Thickness early warning method, system and equipment for galvanized steel sheet and medium
By collecting the full-process parameters of galvanized steel plates and combining them with pre-trained models, accurate modeling and active early warning of the thickness of galvanized steel plates are achieved, which solves the problems of single parameters and delayed early warning in existing technologies and improves prediction accuracy and prevention and control capabilities.
Patent Information
- Application Number
- CN202510708631.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-29
- Publication Date
- 2025-09-09
AI Technical Summary
The existing galvanized steel plate thickness control method has the problems of single parameter dimension, limited coverage and delayed warning, which leads to insufficient thickness prediction accuracy, delayed adjustment and batch quality risks.
By collecting parameters of the entire raw material and galvanizing process, combining the pre-trained model to dynamically integrate the coupling of multiple processes, real-time prediction and warning are carried out, providing galvanized steel plate thickness warning methods, systems and equipment, and realizing accurate modeling and active prevention and control of thickness changes throughout the entire process.
It significantly improves the prediction accuracy of galvanized steel plate thickness, avoids batch quality problems, and realizes the upgrade from passive correction to active prevention and control.
Smart Images
Figure CN120611267A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of quality control of galvanized steel plates, and in particular to a thickness early warning method, system, equipment and medium for galvanized steel plates. Background Art
[0002] Galvanized steel sheet, a key metal material, is widely used in construction, automotive, and home appliances. Its thickness accuracy directly impacts product strength, corrosion resistance, and processability. Galvanizing steel sheet requires multiple steps, including heating, galvanizing, and skin-passing. Thickness is influenced by multiple factors, including raw material characteristics, process parameters, and equipment status. Even millimeter-level deviations can result in out-of-tolerance (OOP) errors. Traditional manual control techniques struggle to meet these high-precision requirements, necessitating the urgent need for intelligent technology to achieve precise control throughout the entire process.
[0003] Existing technologies implement thickness control through empirically statistically based mean reduction setting methods, single-process parameter threshold monitoring methods, or post-tolerance feedback control methods for finished product thickness. The empirically statistical mean reduction setting method uses historical average reduction values for steel grades and specifications to set process parameters (such as furnace tension and skin-pass elongation), reducing thickness fluctuations for similar products. The single-process parameter threshold monitoring method deploys sensors in specific processes (such as the furnace and skin-pass mill) to monitor parameters such as temperature and tension and trigger threshold alarms to assist in local process adjustments. The post-tolerance feedback control method detects thickness deviations and issues an alarm, guiding subsequent production parameter corrections.
[0004] However, the existing thickness control methods for galvanized steel sheets have obvious defects: the thinning amount mean setting method based on empirical statistics has a single input parameter and ignores the full-process data such as raw material thickness, width and zinc layer quality, resulting in insufficient model prediction accuracy; the single-process parameter threshold monitoring method has limited coverage and only statically alarms for single-process parameters, and cannot reflect the coupling effect of multiple processes; the feedback control method has a delayed warning after the finished product thickness exceeds the tolerance, and can only be passively adjusted after the deviation, and cannot predict risks in advance, which is prone to batch quality problems. Summary of the Invention
[0005] In response to the technical problems of the existing galvanized steel plate thickness warning method, such as single parameter dimension, limited coverage and delayed warning, which lead to insufficient thickness prediction accuracy, delayed adjustment and batch quality risks, the present application provides a galvanized steel plate thickness warning method, system, equipment and medium. By comprehensively collecting raw materials and galvanizing process parameters, dynamically integrating multi-process coupling effects of pre-trained models and real-time prediction and warning, the problems of insufficient parameter dimension, local coverage limitation and delayed alarm are solved, the thickness prediction accuracy is significantly improved and active prevention and control of abnormal trends are realized.
