A control management system for seamless steel tube production

Abstract

This invention provides a control and management system for seamless steel pipe production, relating to the field of steel pipe production control system technology. The system includes: real-time monitoring of raw material process management and comprehensive analysis to obtain raw material quality index and raw material matching degree; real-time monitoring of seamless steel pipe production process management and comprehensive analysis to obtain process stability index and equipment efficiency factor; real-time monitoring of seamless steel pipe quality management and comprehensive analysis to obtain product qualification rate and quality fluctuation coefficient; comprehensive analysis of raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, product qualification rate, and quality fluctuation coefficient to obtain optimization schemes; and construction of a predictive model for the entire seamless steel pipe production line based on a machine learning model to generate expected optimization schemes, thereby improving the controllability, stability, and resource utilization rate of the seamless steel pipe production process.

Description

Technical Field

[0001] This invention relates to the field of steel pipe production control system technology, specifically a control and management system for seamless steel pipe production. Background Technology

[0002] Seamless steel pipes, as a key basic material in the industrial field, are widely used in petrochemicals, energy transportation, machinery manufacturing, and infrastructure projects. Their production quality and efficiency directly affect the safety and development of downstream industries. With the global manufacturing industry transforming towards intelligent and refined processes, the demand for real-time monitoring, quality control, and process optimization in the seamless steel pipe production process is becoming increasingly urgent.

[0003] In the traditional seamless steel pipe production process, there are significant limitations in raw material control, production process monitoring, and product quality inspection: Inefficient raw material management: Relying on manual sampling or batch sampling inspection makes it difficult to track the physical properties (such as strength and toughness), chemical properties (such as elemental composition) and source batch information of raw materials in real time. This results in the inability to provide timely warnings of raw material quality fluctuations and a lag in the assessment of the match with the production plan, which can easily lead to subsequent process abnormalities.

[0004] Production process monitoring is lagging: Key process data such as temperature distribution, energy consumption, and rolling parameters are mostly collected through discrete sensors, lacking systematic integration and dynamic analysis; monitoring of equipment operating status (such as production cycle time and utilization rate) relies on manual inspection or historical data statistics, and fault warnings and process anomaly responses are not timely, resulting in low production efficiency and serious energy waste.

[0005] The quality inspection lacks full-process traceability: the inspection of product dimensional accuracy, surface defects and internal quality mostly adopts offline sampling mode, which cannot cover the entire production cycle. It is difficult to quickly locate the cause of quality fluctuations. The improvement of the pass rate depends on experience trial and error, and there is a lack of intelligent analysis and optimization methods based on multi-dimensional data.

[0006] In existing technologies, some companies have attempted to introduce automated monitoring systems, but these systems generally suffer from the following shortcomings: The problem of data silos is prominent: data on raw materials, process parameters, equipment status, and product quality have not been able to be linked across different stages, resulting in insufficient comprehensive analysis capabilities. Limited level of intelligence: It only achieves data visualization, but lacks dynamic modeling and prediction of core indicators such as quality index, matching degree, and equipment efficiency, making it difficult to generate targeted optimization solutions; Lack of real-time and closed-loop control: Production parameters cannot be adjusted in a timely manner based on real-time data feedback, limiting the improvement of process stability and equipment reliability.

[0007] Therefore, how to construct a real-time monitoring system covering the entire process from raw material entry to product exit, and achieve dynamic optimization of quality and efficiency through multi-source data fusion analysis, has become a key technological bottleneck for the intelligent upgrading of the seamless steel pipe industry. This invention addresses the above problems by proposing a real-time monitoring system for seamless steel pipe production lines that integrates multi-dimensional monitoring, intelligent analysis, and closed-loop optimization to improve the controllability, stability, and resource utilization of the production process.

[0008] Therefore, a control and management system for the production of seamless steel pipes is provided. Summary of the Invention

[0009] In order to solve the above-mentioned technical problems, the purpose of this invention is to provide a control and management system for the production of seamless steel pipes.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a control and management system for seamless steel pipe production, the system comprising: a raw material monitoring module, a steel pipe manufacturing monitoring module, a steel pipe quality monitoring module, and an optimization processing module; The raw material monitoring module is used to monitor the physical and chemical properties of raw materials in real time, track the source, batch information and inventory status of raw materials, detect surface defects of raw materials, and evaluate the matching degree of raw materials with the current production plan, thereby obtaining the raw material quality index and raw material matching degree. The steel pipe manufacturing monitoring module is used to monitor the temperature distribution, energy consumption, and rolling parameters in the seamless steel pipe production process in real time; to monitor the operating status, production cycle, equipment utilization rate, and to provide early warnings of equipment failures and process anomalies of the manufacturing equipment in real time; and to perform comprehensive analysis and processing to obtain the process stability index and equipment efficiency factor. The steel pipe quality monitoring module is used to detect the dimensional accuracy of steel pipes online, detect surface defects, evaluate internal quality, and verify mechanical properties, thereby obtaining the overall product pass rate and quality fluctuation coefficient. The optimization processing module receives feature values ​​from the three modules, performs comprehensive analysis, and obtains the corresponding optimization scheme for each module. Based on the machine learning model, a prediction model for the entire seamless steel pipe production line is constructed, thereby generating the corresponding expected optimization scheme.

