Intelligent management method and system for cast pipe production line

Abstract

The present invention discloses an intelligent management method and system for a cast pipe production line, which relates to the technical field of cast pipe production. During the trial production process, the method sets different rotation conditions and combines CFD simulation technology to identify the order of solidification areas under different rotation conditions, and analyzes the composition differences and distribution of solute elements in different areas. By obtaining the composition segregation coefficient Pxs, the distribution of solute elements in the cast pipe production process can be optimized, and the composition uniformity of the cast pipe can be further improved, thereby enhancing the mechanical properties and structural reliability of the cast pipe and reducing the risk of defects caused by uneven composition. In the cast pipe production process, by analyzing the timeliness of shrinkage compensation during the solidification of the molten metal and combining process data, a hole defect risk coefficient Kfx is constructed to lay the foundation for effectively reducing the incidence of hole defects and optimizing the production conditions of centrifugal casting.

Description

Technical Field

[0001] The present invention relates to the technical field of cast pipe production, and in particular to an intelligent management method and system for a cast pipe production line. Background Art

[0002] Intelligent management of cast pipe production lines is a field of modern manufacturing, particularly widespread in the metal casting and machinery manufacturing industries. With the advancement of Industry 4.0, the application of intelligent and automated technologies in the casting process is becoming increasingly widespread, particularly in the management and optimization of cast pipe production lines. Centrifugal casting technology, renowned for its efficiency and precision, has been widely adopted in cast pipe production. In particular, precise analysis of the solid-liquid state in the mold and control of its composition distribution are crucial for improving the overall quality of cast pipes.

[0003] Although the current intelligent management technology of cast pipe production lines has made certain progress, there are still certain problems and shortcomings in specific applications. During the centrifugal casting process, changes in rotation speed will directly affect the solid-liquid state in the mold, which in turn affects the composition distribution and mechanical properties of the cast pipe. Traditional casting processes rely heavily on experience and single parameter settings, making it difficult to achieve precise control of the solid-liquid state. In particular, at different rotation speeds, there are large differences and uncertainties in the changes in the solidification area, composition segregation, and shrinkage compensation. The existing process monitoring and simulation systems fail to provide real-time feedback on the temperature field and composition distribution during the solidification process, resulting in the failure to produce cast pipes according to appropriate production conditions, which can easily cause uneven mechanical properties of the castings and untimely shrinkage compensation, affecting the final quality of the cast pipes. Especially in high-demand casting products, these factors have a greater impact on the final cast pipe quality. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent management method and system for a cast pipe production line, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for intelligent management of a cast pipe production line, comprising the following steps:

[0006] S1: Extract relevant process data from the pipe casting production line and determine the mold making according to casting requirements;

[0007] S2: Based on the mold determination, a trial production of cast pipes is carried out. During the trial production process, different rotation conditions are set and combined with CFD simulation to identify the sequence of solidification zones under different rotation conditions. Based on the identification results under different rotation conditions, the differences in composition and solute element distribution in different solidification zones are analyzed to obtain the composition segregation coefficient Pxs.

[0008] S3: During the solidification process of the molten metal, the timeliness of the shrinkage feeding of the casting pipe production line is analyzed, and the hole defect risk coefficient Kfx is constructed in combination with relevant process data. The loss minimization prediction model is constructed using deep learning technology to fit and output the production selection conditions of centrifugal casting.

[0009] Preferably, the specific steps of S1 include:

[0010] S11: Deploy multiple sets of sensors and data acquisition equipment on the cast pipe production line to extract relevant process data in real time. The relevant process data includes production line equipment operation information, process parameters and material property information. The production line equipment operation information includes the furnace temperature, centrifuge rotation speed and current and voltage of each production equipment in the cast pipe production line; the process parameters include the initial batch ratio of the metal material for casting the cast pipe, the initial batch content of the metal material ... , solidification temperature range width , initial liquid volume TJ, metal material pouring temperature, cooling rate and supplementary volume; material property information includes the density of the metal material of the cast pipe, solidus temperature , viscosity and thermal conductivity;

[0011] S12: According to the casting requirements, a corresponding mold is made and a mold geometry model is obtained. The casting requirements include the specifications and shape of the cast pipe to be cast. The mold type is selected based on the company's production batch requirements, production cycle plan, and production cost control. The mold types include metal molds and sand molds.

[0012] Preferably, the specific steps of S2 include:

[0013] S21: Based on CFD simulation technology, the mold geometry model, production line equipment operation information, and material property information in S12 are input into the CFD software. The process parameters are set as boundary conditions, and different rotation conditions are set to conduct conditional trial production of cast pipes. During the trial production process, the sequence of solidification zones under different rotation conditions is identified. The specific identification steps are as follows:

[0014] S211: Based on numerical simulation technology, the finite difference method is used to discretize time and space, and the heat conduction equation is numerically solved to obtain the temperature field T at each position in the mold under different rotation conditions;

[0015] S212: Based on the temperature field T of each area in the mold and combined with the material property information, obtain the solidification index at each position in the mold under different rotation conditions , specifically:

[0016] ;

[0017] Where, It represents the solidification index at the position r in the mold at the tth time point, represents a unit step function, if ,but =1, otherwise =0; Indicates the current temperature value at position r in the mold, represents the solidus temperature, Indicates adjustment parameters, value range ; represents the temperature gradient at position r in the mold.