[0006] In a first aspect, the present application provides a method for early warning of the thickness of a galvanized steel sheet, comprising the following steps: S1. Collect raw material parameters and galvanizing process parameters of the target galvanized steel sheet; S2. Calculate the predicted thickness of the galvanized steel sheet using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet , the formula is:
[0007] Where, is the thickness of raw strip steel; is the thickness variation of the furnace area; is the amount of thickening caused by the zinc layer; The thickness reduction for finishing; The thickness reduction caused by tension straightening; S3. Compare the predicted thickness of the galvanized steel sheet with the preset product thickness tolerance threshold, and issue an early warning when the predicted thickness of the galvanized steel sheet exceeds the product thickness threshold.
[0008] It should be further explained that in step S1, the raw material parameters include the raw material strip thickness of the target galvanized steel sheet. , raw material strip width w, raw material strip initial temperature , galvanizing process parameters include furnace tension , furnace zone temperature , finishing elongation , tensile elongation , zinc layer mass density .
[0009] It should be further explained that, in step S2, The calculation method is:
[0010] Where, is the tension thickness variation; is the thickness change due to thermal expansion; is the furnace area tension; is the elastic modulus of the raw strip steel; w is the width of the raw steel strip; is the thermal expansion coefficient of the raw steel strip; is the temperature change of the furnace area, ; is the heating temperature of the furnace area, is the initial temperature of the raw strip.
[0011] It should be further explained that the elastic modulus of the raw strip steel and thermal expansion coefficient The method to obtain is: Obtaining raw material parameters, galvanizing process parameters, and actual thickness measurements of multiple historical batches of galvanized steel sheets of the same type as the steel sheets to be galvanized; Substitute the raw material parameters, galvanizing process parameters, thickness and measured values of galvanized steel sheet thickness of each historical batch into the galvanized steel sheet thickness prediction model, and simultaneously solve the elastic modulus of the raw material strip steel. and thermal expansion coefficient .
[0012] It should be further explained that, in step S2, The calculation method is:
[0013] Where, is the surface density of the zinc layer; is the density of zinc.
[0014] It should be further explained that, in step S2, The calculation method is:
[0015] Where, is the smoothing elongation.
[0016] It should be further explained that, in step S2, The calculation method is:
[0017] Where, is the tensile elongation.
[0018] It should be further explained that in step S3, the early warning includes: The deviation between the predicted thickness of galvanized steel sheet and the product thickness threshold; The warning level is divided into three levels: mild, moderate, and severe according to the size of the deviation; Adjustment suggestions, including the adjustment direction and recommended adjustment amount of at least one raw material parameter or galvanizing process parameter.
[0019] In a second aspect, the present application provides a galvanized steel sheet thickness warning system for implementing the above-mentioned thickness warning method, comprising: Data acquisition module, used to collect raw material parameters and galvanizing process parameters of target steel sheets to be galvanized; A thickness prediction module is used to calculate the thickness prediction value of the galvanized steel sheet using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet; The early warning module is used to compare the predicted value of the galvanized steel sheet thickness with the preset product thickness tolerance threshold, and issue an early warning when the predicted value of the galvanized steel sheet thickness exceeds the product thickness threshold.
[0020] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the above-mentioned galvanized steel plate thickness warning method when executing the computer program.
[0021] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, which implements the steps of the above-mentioned galvanized steel plate thickness warning method when the computer program is executed by a processor.
[0022] It can be seen from the above technical solutions that this application has the following advantages: 1. This application breaks through the limitations of traditional methods that rely on single process parameters by collecting the raw material parameters and galvanizing process parameters of the target steel sheets to be galvanized, and completely covers the multi-dimensional data input of raw material characteristics and the entire galvanizing process. It solves the problem of insufficient prediction accuracy caused by the single parameter dimension in the thinning amount mean setting method based on empirical statistics, and provides comprehensive data support for the thickness prediction of galvanized steel sheets.
[0023] 2. This application calculates the predicted value of the thickness of the galvanized steel sheet by using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet, integrates the coupling effect of multiple processes, overcomes the defect that the single process parameter threshold monitoring method can only provide local alarms, and realizes accurate modeling of thickness changes in the entire process, significantly improving the prediction reliability under complex working conditions.