[0011] Furthermore, the process of real-time monitoring of the physical and chemical properties of raw materials; tracking the source, batch information, and inventory status of raw materials; detecting surface defects in raw materials; and assessing the matching degree between raw materials and the current production plan includes: Real-time monitoring of raw material dimensional accuracy, density uniformity, number of defects, severity of defects, main element compliance, and impurity content; pass Labels track the origin, batch information, and inventory status of raw materials; Industrial cameras are used to scan the surface of raw materials and automatically mark any suspected defects. The specifications of steel billets are automatically matched according to the customer's order requirements to obtain the geometric matching degree between the raw materials and the steel billets in the customer's order production plan. The chemical composition deviation between the raw materials and the steel billets in the customer's order production plan is compared and recorded as the composition matching degree. The logistics convenience of the raw materials and the steel billets in the customer's order production plan is also considered, taking into account the storage location of the raw materials and the distance to the production line.

[0012] Furthermore, the process of obtaining the raw material quality index and raw material matching degree includes: Based on customer order requirements, obtain the dimensional accuracy, density uniformity, main element conformity, impurity content, defect quantity, and defect severity of the steel billet corresponding to the customer order production plan; perform standardization processing based on Z-score standardization technology; thereby obtain physical property scores; obtain chemical composition scores based on the main element conformity and impurity content after standardization processing; obtain surface quality scores based on the defect quantity and defect severity after standardization processing. Based on the physical performance score, chemical composition score, and surface quality score, the raw material quality index of the billet for the corresponding customer order production plan is obtained; The raw material matching degree is obtained based on the geometric dimension matching degree, composition matching degree, and logistics convenience.

[0013] Furthermore, the process of real-time monitoring of temperature distribution, energy consumption, and rolling parameters during seamless steel pipe production, as well as real-time monitoring of the operating status, production cycle time, and equipment utilization of manufacturing equipment, includes: Set up corresponding sensors to collect temperature distribution, energy consumption and rolling parameters in real time during the seamless steel pipe production process; Accelerometers are installed on the rolling mill and reducer. Vibration signals are analyzed based on wavelet transform to obtain the vibration intensity of the corresponding equipment. The output torque of the motor is monitored based on strain gauge torque sensors, and the load is fed back in real time to obtain the torque fluctuation of the corresponding equipment. The temperature fluctuation of the corresponding equipment is obtained by scanning the surface temperature of the equipment with an infrared thermal imager. The vibration intensity, torque fluctuation, and temperature fluctuation constitute the operating status of the equipment. By using industrial cameras to identify the location of materials, the logistics time between each process is calculated to obtain the production cycle time; and by using equipment start and stop signals to calculate the effective running time, the corresponding equipment utilization rate is obtained.

[0014] Furthermore, the process of obtaining the process stability index and equipment efficiency factor includes: Based on the temperature distribution, the temperature standard deviation of the corresponding stage, energy consumption, and rolling parameters, the temperature stability, energy consumption ratio, and rolling parameter stability of seamless steel pipe production are obtained. Based on the operating status, the equipment status stability of the manufacturing equipment is obtained, and then the process stability index in the corresponding customer order production plan process is obtained. Based on the production cycle time and equipment utilization rate, the time utilization rate and efficiency utilization rate of the manufacturing equipment are obtained, and then the equipment efficiency factor in the corresponding customer order production plan process is obtained.

[0015] Furthermore, the process of real-time monitoring of the dimensional accuracy of seamless steel pipes, detecting surface defects, internal quality, and mechanical properties includes: The outer diameter is measured online using a laser diameter gauge, and the wall thickness is detected using an ultrasonic thickness gauge; the length of the steel pipe is measured using an encoder; surface defects on the surface of the seamless steel pipe are identified using an industrial camera; and the internal quality of the seamless steel pipe is assessed by using an ultrasonic flaw detector. The mechanical properties of seamless steel pipes are evaluated by online hardness testing, offline tensile testing, and impact toughness testing.

[0016] Furthermore, the process of obtaining the overall product pass rate and quality fluctuation coefficient includes: Based on the dimensional accuracy, surface defects, internal quality, and mechanical properties of the seamless steel pipe, the number of qualified dimensional accuracy, the number of qualified surface defects, the number of qualified internal quality, and the number of qualified mechanical properties of the seamless steel pipe are obtained. Based on the number of qualified dimensional accuracy samples, the number of qualified surface defects samples, the number of qualified internal quality samples, and the number of qualified mechanical properties samples, the overall product pass rate and the quality fluctuation coefficient are obtained; the overall product pass rate is: ;in, Indicates the overall product pass rate; The number of acceptable dimensional accuracy measurements; This represents the number of acceptable surface defects. This represents the number of internally qualified products. The number of mechanical properties that meet the requirements; This refers to the total number of seamless steel pipes in this batch; the quality fluctuation coefficient is: ;in, Indicates the quality fluctuation coefficient; This is a correction factor for production batch size.

[0017] Furthermore, the process of comprehensively analyzing and processing raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, overall product qualification rate, and quality fluctuation coefficient includes: The raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, product comprehensive qualification rate, and quality fluctuation coefficient of the seamless steel pipe production line are input into a multi-index correlation analysis system, and then an optimization solution is output.