[0018] Preferably, the specific identification step further includes:

[0019] S213: Solidification index at various positions in the mold under different rotation conditions , set the coagulation threshold, and compare the coagulation threshold with the coagulation index at each position under different rotation conditions. Comparisons were performed to identify the sequence of solidification zones under different rotation conditions:

[0020] S2131: If the solidification index at the corresponding position under the corresponding rotation conditions If the solidification threshold is exceeded, it is determined that the corresponding position under the corresponding rotation condition is in a solidification state, and statistics are performed to obtain the solidification area under the corresponding rotation condition, and the solidification area is marked as the first solidification area;

[0021] S2132: If the solidification index at the corresponding position under the corresponding rotation conditions If the solidification threshold is not exceeded, it is determined that the corresponding position is not in a solidification state under the corresponding rotation condition. Statistics are performed to obtain the unsolidified area under the corresponding rotation condition, and the unsolidified area is marked as a post-solidification area.

[0022] Preferably, the specific step S2 further includes:

[0023] S22: Based on the first solidification region and the last solidification region identified in S21, the relative contents of different melting point phases in the first solidification region and the last solidification region at different solidification stages within each rotation condition are analyzed to obtain the mass fraction of the high melting point phase in the first solidification region. and the mass fraction of high melting point phase in the post-solidification region , specifically:

[0024]

[0025] Where, It represents the content of high melting point phase in the first solidification region at solidification stage d, It represents the content of high melting point phase in the post-solidification region at solidification stage d, Indicates the initial content of metal materials. represents the mass fraction of the high melting point phase in the first solidification region at solidification stage d, represents the mass fraction of the high melting point phase in the post-solidification region at solidification stage d;

[0026] S23: The mass fraction of the high melting point phase in the first solidification region and the mass fraction of high melting point phase in the post-solidification region Perform subtraction to obtain the mass score difference , specifically:

[0027] S24: Based on mass score difference The acquisition method is to extract the maximum mass fraction difference from multiple solidification stages under the corresponding rotation conditions. As an indicator of the difference in composition distribution between the first solidification area and the last solidification area , used to measure the uniformity of mechanical properties at different positions of the cast pipe.

[0028] Preferably, the specific step S2 further includes:

[0029] S25: Based on the first solidification area and the last solidification area identified in S21, and combined with the phase diagram principle, the distribution relationship of the solute elements in the solid phase and the liquid phase in the mold under different rotation conditions is analyzed to obtain the distribution coefficient Fx under the corresponding rotation conditions, specifically:

[0030]

[0031] Where, represents the solute concentration in the solid phase, represents the solute concentration in the liquid phase;

[0032] S26: The composition distribution difference index between the first solidification area and the last solidification area obtained in S24 and S25 And the distribution coefficient Fx under the corresponding rotation conditions, the component segregation coefficient Pxs is obtained by dimensionless conversion, specifically:

[0033]

[0034] Where, and They are all weight values. The specific values are set by the user according to the situation. 0< <1,0< <1.

[0035] Preferably, the specific steps of S3 include:

[0036] S31: Based on the material manual and database, the solidification shrinkage rate data of the corresponding metal material is retrieved, and the initial liquid volume TJ in the process parameters is extracted. During the solidification process of the liquid metal, the actual replenishment liquid metal volume Vb at the riser is collected to analyze the timeliness of the shrinkage feeding of the cast pipe production line to generate a timeliness function under different rotation conditions. , specifically:

[0037]

[0038] Where, Indicates the solidification shrinkage volume. If the actual volume of liquid metal added Vb is less than the solidification shrinkage volume ,but =1, indicating that the current casting pipe production line is not feeding in time; if the actual replenishment liquid metal volume Vb ≥ solidification shrinkage volume ,but =0, indicating that the current casting pipe production line is in a timely state of shrinkage feeding;

[0039] S32: Before trial production, collecting supplementary data in the corresponding mold on the casting pipe production line under different rotation conditions, the supplementary data including the riser content Hm in the corresponding mold on the casting pipe production line under different rotation conditions;

[0040] S33: Obtain the hole defect risk coefficient Kfx, specifically:

[0041]

[0042] Where, Indicates the width of the solidification temperature range, 、 and All are weight values.

[0043] Preferably, the specific step S3 further includes:

[0044] S34: The hole risk coefficient Kfx and the component segregation coefficient Pxs are used as the input values of the loss minimization prediction model. After dimensionless processing, the failure assessment index DPzs under the corresponding rotation conditions is fitted and output, which is specifically:

[0045] ;

[0046] Where, and Both are influence coefficients, which are used to adjust the influence of pores and component segregation on failure assessment. e is expressed as the Euler number.

[0047] Preferably, the specific step S3 further includes:

[0048] S35: Based on the failure assessment index DPzs under the corresponding rotation conditions obtained in S34, after feature extraction, the failure assessment index DPzs with the smallest value is extracted, and the rotation condition corresponding to the failure assessment index DPzs with the smallest value is used as the production condition of centrifugal casting.

[0049] An intelligent management system for a cast pipe production line, comprising a preparation subsystem, a trial production subsystem and a production management subsystem;

[0050] The preparation subsystem extracts relevant process data from the pipe casting production line and determines the mold making according to the casting requirements;

[0051] The trial production subsystem will conduct conditional trial production for cast pipe production based on the mold determination. During the trial production process, different rotation conditions are set and combined with CFD simulation to identify the sequence of solidification zones under different rotation conditions. Based on the identification results under different rotation conditions, the composition differences and solute element distribution in different solidification zones are analyzed to obtain the composition segregation coefficient Pxs.

[0052] The production management subsystem is used to analyze the timeliness of shrinkage feeding in the casting pipe production line during the solidification process of the molten metal, and to construct the hole defect risk coefficient Kfx based on relevant process data. It uses deep learning technology to build a loss minimization prediction model and fit the output of the production selection conditions for centrifugal casting.

