A method and system for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target.

Abstract

This invention discloses a method and system for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target, belonging to the field of continuous casting process control in iron and steel metallurgy. The method includes: S1. Establishing a quantitative relationship model for the target steel grade and the cross-section of the billet; S2. Determining the target columnar crystal length range based on product quality requirements and the quantitative relationship model; S3. Based on the principle of solidification heat transfer, calculating and determining the optimal cooling process parameter set required for the secondary cooling zone of continuous casting to reach the target columnar crystal length range through numerical simulation or empirical formulas; S4. Applying the optimal cooling process parameter set to production and verification. This invention uses the key intermediate metallurgical state variable of "columnar crystal length" as a direct control target, changing the traditional indirect control mode that uses surface temperature as the target. It achieves precise and scientific design from final quality requirements to process parameters, significantly improving the stability and consistency of the internal quality of the billet and greatly increasing process development efficiency.

Description

Technical Field

[0001] This invention relates to the field of continuous casting process control in iron and steel metallurgy, and in particular to a method and system for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target. Background Technology

[0002] In continuous casting production, the solidification morphology of the billet (the ratio and size of columnar crystals to equiaxed crystals) has a direct causal relationship with its internal defects (such as central segregation, porosity, and shrinkage cavities). Excessive development of columnar crystals is usually accompanied by severe central segregation and transgranular structures, while the expansion of fine equiaxed crystal regions is beneficial for homogenization of composition and structure. Therefore, controlling the solidification structure is the fundamental way to improve the internal quality of the billet.

[0003] Traditional control strategies for secondary cooling in continuous casting mainly fall into two categories: one is control based on the "target surface temperature curve," which involves pre-setting the surface temperature of the billet at key points such as the outlet of the straightening machine and tracking this curve by dynamically adjusting the water volume in each section of the secondary cooling process; the other is proportional control based on casting speed, which involves adjusting the total water volume according to changes in casting speed at a fixed ratio. The control objective of these methods is the surface temperature or thermal state of the billet. However, there are multiple complex and nonlinear heat transfer, mass transfer, and solidification processes between these methods and the ultimately important internal quality indicators (such as segregation), resulting in indirect and delayed correlations. Process engineers often need to make empirical back-calculations and repeated "trial and error" adjustments based on the final quality inspection results (such as segregation discrepancies), which is inefficient, time-consuming, and makes it difficult to achieve quality stability between different heats.

[0004] Therefore, existing technologies lack an intermediate bridge and control variable that can directly and quantitatively link process adjustments with the final quality target. There is an urgent need for a new process design approach that can use the macroscopically visible solidification microstructure as the core control target, and through precise control of this target, stably achieve the expected internal quality. Summary of the Invention

[0005] The purpose of this invention is to overcome the technical problems existing in the prior art and to provide a method and system for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target.

[0006] The objective of this invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target, comprising the following steps: S1. Establish a relational model. Establish a quantitative relational model for the target steel grade and the billet cross section. Through historical production data or system tests, the quantitative relational model describes the quantitative relationship between the average length L of the columnar crystal region on the billet cross section and at least one core internal quality index M under the working condition, denoted as: M = f(L) or L = g(M); S2. Determine the control target. Based on the product quality requirements, determine the expected target value Mt of the core internal quality indicator M, and use the quantitative relationship model established in step S1 to determine the target columnar crystal length range [L_min, L_max] required to achieve the expected target value Mt; S3. Reverse process design. Based on the principles of solidification heat transfer and the target columnar crystal length L, the optimal cooling process parameter set P required for the secondary cooling zone of continuous casting is calculated and determined through numerical simulation or empirical formulas. S4. Execution and Verification. Apply the optimal cooling process parameter set P to continuous casting production, and after production stabilizes, take samples of the cast billet for low-magnification inspection to verify whether the measured average length L_actual of the columnar crystals falls within the target columnar crystal length range [L_min, L_max], in order to confirm the effectiveness of the optimal cooling process parameter set.