[0024] 3. This application calculates the predicted thickness value of the galvanized steel plate in real time and dynamically compares it with the preset tolerance threshold. It immediately issues an alert when the predicted value exceeds the threshold, eliminating the lag problem of the feedback control method after the finished product thickness exceeds the tolerance, identifying abnormal thickness trends in advance and guiding parameter adjustments, avoiding batch tolerance problems, and realizing an upgrade from passive correction to active prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0026] Figure 1 This is a flow chart of a thickness warning method for galvanized steel sheets in one embodiment of the present application.
[0027] Figure 2 It is a schematic block diagram of a thickness warning system for galvanized steel sheets in one embodiment of the present application.
[0028] Figure 3 It is a schematic diagram of the hardware structure of an electronic device in one embodiment of the present application. DETAILED DESCRIPTION
[0029] In order to make the application objectives, features, and advantages of this application more obvious and easy to understand, the technical solutions protected by this application will be clearly and completely described below using specific embodiments and drawings. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this patent, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this patent.
[0030] The thickness warning method of galvanized steel sheets involved in this application is mainly aimed at the field of galvanized steel sheet quality control technology. By collecting the raw material parameters and galvanizing process parameters of the target steel sheets to be galvanized, it breaks through the limitation of the traditional method that relies on single process parameters, completely covers the multi-dimensional data input of raw material characteristics and the entire galvanizing process, and solves the problem of insufficient prediction accuracy caused by the single parameter dimension in the thinning amount mean setting method based on empirical statistics, and provides comprehensive data support for the thickness prediction of the galvanized steel sheets; by using the raw material parameters and galvanizing process parameters of the target steel sheets to be galvanized, the thickness prediction value of the galvanized steel sheets is calculated, and the coupling effect of multiple processes is integrated to overcome the defect that the single process parameter threshold monitoring method can only provide local alarms, and realize accurate modeling of thickness changes in the whole process, and significantly improve the prediction reliability under complex working conditions; by real-time calculation of the galvanized steel sheet thickness prediction value and dynamic comparison with the preset tolerance threshold, an immediate warning is issued when the prediction value exceeds the threshold, eliminating the lag problem of the feedback control method after the finished product thickness exceeds the tolerance, identifying the thickness abnormality trend in advance and guiding parameter adjustment, avoiding batch tolerance problems, and realizing the upgrade from passive correction to active prevention and control.
[0031] The thickness warning method of galvanized steel plates involved in this application is mainly aimed at the technical problems of existing galvanized steel plate thickness warning methods, which have a single parameter dimension, limited coverage and warning lag, resulting in insufficient thickness prediction accuracy, adjustment lag and batch quality risks.
[0032] The following describes in detail the galvanized steel sheet thickness warning method involved in this application. Specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of this application. However, it should be clear to those skilled in the art that this application can also be implemented in other embodiments without these specific details.
[0033] In the galvanized steel sheet thickness early warning method involved in this application, the term "comprising" is used to indicate the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. The terms "including," "comprising," "having" and their variations all mean "including but not limited to," unless otherwise specifically emphasized.
[0034] To facilitate the clear description of the technical solutions of this application, the words "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or order of execution, and the words "first" and "second" do not necessarily mean different.
[0035] The phrases "one embodiment" or "some embodiments" described in this application mean that the specific features, structures, or characteristics described in the embodiment are included in one or more embodiments of the application. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in other embodiments," etc. that appear in different places in this application do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized.
[0036] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.
[0037] The thickness warning method of the galvanized steel plate provided in the embodiment of the present application is executed by a computer device, and accordingly, the thickness warning system of the galvanized steel plate runs in the computer device.
[0038] The following are some explanations of terms in this plan to facilitate a better understanding of this plan: Physics-guided parameter calibration models: These models, derived from the combination of physics-informed modeling and parameter calibration, incorporate physical laws, mechanisms, or prior knowledge as constraints during model construction or parameter adjustment. By combining physical principles with data-driven approaches, these models guide the optimization of model parameters toward those consistent with actual physical laws, ensuring that model outputs accurately reflect real-world physical phenomena or match observed data. Essentially, they leverage physical priors to enhance model reliability and interpretability.