[0053] The present invention provides an intelligent management method and system for a cast pipe production line, which has the following beneficial effects:

[0054] (1) By extracting process data from the cast pipe production line and determining the mold according to casting requirements, the present invention can accurately formulate molds that meet actual production needs, reducing manufacturer losses and fluctuations in cast pipe production caused by improper mold design or mismatch. During the trial production process, the method sets different rotation conditions and combines CFD simulation technology to identify the sequence of solidification regions under different rotation conditions, analyze the composition differences and distribution of solute elements in different regions. By obtaining the composition segregation coefficient Pxs, the distribution of solute elements in the cast pipe production process can be optimized, further improving the composition uniformity of the cast pipe, thereby enhancing the mechanical properties and structural reliability of the cast pipe and reducing the risk of defects caused by uneven composition. In the cast pipe production process, by analyzing the timeliness of shrinkage during the solidification of the molten metal and combining process data, a hole defect risk coefficient Kfx is constructed. This analysis can predict potential hole defects in advance, laying the foundation for effectively reducing the incidence of hole defects. Deep learning technology is used to optimize the production conditions of centrifugal casting, reduce losses and energy waste in the production process, and ensure that the cast pipe is produced under optimal conditions, further improving production efficiency. In summary, this method achieves intelligent optimization of the cast pipe production process by integrating advanced simulation technology, deep learning algorithms and refined production management methods.

[0055] (2) By analyzing the relative contents of different melting point phases in the first solidification region and the last solidification region, the present invention can accurately identify the composition differences of each region at different solidification stages. By calculating the mass fraction of the high melting point phase based on the phase equilibrium principle, the material composition differences of different regions of the cast pipe can be accurately reflected. For example, in different solidification stages (corresponding to different temperatures), the changes in the mass fractions of the high melting point phase in the first solidification region and the high melting point phase in the last solidification region can provide a scientific basis for the subsequent evaluation of the uniformity of the mechanical properties of the cast pipe. If the mass fraction difference is large, it means that the composition is unevenly distributed, which may lead to the presence of more impurities or brittle phases in certain regions, reducing the overall mechanical properties of the cast pipe. Mechanical property optimization: The present invention helps optimize the mechanical properties of the cast pipe by extracting the mass fraction difference with the largest value from multiple solidification stages as a composition distribution difference index. Cast pipes with uneven composition distribution are prone to brittle fracture or fatigue failure when subjected to external forces such as impact, affecting the service life of the product. The method of the present invention can further control composition segregation and improve the toughness and fatigue resistance of the cast pipe.

[0056] (3) By comparing the solidification shrinkage rate data and the actual volume of replenished liquid metal, the timeliness of the shrinkage replenishment of the cast pipe production line is analyzed, thereby ensuring that the cast pipe can replenish liquid metal in time during the solidification process. By defining a timeliness function, it is possible to effectively determine whether the cast pipe production line is in a timely shrinkage replenishment state. By collecting the mold riser content under different rotation conditions and obtaining the hole defect risk coefficient based on dimensionless processing, the present invention can quantify the hole defect risk that may occur in the cast pipe production process. The risk coefficient helps to identify potential defects in the production process and provide effective adjustment directions. In general, the present invention optimizes the process parameters of cast pipe production, reduces the occurrence of defects, and improves the mechanical properties and stability of cast pipes through accurate shrinkage replenishment timeliness analysis, hole defect risk assessment and failure prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of an intelligent management method for a cast pipe production line according to the present invention;

[0058] Figure 2 It is a partial cross-sectional view of the mold of the present invention;

[0059] Figure 3 This is a distribution diagram of solidification conditions in the mold under different rotation conditions in the present invention;

[0060] Figure 4 This is a block diagram of an intelligent management system for a cast pipe production line according to the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] Example 1

[0063] See also Figure 1 The present invention provides an intelligent management method for a cast pipe production line, comprising the following steps:

[0064] S1: Extract relevant process data from the pipe casting production line and determine the mold making according to casting requirements;

[0065] S2: Based on the mold determination, a trial production of cast pipes is conducted. During the trial production process, different rotation conditions (different rotation speeds) are set. Combined with CFD simulation, the sequence of solidification zones under different rotation conditions is identified. Based on the identification results under different rotation conditions, the differences in composition and solute element distribution in different solidification zones are analyzed to obtain the composition segregation coefficient Pxs.

[0066] S3: During the solidification process of the molten metal, the timeliness of the shrinkage feeding of the casting pipe production line is analyzed, and the hole defect risk coefficient Kfx is constructed in combination with relevant process data. The loss minimization prediction model is constructed using deep learning technology to fit and output the production selection conditions of centrifugal casting.

[0067] In this embodiment, by extracting and analyzing the process data in the production line in real time and combining it with CFD simulation technology, the order of solidification zones under different rotation conditions can be accurately identified, thereby providing a scientific basis for mold preparation and rotation condition setting in the cast pipe production process. This method effectively avoids the production instability under traditional empirical or single process conditions, and significantly improves the quality and production efficiency of cast pipes through precise simulation and data-driven. During the solidification process of cast pipes, based on the identification of solidification zones under different rotation conditions, it is possible to deeply analyze the distribution differences of solute elements in different solidification zones, and then obtain the composition segregation coefficient Pxs. By analyzing the composition segregation coefficient, the problem of uneven composition caused by changes in solid-liquid state during the production of cast pipes is effectively solved, thereby improving the mechanical properties and reliability of cast pipes and reducing defects caused by uneven composition. During the solidification process, the present invention constructs a hole defect risk coefficient Kfx by analyzing the timeliness of shrinkage compensation of the cast pipe production line and combining relevant process data. By combining deep learning technology with the optimization and fitting of a loss-minimizing prediction model, production conditions can be accurately predicted during the trial production phase, reducing defects such as holes and cracks caused by untimely shrinkage feeding, and minimizing quality fluctuations and resource waste in cast pipe production. In short, this method achieves intelligent management of the entire cast pipe production process, from mold design and rotation condition optimization to shrinkage feeding timeliness analysis, by precisely controlling every step of the process. This effectively improves the quality stability, production efficiency, and resource utilization of cast pipe products, and has broad application prospects and economic value.