[0007] In some embodiments, if the measured average length of the columnar crystal falls within the target columnar crystal length range in step S4, then the process design is verified to be effective, and the parameters corresponding to the optimal cooling process parameter set P are solidified; otherwise, the optimal cooling process parameter set P is fine-tuned and steps S3-S4 are repeated.

[0008] In some embodiments, the core internal quality indicators include one or more of the following: central carbon segregation index (CI), central porosity level (CSR), or shrinkage level (CR).

[0009] In some embodiments, in step S1, the average length L of the columnar crystal region is defined as: the average distance from the boundary of the chilled layer to the leading edge of the columnar crystals along the inner arc, outer arc, and the center lines of the two side surfaces on a low-magnification specimen of the cast billet cross-section. The quantitative relationship model f or g can be obtained by fitting the production data using methods such as linear / nonlinear regression analysis and neural networks.

[0010] In some embodiments, the reverse process design in step S3 is specifically carried out by using commercial solidification heat transfer software (such as ProCAST, MSC.Marc) or a self-developed mathematical model. The thermal properties of the target steel grade, casting speed, and superheat are used as fixed inputs, while the heat exchange coefficient (or water volume) of the secondary cooling zone is used as an adjustable variable for simulation calculation. Through iterative adjustments, the predicted value L_simulated of columnar crystal length in the solidified structure of the cast billet is made close to the target value L. At this point, the cooling parameter set used in the simulation is the desired design result P.

[0011] In some embodiments, the cooling process parameter set includes one or more of the following: specific water volume (or water volume), air-to-water ratio, and cooling intensity distribution pattern along the casting flow direction for each cooling zone.

[0012] In some embodiments, the method is particularly applicable to steel grades that are sensitive to segregation and internal defects, including but not limited to high-carbon steel (such as GCr15), medium- and low-carbon alloy structural steel (such as 20CrMnTiH, 42CrMo), and some peritectic steels.

[0013] A second aspect of the present invention provides a continuous casting billet cooling process optimization system with columnar crystal length as the control target, comprising: A quantitative relationship model construction module is used to establish a quantitative relationship model for the target steel grade and the cross section of the billet. The quantitative relationship model describes the quantitative relationship between the average length of the columnar crystal region on the cross section of the billet and at least one core internal quality index. The target columnar crystal length determination module is used to determine the expected target value of the core internal quality indicator according to the product quality requirements, and to determine the range of target columnar crystal length that needs to be controlled to achieve the expected target value using the quantitative relationship model. The optimal cooling process parameter set determination module is used to calculate and determine the optimal cooling process parameter set required for the continuous casting secondary cooling zone to reach the target columnar crystal length range based on the principle of solidification heat transfer, through numerical simulation or empirical formulas. The execution and verification module is used to apply the optimal cooling process parameter set to production, and after production stabilizes, to sample and verify whether the measured average length of columnar crystals falls within the target columnar crystal length range, in order to confirm the effectiveness of the optimal cooling process parameter set.

[0014] It should be further noted that the technical features corresponding to the above-mentioned options and embodiments can be combined or substituted with each other to form new technical solutions without conflict.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. Revolutionary Innovation in Control Logic: This invention, for the first time, uses the intuitive metallurgical state variable of "columnar crystal length" as the core target of process control. By establishing a quantitative relationship model between the average length of columnar crystals and core internal quality indicators, and using this model to inversely deduce the target columnar crystal length range, the traditional, indirect control link of "process parameter (water volume) - surface temperature - internal quality" is reconstructed into a direct control link of "process parameter - columnar crystal length (intermediate metallurgical state) - internal quality". By introducing "columnar crystal length," a clear, measurable intermediate variable with a strong correlation to the final quality, this invention fills the control gap between process parameters and final quality, providing process design with a clear and scientific intermediate target, and realizing a fundamental shift from experience-based trial and error to quantitative design.

