35 results about "Process capability index" patented technology

The invention discloses a flexible circuit board production quality monitoring method and system based on data feedback, and the method comprises the steps: collecting the physical parameters of development etching, drilling and copper plating processes, such as line width, line distance, aperture, roundness and copper thickness, and workshop temperature and humidity and dust concentration data; generating a single-variable control chart for a single-process physical parameter, generating a multivariable T2 control chart for a multi-coupling parameter process, and calculating a process stability index and a comprehensive fluctuation index; inputting the abnormal data into a BP neural network, identifying an abnormal mode type and outputting characteristic parameters; generating a parameter correction instruction according to the abnormal mode and the characteristic parameters, and adjusting equipment parameters in real time; recalculating the process capability index based on the adjusted data, and triggering secondary feedback if the process capability index does not reach the standard; and dynamically adjusting the threshold value of the control chart according to the standard deviation and the mean value of the process capability indexes of the continuous batches. Production key parameters are comprehensively covered, abnormity is found in time, accurate and comprehensive detection is achieved, real-time adjustment is achieved, stability and controllability are ensured, and the product quality is improved.

The invention relates to a titanium alloy smelting quality management method and system. The method comprises the steps that real-time monitoring data in the titanium alloy smelting process are collected; classifying and storing the real-time monitoring data to obtain historical process data; performing equipment abnormity early warning based on the equipment operation data and a preset first early warning rule, and generating first early warning information; obtaining a process capability index based on the process control node data, performing process data deviation early warning based on the obtained process capability index and a preset second early warning rule, and generating second early warning information; performing product manufacturing and detection early warning based on the ingot casting product manufacturing and detection data and a preset third early warning rule, and generating third early warning information; and displaying and processing the first early warning information, the second early warning information and the third early warning information in a man-machine interaction mode. Effective guidance is provided for quality management of titanium alloy smelting, and potential problems can be found and eliminated in time.

The invention relates to the technical field of welding, and particularly discloses an anti-fatigue welding process for a long-distance pipeline supporting steel structure. The process comprises the steps that a font groove structure is determined, and pre-welding polishing pretreatment and layered welding are conducted; fatigue samples of weld toe areas with different arc radiuses are collected, crack feature coordinates and stress concentration coefficients are extracted, failure modes are divided based on clustering, the arc radiuses are verified as main control factors of fatigue failure through multiple linear regression, and a core control range is determined by combining process capability indexes; carrying out fatigue verification on the basis of core parameter extension radius gradient, carrying out process compatibility matching in combination with a controllable interval and a thermal deformation interval of equipment, and outputting a radius candidate value through a response surface method; on-site construction constraints are collected, laboratory path space compatibility is analyzed, a formed track is optimized in a segmented mode, and constraint compensation factors are embedded. The anti-fatigue performance of the weld toe area of the supporting steel structure can be improved, and quality guarantee is provided for welding of the long-distance pipeline supporting steel structure.

The invention provides a glyceryl triacetate production parameter control method and system based on reinforcement learning, and relates to the technical field of optimization control methods, and the method comprises the steps: obtaining the production parameters of glyceryl triacetate; based on the kinetic model, constructing a kinetic equation of the esterification reaction of glycerol and acetic acid; constructing a digital twinborn simulation environment of the glyceryl triacetate production process; defining a reinforcement learning framework by taking a digital twin simulation environment as a scene; according to the reinforcement learning framework, taking the Elman neural network as a state predictor, and predicting the state of the glyceryl triacetate; solving the reinforcement learning framework through an MO-MPO algorithm, and determining an optimal control strategy and an optimal production parameter sequence corresponding to the optimal control strategy; judging whether the process capability index is smaller than a process capability index threshold value or not; if yes, continuing to solve the reinforcement learning framework; otherwise, outputting an optimal production parameter sequence; and controlling the optimal production parameter sequence through a PID (Proportion Integration Differentiation) control algorithm.