[0018] Furthermore, the process of constructing a multi-indicator correlation analysis system includes: Obtain the raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, product overall pass rate and quality fluctuation coefficient for the same production line batch, and perform data standardization and outlier handling. Based on Pearson correlation coefficient analysis, the corresponding correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management were obtained. Based on the correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management, a correlation level classification is obtained, and then a corresponding optimization scheme is generated.

[0019] Furthermore, the process of constructing a predictive model for the entire seamless steel pipe production line includes: A predictive model for the entire production line of seamless steel pipes is constructed based on machine learning models. Based on the seamless steel pipe full production line prediction model, the correlation coefficients for predicting raw material management, predicting steel pipe manufacturing management, and predicting steel pipe quality management are obtained. Based on the predicted correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management, an expected optimization plan is generated.

[0020] Compared with the prior art, the beneficial effects of the present invention are: 1. By coordinating the monitoring of three major modules—raw materials, manufacturing process, and quality inspection—a closed-loop system for data collection, analysis, and optimization is constructed to achieve comprehensive control over the production process and systematic improvement in product quality.

[0021] 2. Based on eigenvalue analysis and prediction models, raw materials, equipment operating parameters and energy consumption are dynamically allocated to achieve precise utilization of production resources and cost optimization. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0023] Figure 1 This is a schematic diagram of the steps in a control and management system for seamless steel pipe production.

[0024] Figure 2 This is a schematic diagram of a control and management system for seamless steel pipe production. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0026] like Figure 1 As shown, a control and management system for seamless steel pipe production includes the following steps: Real-time monitoring of the physical and chemical properties of raw materials; tracking the source, batch information, and inventory status of raw materials; detecting surface defects in raw materials; assessing the matching degree between raw materials and the current production plan; and conducting comprehensive analysis to obtain the raw material quality index and raw material matching degree. Real-time monitoring of temperature distribution, energy consumption, and rolling parameters during the seamless steel pipe production process; real-time monitoring of the operating status, production cycle, equipment utilization rate, early warning of equipment failures and process anomalies of manufacturing equipment, and comprehensive analysis and processing to obtain process stability index and equipment efficiency factor. Real-time monitoring of seamless steel pipe dimensional accuracy, detection of surface defects, internal quality and mechanical properties of seamless steel pipe, and comprehensive analysis and processing are carried out to obtain the overall product qualification rate and quality fluctuation coefficient. The raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, product overall qualification rate, and quality fluctuation coefficient are comprehensively analyzed and processed to obtain corresponding optimization solutions. Based on the machine learning model, a prediction model for the entire seamless steel pipe production line is constructed to generate corresponding expected optimization solutions.

[0027] It should be further explained that, in the specific implementation process, the real-time monitoring of the physical and chemical properties of raw materials; tracking the source, batch information, and inventory status of raw materials; detecting surface defects of raw materials; and assessing the matching degree between raw materials and the current production plan include: The specific process of real-time monitoring of raw materials includes: Real-time monitoring of the dimensional accuracy, density uniformity, number of defects, severity of defects, main element conformity, and impurity content of raw materials; the dimensional accuracy, density uniformity, number of defects, and severity of defects are the physical properties of the raw materials; the main element conformity and impurity content are the chemical properties of the raw materials. It should be further noted that the raw materials include, but are not limited to, carbon steel, medium carbon steel, high carbon steel, alloy steel, and high alloy steel.

[0028] For example, using a laser rangefinder to measure the dimensional accuracy of a steel billet, accuracy The density uniformity inside the steel billet was detected using an ultrasonic flaw detector; the composition of the steel billet was analyzed using a direct-reading spectrometer, with three samples taken from each batch for analysis. The content of more than 10 elements was measured by mass spectrometry. Trace elements, with precision reaching The grade is used to obtain the impurity content of the steel billet.

[0029] The specific process of real-time monitoring of raw material sources, batch information, and inventory status includes: When raw materials enter the factory, through The label records supplier information, production furnace number, and rolling date; a blockchain ledger is established for storage. Tags ensure that data cannot be tampered with and enable full lifecycle traceability.

[0030] 3D laser scanning technology is used to monitor inventory status in real time, including quantity and spatial distribution, and temperature and humidity sensors are used to monitor the storage environment to prevent steel billet corrosion.

[0031] The specific process of detecting surface defects in raw materials and assessing the match between raw materials and the current production plan includes: Industrial cameras are used to scan the surface of raw materials, achieving a resolution of [missing information]. It can identify defects such as cracks, scars, and folds, automatically mark suspicious defects, and remind manual review and inspection.

[0032] The system automatically matches billet specifications based on customer order requirements to obtain the geometric dimensional matching degree between raw materials and billets in the customer order production plan; it compares the chemical composition deviation between raw materials and billets in the customer order production plan and records it as the composition matching degree; and it considers the distance between the raw material inventory location and the production line to obtain the logistical convenience of raw materials and billets in the customer order production plan.

[0033] It should be further noted that the billet specifications include both the physical and chemical properties of the billet.

[0034] It should be further explained that, in the specific implementation process, the process of obtaining the raw material quality index and the raw material matching degree includes: Based on the customer's order requirements, obtain the corresponding customer order production plan for the billet's dimensional accuracy, density uniformity, main element conformity, impurity content, number of defects, and severity of defects.

