Rolling mill equipment design parameter optimization method and device, equipment and storage medium

Abstract

The invention provides a rolling mill equipment design parameter optimization method and device, equipment and a storage medium, and relates to the technical field of equipment parameter optimizing.The rolling mill equipment design parameter optimization method comprises the steps that original design data of to-be-optimized rolling mill equipment is obtained, and an equal-proportion roll changing force model is established based on the original design data; analyzing the roll changing force model to obtain key design parameters of the rolling mill equipment to be optimized; and classifying the key design parameters to obtain a plurality of classification data, and performing parameter optimization on each classification data according to a preset condition to obtain actual design parameters of the rolling mill equipment to be optimized. Through systematized data acquisition, model establishment and parameter analysis and optimization, the production requirements can be met, so that the system can operate more efficiently in the production process, the product quality and the production efficiency are improved, and meanwhile, the fault rate and the maintenance cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment parameter optimization, and in particular to a method, device, equipment and storage medium for optimizing design parameters of rolling mill equipment. Background Art

[0002] In the plate and strip rolling process, the rolling mill stand plays a vital role. Its main functions include providing the installation position of the rolls and ensuring the rigidity of the roll system, thereby ensuring the uniform thickness of the materials (such as steel plates, steel strips and other types of plates or strips) during the rolling process. In addition, the rolling mill stand must also provide the reaction force required during the rolling process to ensure the stability and safety of the operation.

[0003] Although different rolling mills are similar in overall structure, their designs often present the characteristics of small-batch customization due to the diversity of rolling tasks. In the traditional design process, the original design is usually selected based on past mature experience to meet the needs of new solutions. However, with the continuous changes in rolling materials and processes, relying solely on experience can no longer fully meet the requirements of modern production. Designers often need to randomly adjust local dimensions and submit the solution to the finite element simulation department for trial calculation. When the simulation results do not meet the requirements, it is often necessary to iterate repeatedly (possibly up to a dozen times) to finally achieve a qualified design. This repeated adjustment process not only reduces work efficiency, but also leads to delays in project progress. Summary of the invention

[0004] The problem solved by the present invention is how to improve the efficiency of the rolling mill equipment design stage and reduce the cost of the rolling mill equipment.

[0005] In order to solve the above problems, the present invention provides a method, device, equipment and storage medium for optimizing design parameters of rolling mill equipment.

[0006] In a first aspect, the present invention provides a method for optimizing design parameters of a rolling mill, comprising:

[0007] Acquire original design data of the rolling mill equipment to be optimized, and establish a proportional roll-changing force model based on the original design data;

[0008] Analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized;

[0009] The key design parameters are classified to obtain a plurality of classified data, and parameters of each of the classified data are optimized according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

[0010] Optionally, the analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized includes:

[0011] Get preset optimization indicators;

[0012] Analyze the roller-changing force model according to each of the preset optimization indicators to obtain corresponding temporary key design parameters;

[0013] A comprehensive analysis is performed on each of the key design parameters according to a preset optimization objective function to obtain corresponding key design parameters.

[0014] Optionally, after comprehensively analyzing the key design parameters according to the preset optimization objective function to obtain the corresponding key design parameters, the method further includes:

[0015] Using a regression analysis model to perform sensitivity analysis on each of the key design parameters to obtain corresponding sensitivity data;

[0016] Each of the sensitivity data is screened to obtain target key design parameters, and the target key design parameters are used as key design parameters of the rolling mill equipment to be optimized.

[0017] Optionally, the key design parameters are classified to obtain a plurality of classified data, including:

[0018] Obtaining a preset classification standard, wherein the preset classification standard includes mechanical parameters, material property parameters and operating parameters;

[0019] The key design parameters are classified according to a preset classification standard to obtain a plurality of classification data.

[0020] Optionally, the preset conditions include performance indicators and cost indicators; and the step of performing parameter optimization on each of the classified data according to the preset conditions to obtain actual design parameters of the rolling mill equipment to be optimized includes:

[0021] When the preset condition is the performance index, the mechanical parameters contained in the classified data are screened to obtain target mechanical parameters, and the target mechanical parameters are optimized by using a genetic algorithm and setting constraints to obtain actual design parameters of the rolling mill equipment to be optimized;

[0022] When the preset condition is the cost index, the material property parameters contained in the classification data are calculated to obtain cost data, and the corresponding weight parameters are adjusted to obtain the optimal material combination with the lowest cost.