The invention discloses a production management method and device based on weight control, equipment and a storage medium. The method comprises the steps of obtaining a target weight interval of a product based on a preset process capability index; acquiring the actual weight of the current product; and comparing the actual weight of the current product with the target weight interval corresponding to the current product to obtain a test result. By utilizing the method, the target weight interval of the product is accurately determined through the preset process capability index, the final test result is determined by comparing the target weight interval with the target weight interval, the product can be timely found to be a box A and a goods B according to the test result, and the risk of mixed loading can be effectively solved.

The present invention discloses a method for evaluating production line stability based on a fault tree, comprising the following steps: obtaining multiple parameters of each device on the production line collected by sensors over a period of time, calculating the process capability index corresponding to each parameter, inputting the obtained process capability index corresponding to each parameter of each device into a pre-trained multiple linear regression model to obtain a stability index for the device, and inputting the stability index of each device into a pre-built fault tree-based stability analysis model to obtain a final stability judgment result for the device. The present invention can solve the technical problem that existing machine learning methods are complex for production lines, require long training times, and are unable to adapt to the real-time requirements of production environments in actual deployment and operation.

The embodiment of the invention discloses a quality problem two-dimensional positioning method and device based on the industrial internet, and relates to the technical field of household appliance production quality analysis, based on the method, a first event triggered by process nodes crossing different production lines can be managed in a centralized manner based on the industrial internet, and after the first event is monitored, the first event can be sent to the industrial internet; the method comprises the following steps: quickly positioning a first topological path based on a pre-constructed topological model, screening out a first candidate topological node set based on a process capability index, screening out a second candidate topological node set based on a process control state, and preferentially identifying a second topological node from the first candidate topological node set. And preferentially identifying the third topological node from the second candidate topological node set, thereby effectively improving the efficiency of positioning the root cause node and the failure handle node by reducing the screening range on the basis of ensuring the screening success rate.

The application discloses a flexible circuit board production quality monitoring method and system based on data feedback, collects physical parameters such as line width, line spacing, hole diameter, roundness and copper thickness of developing etching, drilling and copper plating processes, and workshop temperature and humidity and dust concentration data, generates a single variable control chart for single process physical parameters, generates a multivariate T 2 Control chart for multiple coupling parameter processes, calculates process stability index and comprehensive fluctuation index, inputs abnormal data into a BP neural network, identifies abnormal mode types and outputs characteristic parameters, generates parameter correction instructions according to the abnormal mode and the characteristic parameters, adjusts equipment parameters in real time, recalculates the process capability index based on the adjusted data, triggers secondary feedback if the process capability index does not meet the standard, and dynamically adjusts the control chart threshold value according to the standard deviation and mean value of the process capability index of continuous batches. The application comprehensively covers key production parameters, discovers abnormalities in time, accurately and comprehensively detects, adjusts in real time to ensure stability and controllability, and improves product quality.

A process capability index warning system and a warning method for the same are provided. The system includes a process capability index calculation device, which determines whether a quantity of test samples of the test device within a cycle of a warning interval is less than a quantity threshold. When the quantity of the test samples is less than the quantity threshold, the quantity of the test samples is accumulated until an end of a next cycle of the warning interval. When the quantity of the test samples is not less than the quantity threshold, the process capability index calculation device calculates and obtains process capability index values, and determines whether or not the process capability index values are less than an index threshold. When any of the process capability index values is less than the index threshold, the process capability index calculation device sends a warning message.

This application relates to a method, apparatus, electronic device, and storage medium for dynamic process capability assessment. In this disclosure, an access control threshold is first calculated; in the absence of historical data, multi-level random simulations are performed to generate a virtual sample set, and then initial prior distribution parameters are determined; if it is determined that the measured samples to be assessed in the current group pass the access control threshold verification, the prior distribution parameters are recursively updated based on the sample statistical parameters to obtain posterior distribution parameters characterizing the current manufacturing process. Then, a first probability that the process mean is within a preset mean interval, a second probability that the process standard deviation is less than a preset standard deviation limit, and a third probability that the process capability index is greater than or equal to the process capability index target are calculated to obtain the process capability assessment result for the current manufacturing process. This disclosure has at least one advantage: it does not rely on historical data, has a data quality filtering mechanism, and is applicable to small sample scenarios.