[0035] It should be further explained that the main element conformity is obtained by matching the main elements of the billet in the order requirements with the main elements of the corresponding batch of billets. For example, if the iron and carbon content is consistent, the batch of billets with the highest similarity in iron and carbon content is selected. The number of defects is determined by scanning the billet surface with an industrial camera to identify the number of defects such as cracks, scabs, and folds, and to assess the severity of the defects on the corresponding billet surface.

[0036] Based on Z-score standardization technology, the dimensional accuracy, density uniformity, main element conformity, impurity content, number of defects, and severity of defects of steel billets in the corresponding customer order production plan are standardized to eliminate the influence of dimensions.

[0037] Based on the dimensional accuracy and density uniformity after the standardization process, the physical property score of the steel billet corresponding to the customer order production plan is obtained. The physical performance rating for: ;in, To ensure dimensional accuracy after standardization; This refers to the density uniformity after standardization.

[0038] Based on the standardized principal element compliance and impurity content, the chemical composition score of the billet for the corresponding customer order production plan is obtained. The chemical composition score for: ;in, The conformity of the primary element after standardization; This refers to the impurity content after standardization.

[0039] Based on the number and severity of defects after the standardization process, the surface quality score of the steel billet corresponding to the customer order production plan is obtained. The surface quality score for: ;in, This represents the number of defects after standardization. The severity of the defect after standardization.

[0040] Based on the physical performance score Chemical composition score and surface quality rating Obtain the raw material quality index of the steel billet for the corresponding customer order production plan. The raw material quality index for: ; , as well as The corresponding weighting coefficients are set according to the actual situation; generally, , as well as .

[0041] It should be further explained that the physical performance rating Chemical composition score and surface quality rating The range of values ​​is point.

[0042] Based on the matching degree of raw materials with the geometric dimensions, composition, and logistical convenience of the billets in the customer order production plan, the raw material matching degree of the corresponding customer order production plan billets is obtained. The matching degree of the raw materials for: ;in, For geometric dimension matching degree; For component matching degree; For the convenience of logistics; , as well as The corresponding weighting coefficients are set according to the actual situation; generally, , as well as .

[0043] It should be further explained that the geometric dimension matching degree for: ;in, Indicates the corresponding geometric dimension type. This indicates the total number of corresponding geometric dimensions, such as outer diameter, wall thickness, and length. Indicates the actual size of the corresponding geometric size type; This indicates the standard size corresponding to the geometric size type.

[0044] The component matching degree for: ;in, Indicates the corresponding component type, Indicates the total number of corresponding component types; This indicates the actual component value for the corresponding component type; This indicates the standard component value for the corresponding component type.

[0045] The convenience of logistics for: ;in, Indicates the actual transit time; Indicates standard transit time.

[0046] It should be further explained that, in the specific implementation process, the real-time monitoring of temperature distribution, energy consumption, and rolling parameters during the seamless steel pipe production process, as well as the real-time monitoring of the operating status, production cycle time, and equipment utilization rate of the manufacturing equipment, includes the following: The specific process of real-time monitoring of seamless steel pipe production includes: By setting up corresponding sensors, the temperature distribution, energy consumption, and rolling parameters during the seamless steel pipe production process can be collected in real time. Based on the temperature distribution and the corresponding temperature standard deviation, the temperature stability of seamless steel pipe production is obtained. The temperature stability for: ;in, This represents the temperature standard deviation for the corresponding stage. This refers to the target temperature for the corresponding stage.

[0047] Based on the energy consumption figures, the energy consumption percentage for seamless steel pipe production is obtained. The energy consumption ratio for: This is the weighting coefficient for electricity consumption; This represents the actual total electricity consumption; The target total electricity consumption for planned production; The weighting coefficient for gas energy consumption per ton of steel; This represents the actual total energy consumption per ton of steel. Energy consumption for the planned total tons of steel production.

[0048] It should be further explained that this is based on the actual total electricity consumption. Total electricity consumption relative to planned production target The ratio can be used to determine the proportion of electricity consumption; the actual total energy consumption per ton of steel can be used to determine this proportion. Energy consumption relative to the planned total tons of steel production The ratio of these values ​​can be used to determine the proportion of energy consumption per ton of steel; both the proportion of electricity consumption and the proportion of energy consumption per ton of steel are numerical data.

[0049] Based on the rolling parameters, the stability of rolling parameters for seamless steel pipe production is obtained. The stability of the rolling parameters for: ;in, For rolling force stability; To ensure the stability of the rolling speed.

[0050] It should be further explained that the rolling force stability for: ;in, This represents the standard deviation of the rolling force of the pressure sensor during the set acquisition period. This indicates the target rolling force during the production of seamless steel pipes.

[0051] To further explain, the standard deviation of the rolling force... Outliers need to be removed when the rolling force stability is stable. The seamless steel pipe production process is in a stable state.

[0052] The stability of the rolling speed for: ;in, This indicates the standard deviation of the rolling speed of the speed sensor within the set acquisition period; This indicates the target rolling speed during the seamless steel pipe production process.

[0053] It should be further noted that the pressure sensor and the speed sensor are set to have the same data acquisition period.

[0054] It should be further noted that the energy consumption includes electricity consumption and energy consumption per ton of steel; the rolling parameters include rolling force and rolling speed.

