the top plate, and sliders are slidably connected into the electric guide rails. The device has the advantages that the electric guide rail and the sliding block are used in cooperation so that the anchor rod can be conveyed into a target hole, certain guiding and limiting effects can be achieved, the deviation phenomenon in the anchor rod installation process can be reduced, in the anchor rod installation process, the perpendicularity of the anchor rod can be monitored through the perpendicularity measuring sensor, and therefore the anchor rod can be conveniently installed. The detection information is transmitted to the controller, then the controller controls the air cylinders to carry out deviation correction, at the moment, the two air cylinders are matched with each other, the piston rod of one air cylinder contracts, and the piston rod of the other air cylinder stretches out, so that the anchor rods in the clamping device a and the clamping device b are in an inclined adjustable state until the anchor rods are in a reasonable perpendicularity range; and tedious manual operation is reduced.

Systems and methods for accelerating the identification of process transformation opportunities offer a data-driven, systematic approaches that incorporate generative AI and user input. Organizational processes are modeled to extract customer pain points, create process activity graphs, identify, and compute key performance indicators (KPIs), benchmark against similar organizations, and simulate business value to highlight transformation opportunities. Embodiments leverage existing knowledge base of customer profiles and solutions to enhance the accuracy of process improvement recommendations.

The invention discloses a welding defect monitoring method and system based on production business, and the method comprises the steps: collecting workpiece information and welding seam information of a to-be-welded workpiece after the to-be-welded workpiece is fed and before welding is started; and executing a predetermined welding defect monitoring logic based on an identification result of the welding seam information. According to the invention, fusion of welding defect monitoring and production business is realized, one-to-one correspondence of welding defect monitoring results with workpiece information and welding seam information is realized, guidance is provided for welding seam inspection and repair, a data basis is provided for process improvement, and an effect of deep fusion with a production business process is achieved.

The application provides a preparation method of a graphene porous membrane. Through process improvement and control of different process parameters, a porous graphene oxide is formed by hole etching on a raw material graphene oxide cake, and then the porous graphene oxide is pulped and coated to form a membrane to form a nanopore surface. After chemical reduction and foaming, exhaust passages are formed on the surface and the edge, the problem of bulging and delamination caused by untimely exhaust during industrial production is solved, and the interlayer bonding force is improved.

A method for developing or improving a process for producing a product from a material comprising steps of acquiring process data from at least two different sources for the production process and its relevant parameters by using a Data Collecting computer; using the acquired process data related to the production process to perform a Process Mapping step by using a Process Mapping computer; assigning the acquired process data related to the relevant parameters of the production process to its corresponding process parts by performing a Data Mapping step by using a Data Mapping computer; analyzing the therefore mapped process data with a specific software performed on an Analyzing computer thereby identifying and validating one or more existing characteristics related to the quality or performance of the production process; and using the identified and validated characteristics to develop the production process or improve its performance.

The utility model relates to the technical field of process improvement, in particular to an additive system metering pump PLC linkage device which comprises an EB tank body, a heating pipe is installed on one side of the EB tank body, one end of the heating pipe penetrates through the ED tank body to extend into the ED tank body, a feeding piece is installed on the top of the EB tank body, a three-way pipe is installed at the bottom of the EB tank body, and a PLC is installed on the other side of the EB tank body. The two ends of the three-way pipe are fixedly connected with one ends of metering pumps correspondingly, and one sides of the two metering pumps are fixedly connected with the PLC through data transmission lines. According to the improved additive system metering pump PLC linkage device, a single tank is adopted to be connected with additive pumps of two systems at the same time, the mode is different from an existing mode that a positive pump and a standby pump are adopted, the utilization rate of resources is increased, resource waste is avoided, the volume of the tank body can be utilized to the maximum extent, excessive production capacity is avoided, in addition, the linkage device is additionally arranged between the additive pumps, and the production efficiency is improved. And automatic pump reversing is realized through flow pressure, so that accidents are reduced.

A high-precision cross-scale 3D structure manufacturing method and a use thereof. The method comprises: performing multi-stage imprinting on a processing substrate using an imprint mold, wherein one or more imprint patterns composed of a target 3D structure are formed after each imprinting, and the hardness of the imprint mold is greater than that of a surface to be processed; during the multi-stage imprinting, sequentially forming the scales of the target 3D structure from small to large; and during the multi-stage imprinting, performing bidirectional conversion on the basis of the morphology requirements of the target 3D structure and / or the mechanical properties of the formed imprint patterns as well as operational parameters of imprinting work. Processing and manufacturing of high-precision cross-scale 3D structures can be realized in a simple and efficient manner, the overall solution can be applied to almost all materials by means of process improvement, and key parameters are set to greatly improve industrial operability and production effectiveness.