The application provides a supply chain data traceability monitoring method and system and a storage medium, relates to the technical field of supply chain quality traceability, and comprises the following steps: collecting production data of different material batches in each link of a supply chain, including raw material performance, processing process parameters and quality inspection results; calculating process capability indexes of each node through an industrial internet gateway, synchronously uploading the production data to the cloud, converting the production data into a vector format, and generating a cross-organization data sequence with material batches as the main line; analyzing link correlation based on the sequence, and constructing a quality influence topology graph dynamically reflecting the influence weight of process parameters on the final quality; when a quality inspection finds a batch quality defect, combining the topology graph and the parameter deviation of the defective batch, calculating the prediction probability of the upstream link as the source of the defect, and generating a visual report showing the most possible source link and the conduction path of the defect, so that accurate traceability of the supply chain quality defect and visual presentation of the influence path can be realized.

This invention belongs to the field of substrate manufacturing technology, specifically relating to a real-time quality monitoring data processing method for IC substrate manufacturing. It includes establishing a standardized data collection system for the entire IC substrate manufacturing process, enabling real-time collection, standardized processing, and storage of multi-dimensional quality data; intelligent analysis of the multi-dimensional quality data; construction of a defect identification model; and completion of defect classification and initial cause judgment. This invention extracts core quality features from quality indicator data, equipment operating status data, process parameter data, and environmental data, integrating them into a unified quality feature vector. Combined with a convolutional neural network model and a rule engine fusion algorithm, it overcomes the limitations of traditional single-threshold judgment. In particular, by calculating the process capability index CPK and the defect impact index, it achieves multi-level accurate classification of defects and initial cross-process cause judgment, improving defect identification accuracy and enabling early detection of potential defect trends.

This invention discloses a quality control method, apparatus, and system for printed circuit boards (PCBs), relating to the field of PCB manufacturing technology. The method includes: acquiring data collected by various sensors of a PCB manufacturing equipment at each time point within a preset time period; calculating the process capability index of the PCB manufacturing equipment at each time point; connecting the process capability indices at all time points to form a real-time process capability index curve; comparing the real-time process capability index curve with historical process capability index curves; determining whether the PCB manufacturing equipment is abnormal based on the comparison result; if the PCB manufacturing equipment is abnormal, calculating the abnormality level based on the comparison result, and generating control instructions for controlling the PCB manufacturing equipment to perform corresponding actions based on the abnormality level. This method can effectively avoid the problems of PCB defects and increased PCB production costs caused by abnormal process capability indices.

The present invention relates to the field of CNC machining, and specifically to a method for evaluating the process capability index of CNC machining parts, comprising the following steps: a first step: constructing a skew-normal process capability index of a position-scale distribution; a second step: replacing any quantile with a quantile of a standardized distribution; a third step: calculating the process capability index of a skew-normal distribution; and a fourth step: evaluating the process capability index. The present invention proposes a robust non-normal process capability index. This new index has a reasonable construction principle and a simple structure, can remain robust to changes in skewness, is easy to calculate, has a unified dimension, and can effectively evaluate the capability of a production process. The index not only covers the existing mainstream process capability index forms, but is also applicable to all position-scale family distributions, or distribution types that can be converted into this distribution family.

The invention provides a method for monitoring and analyzing the stability of an injection molding process. The method comprises the following steps: detecting an injection pressure value of injection molding equipment according to a set acquisition frequency; wherein the set acquisition frequency is set according to actual working condition requirements; determining a process capability index of the injection molding equipment according to the injection pressure value; judging whether the process capability index of the injection molding equipment is within a set threshold range or not, if yes, determining that the injection molding equipment is stable, and if not, generating an early warning instruction and executing early warning; according to the invention, the injection pressure of the injection molding equipment is continuously acquired for multiple times, the injection molding process capability index value is determined according to the acquired injection pressure value, and the influence of factors such as temperature and a mold is considered in the determination process, so that the stability of the current injection molding equipment can be accurately judged, frequent shutdown of injection molding production is effectively prevented, and the production efficiency is improved. And the production continuity and yield are ensured.