[0055] For example, 32 temperature sensors are arranged inside the heating furnace to construct a temperature field model and control the temperature difference. A fiber optic grating sensor is used to monitor the temperature of the perforated mandrel in real time to prevent overheating damage. The power consumption of the heating furnace is collected by a smart meter, and the frequency... The gas flow rate is monitored by a gas flow meter to obtain the corresponding energy consumption per ton of steel. The rolling force stability and accuracy are assessed in real time using a pressure sensor. Based on the encoder, the stability of the rolling speed is evaluated, and the stability of the rolling force and the fluctuation range of the rolling speed are controlled. .

[0056] The specific process of real-time monitoring of manufacturing equipment includes: Install acceleration sensors on the rolling mill and reducer, and set the sampling frequency. The vibration intensity of the corresponding equipment is obtained by analyzing the vibration signal based on wavelet transform. The torque output of the motor is monitored by a strain gauge torque sensor, and the load is fed back in real time to obtain the torque fluctuation of the corresponding equipment. The temperature fluctuation of the corresponding equipment is obtained by scanning the surface temperature of the equipment with an infrared thermal imager. The vibration intensity, torque fluctuation, and temperature fluctuation constitute the operating status of the equipment.

[0057] Based on the operating state, the equipment state stability of the manufacturing equipment is obtained. The stability of the device state for: ;in, For vibration intensity stability; For torque ripple stability; For temperature fluctuation stability.

[0058] It should be further explained that the vibration intensity stability for: ;in, Indicates the standard deviation of vibration intensity; This indicates the maximum vibration intensity of the equipment during the seamless steel pipe production process.

[0059] Torque fluctuation stability for: ;in, This represents the standard deviation of torque fluctuation; This indicates the maximum torque fluctuation of the equipment during the seamless steel pipe production process.

[0060] Temperature fluctuation stability for: ;in, This represents the standard deviation of temperature fluctuation; This indicates the maximum temperature fluctuation of the equipment during the seamless steel pipe production process.

[0061] By using industrial cameras to identify the location of materials, the logistics time between each process is calculated to obtain the production cycle time; and by using equipment start and stop signals to calculate the effective running time, the corresponding equipment utilization rate is obtained.

[0062] Based on the production cycle time, the operating time utilization rate of the manufacturing equipment is obtained. The time utilization rate for: ;in, for Total logistics time between each process; To plan logistics time.

[0063] Based on the equipment utilization rate, the operating efficiency and utilization rate of the manufacturing equipment are obtained. The efficiency utilization rate for: ;in, Effective operating time of manufacturing equipment; Planned operating time for manufacturing equipment.

[0064] It should be further explained that, in the specific implementation process, the specific steps for obtaining the process stability index and equipment efficiency factor include: Obtaining temperature stability Energy consumption ratio Stability of rolling parameters Equipment stability Time utilization rate and efficiency utilization rate ; According to the temperature stability Energy consumption ratio Stability of rolling parameters Equipment stability Obtain the process stability index in the production plan of the corresponding customer order. The process stability index for: ; , , as well as The corresponding weighting coefficients are set according to the actual situation; generally, , , as well as .

[0065] According to the time utilization rate and efficiency utilization rate Obtain the equipment efficiency factor in the production plan process for the corresponding customer order. The equipment efficiency factor for: .

[0066] It should be further noted that the process stability index Used to measure the degree of fluctuation of key parameters during the rolling process, with a range of The equipment efficiency factor is [amount]. A standard used to evaluate the actual operating efficiency of equipment compared to its theoretical operating efficiency.

[0067] It should be further explained that, in the specific implementation process, the real-time monitoring of the dimensional accuracy of seamless steel pipes, and the detection of surface defects, internal quality, and mechanical properties of seamless steel pipes include: The accuracy of online measurement of the outer diameter using a laser diameter gauge is [not specified]. And the wall thickness is measured using an ultrasonic thickness gauge, each Set up a measurement point covering the entire pipe length, and use an encoder to measure the pipe length, controlling for deviation. .

[0068] Based on the deployed industrial cameras, the system achieves full-coverage inspection of the steel pipe surface, identifying whether there are defects such as cracks, holes, and scratches on the surface of seamless steel pipes; suspected defects are re-inspected from multiple angles to reduce the false judgment rate.

[0069] It should be further noted that the frame rate of the industrial camera... ; Crack width Record it as a crack defect; record the diameter of the hole. Record it as a hole defect; record the depth of the scratch. This is recorded as a scratch defect.

[0070] Ultrasonic flaw detectors are used to detect internal cracks, holes, scratches, and other defects in seamless steel pipes, thereby assessing their internal quality.

[0071] The specific process for verifying the mechanical properties of seamless steel pipes is as follows: 1. Online hardness testing: Using an electromagnetic induction hardness tester, any three points of the corresponding batch of seamless steel pipes are tested.

[0072] 2. Offline tensile test: Test the yield strength, tensile strength and elongation of the corresponding batch of seamless steel pipes.

[0073] 3. Impact toughness test: Charpy impact test is performed on the corresponding batch of seamless steel pipes to evaluate the low-temperature toughness of the seamless steel pipes.

[0074] Then, the mechanical properties of the seamless steel pipe after testing are evaluated.