According to one aspect, there is provided a computer-implemented system for evaluating and improving strain production using three integrated components: a prototype system for evaluating candidate strains, an optimization system for enhancing strain performance, and a scale-up system for adapting laboratory results to commercial-scale parameters. The system employs artificial intelligence models trained on data from all three components. The system continuously receives performance data from these systems to update its AI models. Using these updated models, the system generates modified operational parameters for the prototype, optimization, and scale-up systems, creating a feedback loop for continuous process improvement.

The application provides a method for monitoring the quality of overlay mark process, providing a design mark; according to the design mark, the design mark is made on a wafer to obtain an overlay mark; the overlay mark is scanned, and a scanned signal is received to generate a photo of the overlay mark; the CD of the contour of the overlay mark is extracted based on the photo; the CD of the contour of the design mark is compared with the CD of the contour of the overlay mark to measure the difference between the two; or the CDs of different positions of the contour of the overlay mark are compared with each other to measure the difference of the CDs of different positions. The application extracts the edge CD from the picture scanned by an electron microscope, and stacks the contour CD of the design mark or the contour CD of the mark at different positions. If there is a difference, the abnormality can be found in time. If the actual mark deviates, the deviation will be reflected on the overlay measurement result, the process and the measurement result are preliminarily judged in advance, the wrong overlay feedback is avoided, and the guidance for process improvement is provided.

The application discloses a process intelligent improvement method based on building block units and a manufacturing industry engineering promotion system. The method comprises the following steps: constructing a three-layer knowledge base comprising a general knowledge base, a factory-specific knowledge base and an improvement case result base; adapting the model to the factory through two-stage training and continuous learning; segmenting the process video shot by the on-site personnel into at least one building block unit, wherein the building block unit is the smallest indivisible independent operation segment; extracting the action features of each building block unit and comparing them with the standard template to calculate the deviation quantization value; when the deviation exceeds the preset threshold, matching the waste type and retrieving similar historical cases to generate an improvement scheme; adopting at least one level of human-computer interaction, wherein the first level is confirmed or interactively modified by the on-site personnel and then submitted, and the second level is audited and approved by the industrial engineering department and then executed; after execution, the video is shot again to verify the effect, and the case is automatically stored in the warehouse; the stored case is used for pushing similar problems, training new people and introducing new products. The application realizes fine-grained quantization, low-cost adaptation, case reuse and controllable closed loop of process improvement.

The invention relates to the technical field of digital twinborn models, and discloses a digital twinborn model for a nuclear-grade additive manufacturing process, and a model construction method comprises the following steps: dividing process state units according to a preset rule and constructing feature vectors based on a standardized manufacturing process data set obtained by preprocessing original manufacturing data; performing backtracking consistency evaluation on the process state unit and calculating a comprehensive consistency evaluation value under the constraint of a process, a material and equipment based on an association probability model constructed by historical data; and screening candidate process state units, and outputting a manufacturing sequence formed by defects and corresponding process state information. According to the method, quantitative tracing of the nuclear-grade additive manufacturing defect forming stage can be achieved, reliable support is provided for quality responsibility definition and process improvement, and nuclear-grade manufacturing quality control requirements are met.

The invention belongs to the technical field of spaceflight product quality reliability, and particularly relates to a spaceflight product implementation process improvement and predominance grade authentication method, which specifically comprises the following steps: establishing a predominance grade evaluation element model: adopting three-level step-by-step progressive basic architecture of aspects, elements and sub-elements to describe model elements; a predominant grade authentication evaluation element grading matrix is established, wherein predominant grades are determined to be divided into a fine grade, a lean grade and a predominant grade, and standards which need to be achieved are formulated for each sub-element according to the fine grade, the lean grade and the predominant grade; sub-element collection and evaluation: collecting corresponding sub-elements according to the predominance grade evaluation element model, and judging whether each sub-element reaches the standard of the corresponding grade step by step from the fine grade according to the predominance grade authentication evaluation element grading matrix; and predominant grade evaluation: determining the grade of each engineering stage according to the grade of the evaluation sub-element, and determining the predominant grade of the aerospace equipment model according to the grade of each engineering stage.