[0023] Optionally, the original design data includes geometric parameter data and material parameter data; and establishing the proportional roller-changing force model based on the original design data includes:

[0024] Determining contact length data according to the geometric parameter data, and calculating corresponding rolling force according to the contact length data and the material parameter data;

[0025] The roll-changing force model is obtained by performing proportional conversion based on the conversion coefficient and the rolling force.

[0026] Optionally, determining the contact length data according to the geometric parameter data comprises:

[0027] Determine the contact length data according to the geometric parameter data through formula 1;

[0028] Wherein, the formula 1 is:

[0029]

[0030] Wherein, L is the contact length data, D is the roller diameter, and h is the roller spacing.

[0031] In a second aspect, the present invention provides a device for optimizing design parameters of a rolling mill, comprising:

[0032] An acquisition unit, used for acquiring original design data of the rolling mill equipment to be optimized, and establishing a proportional roll-changing force model based on the original design data;

[0033] A processing unit, used for analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized;

[0034] The processing unit is also used to classify the key design parameters to obtain a plurality of classification data, and optimize the parameters of each classification data according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

[0035] In a third aspect, the present invention provides a rolling mill equipment design parameter optimization device, including a memory and a processor;

[0036] The memory is used to store computer programs;

[0037] The processor is used to implement the rolling mill equipment design parameter optimization method as described in the first aspect when executing the computer program.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing the design parameters of the rolling mill equipment as described in the first aspect is implemented.

[0039] The beneficial effects of the rolling mill equipment design parameter optimization method and device of the present invention are:

[0040] First, collect all the original design data related to the rolling mill equipment to be optimized. These data usually include geometric parameters (such as roller diameter, roller spacing, contact length, etc.), material parameters (such as material strength, hardness, ductility, etc.), operating parameters (such as running speed, temperature, etc.) and historical operation data. By accurately collecting the original design data, reliable basic data is provided for subsequent model establishment and parameter analysis to ensure the accuracy and feasibility of the analysis.

[0041] Then, based on the collected geometric parameters and material parameters, the roll-changing force of the rolling mill is calculated using physical formulas (such as mechanical equations), and a proportional roll-changing force model is established. This model can describe the mechanical relationship in the rolling process and help predict the change of roll-changing force under different conditions. After establishing an accurate roll-changing force model, the mechanical characteristics of the rolling process can be understood more effectively, which helps to optimize the design of the rolling mill so that it can maintain stable performance when dealing with different working conditions.

[0042] The obtained roll-changing force model is then used to analyze the key design parameters, identify and extract the key parameters that affect the performance of the rolling mill. These parameters may include roll spacing, roll speed, material yield strength, etc. By analyzing the model and identifying key design parameters, resources and time can be effectively concentrated on the parameters that most affect the performance of the rolling mill, laying a good foundation for subsequent optimization.

[0043] Finally, the key design parameters obtained by analysis are classified, such as mechanical parameters, material property parameters and operating parameters. Each category of parameters has different effects on the operating performance of the rolling mill. Parameter classification helps to systematically manage and optimize different types of parameters, making the optimization process clearer and providing a framework for subsequent refinement and optimization. And optimize each classified data according to project requirements and preset conditions (such as performance indicators and cost constraints). For example, if the preset condition is to improve performance, genetic algorithms may be used to optimize mechanical parameters; if the preset condition is to reduce costs, material property parameters are calculated and adjusted. Through the targeted optimization of parameters of different categories, the overall performance and economy of the rolling mill equipment can be effectively improved, ensuring that the desired effect is achieved in actual production while reducing operating costs. After classification and optimization, the actual design parameters based on the optimization of various parameters are finally obtained. At this time, the design parameters are more in line with the expected goals and can meet production needs, so that it can operate more efficiently during the production process, thereby improving product quality and production efficiency, while reducing failure rates and maintenance costs.

[0044] In summary, the present invention constructs an effective and efficient method for optimizing the design parameters of rolling mill equipment through systematic data acquisition, model building, parameter analysis and optimization. Its beneficial effects are not only reflected in the equipment performance and cost-effectiveness, but more importantly, it provides a solid foundation for the innovative design of rolling mill equipment and its application in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic flow chart of a method for optimizing design parameters of a rolling mill equipment according to an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of sensitivity data according to an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of the structure of a device for optimizing design parameters of a rolling mill according to an embodiment of the present invention;

[0048] Figure 4 It is a structural schematic diagram of a rolling mill equipment design parameter optimization device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0050] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0051] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0052] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0053] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0054] This embodiment provides a method, device, equipment and storage medium for optimizing design parameters of rolling mill equipment.