[0016] 2. Significantly Improved Stability and Consistency of Billet Internal Quality: This invention applies the optimal cooling process parameter set for production and samples to verify whether the actual columnar crystal length falls within the target range, constructing a closed-loop verification mechanism with columnar crystal length as the direct control target. Since the quantitative relationship model has revealed a strong correlation between columnar crystal length and internal quality indicators, as long as the columnar crystal length is stabilized within the target range through process control, the internal quality of the billet (such as center segregation and porosity) can be stabilized from the root by precisely controlling the morphology of the solidification structure. Compared to traditional methods that use surface temperature as an indirect target, this invention directly acts on key metallurgical variables affecting quality, effectively eliminating quality inconsistencies caused by fluctuations in casting speed, superheat, etc., and significantly improving the stability and consistency of billet internal quality across different heats and casting cycles.

[0017] 3. Significantly Improved Process Development Efficiency: This invention utilizes a reverse design principle—using iterative adjustments to bring the predicted columnar crystal length calculated through solidification simulation closer to the target columnar crystal length—achieving rapid and accurate optimization of process parameters. In traditional methods, process engineers must repeatedly cycle through "production-inspection-adjustment" based on the final quality inspection results. This invention, however, places the process design process before production, replacing actual production testing with numerical simulation. The optimization process, which previously required multiple on-site trials, is transformed into iterative calculations within a computer. Only a limited number of experiments are needed to establish or correct the relationship model; then, simulation calculations can quickly determine the optimal process window, shortening the development cycle and reducing testing costs.

[0018] 4. Achieving Digitalization and Intelligentization of the Process: This invention transforms the design process of the continuous casting secondary cooling process from relying on manual experience to model-based quantitative calculation. The logical architecture of this method (quality target → intermediate metallurgical state → process parameters → verification feedback) can be embedded into an advanced process control system to form a digital model. In actual production, when production conditions (such as casting speed and superheat) fluctuate, the system can dynamically adjust the cooling process parameter set based on real-time data and iterative calculation principles to maintain the columnar crystal length within the target range, thereby achieving an intelligent upgrade from "post-production inspection" to "predictive control." Therefore, this invention not only provides a specific process optimization method but also provides a core theoretical foundation and algorithmic framework for realizing intelligent and adaptive control of the continuous casting process. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an optimization method for the cooling process of continuously cast billets with columnar crystal length as the control target, as shown in an embodiment of the present invention. Figure 2 This is a scatter plot of L and CI shown in an embodiment of the present invention; Figure 3This is a schematic diagram comparing the conventional mode with the secondary cooling water distribution mode of the present invention, as shown in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that the defects in the solutions in the prior art are all the results of the inventors' practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of this application in the following text should be the inventors' contributions to this application in the process of invention and creation, and should not be understood as technical content known to those skilled in the art.

[0022] In view of the technical problems pointed out in the background art, the present invention provides the following embodiments: In an exemplary embodiment, a method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target is provided, such as... Figure 1 As shown, it includes the following steps: S1. Establish a relational model. Establish a quantitative relational model for the target steel grade and the billet cross section. Through historical production data or system tests, the quantitative relational model describes the quantitative relationship between the average length L of the columnar crystal region on the billet cross section and at least one core internal quality index M under the working condition, denoted as: M = f(L) or L = g(M); S2. Determine the control target. Based on the product quality requirements, determine the expected target value Mt of the core internal quality indicator M, and use the quantitative relationship model established in step S1 to determine the target columnar crystal length range [L_min, L_max] required to achieve the expected target value Mt; S3. Reverse process design. Based on the principles of solidification heat transfer and the target columnar crystal length L, the optimal cooling process parameter set P required for the secondary cooling zone of continuous casting is calculated and determined through numerical simulation or empirical formulas. S4. Execution and Verification. Apply the optimal cooling process parameter set P to continuous casting production, and after production stabilizes, take samples of the cast billet for low-magnification inspection to verify whether the measured average length L_actual of the columnar crystals falls within the target columnar crystal length range [L_min, L_max], in order to confirm the effectiveness of the optimal cooling process parameter set.