[0039] Parameter identification equation: The parameter identification equation is an expression used to establish the mathematical relationship between the parameters to be identified and the measurable variables in the system. It usually contains the unknown parameters of the system. By analyzing the input and output data of the system and solving the equation using mathematical modeling or optimization algorithms, the specific values of the parameters can be determined, and the quantitative identification and accurate description of the system parameters can be achieved. It is the core mathematical tool for obtaining system parameter information from observation data.
[0040] Figure 1 This is a flow chart of a thickness warning method for galvanized steel sheets according to an embodiment of the present application. Figure 1 The execution subject may be a galvanized steel plate thickness warning system. According to different requirements, the order of the steps in the flow chart may be changed, and some steps may be omitted.
[0041] like Figure 1 As shown, the thickness warning method of the galvanized steel plate includes: Step S1, collecting raw material parameters and galvanizing process parameters of the target steel sheet to be galvanized.
[0042] By collecting the raw material parameters and galvanizing process parameters of the target galvanized steel sheet, comprehensive data acquisition of the initial state and process conditions of the raw steel strip is achieved, providing a complete input variable basis for the subsequent thickness prediction model and ensuring the integrity of key influencing factors in the prediction process.
[0043] In some specific embodiments, the raw material parameters include the target raw material strip thickness of the galvanized steel sheet. , raw material strip width w, raw material strip initial temperature , galvanizing process parameters include furnace tension , furnace zone temperature , finishing elongation , tensile elongation , zinc layer mass density .
[0044] By expanding the raw material parameters to the thickness, width, and initial temperature of the raw strip, and covering the galvanizing process parameters to the furnace zone tension, temperature, skin-pass elongation, straightening elongation, and zinc layer mass surface density, a refined characterization of the coupling effect of raw material characteristics and process parameters is achieved, providing more comprehensive data support for thickness prediction under the synergistic influence of multiple variables.
[0045] Step S2, using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet to calculate the thickness prediction value of the galvanized steel sheet , the formula is:
[0046] Where, is the thickness of raw strip steel; is the thickness variation of the furnace area; is the amount of thickening caused by the zinc layer; The thickness reduction for finishing; It is the thickness reduction caused by tension straightening.
[0047] By calculating the predicted value of galvanized steel sheet thickness based on raw material parameters and galvanizing process parameters, a comprehensive quantitative modeling of furnace area thickness changes, zinc layer thickening, and skin pass and straightening thinning is achieved. This can accurately predict the superimposed effect of thickness changes in each process stage and avoid systematic deviations caused by single factor correction.
[0048] In some specific embodiments, The calculation method is:
[0049] Where, is the tension thickness variation; is the thickness change due to thermal expansion; is the furnace area tension; is the elastic modulus of the raw strip steel; w is the width of the raw steel strip; is the thermal expansion coefficient of the raw steel strip; is the temperature change of the furnace area, ; is the heating temperature of the furnace area, is the initial temperature of the raw strip.
[0050] By combining the calculation methods of furnace zone tension thickness change and thermal expansion thickness change, physical modeling of the influence of furnace zone tension and temperature changes on strip thickness is realized. This can accurately separate the elastic deformation and thermal expansion effects and avoid the errors introduced by single empirical coefficient estimation.
[0051] In some specific embodiments, the elastic modulus of the raw steel strip is and thermal expansion coefficient The method to obtain is: Obtaining raw material parameters, galvanizing process parameters, and actual thickness measurements of multiple historical batches of galvanized steel sheets of the same type as the steel sheets to be galvanized; Substitute the raw material parameters, galvanizing process parameters, thickness and measured values of galvanized steel sheet thickness of each historical batch into the galvanized steel sheet thickness prediction model, and simultaneously solve the elastic modulus of the raw material strip steel. and thermal expansion coefficient .
[0052] By simultaneously solving the elastic modulus and thermal expansion coefficient of the raw strip steel based on historical data, dynamic calibration of material characteristic parameters is achieved, eliminating the applicability deviation of fixed parameters under different batches of raw materials or process conditions, and improving the generalization ability of the thickness prediction model.