[0055] like Figure 1 As shown, a method for optimizing design parameters of a rolling mill equipment provided by an embodiment of the present invention includes:

[0056] S100, obtaining original design data of the rolling mill equipment to be optimized, and establishing a proportional roll-changing force model based on the original design data.

[0057] Specifically, first, it is necessary to collect all the original design data related to the rolling mill equipment to be optimized. This includes but is not limited to: geometric parameters of the rolling mill (such as roll diameter, roll spacing, rolling mill width, etc.); material properties (such as yield strength, tensile strength, elastic modulus, etc.); working parameters (such as rolling speed, temperature, amplitude change, etc.); rolling force data (such as maximum rolling force, average rolling force, etc.). A proportional roll change force model is established based on the original design data. This model refers to standardizing the roll force through a proportional relationship on the basis of considering the geometric parameters, material properties and working conditions of the rolling mill to simplify the model calculation process. Representative parameters can be selected for proportional conversion, such as the surface pressure, contact area, thickness change, etc. of the roll. For example, the relationship equation between the roll change force and the rolling force is established based on a virtual experiment, such as:

[0058] F = k·A·Δh;

[0059] Among them, F is the rolling force, k is the material constant, A is the contact area, and Δh is the thickness change.

[0060] The established model can be verified by using finite element simulation software. The original design data is input into the model to observe whether the rolling force output by the model is consistent with the measured data.

[0061] By establishing a proportional roll-changing force model, the changes in rolling force can be efficiently reflected, the design accuracy can be improved, and a more scientific basis can be provided for subsequent designs. Using this model, designers can quickly evaluate the impact of different parameter adjustments on rolling force, thereby reducing dependence on actual equipment, reducing the number of experiments, and improving design efficiency. With a stable and reliable roll-changing force model, designers can make reasonable parameter adjustments in the early stages of design, reduce the number of iterations, and thus shorten the project development cycle.

[0062] S200, analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized.

[0063] Specifically, after establishing the proportional roll-changing force model, the model needs to be analyzed in depth to extract the key design parameters of the rolling mill equipment to be optimized. Usually, the actual operating data of the existing rolling mill equipment and rolling process are compared with the roll-changing force model, and the key parameters affecting the roll-changing force, such as roll diameter, roll spacing, rolling speed, etc., are identified through optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.). And determine the sensitivity of each parameter to the roll-changing force, that is, analyze the degree of influence of its changes on the rolling force and the quality of the finished product. Among them, sensitivity analysis is performed to evaluate the impact of each design parameter on the output of the roll-changing force model (i.e., rolling force). This can be achieved through differential methods or simulation methods (such as Monte Carlo simulation). Determine which parameters are the most critical, usually focusing on parameters with higher sensitivity, where small changes may lead to significant changes in rolling force.

[0064] According to the results of sensitivity analysis, key parameters are optimized. For example, if it is found that the roll diameter has a greater impact on the roll-changing force, you can consider optimizing the roll diameter or replacing wear-resistant materials to improve the performance during rolling. Develop a parameter optimization plan, and if necessary, combine it with finite element analysis to simulate the rolling force performance under different parameters to find the best design solution.

[0065] By analyzing the roll-changing force model and extracting key design parameters, enterprises can achieve more scientific design decisions, ensuring that the rolling mill equipment meets various production requirements while optimizing the design scheme, improving overall efficiency, and ultimately achieving both economic and technological breakthroughs.

[0066] S300, classifying the key design parameters to obtain a plurality of classification data, and optimizing the parameters of each of the classification data according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

[0067] Specifically, the key design parameters can be classified into the following categories according to their functions and influencing factors: geometric parameter category, material parameter category, working condition category, and production parameter category. The geometric parameter category may include roll diameter (D), roll spacing (H), mill width (W), etc. The material parameter category may include the yield strength ( y ), the tensile strength of the material ( t ), elastic modulus of the material (E), etc.; working conditions may include rolling speed (V), rolling temperature (T), rolling pressure (P), etc.; production parameters may include raw material thickness (h0), cooling intensity (Q), rolling times (N), etc.

[0068] To optimize the parameters of each classification data, it is necessary to set the objective function according to the preset conditions (such as production efficiency, product quality, cost, etc.) and use the optimization algorithm to adjust it. Choose a suitable optimization algorithm for parameter adjustment, such as: Genetic algorithm (GA): suitable for multi-peak optimization problems, capable of searching the solution space extensively. Particle swarm optimization (PSO): fast convergence based on the optimal solution of the group, suitable for continuous optimization problems. Simulated annealing (SA): find the global optimal solution by simulating the physical annealing process.

