Manufacturing method of-50 DEG C high-strength and high-toughness steel plate for wind power tower drum
By combining precision rolling and controlled rolling cooling processes with segmented heating and cooling adjustments using a cooling control module and a self-learning module, the problem of poor microstructure uniformity in thick steel plates at low temperatures was solved, improving the weldability and mechanical properties of wind turbine tower steel plates and ensuring their safety and reliability in an environment of -50℃.
Patent Information
- Application Number
- CN202510866028.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-26
- Publication Date
- 2025-10-31
AI Technical Summary
In existing technologies, it is difficult to balance the uniformity of the microstructure between the core and the surface of thick steel plates after adding alloying elements such as nickel and chromium through conventional controlled rolling and cooling processes. This leads to deterioration of welding performance and insufficient toughness. In particular, the steel is prone to brittle fracture in low-temperature environments of -50℃, which affects the service safety of wind turbine towers.
By combining the leveling rolling process in the finishing rolling stage with the cooling control module and the VSG self-learning module, and through segmented heating and controlled rolling and cooling processes, the cooling water volume and time are adjusted in real time to ensure the temperature uniformity of the slab, avoid insufficient or excessive cooling, and improve the strength and toughness of the steel.
It achieves uniform cooling of 12-50mm steel plates in low-temperature environments, improves welding performance and mechanical properties, reduces energy waste, and increases production efficiency and steel reliability.
Smart Images
Figure CN120861604A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wind turbine tower steel plate smelting technology, specifically to a method for manufacturing high-strength and high-toughness wind turbine tower steel plates at -50℃. Background Technology
[0002] The wind turbine tower is a key supporting structure for wind turbine generators. It is typically a cylindrical steel structure, constructed from multiple sections of rolled and welded steel plates. The base diameter is relatively large, gradually decreasing with height. The overall height is designed based on the turbine's power and wind resource conditions, and can range from tens to over a hundred meters. Its main function is to support the wind turbine nacelle and blades at a specific height to achieve optimal wind speeds. It also withstands complex loads such as gravity, wind loads, and vibration loads during turbine operation, requiring high strength, high toughness, good weldability, and resistance to environmental corrosion. As wind power installations expand into high-altitude and cold regions, traditional Q355 grade wind power steel faces two technical bottlenecks in engineering applications: First, its impact toughness index at -50℃ is often below 70J. Low-temperature impact toughness reflects the steel's ability to resist impact loads at low temperatures. When this value is below 70J, the material is prone to brittle fracture, affecting the service safety of wind turbine towers and other structures in frigid regions. Second, the strength uniformity of thick (12-50mm) steel plates is insufficient, which may lead to insufficient local load-bearing capacity of the structure, restricting the reliability and service life of large-scale wind power equipment.
[0003] In existing technologies, the addition of alloying elements such as nickel (Ni) and chromium (Cr) can effectively improve the low-temperature mechanical properties of steel. However, conventional controlled rolling and cooling processes are difficult to balance the uniformity of the microstructure between the core and the surface of thick (12-50mm) steel plates, which leads to deterioration of welding performance, long-term reliability issues and non-compliance with flaw detection in extreme environments, and a surge in costs. Summary of the Invention
[0004] The purpose of this invention is to provide a method for manufacturing steel plates for high-strength and high-toughness wind turbine towers at -50℃, in order to solve the problem that in the prior art, it is difficult to balance the uniformity of the core and surface of 12-50mm steel plates after adding alloying elements such as nickel and chromium through conventional controlled rolling and cooling processes, which leads to deterioration of welding performance and easy occurrence of insufficient toughness.