[0075] It should be further explained that, in the specific implementation process, the process of obtaining the overall product pass rate and quality fluctuation coefficient includes: To obtain the dimensional accuracy of seamless steel pipes, inspect surface defects, internal quality, and mechanical properties; Based on the dimensional accuracy, surface defects, internal quality, and mechanical properties of the seamless steel pipe, the number of qualified dimensional accuracy, surface defects, internal quality, and mechanical properties of the seamless steel pipe are obtained. It should be noted that if the dimensional accuracy, surface defects, internal quality, and mechanical properties of a seamless steel pipe meet the corresponding qualification standards, then the seamless steel pipe is counted as the corresponding qualified number. For example, if the dimensional accuracy and surface defects of a seamless steel pipe both meet the corresponding qualification requirements, then the qualified number of dimensional accuracy and the qualified number of surface defects of the seamless steel pipe are both increased by one. If the internal quality and mechanical properties of the seamless steel pipe do not meet the corresponding qualification standards, then the qualified number of internal quality and the qualified number of mechanical properties are not counted.

[0076] The overall product pass rate is obtained based on the number of qualified dimensional accuracy samples, the number of qualified surface defects samples, the number of qualified internal quality samples, and the number of qualified mechanical performance samples. and quality fluctuation coefficient ; The overall pass rate of the products for: ;in, The number of acceptable dimensional accuracy measurements; This represents the number of acceptable surface defects. This represents the number of internally qualified products. The number of mechanical properties that meet the requirements; This represents the total number of seamless steel pipes in this batch; It should be noted that if the overall product pass rate If the product's overall quality is below the preset minimum threshold, then there is a process problem with the seamless steel pipes produced in that batch. Technicians are reminded to carry out repairs. Generally, the minimum threshold for overall product quality is set to 2.1.

[0077] The quality fluctuation coefficient for: ;in, This is a correction factor for production batch size. It should be further explained that the testing process for the dimensional accuracy, surface defects, internal quality, and mechanical properties of seamless steel pipes is progressive. If there is a problem with the dimensional accuracy, the corresponding seamless steel pipe is directly rejected without proceeding to the next step of testing, thus improving efficiency.

[0078] For example, if the number of qualified seamless steel pipes in a batch is 95 for dimensional accuracy, 94 for surface defects, 92 for internal quality, and 90 for mechanical properties, and the total number of seamless steel pipes in the batch is 100, then the overall pass rate of that batch of seamless steel pipes is... If the product comprehensive qualification rate is 3.71, which is greater than the minimum threshold for product comprehensive qualification, then the comprehensive qualification rate of this batch of seamless steel pipes is excellent. In actual production, a comprehensive qualification rate of 3.2 or higher is recorded as excellent, a comprehensive qualification rate between 2.1 (inclusive) and 3.2 (exclusive) is recorded as qualified, a comprehensive qualification rate between 3.2 (inclusive) and 3.8 (exclusive) is recorded as excellent, and a comprehensive qualification rate between 3.8 (inclusive) and 4 (inclusive) is recorded as outstanding.

[0079] It should be further explained that, in the specific implementation process, the comprehensive analysis and processing of raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, product overall qualification rate, and quality fluctuation coefficient includes: A multi-index correlation analysis system was established; it should be further explained that the multi-index correlation analysis system is applicable to seamless steel pipe production lines. , , , , , A quantitatively integrated mathematical model enables precise evaluation and optimization decisions for seamless steel pipe production lines.

[0080] Seamless steel pipe production line , , , , , The data is input into the multi-index correlation analysis system, which then outputs optimization schemes; the optimization schemes include raw material management optimization schemes, steel pipe manufacturing management optimization schemes, and steel pipe quality management optimization schemes.

[0081] It should be further explained that, in the specific implementation process, the establishment of a multi-indicator correlation analysis system includes: Obtain batches from the same production line , , , , , Data standardization and outlier handling are performed to eliminate the impact of dimensional differences and outliers.

[0082] Based on Pearson correlation coefficient analysis, and for batches from the same production line , , , , , Analysis was conducted to obtain the correlation coefficient for raw material management. Correlation coefficient of steel pipe manufacturing management and the correlation coefficient of steel pipe quality management The specific analysis process based on the Pearson correlation coefficient will not be elaborated here.

[0083] It should be further explained that the aforementioned raw material management correlation coefficient and , Related, the correlation coefficient of the steel pipe manufacturing management and , Related to and the correlation coefficient of steel pipe quality management and , Related.

[0084] Based on the aforementioned raw material management correlation coefficient Correlation coefficient of steel pipe manufacturing management and the correlation coefficient of steel pipe quality management To obtain the correlation level classification; As shown in Table 1: For example, the correlation coefficient of raw material management ; When improving The simultaneous improvement indicates a high correlation between raw material quality and production planning, suggesting a high degree of correlation in raw material management and eliminating the need for optimization of the raw material management plan; the correlation coefficient of raw material management... ; When improving A slow increase indicates a decreasing correlation between raw material quality and production planning, suggesting that raw material management can be further improved and the management plan optimized; the correlation coefficient of raw material management... ; When improving The lack of improvement indicates serious problems in the raw material management process, necessitating modifications to the raw material management plan.

[0085] Based on the correlation level classification, corresponding optimization schemes are generated.