[0069] Through the implementation of the above optimization, the design of the rolling mill equipment to be optimized not only improves efficiency and quality, but also achieves better cost control and market adaptability, providing strong technical support for the enterprise in the highly competitive market.

[0070] In this embodiment, first, all the original design data related to the rolling mill equipment to be optimized are collected. These data usually include geometric parameters (such as roller diameter, roller spacing, contact length, etc.), material parameters (such as material strength, hardness, ductility, etc.), operating parameters (such as operating speed, temperature, etc.) and historical operation data, etc. By accurately collecting the original design data, reliable basic data is provided for subsequent model establishment and parameter analysis, ensuring the accuracy and feasibility of the analysis.

[0071] Then, based on the collected geometric parameters and material parameters, the roll-changing force of the rolling mill is calculated using physical formulas (such as mechanical equations), and a proportional roll-changing force model is established. This model can describe the mechanical relationship in the rolling process and help predict the change of roll-changing force under different conditions. After establishing an accurate roll-changing force model, the mechanical characteristics of the rolling process can be understood more effectively, which helps to optimize the design of the rolling mill so that it can maintain stable performance when dealing with different working conditions.

[0072] The obtained roll-changing force model is then used to analyze the key design parameters, identify and extract the key parameters that affect the performance of the rolling mill. These parameters may include roll spacing, roll speed, material yield strength, etc. By analyzing the model and identifying key design parameters, resources and time can be effectively concentrated on the parameters that most affect the performance of the rolling mill, laying a good foundation for subsequent optimization.

[0073] Finally, the key design parameters obtained by analysis are classified, such as mechanical parameters, material property parameters and operating parameters. Each category of parameters has different effects on the operating performance of the rolling mill. Parameter classification helps to systematically manage and optimize different types of parameters, making the optimization process clearer and providing a framework for subsequent refinement and optimization. And optimize each classified data according to project requirements and preset conditions (such as performance indicators and cost constraints). For example, if the preset condition is to improve performance, genetic algorithms may be used to optimize mechanical parameters; if the preset condition is to reduce costs, material property parameters are calculated and adjusted. Through the targeted optimization of parameters of different categories, the overall performance and economy of the rolling mill equipment can be effectively improved, ensuring that the desired effect is achieved in actual production while reducing operating costs. After classification and optimization, the actual design parameters based on the optimization of various parameters are finally obtained. At this time, the design parameters are more in line with the expected goals and can meet production needs, so that it can operate more efficiently during the production process, thereby improving product quality and production efficiency, while reducing failure rates and maintenance costs.

[0074] In summary, this embodiment constructs an effective and efficient method for optimizing the design parameters of rolling mill equipment through systematic data acquisition, model building, parameter analysis and optimization. Its beneficial effects are not only reflected in equipment performance and cost-effectiveness, but more importantly, it provides a solid foundation for the innovative design of rolling mill equipment and its application in the market.

[0075] Optionally, the analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized includes:

[0076] Get preset optimization indicators;

[0077] Analyze the roller-changing force model according to each of the preset optimization indicators to obtain corresponding temporary key design parameters;

[0078] A comprehensive analysis is performed on each of the key design parameters according to a preset optimization objective function to obtain corresponding key design parameters.

[0079] Optionally, after comprehensively analyzing the key design parameters according to the preset optimization objective function to obtain the corresponding key design parameters, the method further includes:

[0080] Using a regression analysis model to perform sensitivity analysis on each of the key design parameters to obtain corresponding sensitivity data;

[0081] Each of the sensitivity data is screened to obtain target key design parameters, and the target key design parameters are used as key design parameters of the rolling mill equipment to be optimized.

[0082] Specifically, before conducting the roll-changing force model analysis, it is first necessary to clarify the indicators to be optimized. For example: common preset optimization indicators may include: production efficiency, product quality, energy consumption, equipment life and operating costs, etc. For each preset optimization indicator, the roll-changing force model is analyzed in detail to determine the temporary key design parameters. The following are possible temporary key parameters: Production efficiency may include rolling speed (V): set to 1.5m / s to achieve higher output. Mill width (W): set to 1000mm to increase production capacity. Product quality may include: Roller diameter (D): set to 400mm to ensure good contact and rolling performance. Material yield strength (σ y ) and tensile strength (σ t ): Select materials with 400MPa and 550MPa. Energy consumption may include roller spacing (H): Select 15mm to optimize energy transfer and reduce power consumption. Equipment life may include the use of wear-resistant materials: Select materials with high wear resistance to reduce wear rate. Operating costs may include cooling efficiency (Q): Optimize to 30℃ / s to reduce energy consumption and heat loss.