The invention provides an instance-based unlocking and locking mechanism tolerance optimization method and system. Belongs to the field of intelligent firearm manufacturing. According to the method, on the basis of ensuring that the assembly precision is qualified, the machining cost and the procedure capability index are comprehensively considered, and the tolerance of the unlocking and locking mechanism is designed and optimized. According to the method, on the basis of an unlocking and locking mechanism tolerance instance library, through analysis and calculation of contribution degrees, product processing cost and quality loss optimization targets are constructed, a target optimization model with process capability indexes and tolerance accumulation as constraint conditions is established, and the processing cost and the assembly quality loss are balanced. The design precision under precision manufacturing can be met, the production efficiency is improved, the part quality is improved, and the production cost is further reduced.

An automatic data analysis and report generation method, an electronic device and a storage medium, the method comprising: uploading a plurality of logs of a plurality of test stations to a log storage directory of a log storage server through an internal network, the plurality of test stations of a production line generating the plurality of logs; according to a set screening condition, a plurality of target logs meeting the screening condition in the log storage catalog are transmitted to a process capability index (CPK) and a measurement repeatability and reproducibility (GRAMP), and a plurality of target logs meeting the screening condition in the log storage catalog are transmitted to the CPK and the GRAMP; r) a report generation server; and analyzing the plurality of target logs through the CPK and GRR report generation server, automatically generating a CPK / GRR report, and sending the CPK / GRR report to a user personal computer.

Embodiments of the present application relate to the field of data processing, and disclose a factory process capability index evaluation method and device, a server and a storage medium. In the embodiments of the present application, a to-be-downloaded file is obtained according to each download address; wherein the to-be-downloaded file in the download address is updated according to a preset time period; data cleaning is performed on the to-be-downloaded file to obtain parameters used for calculating a process capability index; the parameters are loaded to a database; the process capability index is calculated according to the parameters in the database; and the calculation result and the parameters are displayed. The implementation of the present application is not limited to the evaluation of the factory process capability index in only a certain specific format, supports multiple data formats and performs data cleaning, can realize batch processing and calculation of parameters, automatically downloads the massive test data of each factory to the local in increments, loads the increments to the database, integrates the calculation results, and displays the results by using a report.

The invention discloses a monitoring method and device of test equipment, equipment, a medium and a product. The method comprises the following steps: acquiring test data from test sites; calculating a process capability index and a standard deviation value of each test site through a rolling window in a preset time interval based on the test data; and comparing the process capability index and the standard deviation value with matched preset thresholds, when the process capability index or the standard deviation value meets an abnormal alarm condition, triggering a multi-mode alarm of the corresponding test site, and forbidding at least one abnormal test site, thereby realizing real-time monitoring of each test site, and improving the test efficiency. Abnormities of a process capability index or a standard deviation value of a certain station can be found in time, unqualified devices are prevented from being continuously produced, multi-mode alarm can adapt to different environments, and response efficiency is improved; only the abnormal site is forbidden, and the parallel working state of the normal site is reserved, so that the shutdown of the whole equipment can be avoided, and the equipment utilization rate and the whole production efficiency are improved.