The invention provides a process parameter optimization method and device, electronic equipment and a storage medium, and the process parameter optimization method comprises the steps: obtaining real-time process parameters of a product in a plurality of manufacture procedures, and carrying out the preprocessing of the real-time process parameters, and obtaining key process parameters; based on the trained quality prediction model, obtaining a plurality of prediction quality indexes of the product according to the key process parameters; under a preset process constraint condition, taking the key process parameters as variables, taking minimization of a weighted total loss function value as a target, performing minimization solution on the weighted total loss function, and determining a target process parameter combination, the weighted total loss function being a loss weighted sum corresponding to each quality index in the plurality of predicted quality indexes; and optimizing the real-time process parameters according to the target process parameter combination. On the basis, the global optimal process parameters crossing multiple processes can be dynamically found, the improvement period of an existing process is shortened, and the production efficiency and the product yield are improved.

The embodiment of the present specification provides an abnormality monitoring method for electrolytic production, comprising: acquiring production monitoring information; determining production abnormality information based on the production monitoring information; determining process improvement information or an abnormality processing scheme based on the production abnormality information.

Techniques disclosed herein pertain to region building for cloud networks and, particularly, for region building process improvements. The techniques include accessing first configuration instructions for building a physical region of a cloud service provider and executing the first configuration instructions. Executing the first configuration instructions causes a first graph that includes nodes to be traversed. A second graph for replacing the first graph can be selected from among candidate graphs. The candidate graphs are generated by reducing an execution time associated with a node of the nodes of the first graph. Second configuration instructions that include instructions for traversing the second graph are generated and executed. Executing the second configuration instructions causes a second graph that includes the nodes to be traversed.

This invention discloses an ultra-high purity oxygen air separation system and a process improvement method, belonging to the field of air separation technology. The system includes an air compressor, a heat exchanger, a subcooler, a high-pressure distillation column, a crude argon column, a high-purity oxygen column, and connecting pipelines. The high-pressure distillation column includes a lower column, an upper column, and a condenser-evaporator A. The high-purity oxygen column contains a condenser-evaporator B. The oxygen-enriched liquid air obtained from the bottom of the lower column is directly fed into the condenser-evaporator B in the high-purity oxygen column as a heat source. After heat exchange, it enters the condenser of the crude argon column, making the condenser-evaporator B simultaneously the hot end of the subcooler. This invention also discloses a process improvement method based on this system, including preliminary separation of the raw material air, heat exchange between the upper and lower columns, extraction and crude separation of the argon fraction, and using the oxygen-enriched liquid air as a heat source for the high-purity oxygen column to achieve subcooling. This invention integrates the functions of the independently set subcooler hot end and the high-purity oxygen column evaporator in the traditional process, eliminating the need for the subcooler hot end equipment and related pipelines, reducing equipment investment costs and cold box design complexity, while improving the recovery rate of oxygen and argon.

The application discloses an image recognition-based inspection method and inspection device for differential shell end cover screw holes, and relates to the technical field of precision detection.The inspection method combines high-precision indexing rotation, isochronous sequence visual trigger collection and image coincidence rate intelligent analysis, realizes non-contact, full-automatic and online comprehensive detection and intelligent diagnosis of the position degree and perpendicularity of the annularly distributed screw holes of a differential shell, achieves high-efficiency, high-precision and high-robustness synchronous quantitative detection and defect classification, and effectively guides process improvement, and has better application value.

The invention relates to a post-epitaxy film thickness and slip line improvement method, which belongs to the technical field of semiconductor silicon wafer processing, and comprises the following operation steps: a first stage: diagnosis and data acquisition: accurately quantifying resistivity, film thickness distribution and relevance of an epitaxial wafer in a current process; and a second stage: establishing a regulation and control model, and designing a targeted process improvement scheme. A resistivity-film thickness compensation model is established, and target resistivity distribution is achieved by adjusting the flow of dopant gas and the temperature / pressure of a reaction cavity. And a third stage: implementing the new scheme, and verifying the validity of the new scheme. And a fourth stage: solidifying the successfully verified process into a standard operation program, and establishing a daily monitoring mechanism. Verification of resistivity uniformity is a common key for connecting and solving the two problems, and a set of complete closed-loop process from diagnosis, modeling, optimization to solidification is established.

The invention relates to the technical field of semiconductor manufacturing process control, and provides a semiconductor process improvement effect analysis method, system and device and a medium. The method is realized through an automatic process: a reference group data set before process improvement and a test data set after process improvement are acquired, and the acquisition of the reference group data set comprises the steps of receiving new condition grouping information and process inevitable station information, pulling wafer batch timestamps and sequencing to determine a time window; performing N-section sampling (N is a natural number not less than 3) to dynamically construct a reference group, and automatically filtering a control batch, a verification batch and a test batch to ensure data purity; and calculating a mean shift index and a variable coefficient index based on the data set, and generating a process improvement evaluation result. Through full-automatic data acquisition and dynamic sampling, objective and efficient reference group construction is realized, manual deviation is eliminated, offset detection sensitivity is improved through a time weighting algorithm, and accurate monitoring and improved decision making of a semiconductor process are effectively supported.