[0068] Example 2

[0069] Please refer to Figure 1 、 Figure 2 and Figure 3 , specifically: S1 specific steps include:

[0070] S11: Deploy multiple sets of sensors and data acquisition equipment on the cast pipe production line to extract relevant process data in real time. The relevant process data includes production line equipment operation information, process parameters and material property information. The production line equipment operation information includes the furnace temperature, centrifuge rotation speed and current and voltage of each production equipment in the cast pipe production line; the process parameters include the initial batch ratio of the metal material for casting the cast pipe, the initial batch content of the metal material ... , solidification temperature range width , initial liquid volume TJ, metal material pouring temperature, cooling rate and supplementary volume; material property information includes the density of the metal material of the cast pipe, solidus temperature , viscosity and thermal conductivity;

[0071] Among them, the initial ingredient content of the metal material is directly obtained based on the ingredient plan before casting and the known raw material composition , which is usually based on the design requirements and target performance of the material.

[0072] Differential scanning calorimetry or differential thermal analysis can be used to measure the heat flow changes of the alloy at different temperatures during the cooling process, thereby obtaining the solidification temperature range (including the solidification start temperature and the solidification end temperature). The width of the solidification temperature range usually refers to the temperature range from liquid to completely solid, and is usually determined by measuring the solid phase solidification start temperature (solidus line) and the temperature at which the liquid phase is completely solidified (liquidus line) during the liquid cooling process of the alloy.

[0073] By measuring the density of liquid metal using physical experiments and combining it with the mass of the liquid metal at a known temperature, the liquid volume can be obtained.

[0074] The solidus temperature can be obtained by consulting a phase diagram, which usually provides the solidus (the temperature at which a solid begins to solidify) for different alloy compositions and temperatures.

[0075] S12: According to the casting requirements, a corresponding mold is made and a mold geometry model is obtained. The casting requirements include the specifications and shape of the cast pipe to be cast. The mold type is selected based on the company's production batch requirements, production cycle plan, and production cost control. The mold types include metal molds and sand molds.

[0076] The specific steps of S2 include:

[0077] S21: Based on CFD simulation technology, the mold geometry model, production line equipment operation information, and material property information in S12 are input into the CFD software. The process parameters are set as boundary conditions, and different rotation conditions are set to conduct conditional trial production of cast pipes. During the trial production process, the sequence of solidification zones under different rotation conditions is identified. The specific identification steps are as follows:

[0078] S211: Based on numerical simulation technology, the finite difference method is used to discretize time and space, and the heat conduction equation is numerically solved to obtain the temperature field T at each position in the mold (after high-temperature melting) under different rotation conditions, that is, the temperature distribution at any position in the casting;

[0079] S212: Based on the temperature field T of each area in the mold and combined with the material property information, obtain the solidification index at each position in the mold under different rotation conditions , specifically:

[0080] ;

[0081] Where, It represents the solidification index at the position r in the mold at the tth time point, represents a unit step function, if ,but =1, otherwise =0; Indicates the current temperature value at position r in the mold, represents the solidus temperature, which is the solidification temperature of the material (the temperature at which a liquid turns into a solid). Indicates adjustment parameters, value range , used to describe the effect of temperature gradient on solidification rate; It represents the temperature gradient at position r in the mold, reflecting the rate of temperature change with spatial position. r represents the position, and t represents the time point;

[0082] For details, please refer to Figure 3 , Figure 3 The pilot test lists multiple sets of rotation conditions and multiple positions, combined with the solidification threshold, to clearly show the solidification index at the corresponding position under different rotation conditions and whether it is above the solidification threshold;

[0083] Among them, the temperature gradient at position r in the mold is The specific acquisition method is as follows:

[0084] Based on the central difference method in the discretization method:

[0085] ;

[0086] Where, 、 and are the step size of the spatial grid, that is, the distance between two adjacent points in different directions, which determines the accuracy of spatial discretization; for example, if we use a grid step size in the x direction , then the spatial distribution of the calculation points is discrete, and the step size It represents the distance from one grid point to the adjacent grid point. 、 、 、 、 and These are the temperature values of adjacent grid points, respectively. and Indicates the temperature value after the current point r moves forward and backward one step in the x direction; and Indicates the temperature value in the y direction; and Indicates the temperature value in the z direction;

[0087] The central difference method approximates the derivative by taking points on both sides of the current point. In this way, it can better capture the trend of the function (in this case, the temperature distribution) at that point.

[0088] The specific identification steps also include:

[0089] S213: Solidification index at various positions in the mold under different rotation conditions , set the coagulation threshold, and compare the coagulation threshold with the coagulation index at each position under different rotation conditions. Comparisons were performed to identify the sequence of solidification zones under different rotation conditions:

[0090] S2131: If the solidification index at the corresponding position under the corresponding rotation conditions If the solidification threshold is exceeded, it is determined that the corresponding position under the corresponding rotation condition is in a solidification state, and statistics are performed to obtain the solidification area under the corresponding rotation condition, and the solidification area is marked as the first solidification area;

[0091] S2132: If the solidification index at the corresponding position under the corresponding rotation conditions If the solidification threshold is not exceeded, it is determined that the corresponding position is not in a solidification state under the corresponding rotation condition. Statistics are performed to obtain the unsolidified area under the corresponding rotation condition, and the unsolidified area is marked as a post-solidification area.