[0053] In some specific embodiments, The calculation method is:
[0054] Where, is the surface density of the zinc layer; is the density of zinc.
[0055] The zinc layer thickness increase is calculated by the ratio of the zinc layer mass surface density to the zinc density, which directly quantifies the contribution of the zinc layer coverage to the thickness and avoids the estimation error of the thickness increase caused by the traditional empirical formula ignoring the uniformity of the zinc layer distribution or density fluctuation. In some specific embodiments, The calculation method is:
[0056] Where, is the smoothing elongation.
[0057] Through the dynamic correction formula of the skin-pass elongation to the skin-pass thinning amount, the nonlinear relationship between the strip extension deformation and thickness thinning in the skin-pass process is modeled, which can accurately reflect the continuity of the thickness change before and after skin-passing and avoid the problem of ignoring the strain strengthening effect by fixing the thinning coefficient.
[0058] In some specific embodiments, The calculation method is:
[0059] Where, is the tensile elongation.
[0060] Through the dynamic correction formula of the tensile elongation to the tensile thinning amount, the staged influence of elastic-plastic deformation on thickness thinning in the tensile straightening process is modeled, which can accurately quantify the sensitivity of the tensile straightening process parameter adjustment to the final thickness and provide a quantitative basis for process optimization.
[0061] Step S3, comparing the predicted value of the galvanized steel sheet thickness with a preset product thickness tolerance threshold, and issuing an early warning when the predicted value of the galvanized steel sheet thickness exceeds the product thickness threshold.
[0062] By dynamically comparing the predicted thickness of galvanized steel plates with the preset tolerance threshold in real time, an early warning is triggered immediately when the predicted value exceeds the threshold, eliminating the hysteresis defect of the traditional alarm mechanism after the tolerance is exceeded. Abnormal thickness trends are identified in advance, and production personnel are guided to adjust key parameters such as furnace tension and finishing elongation. This solves the batch quality problems caused by passive correction and realizes the upgrade of active control from "post-processing" to "pre-emptive prevention and control".
[0063] In some specific embodiments, the early warning includes: The deviation between the predicted thickness of galvanized steel sheet and the product thickness threshold; The warning level is divided into three levels: mild, moderate, and severe according to the size of the deviation; Adjustment suggestions, including the adjustment direction and recommended adjustment amount of at least one raw material parameter or galvanizing process parameter.
[0064] Through the graded warning mechanism, the thickness deviation is divided into three levels: mild, moderate and severe, and specific parameter adjustment suggestions are associated. This solves the problem of low adjustment efficiency caused by the ambiguity of traditional alarm information and provides operators with clear risk level determination and intervention measures guidance.
[0065] In a specific embodiment, the steps of the galvanized steel sheet thickness early warning method include: Step S1, collecting the raw material parameters and galvanizing process parameters of the target galvanized steel sheet, the raw material parameters include the raw material strip thickness of the target galvanized steel sheet , raw material strip width w, raw material strip initial temperature , galvanizing process parameters include furnace tension , furnace zone temperature , finishing elongation , tensile elongation , zinc layer mass density .
[0066] Step S2, using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet to calculate the thickness prediction value of the galvanized steel sheet , the formula is:
[0067]
[0068]
[0069]
[0070]
[0071] Where, is the thickness of raw strip steel; is the thickness variation of the furnace area; is the tension thickness variation; is the thickness change due to thermal expansion; is the furnace area tension; is the elastic modulus of the raw strip steel material; w is the width of the raw steel strip; is the thermal expansion coefficient of the raw steel strip; is the temperature change of the furnace area, , is the heating temperature of the furnace area, is the initial temperature of the raw strip; is the amount of thickening caused by the zinc layer; is the surface density of the zinc layer; is the density of zinc; The thickness reduction for finishing; The thickness reduction caused by tension straightening; is the skin-finishing elongation; is the tensile elongation; Elastic modulus of raw strip steel and thermal expansion coefficient The method to obtain is: Obtaining raw material parameters, galvanizing process parameters, and actual thickness measurements of multiple historical batches of galvanized steel sheets of the same type as the steel sheets to be galvanized; Substitute the raw material parameters, galvanizing process parameters, thickness and measured values of galvanized steel sheet thickness of each historical batch into the galvanized steel sheet thickness prediction model, and simultaneously solve the elastic modulus of the raw material strip steel. and thermal expansion coefficient .