[0023] Based on the above method, this embodiment takes the production of small square billets (150mm×150mm) of 20CrMnTiH gear steel as an example, aiming to stably control the central carbon segregation index (CI) at ≤1.08. The specific implementation process of optimizing the secondary cooling process of 20CrMnTiH gear steel is as follows: S1: Establish a quantitative relationship model Fifty sets of historical production data for 20CrMnTiH steel in this section were collected. Each set of data included: secondary cooling process parameters, the average length L (mm) of columnar crystals measured by low magnification inspection of the billet, and the central carbon segregation index CI measured by the drill cuttings method. The quantitative relationship model is shown in Table 1.

[0024] Table 1. Schematic diagram of quantitative relationship model for 20CrMnTiH steel Perform statistical analysis on the data and plot a scatter plot of L versus CI, such as... Figure 2 As shown, the blue scatter plots represent 50 sets of measured data, exhibiting a clear positive correlation trend. The red dashed line represents the linear regression line with a slope of 0.0023. The green box / line represents the columnar crystal control range (65-75mm) inferred from the quality requirement (≤1.08). Through regression analysis, the empirical formula between the two is obtained: CI = 0.0023 × L + 0.92, with a correlation coefficient R² = 0.85. This model shows that under this production system, the center segregation increases linearly with the increase of columnar crystal length, and the model has good explanatory power. Figure 2 The study directly verified the strong correlation between columnar crystal length and central segregation, and that CI can be indirectly controlled by controlling L, providing a quantitative basis for subsequent reverse process design.

[0025] Furthermore, to verify the reliability of the model, 10 additional sets of data that were not involved in the modeling were used for verification. The average relative error between the predicted and measured values ​​was within the preset threshold, indicating that the model has good prediction accuracy.

[0026] S2: Determine the control objective Product quality requirement CI ≤ 1.08.

[0027] Substituting CI_t = 1.08 into the above relational model: 1.08 = 0.0023 × L_t + 0.92. Solving for L_t, we get L_t ≈ 69.6 mm.

[0028] Considering production fluctuations, the target columnar crystal length control range is set as follows: 65 mm ≤ L ≤ 75 mm. As long as L is controlled within this range, theoretically, CI can be stabilized between 1.07 and 1.09.

[0029] S3: Reverse Engineering Design Commercial software was used to input the thermal properties of 20CrMnTiH steel, the target drawing speed (1.2 m / min), and the typical superheat (25℃).

[0030] Simulations using the existing traditional secondary cooling mode as initial values ​​revealed that the simulated columnar crystal length L_sim was 85 mm, exceeding the target range, with a corresponding predicted CI of 1.12, which is consistent with historical poor data.

[0031] With L_target = 70mm as the optimization objective and the water volume in each secondary cooling zone as the design variable, the optimization algorithm was initiated for iterative calculation. After multiple iterations, the software recommended a new set of secondary cooling parameters, P_new. Its main characteristics are: appropriately strengthening the cooling of the front section (foot roller zone and zone I) of the secondary cooling system, while moderately weakening the cooling of the rear section (zones II and III), such as... Figure 3 As shown, the horizontal axis represents the different sections of the secondary cooling process (foot roller zone, zone I, zone II, zone III, and zone IV), and the vertical axis represents the cooling water volume (relative value, dimensionless, reflecting the distribution trend). Blue bars represent the traditional mode (relatively uniform water volume in each zone, with a slight decrease in the later stages), while orange bars represent the new reverse-design mode of this invention. It can be seen that the foot roller zone + zone I is significantly strengthened, while zones II to IV are moderately weakened, forming a "strong at the front and stable at the back" distribution. Strengthening the front-end cooling promotes equiaxed crystal nucleation, while weakening the rear-end cooling suppresses excessive columnar crystal growth. In the traditional mode, the simulated columnar crystal length L_sim = 85 mm; this invention optimizes L to approximately 70 mm. The relative water volume values ​​in the figure are based on empirical settings of a typical small billet secondary cooling system and are only used to illustrate the distribution trend, not precise absolute values. This figure visually presents the core adjustment direction of the cooling process parameter set P_new output by the reverse-design method of this invention, and... Figure 2 The relational model forms a complete technological closed loop.

[0032] S4: Execution and Verification The designed P_new parameter set was applied to continuous casting machine production.