[0083] In some embodiments, after determining the temporary key design parameters, a preset optimization objective function is used to perform a comprehensive analysis on these parameters. The objective function is in the following form:

[0084] Objective·Function·

[0085] =w1*Efficiency·+·w2Quality-w3*Energy·Consumption-w4*Cost

[0086] Among them, w1, w2, w3, and w4 are weight coefficients, reflecting the importance of each indicator. Objective Function is the optimization objective function, which indicates the value of the target to be maximized or minimized, which is usually used to evaluate the pros and cons of design, operation, or other decisions; Efficiency is production efficiency, which is usually used to indicate the number or quality of products produced per unit time. The higher the efficiency, the more efficient the production process; Quality: product quality, which usually involves indicators such as product precision, strength, and surface finish. The higher the quality, the stronger the customer satisfaction and market competitiveness; Energy Consumption is energy consumption, which indicates the energy consumed in the production process. It is usually expressed as the energy consumption per unit of product production. The lower the energy consumption, the less production cost and environmental impact; Cost is the operating cost, which involves the total cost incurred in the production process, including raw material costs, labor costs, equipment maintenance costs, etc. The lower the cost, the greater the financial benefit to the enterprise.

[0087] After obtaining the key design parameters, sensitivity analysis can be performed to evaluate the degree of influence of each parameter on the optimization target. The specific process can establish a regression analysis model: take the key design parameters as independent variables, take the value of the preset optimization objective function as the dependent variable, perform regression analysis, and construct a mathematical model. Through the regression model, the sensitivity data of each design parameter to the objective function is obtained, reflecting the influence of parameter changes on production efficiency, product quality, energy consumption and cost.

[0088] Finally, select the target key design parameters: According to the sensitivity data, select the design parameters that have the greatest impact on the objective function and finally determine them as the target key design parameters. These parameters will be the focus of subsequent optimization.

[0089] Through the above process, sensitivity analysis enables designers to have a deep understanding of key parameters, which helps to reduce design risks and optimize decisions during the adjustment process. And through these optimizations and analyses, the design of the rolling mill to be optimized not only improves various production and quality indicators, but also ensures the competitiveness of the enterprise in the market, laying the foundation for future development.

[0090] In some embodiments, such as Figure 2 As shown, the sensitivity data diagram is Figure 2 The correlation coefficient and sensitivity coefficient of the parameters with relatively large lateral deformation in the rolling mill equipment are shown in the figure. The parameters appearing in the figure are all parameters that have been screened according to the preset sensitivity index, for example, the preset sensitivity index is 10%. Among them, local sensitivity is local sensitivity, P1-beam-width is beam width, P2-beam-thick is beam thickness, P3-up-height is upper height, P6-foot-height is foot height, P13 is R (radius), P7-Geometry Mass is geometry mass, P8-X-disp Maximum is the maximum displacement in the X direction, P9-Z-disp-up Maximum is the maximum displacement in the Z direction, and P10-Zdisp-down Maximum is the minimum displacement in the Z direction. Output Parameters is the output parameters.

[0091] Optionally, the key design parameters are classified to obtain a plurality of classified data, including:

[0092] Obtaining a preset classification standard, wherein the preset classification standard includes mechanical parameters, material property parameters and operating parameters;

[0093] The key design parameters are classified according to a preset classification standard to obtain a plurality of classification data.

[0094] Specifically, first, determine the classification criteria. The preset classification criteria can be divided into the following three categories: Mechanical Parameters: Parameters related to the structure, shape, geometric characteristics and motion characteristics of the equipment. Material Properties Parameters: Parameters related to the physical, chemical and mechanical properties of the materials used, such as strength, toughness and wear resistance. Operational Parameters: Parameters related to the operating conditions, control strategies and working environment in the production process, such as temperature, pressure and rolling speed. After clarifying the classification criteria, classify the identified key design parameters. For example: Mechanical parameters may include roll diameter (D): affects rolling quality and finished product size. Mill width (W): determines production capacity and the type of products it can accommodate. Roller spacing (H): affects material flow and energy consumption in the rolling process. Material property parameters: material yield strength ( y ): Affects the forming ability during rolling. Tensile strength (τ t ): affects the durability and reliability of the finished product. Use of wear-resistant materials: improves the wear resistance of the equipment and reduces the wear rate. Operating parameters: Rolling speed (V): affects production efficiency and product quality. Cooling efficiency (Q): controls the temperature change of the product, affecting quality and energy consumption. Working temperature (T): affects the performance of the material and processing effect.