[0072] Step S3: Compare the predicted thickness of the galvanized steel sheet with a preset product thickness tolerance threshold. When the predicted thickness of the galvanized steel sheet exceeds the product thickness threshold, an early warning is issued. The early warning includes: The deviation between the predicted thickness of galvanized steel sheet and the product thickness threshold; The warning level is divided into three levels: mild, moderate, and severe according to the size of the deviation; Adjustment suggestions, including the adjustment direction and recommended adjustment amount of at least one raw material parameter or galvanizing process parameter.
[0073] The galvanized steel sheet thickness prediction model in this embodiment is used to process two batches of galvanized steel sheets. The raw material steel grade of the galvanized steel sheets in batch 1 is DX51D+Z, with a thickness of 0.65 mm, a raw material width of 1050 mm, a yield strength of 245 MPa, a heating temperature of 760°C, a furnace zone tension of 8.1 kN, a skin-pass mill elongation of 1.5%, a tension leveler elongation of 0.2%, a zinc layer thickness of 280 g / m², and tensions of both the skin-pass and tension leveler are 20 kN. The raw material parameters and galvanizing process parameters of batch 1 were used to obtain a predicted thickness of galvanized steel sheet of 0.68376 mm. The actual thickness of batch 1 after galvanizing was 0.68392 mm, with a theoretical error of 0.00016 mm. The raw material steel grade of the galvanized sheet of batch 2 is DX53D+Z, with a thickness of 0.573mm, a raw material width of 1354mm, a yield strength of 149MPa, a heating temperature of 840℃, a furnace zone tension of 6.0KN, a skin-pass mill elongation of 0.6%, a straightening mill elongation of 0%, a zinc layer thickness of 80g / m², and a skin-pass and straightening mill tension of 23KN. The raw material parameters and galvanizing process parameters of batch 2 were input into the pre-trained galvanized steel plate thickness prediction model, and the predicted value of the galvanized steel plate thickness was 0.58624 mm. The actual thickness of batch 2 after galvanizing was 0.579927 mm, and the theoretical error was 0.006313 mm.
[0074] The following is an embodiment of the thickness warning system of the galvanized steel plate provided in the embodiments of the present application. The thickness warning system and the thickness warning method of the galvanized steel plate in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the thickness warning system of the galvanized steel plate, please refer to the embodiment of the thickness warning method of the galvanized steel plate.
[0075] like Figure 2 As shown, the thickness warning system of galvanized steel sheet includes: Data acquisition module, used to collect raw material parameters and galvanizing process parameters of target steel sheets to be galvanized; A thickness prediction module is used to calculate the thickness prediction value of the galvanized steel sheet using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet; The early warning module is used to compare the predicted value of the galvanized steel sheet thickness with the preset product thickness tolerance threshold, and issue an early warning when the predicted value of the galvanized steel sheet thickness exceeds the product thickness threshold.
[0076] The thickness warning system of this embodiment is used to implement a thickness warning method for galvanized steel sheets, and the steps include: S1. Collect raw material parameters and galvanizing process parameters of the target galvanized steel sheet; S2. Calculate the predicted thickness of the galvanized steel sheet using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet , the formula is:
[0077] Where, is the thickness of raw strip steel; is the thickness variation of the furnace area; is the amount of thickening caused by the zinc layer; The thickness reduction for finishing; The thickness reduction caused by tension straightening; S3. Compare the predicted thickness of the galvanized steel sheet with the preset product thickness tolerance threshold, and issue an early warning when the predicted thickness of the galvanized steel sheet exceeds the product thickness threshold.
[0078] This application also provides an electronic device for implementing each embodiment of this application. Figure 3 A hardware structure diagram of an electronic device for implementing various embodiments of the present application is shown in FIG. Figure 3 As shown, the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor.