[0086] For example, the correlation coefficient of raw material management Raw material quality index Matching degree with raw materials The low correlation may be due to a mismatch between procurement specifications and order requirements; it is necessary to restructure the procurement process and establish an order-raw material specification mapping database to automatically match the raw material matching degree during procurement. ≥80% of steel billets; raw material quality index >90 and raw material matching degree For raw materials with a content of <70, secondary processing (such as surface heat treatment) should be initiated to improve the matching degree.

[0087] It should be further explained that, in the specific implementation process, the construction of a predictive model for the entire seamless steel pipe production line includes: Obtain several sets of historical batches from the same production line. , , , , , ; Based on several historical batches from the same production line , , , , , The correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management for the same historical batch were obtained. The correlation coefficients of raw material management, steel pipe manufacturing management, and steel pipe quality management for several historical batches are grouped and labeled, and denoted as follows: It is a natural number; Will The correlation coefficients of raw material management, steel pipe manufacturing management, and steel pipe quality management for the same batch in the past were used as sample data, and Less than The natural numbers, and using the sample data, the mean of the sample data is obtained, denoted as the sample set; The correlation coefficients of raw material management, steel pipe manufacturing management, and steel pipe quality management of the same batch in the remaining historical data were used as the test set. A training sample set is formed based on the aforementioned sample set and test set; Construct a standard prediction model based on the LSTM model; The training sample set is then input into the standard prediction model to train it. The trained standard prediction model is then denoted as the seamless steel pipe full production line prediction model.

[0088] Based on the seamless steel pipe full production line prediction model, the correlation coefficients for predicting raw material management, predicting steel pipe manufacturing management, and predicting steel pipe quality management are obtained. Based on the predicted correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management, an expected optimization plan is generated.

[0089] like Figure 2As shown, a control and management system for seamless steel pipe production includes the following modules: raw material monitoring module, steel pipe manufacturing monitoring module, steel pipe quality monitoring module, and optimization processing module; The raw material monitoring module is used to monitor the physical and chemical properties of raw materials in real time, track the source, batch information and inventory status of raw materials, detect surface defects of raw materials, and evaluate the matching degree between raw materials and the current production plan, thereby obtaining the raw material quality index and raw material matching degree.

[0090] The steel pipe manufacturing monitoring module is used to monitor the temperature distribution, energy consumption, and rolling parameters in the seamless steel pipe production process in real time; to monitor the operating status, production cycle, equipment utilization rate, and to provide early warnings of equipment failures and process anomalies of the manufacturing equipment in real time; and to perform comprehensive analysis and processing to obtain the process stability index and equipment efficiency factor. The steel pipe quality monitoring module is used to detect the dimensional accuracy of steel pipes online, detect surface defects, evaluate internal quality, and verify mechanical properties, thereby obtaining the overall product pass rate and quality fluctuation coefficient. The optimization processing module receives feature values ​​from the three modules, performs comprehensive analysis, and obtains the corresponding optimization scheme for each module. Based on the machine learning model, a prediction model for the entire seamless steel pipe production line is constructed, thereby generating the corresponding expected optimization scheme.

[0091] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A control management system for seamless steel tube production, characterized by, The system includes: a raw material monitoring module, a steel pipe manufacturing monitoring module, a steel pipe quality monitoring module, and an optimization processing module; The raw material monitoring module is used to monitor the physical and chemical properties of raw materials in real time, track the source, batch information and inventory status of raw materials, detect surface defects of raw materials, and evaluate the matching degree of raw materials with the current production plan, thereby obtaining the raw material quality index and raw material matching degree. The steel pipe manufacturing monitoring module is used to monitor the temperature distribution, energy consumption, and rolling parameters in the seamless steel pipe production process in real time; to monitor the operating status, production cycle, equipment utilization rate, and to provide early warnings of equipment failures and process anomalies of the manufacturing equipment in real time; and to perform comprehensive analysis and processing to obtain the process stability index and equipment efficiency factor. The steel pipe quality monitoring module is used to detect the dimensional accuracy of steel pipes online, detect surface defects, evaluate internal quality, and verify mechanical properties, thereby obtaining the overall product pass rate and quality fluctuation coefficient. The optimization processing module receives feature values ​​from the three modules, performs comprehensive analysis, and obtains the corresponding optimization scheme for each module. Based on the machine learning model, a prediction model for the entire seamless steel pipe production line is constructed, thereby generating the corresponding expected optimization scheme.

2. A control management system for seamless tube production according to claim 1, characterized in that, Real-time monitoring of the physical and chemical properties of raw materials; tracking the source, batch information, and inventory status of raw materials; The process of inspecting surface defects in raw materials and assessing their suitability for the current production plan includes: Real-time monitoring of raw material dimensional accuracy, density uniformity, number of defects, severity of defects, main element compliance, and impurity content; By Tags track the origin of raw materials, lot information, and inventory status; Industrial cameras are used to scan the surface of raw materials and automatically mark any suspected defects. The specifications of steel billets are automatically matched according to the customer's order requirements to obtain the geometric matching degree between the raw materials and the steel billets in the customer's order production plan. The chemical composition deviation between the raw materials and the steel billets in the customer's order production plan is compared and recorded as the composition matching degree. The logistics convenience of the raw materials and the steel billets in the customer's order production plan is also considered, taking into account the storage location of the raw materials and the distance to the production line.