[0092] In this embodiment, in step S12, a suitable mold is selected based on the casting requirements and production batch requirements, and the geometric model of the mold is obtained. This process combines the production cycle and cost control. By selecting a metal mold or a sand mold, production efficiency can be maximized while meeting production needs. For example, metal molds are generally suitable for large-scale production, which can improve production efficiency and reduce the production cost of a single cast pipe, while sand molds are suitable for small-batch, high-complexity cast pipe production. The choice of this mold optimizes the production process, makes production more flexible, and can adjust the production mode according to demand. In step S2, CFD simulation technology and numerical solutions are used, combined with mold geometric models, production data and material property information, to conduct conditional trial production of cast pipes. By identifying the sequence of solidification zones under different rotation conditions, the temperature distribution and composition segregation during the solidification process can be more accurately predicted and controlled.

[0093] Suppose that during a cast pipe production run, the rotation speed is set to a low setting. During trial production, CFD simulation technology identifies certain locations in the cast pipe mold as having lower temperatures and slower solidification. Numerical solutions are used to determine the solidification indicators for these locations, identifying these areas as late-solidification regions. Adjusting the rotation speed can alter the order of solidification regions, making the temperature gradient more uniform, thereby optimizing the solidification process and reducing solute element segregation. For example, if solute element segregation occurs in certain locations during solidification, it can lead to uneven performance in the cast pipe, impacting subsequent use and processing. By setting solidification thresholds and comparing them with simulation results, the present invention can identify regional variations in the solidification process based on different rotation conditions, enabling timely production line management and ensuring cast pipe quality. Accurate identification of solidification regions, particularly the accurate determination of early and late solidification regions, can avoid defects caused by uneven solidification (such as pores and cracks). These optimization processes, through intelligent analysis and prediction, effectively improve the structural integrity and mechanical properties of the cast pipe.

[0094] Example 3

[0095] Please refer to Figure 1 Specifically: S2 specific steps also include:

[0096] S22: Based on the first solidification region and the last solidification region identified in S21, analyze the relative content of different melting point phases in the first solidification region and the last solidification region at different solidification stages (corresponding to different temperatures) under each rotation condition to obtain the mass fraction of the high melting point phase in the first solidification region and the mass fraction of high melting point phase in the post-solidification region , specifically:

[0097]

[0098] Where, It represents the content of high melting point phase in the first solidification region at solidification stage d, It represents the content of high melting point phase in the post-solidification region at solidification stage d, Indicates the initial content of metal materials. represents the mass fraction of the high melting point phase in the first solidification region at solidification stage d, represents the mass fraction of the high melting point phase in the post-solidification region at solidification stage d;

[0099] Among them, the melting point can be monitored and obtained by differential scanning calorimetry, differential thermal analysis, melting point method and other methods. The high melting point phase here refers to a substance or phase with a higher melting point in a multiphase system. In metal alloys or other solid-liquid phase change systems, different phases of the material will transform at different temperatures. Some phases have a higher melting point and are usually called high melting point phases. Therefore, the high melting point phase is a relatively high melting point phase.

[0100] It should be noted that the mass fraction reflects the respective proportions of the melting point phases in the corresponding solidification region. It is based on the principle of phase equilibrium, which indicates that at a given temperature, the relative content of the two phases in the metal material depends on the overall composition of the metal material and the equilibrium composition of the two phases (the content of the corresponding melting point phase in the corresponding solidification region at a certain temperature).

[0101] By analyzing the relative content of the melting point phase in the first and last solidification regions at different solidification stages (corresponding to different temperatures), the compositional differences can be reflected from the perspective of phase composition. During the solidification process, as the temperature decreases, the lever principle can be used to determine the change in the content of the high-melting-point phase in the first and last solidification regions, thus indirectly reflecting the compositional differences.

[0102] S23: The mass fraction of the high melting point phase in the first solidification region and the mass fraction of high melting point phase in the post-solidification region Perform subtraction to obtain the mass score difference , specifically:

[0103] S24: Based on mass score difference The acquisition method is to extract the maximum mass fraction difference from multiple solidification stages under the corresponding rotation conditions. As an indicator of the difference in composition distribution between the first solidification area and the last solidification area , used to measure the uniformity of mechanical properties at different positions of the cast pipe.

[0104] It should be noted that uneven distribution of components may cause more impurities or brittle phases to exist in certain areas. When subjected to dynamic loads such as impact, these areas are prone to brittle fracture, reducing the toughness and fatigue resistance of the casting.

[0105] In this embodiment, by analyzing the relative contents of different melting point phases in the early solidification area and the late solidification area, this method can further evaluate the distribution of high melting point phases in the cast pipe at different solidification stages. This analysis helps to deeply understand the differences in composition in the cast pipe, especially the changes in the mass fraction of high melting point phases under different temperature conditions, thereby optimizing the production process and reducing segregation. The present invention obtains the mass fraction difference and then quantifies the difference in composition distribution between different positions of the cast pipe. This indicator can further reflect the uniformity of the composition in the cast pipe, avoid inconsistent mechanical properties due to uneven composition, especially when subjected to dynamic loads, avoid brittle fracture and fatigue failure.