[0095] By classifying key design parameters, different types of design parameters can be more clearly identified and managed after classification, which is helpful for subsequent analysis and optimization. And by classifying parameters, it is easier to identify the parameters in each category that have the greatest impact or sensitivity on the objective function, providing a basis for subsequent optimization and decision-making. Special discussions and optimizations can be conducted for categories to develop improvement plans more efficiently. In the decision-making process, performance requirements and constraints can be quickly evaluated based on classification, which facilitates making wise decisions among various design options. That is, through this systematic parameter classification, design strategies can be more effectively managed and adjusted throughout the optimization process, thereby improving the performance and stability of rolling mill equipment.

[0096] Optionally, the preset conditions include performance indicators and cost indicators; and the step of performing parameter optimization on each of the classified data according to the preset conditions to obtain actual design parameters of the rolling mill equipment to be optimized includes:

[0097] When the preset condition is the performance index, the mechanical parameters contained in the classified data are screened to obtain target mechanical parameters, and the target mechanical parameters are optimized by using a genetic algorithm and setting constraints to obtain actual design parameters of the rolling mill equipment to be optimized;

[0098] When the preset condition is the cost index, the material property parameters contained in the classification data are calculated to obtain cost data, and the corresponding weight parameters are adjusted to obtain the optimal material combination with the lowest cost.

[0099] Specifically, in the design parameter optimization process, in order to achieve the design goal of the rolling mill equipment to be optimized, the classified data can be optimized according to preset conditions such as performance indicators and cost indicators. The preset conditions can include two types of indicators: Performance indicators: such as production efficiency, product quality, energy consumption, etc., the purpose is to improve the overall performance of the equipment. Cost indicators: including material costs, operating costs, etc., the purpose is to reduce the overall cost. According to the requirements of the performance indicators, the mechanical parameters contained in the classified data (such as roll diameter, mill width, roll spacing, etc.) are screened to determine the target mechanical parameters that need to be optimized. Genetic algorithm (GA) can be used as an optimization tool because it exhibits good global search capabilities when dealing with complex optimization problems. Set the relevant parameters of the genetic algorithm, such as population size, crossover rate, mutation rate, etc. And add specific constraints for the target mechanical parameters, such as physical boundaries, safety standards and performance upper limits, to ensure that the optimization results are within a feasible range.

[0100] Genetic algorithms are used to iteratively optimize the target mechanical parameters. Starting from multiple initial solutions, the quality of the solutions is continuously improved through operations such as selection, crossover and mutation, and ultimately the actual design parameters of the rolling mill equipment to be optimized are obtained.

[0101] Optimize material attribute parameters according to cost indicators, perform cost calculation based on material attribute parameters in classified data (such as strength, toughness, wear resistance, etc.), and consider the use cost of different materials in the entire production process. And set corresponding weight parameters for the performance and cost of different materials (for example, the improvement of material quality may correspond to higher costs), and seek a balance between performance and cost by adjusting these weight parameters.

[0102] Through optimization algorithms (such as linear programming, mixed integer programming, etc.), optimize each material combination to find the material combination with the lowest cost. Evaluate the overall performance of each material combination to ensure that it meets the preset performance requirements while reducing costs.

[0103] Through the above optimization process, 1. Through the optimization of performance indicators, the actual design parameters obtained can significantly improve the production efficiency and product quality of the rolling mill, thereby enhancing the market competitiveness of the enterprise.

[0104] 2. Optimization of cost indicators can effectively reduce material costs and operating costs, improve the utilization efficiency of production resources, and increase the economic benefits of the enterprise.

[0105] The optimization of mechanical and material parameters is combined with constraints to ensure that the resulting design not only improves performance but is also feasible and safe in actual production. At the same time, through the analysis and optimization of preset conditions, a systematic decision support is formed to provide a basis for future design and improvement.

[0106] Overall, this classification optimization process for performance and cost indicators provides a comprehensive and systematic approach to rolling mill equipment design, bringing significant economic and technical advantages.