[0005] To achieve the above objectives, the basic solution provided by this invention is: a method for manufacturing steel plates for high-strength and high-toughness wind turbine towers at -50℃, comprising the following steps: S1. The leveling rolling process in the finishing rolling stage sends relevant information about the slab to the cooling control module, and collects the actual final rolling temperature and actual reddening temperature in real time at the finishing rolling exit. S2. The slab is sent into a heating furnace for heat treatment by segmented heating; S3. The heat-treated slab is sent to the controlled rolling and cooling stage. The cooling control module in the controlled rolling and cooling stage determines whether there is a record in the database based on the slab information. If there is a record, the historical data in the database is called. If there is no record, the cooling time and cooling water flow rate are calculated based on the actual final rolling temperature, actual reddening temperature, target final rolling temperature and target reddening temperature in S1. S4. The VSG self-learning module in the cooling control module monitors the red-hot temperature of the cooling zone in S1 online, thereby diagnosing whether there is a problem with three consecutive slabs. If the red-hot temperature of the three consecutive slabs is normal, proceed to the next step. If the red-hot temperature of the three consecutive slabs is abnormal, adjust the cooling time until the red-hot temperature reaches the target temperature of the slab before proceeding to the next step. The S5.VSG self-learning module collects and saves the adjusted cooling parameters to the database.
[0006] The principle and beneficial effects of this invention are as follows: In use, the leveling rolling process in the finishing rolling stage sends relevant information about the slab to the cooling control module, and then the slab is sent into the heating furnace for heat treatment through segmented heating. After heat treatment, the slab is cooled. The cooling control module can control the cooling according to the actual situation of the slab. The cooling control module and the VSG self-learning module can adjust the cooling water volume and cooling time in the controlled rolling and controlled cooling process in real time, thereby reducing the deterioration of steel welding performance caused by uneven cooling of 12-50mm slabs, and eliminating energy waste and production efficiency loss caused by over- or under-cooling.
[0007] Option 2 is an optimal choice of the basic option. In step S1, the relevant information of the slab includes: the finished steel grade, length, width and thickness, as well as the target final rolling temperature and target red-hot temperature of the corresponding steel grade; by controlling the cooling termination temperature, the strength, toughness and other mechanical properties of the steel can be locked, and the amount of alloy can be reduced and the performance stability can be improved. By controlling the target red-hot temperature, the deterioration of welding performance caused by overcooling or undercooling can be reduced, and problems such as insufficient toughness and failure of flaw detection can also occur.
[0008] Option 3, the preferred option of the basic option, in step S2, the control requirements of the heating furnace are as follows: the temperature of the first heating section is 1080-1100℃, the temperature of the second heating section is 1220-1270℃, the temperature of the soaking section is between 1200-1250℃, the soaking time is 45min, the heating rate is 10min / cm, the furnace time is ≥230min, and the temperature difference between the upper and lower surfaces of the slab should be ≤50℃; segmented heat treatment is adopted, and the core and surface temperature fields are homogenized through gradient heating and zoned temperature control to reduce thermal stress deformation caused by temperature difference. At the same time, the temperature and holding time of each section are flexibly adjusted for different steel grades.
[0009] Option 4, this is the preferred option of the basic option. In step S3, the formula for calculating the cooling time is: Where t is time. Where is the density of the steel, c is the specific heat capacity of the steel, d is the thickness of the steel, T1 is the actual final rolling temperature, T2 is the target final rolling temperature, and T... a Let be the cooling water temperature, and h be the convective heat transfer coefficient; the formula for calculating the cooling water flow rate is: Where Q is the cooling water volume, V is the steel volume, c is the specific heat capacity of the steel, T1 is the actual final rolling temperature, and T2 is the target final rolling temperature. The temperature difference between the inlet and outlet of the cooling water. For cooling efficiency, it can accurately calculate the required heat dissipation based on the heat capacity of the steel, ensure heat exchange efficiency by regulating the cooling water flow, and accurately predict the cooling time by combining the convective heat transfer model, avoiding insufficient or excessive cooling due to empirical estimation.