[0106] For example, suppose that during a cast pipe production run, CFD simulation analysis reveals that under certain rotational conditions, the mass fraction of the high-melting-point phase in the first-solidifying region at a certain temperature stage is 0.65, while the mass fraction of the high-melting-point phase in the last-solidifying region is 0.35 at the same temperature stage. Subtraction yields a mass fraction difference of 0.30, indicating a compositional difference within the pipe, which may result in superior mechanical properties in the first-solidifying region compared to the last-solidifying region. By further extracting the maximum mass fraction difference across multiple solidification stages and using this as an indicator of compositional variability, rotational conditions can be optimized, reducing the brittle phase problem caused by compositional inhomogeneity within the pipe and ultimately improving its mechanical properties and service life.

[0107] Example 4

[0108] Please refer to Figure 1 Specifically: S2 specific steps also include:

[0109] S25: Based on the first solidification area and the last solidification area identified in S21, and combined with the phase diagram principle, the distribution relationship of the solute elements in the solid phase and the liquid phase in the mold under different rotation conditions is analyzed to obtain the distribution coefficient Fx under the corresponding rotation conditions, specifically:

[0110]

[0111] Where, represents the solute concentration in the solid phase, represents the solute concentration in the liquid phase;

[0112] when When <1, it indicates that the solubility of the solute in the liquid phase is higher than that in the solid phase. As the solid phase precipitates, the solute concentration in the liquid phase will gradually increase. For example, in the solidification process of some aluminum alloys, the solute concentration of copper in the liquid phase will gradually increase. <1, as solidification proceeds from the outside to the inside, copper elements will be continuously squeezed to the center of the casting, resulting in an increase in the concentration of copper elements in the center. During solidification, copper will gradually be enriched in the liquid phase, which is positive segregation;

[0113] Reverse segregation: In contrast to positive segregation, the solute concentration on the surface or outer layer of the casting is higher than that in the center. This is usually caused by factors such as temperature gradients inside and outside the casting during solidification, shrinkage stress, etc., which lead to the flow of liquid phase, causing the solute elements to be brought to the surface of the casting.

[0114] Among them, the phase diagram principle is a graphical tool used to represent the distribution and change laws of the phase (such as solid, liquid, gas, etc.) of a substance under different conditions (such as temperature, pressure, composition, etc.). It is a diagram that describes the thermodynamic properties, phase changes and their mutual relationships of multi-component substances or multi-phase material systems.

[0115] Solute concentration in the solid phase It can be determined by X-ray diffraction (XRD) method;

[0116] Solute concentration in the liquid phase It can be measured by thermal analysis methods such as differential scanning calorimetry (DSC) or thermogravimetric analysis (TGA).

[0117] It should be noted that during the solidification process of a binary alloy, as the temperature decreases, the alloy begins to transform from a liquid to a solid state when the solidus temperature is reached. The areas that begin to solidify first form a solid phase, typically in the form of crystal nuclei, which then gradually grow. At this stage, the first solidified area can indeed be considered a solid phase, possessing the crystal structure and physical properties of a solid phase, distinct from the unsolidified liquid phase.

[0118] Initial solidification: In the initial solidification phase, the first solidification area is mainly composed of some tiny solid phase nuclei. These nuclei form in the liquid alloy and gradually grow as time and temperature change, continuously absorbing atoms from the surrounding liquid alloy. At this time, the first solidification area is mainly solid phase, but it is still surrounded by a large amount of liquid phase. There is a clear phase interface between the solid and liquid phases, and the solid and liquid phases are constantly exchanging matter and energy.

[0119] Mid-solidification: As the solidification process progresses, the solid phase in the first-solidifying region continues to increase, and the individual solid phase nuclei gradually grow and connect with each other, forming a continuous solid phase skeleton. However, at this time, some liquid phase still exists between the solid phase skeletons. As solidification continues, this liquid phase will gradually be squeezed into the gaps between the solid phases. At this stage, the first-solidifying region has formed a solid-dominated structure as a whole, but it also contains a liquid phase that has not yet fully solidified. It is a state of coexistence of liquid and solid phases, but the solid phase has already taken a dominant position.

[0120] Late solidification: In the late solidification stage, most of the liquid alloy has transformed into the solid phase, with only a small amount of liquid remaining at locations such as grain boundaries. At this point, the first-solidifying region is essentially completely solid, with only a very small amount of residual liquid possibly solidifying last. However, due to factors such as compositional segregation during the solidification process, there may be some differences in composition and microstructure between the first-solidifying and last-solidifying regions. However, from a phase perspective, the first-solidifying region is essentially solid.

[0121] S26: The composition distribution difference index between the first solidification area and the last solidification area obtained in S24 and S25 And the distribution coefficient Fx under the corresponding rotation conditions, the component segregation coefficient Pxs is obtained by dimensionless conversion, specifically:

[0122]

[0123] Where, and They are all weight values. The specific values are set by the user according to the situation. 0< <1,0< <1.

[0124] In this embodiment, by combining the principle of phase diagrams to analyze the distribution relationship of solute elements in the solid and liquid phases in the first and second solidification regions, the segregation behavior of solute elements in castings under different rotation conditions can be accurately identified. By calculating the distribution coefficient Fx, the concentration changes of solute elements in the solid and liquid phases can be reflected, thereby understanding the distribution of solutes during the solidification process. For example, during the solidification process of aluminum alloys, the distribution coefficient Fx of copper elements may be less than 1, causing the copper elements to be gradually repelled from the outside to the center of the casting, forming a positive segregation phenomenon. Scientific identification of segregation phenomena: By calculating the distribution coefficient Fx, positive segregation and negative segregation phenomena in cast pipes can be identified. Accurate identification of this process can help adjust the casting process, reduce the impact of segregation phenomena, and thus improve the quality and uniformity of cast pipes. By non-dimensionalizing the obtained composition segregation coefficient Pxs, this method can quantify the degree of composition segregation in cast pipes, paving the way for optimizing process conditions and reducing mechanical property problems caused by composition segregation.