[0107] Optionally, the original design data includes geometric parameter data and material parameter data; and establishing the proportional roller-changing force model based on the original design data includes:

[0108] Determining contact length data according to the geometric parameter data, and calculating corresponding rolling force according to the contact length data and the material parameter data;

[0109] The roll-changing force model is obtained by performing proportional conversion based on the conversion coefficient and the rolling force.

[0110] Optionally, determining the contact length data according to the geometric parameter data comprises:

[0111] Determine the contact length data according to the geometric parameter data through formula 1;

[0112] Wherein, the formula 1 is:

[0113]

[0114] Wherein, L is the contact length data, D is the roller diameter, and h is the roller spacing.

[0115] Specifically, first, it is necessary to collect the original design data of the rolling mill equipment. This data can be obtained from the equipment manufacturer's technical documents, design drawings or directly from existing equipment. It may mainly include

[0116] Geometric parameters: Roller diameter (D): The diameter of the roller, which affects the rolling force and contact area. Roller spacing (h): The vertical spacing between the rollers, which affects the material deformation. Roller width (W): The width of the roller, which affects the distribution of rolling force. Roller shape: such as cylindrical, conical, etc.

[0117] Material parameters: yield strength (τ y ): The material's ability to resist deformation during rolling. Elastic modulus (E): The material's stiffness, affecting its deformation behavior during rolling. Friction coefficient (μ): Affects the friction between the roller and the material, thereby affecting the rolling force.

[0118] Operation parameters: Input thickness (H in): The initial thickness of the material before entering the rolling mill. Output thickness (H out ): The final thickness of the material after it passes through the rolling mill. Rolling speed (v): The speed at which the material moves in the rolling mill. Rolling force (F): The force applied to the material to be rolled.

[0119] Equipment Limitations: Maximum Load: The maximum rolling force that the equipment is designed to withstand.

[0120] Operating Temperature Range: The temperature at which a material can operate safely.

[0121] The proportional roll-changing force model is based on the theory of plastic deformation, and the commonly used models include the Von Mises yield criterion. The rolling force during the rolling process is related to the flow and geometric parameters of the material. The modeling process usually includes: 1. Calculating the contact length data; 2. Calculating the rolling force. The calculation of the rolling force can be determined by the following formula:

[0122] F=W·L·σ avg ;

[0123] Among them, σ avg is the average yield strength of the material, which can be calculated using the following calculation:

[0124] This indicates that as the thickness changes, the yield strength of the material will adjust accordingly.

[0125] 3. Proportional conversion: The rolling force needs to be proportionally converted. It can be assumed that the relationship between the rolling force and the input and output thickness is as follows:

[0126]

[0127] If you need to set the conversion factor k: F out = k·F in ; where k is a constant obtained from experimental data, usually between 1 and 1.5.

[0128] After the model is established, model verification and data analysis are required. The rolling force predicted by the model should be compared with the rolling force actually obtained in the experiment or production, the error should be calculated and the model should be adjusted. The influence of different parameters (such as roller diameter, material properties, etc.) on the rolling force should be studied, and the variables with the most significant influence should be identified for adjustment and optimization.

[0129] like Figure 3 As shown, an embodiment of the present invention provides a device for optimizing design parameters of a rolling mill, comprising:

[0130] An acquisition unit, used for acquiring original design data of the rolling mill equipment to be optimized, and establishing a proportional roll-changing force model based on the original design data;

[0131] A processing unit, used for analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized;

[0132] The processing unit is also used to classify the key design parameters to obtain a plurality of classification data, and optimize the parameters of each classification data according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

[0133] The rolling mill equipment design parameter optimization device of this embodiment is used to implement the rolling mill equipment design parameter optimization method as described above. Its advantages over the prior art are the same as the advantages of the above-mentioned rolling mill equipment design parameter optimization method over the prior art, and will not be repeated here.

[0134] like Figure 4 As shown, an embodiment of the present invention provides a rolling mill equipment design parameter optimization device 400, including a memory 410 and a processor 420; the memory 410 is used to store computer programs; the processor 420 is used to implement the rolling mill equipment design parameter optimization method as described above when executing the computer program.

[0135] In other words, a rolling mill equipment design parameter optimization device 400 includes a memory 410 and a processor 420 coupled to the memory 410; the memory 410 is configured to store a computer program; the processor 420 is configured to perform the following operations when executing the computer program:

[0136] Acquire original design data of the rolling mill equipment to be optimized, and establish a proportional roll-changing force model based on the original design data;

[0137] Analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized;

[0138] The key design parameters are classified to obtain a plurality of classified data, and the parameters of each of the classified data are optimized according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

[0139] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for optimizing the design parameters of a rolling mill equipment as described above is implemented.