Techniques disclosed herein pertain to region building for cloud networks and, particularly, for region building process improvements. The techniques include accessing first configuration instructions for building a physical region of a cloud service provider and executing the first configuration instructions. Executing the first configuration instructions causes a first graph that includes nodes to be traversed. A second graph for replacing the first graph can be selected from among candidate graphs. The candidate graphs are generated by reducing an execution time associated with a node of the nodes of the first graph. Second configuration instructions that include instructions for traversing the second graph are generated and executed. Executing the second configuration instructions causes a second graph that includes the nodes to be traversed.

The application provides a data analysis method, system, medium and electronic equipment. The method comprises: acquiring at least one group of detection data of a wafer to be analyzed; for any one group of detection data, identifying the wafer to be analyzed according to a corresponding detection standard to generate a defect identification map of the wafer to be analyzed; performing a superposition operation on each defect identification map to generate a comprehensive defect map of the wafer to be analyzed, and performing data analysis on the wafer to be analyzed according to the comprehensive defect map. The technical scheme of the application can integrate the detection data of each process section, clearly show the evolution of defects and process problems, and provide effective data support for process improvement and equipment monitoring.

This invention discloses a method and system for precise control of cutting parameters and tool life, belonging to the field of cutting tool life management. Based on strict control of machining parameters and tool life determined by the part, machining step, and cutting parameters, it can accurately calculate the remaining tool life and remind users to change tools in a timely manner. This method enables comprehensive monitoring of the usage of all tools on all equipment in the entire workshop. Compared to the traditional model where each person oversees several machine tools, only one person is needed to manage the usage of hundreds of machine tools and tens of thousands of tools. It can record the specific tools used in each step of the part machining process and the execution of machining parameters at that time based on the part machining history, facilitating process engineers to analyze quality problems and improve process parameters, promoting continuous process improvement. The method proposed in this invention significantly improves the precision of life management compared to traditional life management models, increases tool utilization, and reduces part quality problems caused by tool malfunctions.

The invention belongs to the technical field of process improvement, and provides a forge piece manufacturing process improvement optimization method which comprises the following steps: performing static data acquisition on a forge piece under the current working condition, performing a Deform-3D-based rigid-plastic finite element algorithm, constructing a rigid-plastic finite element simulation model, and constructing a simulation model; the influence of different forging parameters on the forging forming quality in the forging process is simulated, and the defect risk of the forging in the simulated forging process under the forging parameters is evaluated; if the defect risk is low, extracting a defect risk coefficient corresponding to the low-risk parameter, and determining a simulation optimal parameter; performing test operation on the forge piece based on the simulation optimal parameter, presetting a test operation time period, and judging whether the qualification rate of the forge piece is abnormal or not by performing quantitative deviation analysis on the actual qualification rate of the forge piece in the test operation time period; according to the method, aiming at the abnormal condition of the qualified rate, a quantitative correction model is established through the mold abrasion loss and the surface roughness increment, and the friction coefficient is accurately corrected to obtain an actual optimal parameter.

A system for crystal growth optimization includes a plurality of sensors integrated into crystal growth equipment for continuous monitoring and data collection, a data acquisition device for receiving data from the sensors, a computing system for storing and processing the received data, a machine learning algorithm implemented on the computing system for analyzing the received data and predicting process improvements, and a user interface for displaying real-time data, process trends, root-cause analysis of defects, and suggested process improvements. The sensors may include various types such as viscometers, pH meters, thermometers, and imaging sensors. The machine learning algorithm may be selected from Linear Regression, Logistic Regression, Neural Network, and Decision Tree algorithms. The system enables automated adjustments of crystal growth parameters and utilizes historical and crowdsourced data to enhance prediction accuracy across different materials and growth conditions.

Techniques disclosed herein pertain to region building for cloud networks and, particularly, for region building process improvements. The techniques include accessing first configuration instructions for building a physical region of a cloud service provider and executing the first configuration instructions. Executing the first configuration instructions causes a first graph that includes nodes to be traversed. A second graph for replacing the first graph can be selected from among candidate graphs. The candidate graphs are generated by reducing an execution time associated with a node of the nodes of the first graph. Second configuration instructions that include instructions for traversing the second graph are generated and executed. Executing the second configuration instructions causes a second graph that includes the nodes to be traversed.