[0033] After five consecutive heats, a sample of the cast billet was taken for low-magnification inspection. The measured average lengths of the columnar crystals were 68mm, 71mm, 70mm, 72mm, and 69mm, all falling within the target range of 65-75mm.

[0034] Carbon segregation tests were conducted on the same batch of billets, and the measured central carbon segregation indices were 1.065, 1.072, 1.070, 1.074, and 1.068, respectively, all of which met the requirement of ≤1.08 and showed minimal fluctuation.

[0035] Conclusion: The new process design was successful. This method successfully transformed the qualitative requirement of "segregation" into quantitative control of "columnar crystal length," and through reverse engineering, found a precise process path to achieve this control. These parameters were solidified as the standard secondary cooling regime for 20CrMnTiH steel.

[0036] For example, to verify the universality of the present invention, the above steps were repeated using a 220mm×260mm large square billet of GCr15 bearing steel.

[0037] S1: Establish a quantitative relationship model Forty-five sets of historical production data were collected, and a nonlinear regression analysis was performed on the average length L of columnar crystals and the central carbon segregation index CI, yielding the fitted equation: CI = 0.00015 × L² + 0.012 × L + 0.85, R² = 0.92. This model indicates that for GCr15 steel, which is more sensitive to segregation, the length of columnar crystals and the segregation index exhibit a nonlinear positive correlation, further demonstrating the necessity of the model construction.

[0038] Meanwhile, a BP neural network was used to train the same data, and a 3-layer network structure was constructed (input layer: L, hidden layer: 5 nodes, output layer: CI). The prediction accuracy of the trained network model (R² = 0.94) was slightly better than that of the linear regression model, and it can be selected according to actual needs.

[0039] S2: Determine the control objective The product quality requirement is CI ≤ 1.05. Substituting CI_t = 1.05 into the nonlinear regression model, we obtain L_t ≈ 52 mm. The target columnar crystal length control range is set as: 48 mm ≤ L ≤ 56 mm.

[0040] S3: Reverse Engineering Design The MSC.Marc solidification heat transfer software was used, with input thermophysical parameters of GCr15 steel, a target drawing speed of 0.9 m / min, and a superheat of 20℃. With L_target = 52 mm as the optimization objective, gradient descent algorithm was employed for iterative calculations. After 15 iterations, the optimal secondary cooling parameter set P_new was obtained, with the specific water content in each zone as follows: foot roll zone 0.38 L / kg, zone I 0.45 L / kg, zone II 0.32 L / kg, zone III 0.22 L / kg, and zone IV 0.18 L / kg.

[0041] S4: Execution and Verification P_new was applied to production, and samples were taken for testing after four consecutive furnace runs. The measured average lengths of the columnar crystals were 53 mm, 51 mm, 54 mm, and 50 mm, all falling within the target range. The central carbon segregation index test results were 1.038, 1.042, 1.035, and 1.045, all meeting the requirement of ≤1.05.

[0042] The above embodiments demonstrate that the method provided by the present invention is applicable to different steel grades and different cross sections. It is a scientific, efficient, and precise means of controlling the internal quality of continuously cast billets, and has significant practical value and prospects for promotion.

[0043] In another exemplary embodiment, based on the same inventive concept as the method embodiment, a continuous casting billet cooling process optimization system is provided, with columnar crystal length as the control target, comprising: A quantitative relationship model construction module is used to establish a quantitative relationship model for the target steel grade and the cross section of the billet. The quantitative relationship model describes the quantitative relationship between the average length of the columnar crystal region on the cross section of the billet and at least one core internal quality index. The target columnar crystal length determination module is used to determine the expected target value of the core internal quality indicator according to the product quality requirements, and to determine the range of target columnar crystal length that needs to be controlled to achieve the expected target value using the quantitative relationship model. The optimal cooling process parameter set determination module is used to calculate and determine the optimal cooling process parameter set required for the continuous casting secondary cooling zone to reach the target columnar crystal length range based on the principle of solidification heat transfer, through numerical simulation or empirical formulas. The execution and verification module is used to apply the optimal cooling process parameter set to production, and after production stabilizes, to sample and verify whether the measured average length of columnar crystals falls within the target columnar crystal length range, in order to confirm the effectiveness of the optimal cooling process parameter set.