[0140] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0141] Acquire original design data of the rolling mill equipment to be optimized, and establish a proportional roll-changing force model based on the original design data;

[0142] Analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized;

[0143] The key design parameters are classified to obtain a plurality of classified data, and parameters of each of the classified data are optimized according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

[0144] The rolling mill equipment design parameter optimization device 400, which can be used as a server or client of the present invention, will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The rolling mill equipment design parameter optimization device 400 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The rolling mill equipment design parameter optimization device 400 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable 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 present invention described and / or required herein.

[0145] The rolling mill equipment design parameter optimization device 400 includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0146] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0147] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for optimizing design parameters of rolling mill equipment, characterized in that: include: Acquire original design data of the rolling mill equipment to be optimized, and establish a proportional roll-changing force model based on the original design data; Analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized; The key design parameters are classified to obtain a plurality of classified data, and parameters of each of the classified data are optimized according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

2. The method for optimizing design parameters of rolling mill equipment according to claim 1, characterized in that: The analysis of the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized includes: Get preset optimization indicators; Analyze the roller-changing force model according to each of the preset optimization indicators to obtain corresponding temporary key design parameters; A comprehensive analysis is performed on each of the key design parameters according to a preset optimization objective function to obtain corresponding key design parameters.

3. The method for optimizing design parameters of rolling mill equipment according to claim 2, characterized in that: After comprehensively analyzing the key design parameters according to the preset optimization objective function to obtain the corresponding key design parameters, the following further comprises: Performing sensitivity analysis on each of the key design parameters using a regression analysis model to obtain corresponding sensitivity data; Each of the sensitivity data is screened to obtain target key design parameters, and the target key design parameters are used as key design parameters of the rolling mill equipment to be optimized.

4. The method for optimizing design parameters of rolling mill equipment according to claim 3, characterized in that: The key design parameters are classified to obtain a plurality of classification data, including: Obtaining a preset classification standard, wherein the preset classification standard includes mechanical parameters, material property parameters and operating parameters; The key design parameters are classified according to a preset classification standard to obtain a plurality of classification data.

5. The method for optimizing design parameters of rolling mill equipment according to claim 4, characterized in that: The preset conditions include performance indicators and cost indicators; the parameters of each of the classification data are optimized according to the preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized, including: When the preset condition is the performance index, the mechanical parameters contained in the classified data are screened to obtain target mechanical parameters, and the target mechanical parameters are optimized by using a genetic algorithm and setting constraints to obtain actual design parameters of the rolling mill equipment to be optimized; When the preset condition is the cost index, the material property parameters contained in the classification data are calculated to obtain cost data, and the corresponding weight parameters are adjusted to obtain the optimal material combination with the lowest cost.

6. The method for optimizing design parameters of rolling mill equipment according to claim 1, characterized in that: The original design data includes geometric parameter data and material parameter data; the method of establishing a proportional roller changing force model based on the original design data includes: Determining contact length data according to the geometric parameter data, and calculating corresponding rolling force according to the contact length data and the material parameter data; The roll-changing force model is obtained by performing proportional conversion based on the conversion coefficient and the rolling force.

7. The method for optimizing design parameters of rolling mill equipment according to claim 6, characterized in that: Determining the contact length data according to the geometric parameter data comprises: Determine the contact length data according to the geometric parameter data through formula 1; Wherein, the formula 1 is: Wherein, L is the contact length data, D is the roller diameter, and h is the roller spacing.

8. A rolling mill equipment design parameter optimization device, characterized in that: include: An acquisition unit, used for acquiring original design data of the rolling mill equipment to be optimized, and establishing a proportional roll-changing force model based on the original design data; A processing unit, used for analyzing the roll-changing force model to obtain key design parameters of the rolling mill equipment to be optimized; The processing unit is also used to classify the key design parameters to obtain a plurality of classification data, and optimize the parameters of each classification data according to preset conditions to obtain the actual design parameters of the rolling mill equipment to be optimized.

9. A rolling mill equipment design parameter optimization device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for optimizing the design parameters of a rolling mill equipment as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored on the storage medium. When the computer program is executed by the processor, the method for optimizing the design parameters of the rolling mill equipment as described in any one of claims 1 to 7 is implemented.

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