[0010] Option 5, which is the preferred option of the basic option, involves the following steps in step S4: The VSG self-learning module monitors the online temperature return. a. Use clustering algorithms to filter the raw data of actual red temperature and extract the temperature integrals from the raw data; b. The variable step size grid automatically refines the grid in areas of severe temperature fluctuation based on temperature characteristics, accurately capturing the actual reddening temperature; c. The variable step size grid calculates the required cooling time based on the actual and target reddening temperatures, driving the VSG self-learning module to adjust the cooling time in S3. When applied to controlled rolling and controlled cooling processes, the VSG self-learning module can deeply mine historical production data and real-time process parameters through machine learning algorithms, dynamically optimize cooling model parameters, and achieve adaptive iteration of cooling strategies. It can match the optimal cooling time and water volume allocation in real time based on steel specifications (such as thickness and steel grade) and target performance, avoiding insufficient or excessive cooling caused by traditional fixed parameter control.
[0011] Option 6, an optimal choice from the basic option, involves further hierarchical storage and compression of the clustering algorithm in the VSG self-learning module in step S5. Hierarchical organization and redundant information removal of multidimensional production data can be achieved through clustering based on data feature similarity. On one hand, hierarchical storage based on clustering results can construct a tree-like storage structure for similar process parameters, equipment status, and other data according to feature dimensions, improving data retrieval efficiency by more than 30% and facilitating rapid retrieval of historical similar operating condition data to support real-time decision-making. On the other hand, compression encoding using the distribution patterns of clustered data can reduce the amount of industrial big data storage by 40%-60%, lowering storage costs while shortening data transmission latency, providing efficient data support for real-time model training.
[0012] Option 7, which is the preferred option of Option 5, requires the following formula for adjusting the cooling time in step c: in This refers to the actual temperature at which the red light returns. The target induction temperature is given by d, where d is the steel thickness and t is the required adjustment time. Let C be the density of the steel, c be the specific heat capacity of the steel, and T be the density of the steel. a Here, denoted as cooling water temperature and h as convective heat transfer coefficient, the method can accurately deduce the required cooling time based on the steel's heat capacity and the temperature difference before the reddening, avoiding cooling deviations caused by empirical estimations. Furthermore, it can dynamically adapt to the phase change requirements of different steel specifications through real-time temperature feedback and thickness characteristics, controlling the temperature gradient between the core and surface within a certain range. This method optimizes the cooling water heat exchange efficiency through a quantitative model, reducing water consumption by 10%-15% while improving the uniformity of the steel's mechanical properties. Attached Figure Description
[0013] Figure 1 This is a flowchart of a method for manufacturing a steel plate for a wind turbine tower with high strength and toughness at -50℃ according to the present invention. Detailed Implementation
[0014] The present invention will be further described in detail below through specific embodiments: Example like Figure 1 The following steps are shown: A method for manufacturing a steel plate for a wind turbine tower with high strength and toughness at -50℃. S1. The leveling rolling process in the finishing rolling stage sends relevant information about the slab to the cooling control module. The relevant information about the slab includes: finished steel grade, length, width and thickness, as well as the target final rolling temperature and target red-heating temperature of the corresponding steel grade. The actual final rolling temperature and actual red-heating temperature are collected in real time at the finishing rolling exit. S2. The slab is sent into a heating furnace for heat treatment by segmented heating. The control requirements of the heating furnace are as follows: the temperature of the first heating segment is 1080-1100℃, the temperature of the second heating segment is 1220-1270℃, the temperature of the soaking segment is between 1200-1250℃, the soaking time is 45min, the heating rate is 10min / cm, the time in the furnace is ≥230min, and the temperature difference between the upper and lower surfaces of the slab should be ≤50℃. S3. The heat-treated slab is sent to the controlled rolling and cooling stage. The cooling control module in this stage checks the database for records based on the slab information. If a record exists, historical data is retrieved. If no record exists, the cooling time is pre-calculated based on the actual final rolling temperature, actual reheating temperature, target final rolling temperature, and target reheating temperature from S1. The formula for calculating the cooling time is: Where t is time. Where is the density of the steel, c is the specific heat capacity of