[0079] Those skilled in the art will understand that the electronic device structure involved in the embodiments of the present application does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.
[0080] In the embodiments of the present application, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or claimed herein.
[0081] In the embodiment of the present application, the processor can be implemented by using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an embodiment can be implemented in a controller. For software implementation, an embodiment such as a process or function can be implemented with a separate software module that allows the execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.
[0082] In addition, the electronic device includes some functional modules not shown, which will not be described here.
[0083] Those skilled in the art will appreciate that various aspects of the electronic device provided herein may be implemented as a system, method, or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."
[0084] The present application also provides a storage medium storing a program product capable of implementing a method for early warning the thickness of a galvanized steel sheet. In some possible implementations, various aspects of the present application may also be implemented in the form of a program product comprising program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps described in the "Exemplary Methods" section above according to various exemplary implementations of the present application.
[0085] The storage medium can be any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
[0086] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for early warning of the thickness of a galvanized steel sheet, characterized in that: include: S1. Collect raw material parameters and galvanizing process parameters of the target galvanized steel sheet; S2. Calculate the predicted thickness of the galvanized steel sheet using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet , the formula is: Where, is the thickness of raw strip steel; is the thickness variation of the furnace area; is the amount of thickening caused by the zinc layer; The thickness reduction for finishing; The thickness reduction caused by tension straightening; S3. Compare the predicted thickness of the galvanized steel sheet with the preset product thickness tolerance threshold, and issue an early warning when the predicted thickness of the galvanized steel sheet exceeds the product thickness threshold.
2. The thickness warning method according to claim 1, characterized in that: In step S1, the raw material parameters include the raw material strip thickness of the target galvanized steel sheet , raw material strip width w, raw material strip initial temperature , galvanizing process parameters include furnace tension , furnace zone temperature , finishing elongation , tensile elongation , zinc layer mass density .
3. The thickness warning method according to claim 2, characterized in that: In step S2, The calculation method is: Where, is the tension thickness variation; is the thickness change due to thermal expansion; is the furnace area tension; is the elastic modulus of the raw strip steel; w is the width of the raw steel strip; is the thermal expansion coefficient of the raw steel strip; is the temperature change of the furnace area, ; is the heating temperature of the furnace area, is the initial temperature of the raw strip.
4. The thickness warning method according to claim 3, characterized in that: Elastic modulus of raw strip steel and thermal expansion coefficient The method to obtain is: Obtaining raw material parameters, galvanizing process parameters, and actual thickness measurements of multiple historical batches of galvanized steel sheets of the same type as the steel sheets to be galvanized; Substitute the raw material parameters, galvanizing process parameters, thickness and measured values of galvanized steel sheet thickness of each historical batch into the galvanized steel sheet thickness prediction model, and simultaneously solve the elastic modulus of the raw material strip steel. and thermal expansion coefficient .
5. The thickness warning method according to claim 2, characterized in that: In step S2, The calculation method is: Where, is the surface density of the zinc layer; is the density of zinc.
6. The thickness warning method according to claim 2, characterized in that: In step S2, The calculation method is: Where, is the smoothing elongation.
7. The thickness warning method according to claim 2, characterized in that: In step S2, The calculation method is: Where, is the tensile elongation.
8. A galvanized steel plate thickness warning system, characterized in that: A method for implementing the thickness warning method according to any one of claims 1 to 7, comprising: Data acquisition module, used to collect raw material parameters and galvanizing process parameters of target steel sheets to be galvanized; A thickness prediction module is used to calculate the thickness prediction value of the galvanized steel sheet using the raw material parameters and galvanizing process parameters of the target galvanized steel sheet; The early warning module is used to compare the predicted value of the galvanized steel sheet thickness with the preset product thickness tolerance threshold, and issue an early warning when the predicted value of the galvanized steel sheet thickness exceeds the product thickness threshold.
9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the thickness warning method according to any one of claims 1 to 7 when executing the computer program.
10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the thickness warning method according to any one of claims 1 to 7.