[0125] Example 5

[0126] Please refer to Figure 1 , specifically: S3 specific steps include:

[0127] S31: Based on the material manual and database, the solidification shrinkage rate data of the corresponding metal material is retrieved, and the initial liquid volume TJ in the process parameters is extracted. During the solidification process of the liquid metal, the actual replenishment liquid metal volume Vb at the riser is collected to analyze the timeliness of the shrinkage feeding of the cast pipe production line to generate a timeliness function under different rotation conditions. , specifically:

[0128]

[0129] Where, Indicates the solidification shrinkage volume. If the actual volume of liquid metal added Vb is less than the solidification shrinkage volume ,but =1, indicating that the current casting pipe production line is not feeding in time; if the actual replenishment liquid metal volume Vb ≥ solidification shrinkage volume ,but =0, indicating that the current casting pipe production line's shrinkage compensation is in a timely state.

[0130] It should be noted that the solidification shrinkage rate data refers to the solidification shrinkage rate of the casting, which can also be expressed by the formula: Casting solidification shrinkage rate = (TJ-gj) / TJ; where TJ represents the initial liquid volume and gj represents the volume of the solid metal after solidification;

[0131] The difference between the initial liquid volume TJ and the volume of the solid metal after solidification gj is expressed as the solidification shrinkage volume ;

[0132] Specifically, material manuals include but are not limited to the Mechanical Design Manual, the Casting Manual, and the Metal Materials Handbook. They can also be found through the China National Knowledge Infrastructure (CNKI) database, the Web of Science database, and the Materials Project database.

[0133] Among them, the "Mechanical Design Manual" is a comprehensive reference book in the field of mechanical design, which contains the performance data of various mechanical engineering materials. The casting performance part of the materials contains relevant data such as the solidification shrinkage rate of common metal materials, which can provide a reference for mechanical design and casting process design.

[0134] Casting Handbook: This handbook provides detailed information on casting processes and materials, with dedicated chapters discussing the properties of various cast metal materials, including solidification shrinkage data for various cast metal materials.

[0135] "Metal Materials Handbook": Systematically introduces the performance and characteristics of various metal materials. In addition to basic mechanical properties, it also involves thermal physical performance data such as the shrinkage rate of materials during solidification, which facilitates material selection and process development.

[0136] CNKI database: contains a large number of academic journals, dissertations and other literature resources. By searching the keyword "solidification shrinkage rate of common metal materials", many relevant research papers can be found, which include experimental data and research analysis of the solidification shrinkage rate of specific metal materials.

[0137] Web of Science database: It is a globally authoritative academic database that covers high-quality literature in many disciplines and can be used to find cutting-edge research results and data on the solidification shrinkage of metal materials internationally.

[0138] Materials Project Database: This is a database focused on materials science, providing a large amount of computational and experimental data on materials, including thermodynamic and physical properties data such as the solidification shrinkage of metal materials. Users can obtain the required information through search and filtering functions.

[0139] S32: Before trial production, collecting supplementary data in the corresponding mold on the casting pipe production line under different rotation conditions, the supplementary data including the riser content Hm in the corresponding mold on the casting pipe production line under different rotation conditions;

[0140] S33: After dimensionless processing, the hole defect risk coefficient Kfx is obtained, specifically:

[0141]

[0142] Where, Indicates the width of the solidification temperature range, 、 and These are all weight values, and the specific values are set by the user according to the situation.

[0143] Among them, the solidification temperature range width The temperature difference between the initial and complete solidification of a liquid metal. It describes the transition range between different components or phases of an alloy during its solidification process. The width of the solidification temperature range is an important indicator of the stability of the metal solidification process and the uniformity of the alloy's composition.

[0144] Alloys with a wide solidification temperature range are more likely to produce shrinkage cavities. Within this temperature range, liquid metal and solid metal coexist for a long time, and the liquid metal's shrinkage feeding channel is easily blocked by the solid phase that solidifies first, so that the later solidified part cannot get enough liquid metal to supplement, thus producing shrinkage cavities.

[0145] Riser is a liquid reservoir used to compensate for the solidification shrinkage of a casting. The volume within the riser directly impacts its shrinkage-feeding effectiveness. If the riser capacity is insufficient, it will be difficult to effectively supply liquid metal to the casting, resulting in shrinkage porosity and shrinkage cavities.

[0146] The specific steps of S3 also include:

[0147] S34: The hole risk coefficient Kfx and the component segregation coefficient Pxs are used as the input values of the loss minimization prediction model. After dimensionless processing, the failure assessment index DPzs under the corresponding rotation conditions is fitted and output, which is specifically:

[0148] ;

[0149] Where, and Both are influence coefficients, which are used to adjust the influence of pore defects and composition segregation on failure assessment. e is expressed as the Euler number, which is approximately 2.71828.

[0150] The specific steps of S3 also include:

[0151] S35: Based on the failure assessment index DPzs under the corresponding rotation conditions obtained in S34, after feature extraction, the failure assessment index DPzs with the smallest value is extracted, and the rotation condition corresponding to the failure assessment index DPzs with the smallest value is used as the production condition of centrifugal casting.