the steel, d is the thickness of the steel, T1 is the actual final rolling temperature, T2 is the target final rolling temperature, and T... a Let be the cooling water temperature, and h be the convective heat transfer coefficient; the formula for calculating the cooling water flow rate is: Where Q is the cooling water volume, V is the steel volume, c is the specific heat capacity of the steel, T1 is the actual final rolling temperature, and T2 is the target final rolling temperature. The temperature difference between the inlet and outlet of the cooling water. For cooling efficiency; S4. The VSG self-learning module in the cooling control module monitors the red-hot temperature of the cooling zone in S1 online to diagnose whether there is a problem with three consecutive slabs. If the red-hot temperature of the three consecutive slabs is normal, proceed to the next step. If the red-hot temperature of the three consecutive slabs is abnormal, adjust the cooling time until the red-hot temperature reaches the target temperature of the slab before proceeding to the next step. The steps of the VSG self-learning module in monitoring the red-hot temperature online are as follows: a. Use clustering algorithms to filter the raw data of actual red temperature and extract the temperature integrals from the raw data; b. The variable step size grid automatically refines the grid in areas of severe temperature fluctuation based on temperature characteristics, accurately capturing the actual reddening temperature; c. The variable step size mesh calculates the required cooling time adjustment based on the actual and target reddening temperatures, driving the VSG self-learning module to adjust the cooling time in S3. The formula for the required cooling time adjustment is: in This refers to the actual temperature at which the red light returns. The target induction temperature is given by d, where d is the steel thickness and t is the required adjustment time. Let C be the density of the steel, c be the specific heat capacity of the steel, and T be the density of the steel. a Where is the cooling water temperature, and h is the convective heat transfer coefficient; The S5.VSG self-learning module collects the adjusted cooling parameters, performs hierarchical storage and compression through clustering algorithms, and finally saves them to the database.
[0015] The implementation method of this embodiment is as follows: In use, the leveling rolling process in the finishing rolling stage first sends the steel grade, length, width, and thickness information of the slab, as well as the target final rolling temperature and target reddening temperature for the corresponding steel grade, to the cooling control module. Then, the slab is sent into the heating furnace for segmented heating: the first segment temperature is 1080-1100℃, the second segment temperature is 1220-1270℃, the soaking segment temperature is 1200-1250℃, the soaking time is 45min, the heating rate is 10min / cm, the furnace time is ≥230min, and the temperature difference between the upper and lower surfaces of the slab should be ≤50℃. Then, the cooling control module checks whether there is historical data in the database based on the slab information. If so, it directly calls the cooling time and cooling water flow rate; otherwise, it calculates the cooling time and cooling water flow rate based on the target final rolling temperature and the actual final rolling temperature. The formula for calculating the cooling time is: Where t is time. Where is the density of the steel, c is the specific heat capacity of the steel, d is the thickness of the steel, T1 is the actual final rolling temperature, T2 is the target final rolling temperature, and T... a Let be the cooling water temperature, and h be the convective heat transfer coefficient; the formula for calculating the cooling water flow rate is: Where Q is the cooling water volume, V is the steel volume, c is the specific heat capacity of the steel, T1 is the actual final rolling temperature, and T2 is the target final rolling temperature. The temperature difference between the inlet and outlet of the cooling water. For cooling efficiency, the temperatures for each stage are shown in Table 1 below for slabs of different thicknesses: Table 1 Temperatures at different stages for slabs of different thicknesses Thickness (mm) Final rolling temperature (°C) Cooling rate (°C / s) Redness temperature (°C) 16 781 20 632 25.5 790 20 640 30.5 765 20 625 50 750 18 535 The VSG self-learning module monitors the actual red-hot temperatures of three consecutive slabs online. If no abnormalities are found, the cooling parameters for this group are saved to the database. If abnormalities are found, the required cooling time is calculated based on the target red-hot temperature and the actual red-hot temperature. The formula for the required cooling time adjustment is as follows: in This refers to the actual temperature at which the red light returns. The target induction temperature is given by d, where d is the steel thickness and t is the required adjustment time. Let C be the density of the steel, c be the specific heat capacity of the steel, and T be the density of the steel. a is the cooling water temperature, and h is the convective heat transfer coefficient.
[0016] The above descriptions are merely embodiments of the present invention, and common knowledge regarding specific structures and characteristics is not elaborated upon here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.