3. A control management system for seamless tube production according to claim 2, characterized in that, The process of obtaining raw material quality index and raw material matching degree includes: Based on customer order requirements, obtain the dimensional accuracy, density uniformity, main element conformity, impurity content, defect quantity, and defect severity of the steel billet corresponding to the customer order production plan; perform standardization processing based on Z-score standardization technology; thereby obtain physical property scores; obtain chemical composition scores based on the main element conformity and impurity content after standardization processing; obtain surface quality scores based on the defect quantity and defect severity after standardization processing. Based on the physical performance score, chemical composition score, and surface quality score, the raw material quality index of the billet for the corresponding customer order production plan is obtained; The raw material matching degree is obtained based on the geometric dimension matching degree, composition matching degree, and logistics convenience.

4. A control management system for seamless tube production according to claim 3, characterized in that, Real-time monitoring of temperature distribution, energy consumption, and rolling parameters during the seamless steel pipe production process; The process of real-time monitoring of manufacturing equipment operating status, production cycle time, and equipment utilization includes: Set up corresponding sensors to collect temperature distribution, energy consumption and rolling parameters in real time during the seamless steel pipe production process; Accelerometers are installed on the rolling mill and reducer. Vibration signals are analyzed based on wavelet transform to obtain the vibration intensity of the corresponding equipment. The output torque of the motor is monitored based on strain gauge torque sensors, and the load is fed back in real time to obtain the torque fluctuation of the corresponding equipment. The temperature fluctuation of the corresponding equipment is obtained by scanning the surface temperature of the equipment with an infrared thermal imager. The vibration intensity, torque fluctuation, and temperature fluctuation constitute the operating status of the equipment. By using industrial cameras to identify the location of materials, the logistics time between each process is calculated to obtain the production cycle time; and by using equipment start and stop signals to calculate the effective running time, the corresponding equipment utilization rate is obtained.

5. A control management system for seamless tube production according to claim 4, characterized in that, The process of obtaining the process stability index and equipment efficiency factor includes: Based on the temperature distribution, the temperature standard deviation of the corresponding stage, energy consumption, and rolling parameters, the temperature stability, energy consumption ratio, and rolling parameter stability of seamless steel pipe production are obtained. Based on the operating status, the equipment status stability of the manufacturing equipment is obtained, and then the process stability index in the corresponding customer order production plan process is obtained. Based on the production cycle time and equipment utilization rate, the time utilization rate and efficiency utilization rate of the manufacturing equipment are obtained, and then the equipment efficiency factor in the corresponding customer order production plan process is obtained.

6. A control management system for seamless tube production according to claim 5, characterized in that, The process of real-time monitoring of the dimensional accuracy of seamless steel pipes, detecting surface defects, internal quality, and mechanical properties includes: The outer diameter is measured online using a laser diameter gauge, and the wall thickness is detected using an ultrasonic thickness gauge; the length of the steel pipe is measured using an encoder; surface defects on the surface of the seamless steel pipe are identified using an industrial camera; and the internal quality of the seamless steel pipe is assessed by using an ultrasonic flaw detector. The mechanical properties of seamless steel pipes are evaluated by online hardness testing, offline tensile testing, and impact toughness testing.

7. A control management system for seamless tube production according to claim 6, characterized in that, The process of obtaining the overall product pass rate and quality fluctuation coefficient includes: Based on the dimensional accuracy, surface defects, internal quality, and mechanical properties of the seamless steel pipe, the number of qualified dimensional accuracy, the number of qualified surface defects, the number of qualified internal quality, and the number of qualified mechanical properties of the seamless steel pipe are obtained. According to the size accuracy qualified number, the surface defect qualified number, the internal quality qualified number and the mechanical performance qualified number, a product comprehensive qualified rate and a quality fluctuation coefficient are obtained; the product comprehensive qualified rate is: ; wherein, is the size accuracy qualified number; is the surface defect qualified number; is the internal quality qualified number; is the mechanical performance qualified number; is the total number of the batch of seamless steel pipes; and the quality fluctuation coefficient is: ; wherein, is a production batch correction coefficient.

8. A control management system for seamless tube production according to claim 7, characterized in that, The process of comprehensively analyzing and processing raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, overall product qualification rate, and quality fluctuation coefficient includes: The raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, product comprehensive qualification rate, and quality fluctuation coefficient of the seamless steel pipe production line are input into a multi-index correlation analysis system, and then an optimization solution is output.

9. A control and management system for seamless steel pipe production according to claim 8, characterized in that, The process of constructing a multi-indicator correlation analysis system includes: Obtain the raw material quality index, raw material matching degree, process stability index, equipment efficiency factor, product overall pass rate and quality fluctuation coefficient for the same production line batch, and perform data standardization and outlier handling. Based on Pearson correlation coefficient analysis, the corresponding correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management were obtained. Based on the correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management, a correlation level classification is obtained, and then a corresponding optimization scheme is generated.

10. A control and management system for seamless steel pipe production according to claim 9, characterized in that, The process of constructing a predictive model for the entire seamless steel pipe production line includes: A predictive model for the entire production line of seamless steel pipes is constructed based on machine learning models. Based on the seamless steel pipe full production line prediction model, the correlation coefficients for predicting raw material management, predicting steel pipe manufacturing management, and predicting steel pipe quality management are obtained. Based on the predicted correlation coefficients for raw material management, steel pipe manufacturing management, and steel pipe quality management, an expected optimization plan is generated.

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