To provide an information processing apparatus which determines validity of a process capability index of a manufacturing process.SOLUTION: In a manufacturing system comprising a manufacturing device which manufactures a product, an inspection device 30 which inspects the manufactured product, and an information processing apparatus which performs process management, the information processing apparatus comprises an acquisition unit for acquiring, from the inspection device, data associating measurement data of measurement items and measurement time or required time in a process with each other, a process capability index generation unit for generating a process capability index on the basis of the measurement data, a time interval detection unit for detecting time intervals on the basis of the measurement time or the required time in the process, and a variation detection unit for detecting a variation in the time intervals, and determines validity of the generated process capability index and transmits the process capability index and a result of determining the validity of the process capability index to a server 4.SELECTED DRAWING: Figure 2

This application provides a method, apparatus, device, and storage medium for evaluating the bias of a measuring device. The method includes: obtaining a bias tolerance limit determined based on the upper and lower limits of a product's quality parameters and a process capability index, wherein the measuring device is used to measure the product's quality parameters; obtaining multiple parameter measurement values ​​obtained by repeatedly measuring the same standard sample using the measuring device; wherein the measured quality parameters of the standard sample are of the same type as the product's quality parameters; calculating the measured bias value and the confidence interval of the bias based on the multiple parameter measurement values ​​and the reference values ​​of the measured quality parameters of the standard sample; and determining whether the bias of the measuring device is acceptable within the tolerance interval defined by the bias tolerance limit based on the measured bias value using a pre-set equivalence test method. Using this application, the bias evaluation results can be consistent with actual engineering requirements.

The present disclosure provides a method and system for machine trial running, the method comprising: providing a product batch; checking historical data of the product batch, and marking the product batch whose historical data meets a preset condition; determining a process machine to be trial run; and identifying and using the marked product batch to trial run the process machine to be trial run. The present disclosure checks the historical data of the product batch, and marks the product batch whose historical data meets the preset condition, filters out the process machine or process formula to be trial run, selects a suitable range of batches from the marked product batch by an equipment automatic management system to trial run the process machine or process formula to be trial run, improves the accuracy of machine trial running, improves the process capability index of the product, and improves the product yield.

The invention provides an evaluation method for controlling based on temperature and humidity measuring points in a factory. The method comprises the following steps: arranging the temperature and humidity measuring points and collecting temperature and humidity data of all quarters; calculating process capability indexes of all quarterly data of each measuring point and carrying out first-order classification; carrying out clustering analysis on all quarterly data of each measuring point and carrying out second-order classification; determining a final evaluation category of each measuring point based on the first-order classification and the second-order classification; and setting a jump category condition, analyzing the final evaluation category of each measuring point, and readjusting the final evaluation category of each measuring point. According to the method, the real-time performance and accuracy of process control and the accuracy of decision making can be improved.

The invention provides a counting and sampling inspection method for small-batch multi-feature products, and relates to the technical field of aero-engine manufacturing. The method comprises the following steps: measuring and calculating a process capability index of a part, and then calculating a virtual batch of products; if the product batch is N and each product has M quality characteristics, calculating a virtual batch P = N * M; checking the sample size character code to obtain the sample size character code; checking the normal inspection sampling scheme table once through the sample size character code and the receiving quality limit AQL to obtain a virtual sample size n; according to the virtual sample size n, the actual sampling inspection number X = n / M of the batch of products is calculated, and if the calculation result is not an integer, an integer is taken through carry; the final sampling inspection scheme is that X products are extracted from the current product batch N to carry out full inspection on the M quality characteristics; sPC process control is continuously carried out on the size of the product subjected to sampling inspection.

The invention provides a roll-packing machine comprehensive quality capability evaluation method and system, a storage medium and equipment. The method comprises the following steps: collecting quality data of a roll-packing machine under a preset index; obtaining a process capability index and a defect rate of the roll-packing machine based on the quality data, carrying out mean value processing on the process capability index and the defect rate within a preset duration, and obtaining an updated process capability index and an updated defect rate based on the process capability index and the defect rate of the current month and the mean value within the preset duration; and obtaining a single quality capability evaluation value under the corresponding index based on the preset allocation weight of each index and in combination with the updated process capability index and the defect rate. According to the roll-packing machine comprehensive quality capability evaluation method and system, the storage medium and the equipment, the comprehensive quality capability of the roll-packing machine can be more comprehensively understood, measures are taken according to the comprehensive quality capability, the product quality is improved, the stability and consistency of products are guaranteed, and the requirements of consumers are met.