Claims
1. A method for manufacturing steel plates for high-strength and high-toughness wind turbine towers at -50℃, characterized in that, Includes the following steps: S1. The leveling rolling process in the finishing rolling stage sends relevant information about the slab to the cooling control module, and collects the actual final rolling temperature and actual reddening temperature in real time at the finishing rolling exit. S2. The slab is sent into a heating furnace for heat treatment by segmented heating; S3. The cooling control module determines whether there is a record in the database based on the information of the slab. If there is a record, it calls the historical data in the database. If there is no record, it performs pre-calculation based on the actual final rolling temperature, actual red-hot temperature, target final rolling temperature and target red-hot temperature in S1, and calculates the cooling time and cooling water flow rate. S4. The VSG self-learning module in the cooling control module monitors the red-hot temperature of the cooling zone in S1 online, thereby diagnosing whether there is a problem with three consecutive slabs. If the red-hot temperature of the three consecutive slabs is normal, proceed to the next step. If the red-hot temperature of the three consecutive slabs is abnormal, adjust the cooling time until the red-hot temperature reaches the target temperature of the slab before proceeding to the next step. The S5.VSG self-learning module collects and saves the adjusted cooling parameters to the database.
2. The method for manufacturing a high-strength and high-toughness steel plate for wind turbine towers at -50℃ according to claim 1, characterized in that, In step S1, the relevant information of the slab includes: the finished steel grade, length, width and thickness, as well as the target final rolling temperature and target red-heating temperature of the corresponding steel grade.
3. The method for manufacturing a high-strength and high-toughness steel plate for wind turbine towers at -50℃ according to claim 1, characterized in that, In step S2, the control requirements for the heating furnace are as follows: the temperature of the first heating section is 1080-1100℃, the temperature of the second heating section is 1220-1270℃, the temperature of the soaking section is between 1200-1250℃, the soaking time is 45min, the heating rate is 10min / cm, the furnace time is ≥230min, and the temperature difference between the upper and lower surfaces of the slab should be ≤50℃.
4. The method for manufacturing a high-strength and high-toughness steel plate for wind turbine towers at -50℃ according to claim 1, characterized in that, In step S3, the formula for calculating the cooling time is: Where t is time. Where is the density of the steel, c is the specific heat capacity of the steel, d is the thickness of the steel, T1 is the actual final rolling temperature, T2 is the target final rolling temperature, and T... a Let be the cooling water temperature, and h be the convective heat transfer coefficient; the formula for calculating the cooling water flow rate is: Where Q is the cooling water volume. Let V be the density of the steel, C be the volume of the steel, T1 be the actual final rolling temperature, and T2 be the target final rolling temperature. The temperature difference between the inlet and outlet of the cooling water. For cooling efficiency.
5. The method for manufacturing a high-strength and high-toughness steel plate for wind turbine towers at -50℃ according to claim 1, characterized in that, In step S4, the VSG self-learning module monitors the online return temperature as follows: a. Use clustering algorithms to filter the raw data of actual red temperature and extract the temperature integrals from the raw data; b. The variable step size grid automatically refines the grid in areas of severe temperature fluctuation based on temperature characteristics, accurately capturing the actual reddening temperature; c. The variable step size grid calculates the required cooling time based on the actual and target reddening temperatures, driving the VSG self-learning module to adjust the cooling time in S3.
6. The method for manufacturing a steel plate for a -50℃ high-strength and tough wind turbine tower according to claim 1, characterized in that, In step S5, the clustering algorithm in the VSG self-learning module performs further hierarchical storage and compression.
7. The method for manufacturing a steel plate for a -50℃ high-strength and tough wind turbine tower according to claim 5, characterized in that, In step c, the formula for adjusting the cooldown time is: in This refers to the actual temperature at which the red light returns. The target induction temperature is given by d, where d is the steel thickness and t is the required adjustment time. Let C be the density of the steel, c be the specific heat capacity of the steel, and T be the density of the steel. a is the cooling water temperature, and h is the convective heat transfer coefficient.