[0044] The above detailed embodiments are a description of the present invention. It should not be considered that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art, several simple deductions and substitutions can be made without departing from the concept of the present invention, and all of these should be considered to fall within the protection scope of the present invention.

Claims

1. A method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target, characterized in that, Includes the following steps: S1. Establish a quantitative relationship model for the target steel grade and the cross-section of the billet, wherein the quantitative relationship model describes the quantitative relationship between the average length of the columnar crystal region on the cross-section of the billet and at least one core internal quality index; S2. Determine the expected target value of the core internal quality indicator according to the product quality requirements, and use the quantitative relationship model to determine the target columnar crystal length range that needs to be controlled to achieve the expected target value; S3. Based on the principle of solidification heat transfer, the optimal set of cooling process parameters required to achieve the target columnar crystal length range in the secondary cooling zone of continuous casting is calculated and determined by numerical simulation or empirical formula. S4. Apply the optimal cooling process parameter set to production, and after production stabilizes, take samples to verify whether the measured average length of columnar crystals falls within the target columnar crystal length range, in order to confirm the effectiveness of the optimal cooling process parameter set.

2. The method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target according to claim 1, characterized in that, If the measured average length of the columnar crystal falls within the target columnar crystal length range in step S4, then the optimal cooling process parameter set is solidified; otherwise, the optimal cooling process parameter set is fine-tuned and steps S3-S4 are repeated.

3. The method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target as described in claim 1, characterized in that, The core internal quality indicators include one or more of the following: central carbon segregation index, central porosity level, and shrinkage level.

4. The method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target according to claim 1, characterized in that, In step S1, the measurement of the average length of the columnar crystal region is defined as follows: On the low magnification specimen of the cross section of the billet, the average distance from the boundary of the chilled layer to the front edge of the columnar crystal along the inner arc, outer arc and the center line of the two sides is measured. The quantitative relationship model is obtained by fitting production data through linear / nonlinear regression analysis or neural networks.

5. The method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target according to claim 1, characterized in that, The reverse calculation in step S3 specifically includes: Simulation calculations were performed using the thermal properties, drawing speed, and superheat of the target steel grade as fixed inputs, and the heat exchange coefficient of the secondary cooling zone as an adjustable variable. Iterative adjustments were made to bring the predicted columnar crystal length in the solidified structure of the cast billet, calculated by simulation, closer to the target columnar crystal length range.

6. The method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target according to claim 1, characterized in that, The cooling process parameter set includes one or more of the following: specific water volume, air-to-water ratio, and cooling intensity distribution pattern along the casting flow direction for each cooling zone.

7. The method for optimizing the cooling process of continuously cast billets with columnar crystal length as the control target according to claim 1, characterized in that, The target steel type is high-carbon steel, medium-low carbon alloy structural steel, or peritectic steel.

8. A continuous casting billet cooling process optimization system with columnar crystal length as the control target, characterized in that, include: A quantitative relationship model construction module is used to establish a quantitative relationship model for the target steel grade and the cross section of the billet. The quantitative relationship model describes the quantitative relationship between the average length of the columnar crystal region on the cross section of the billet and at least one core internal quality index. The target columnar crystal length determination module is used to determine the expected target value of the core internal quality indicator according to the product quality requirements, and to determine the range of target columnar crystal length that needs to be controlled to achieve the expected target value using the quantitative relationship model. The optimal cooling process parameter set determination module is used to calculate and determine the optimal cooling process parameter set required for the continuous casting secondary cooling zone to reach the target columnar crystal length range based on the principle of solidification heat transfer, through numerical simulation or empirical formulas. The execution and verification module is used to apply the optimal cooling process parameter set to production, and after production stabilizes, to sample and verify whether the measured average length of columnar crystals falls within the target columnar crystal length range, in order to confirm the effectiveness of the optimal cooling process parameter set.

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