This invention discloses an integrated control scheme for optimal four-parameter continuous sampling inspection and first-generation process capability index. The specific steps are: (1) Given; (2) Determine the limit process and calculate; (3) Determine the process capability of the boundary process; (4) Calculate the integrated control scheme parameters (); (5) Execute the integrated scheme; (6) Collect data for normality test. If the normality is ≥ 0, proceed to (7); otherwise, stop production for maintenance; (7) Record data and calculate; (8) Compare with : if ≥ 0, continue to step (9); otherwise, stop production for maintenance; (9) Compare with : if ≥ 0, proceed to (10) without inspection; otherwise, return to (5); (10) Observe whether the system variables change. If they change, return to (5); otherwise, do not inspect. This invention is used for production lines with limited inspection capabilities. While meeting quality and cost requirements, it reduces the first and second types of risks. It can be used for quality control of production processes with tolerance upper and lower limits and limited inspection capabilities.

This invention proposes an example-based tolerance optimization method and system for locking and unlocking mechanisms, belonging to the field of intelligent firearms manufacturing. The method optimizes the tolerance design of the locking and unlocking mechanism by comprehensively considering processing costs and process capability indices while ensuring qualified assembly accuracy. Based on a tolerance example library of locking and unlocking mechanisms, the method constructs optimization objectives for product processing costs and quality losses through contribution analysis and calculation. It establishes an objective optimization model with process capability indices and tolerance accumulation as constraints, achieving a balance between processing costs and assembly quality losses. This invention can meet the design accuracy requirements of precision manufacturing, improve production efficiency, enhance part quality, and further reduce production costs.

This application discloses a method, apparatus, electronic device, and storage medium for determining matching risk. The method includes: acquiring first dimension data of multiple first matching parts and second dimension data of multiple second matching parts; constructing a first normal distribution of the first dimension data and a second normal distribution of the second dimension data; obtaining process capability indices of the first and second matching parts based on the first normal distribution, the second normal distribution, and a target formula; and determining the matching risk of the first and second matching parts based on the process capability indices. This method can determine the matching risk of the first and second matching parts based on the first dimension data of the first matching parts and the second dimension data of the second matching parts, revealing the impact of interactions between different matching parts on the final assembly result, avoiding incompatibility issues when assembling the first and second matching parts, improving the success rate of the assembly result, and reducing structural quality risks.

The application discloses a kind of based on gradient lifting decision coal mine underground drilling process working condition identification method, comprising the following steps: step 1: coal mine drilling process operating state grade division: the state grade division corresponding to process capability index is established, i.e. normal, conservative, three kinds of working conditions of overexcited;Step 2: coal mine drilling process working condition identification model parameter selection: select fixed threshold drilling operating parameter as the input parameter of drilling process working condition identification model;Step 3: coal mine drilling process working condition identification model realization: gradient lifting decision tree method is used to realize model, finally realizes coal mine drilling process working condition identification.The method of the application overcomes the limitations brought by regional restrictions in traditional methods, not only improves the accuracy of coal mine drilling process working condition identification result, but also provides guidance for the research of coal mine drilling process state monitoring, performance optimization and intelligent control, with practicality and applicability.

The invention discloses a wireless switch performance optimization method and system based on process capability analysis, and relates to the technical field of wireless switch performance optimization, and the method comprises the following steps: calculating a process capability index based on time data of wireless switch response and a preset specification upper limit; acquiring time deviation and stability deviation according to the process capability index and a preset capability threshold value; generating a time consumption weight and a fluctuation contribution degree based on the time consumption deviation and the stability deviation, and obtaining an alternative optimization scheme; predicting the process capability index and the cost increment of the alternative optimization scheme, calculating the scheme improvement efficiency, and selecting a final optimization scheme based on the scheme improvement efficiency. The method is used for solving the problems of lack of quantitative evaluation of stability performance and lack of data drive in optimization of resource allocation in the prior art.