[0152] In this embodiment, based on the comparison of the actual collected volume of liquid metal replenished at the riser and the solidification shrinkage volume, the present invention can monitor the timeliness of shrinkage replenishment in a cast pipe production line in real time. By generating a timeliness function, it can quickly identify and adjust production conditions when replenishment is not timely, effectively avoiding defects such as porosity caused by insufficient replenishment. This optimization measure helps ensure the stable quality of castings during cast pipe production and avoid unnecessary quality losses. By constructing a porosity risk coefficient, the present invention can quantify the risk of porosity during cast pipe production, providing targeted data support to help production personnel adjust rotation conditions and replenishment volume to reduce the probability of porosity. This risk coefficient, combined with the analysis results after dimensionless processing, can effectively guide the optimization of the casting process and reduce production failures caused by porosity. Failure assessment and production condition optimization: By constructing a loss minimization prediction model and combining input data of the porosity risk coefficient and the component segregation coefficient, the failure risk under different rotation conditions can be assessed. Based on the resulting failure assessment index, the present invention helps users identify relatively optimal production conditions. By extracting the failure assessment index with the smallest value and combining it with the prediction results of deep learning technology, the production conditions of centrifugal casting can be optimized to improve production efficiency and casting quality.

[0153] Specifically, suppose that during the cast pipe production process, real-time monitoring reveals that the actual volume of liquid metal replenished at the riser is less than the solidification shrinkage volume. This indicates that the current cast pipe production line is not replenishing shrinkage in a timely manner. To avoid hole defects caused by untimely replenishment, the production system will adjust the replenishment speed according to the timeliness function. After further calculation of the hole defect risk coefficient, it was found that the value was too high, indicating that the hole defect risk is relatively high under the current rotation conditions. Then, the failure assessment index output by the deep learning model fitting shows that the failure risk under a certain rotation condition is the lowest. The system automatically selects this condition as the optimal production condition. In this way, the quality of the entire production process is effectively improved, while rework or scrap caused by process problems is reduced.

[0154] Example 6

[0155] Please refer to Figure 4 ,Specifically: An intelligent management system for a cast pipe production line, including a preparation subsystem, a trial production subsystem and a production management subsystem;

[0156] The preparation subsystem extracts relevant process data from the pipe casting production line and determines the mold making according to the casting requirements;

[0157] The trial production subsystem will conduct conditional trial production for cast pipe production based on the mold determination. During the trial production process, different rotation conditions are set and combined with CFD simulation to identify the sequence of solidification zones under different rotation conditions. Based on the identification results under different rotation conditions, the composition differences and solute element distribution in different solidification zones are analyzed to obtain the composition segregation coefficient Pxs.

[0158] The production management subsystem is used to analyze the timeliness of shrinkage feeding in the casting pipe production line during the solidification process of the molten metal, and to construct the hole defect risk coefficient Kfx based on relevant process data. It uses deep learning technology to build a loss minimization prediction model and fit the output of the production selection conditions for centrifugal casting.

[0159] During system operation, the preparation subsystem's primary function is to extract relevant process data from the pipe casting line and determine the mold design based on casting requirements. This subsystem collects process parameters from the line to provide data support for subsequent casting operations. The trial production subsystem conducts conditional trial production based on the determined mold conditions. During trial production, the system sets different rotation conditions and uses computational fluid dynamics (CFD) simulations to identify the solidification sequence of the pipe casting line under different rotation conditions. By simulating the solidification process under different rotation conditions, the trial production subsystem analyzes the compositional differences and solute element distribution within different solidification zones of the pipe. This data is used to determine the composition segregation coefficient, which is crucial for subsequent production optimization. By accurately identifying the solidification sequence and composition distribution, the trial production subsystem provides accurate process data support to the production management subsystem, helping to improve pipe quality. During the solidification process of the molten metal, the production management subsystem analyzes the timely feeding of the pipe casting line and, based on relevant process data, constructs a porosity risk factor. By real-time monitoring of changes in the volume of replenished liquid metal and the solidification shrinkage volume, the production management subsystem determines the timely feeding and identifies potential production issues. Subsequently, the production selection conditions for centrifugal casting are fitted and output. These optimized production conditions can effectively reduce the risk of defects and failure in cast pipes and improve production efficiency.

[0160] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent management method for a cast pipe production line, characterized by: The following steps are included: S1: Extract relevant process data from the pipe casting production line and determine the mold making according to casting requirements; S1 includes, S11: deploy multiple sets of sensors and data acquisition equipment on the cast pipe production line to extract relevant process data in the production line in real time. The relevant process data includes production line equipment operation information, process parameters and material property information. Among them, the production line equipment operation information includes the furnace temperature, centrifuge rotation speed and current and voltage of each production equipment in the cast pipe production line; the process parameters include the initial batch ratio of the metal material for casting the cast pipe, the initial batch content of the metal material , solidification temperature range width , initial liquid volume TJ, metal material pouring temperature, cooling rate and supplementary volume; material property information includes the density of the metal material of the cast pipe, solidus temperature , viscosity and thermal conductivity; S2: Based on the mold determination, a trial production of cast pipes is conducted. During the trial production process, different rotation conditions are set and combined with CFD simulation to identify the sequence of solidification zones under different rotation conditions, including the first solidification zone and the last solidification zone. Based on the identification results under different rotation conditions, the composition differences and solute element distribution in different solidification zones are analyzed to obtain the composition segregation coefficient Pxs. S2 includes, S25: Based on the identified early solidification area and late solidification area, and combined with the phase diagram principle, the distribution relationship of the solute elements in the solid phase and liquid phase in the mold under different rotation conditions is analyzed to obtain the distribution coefficient Fx under the corresponding rotation conditions, specifically: Where, represents the solute concentration in the solid phase, represents the solute concentration in the liquid phase; S26: Based on the obtained composition distribution difference index between the first solidification area and the last solidification area And the distribution coefficient Fx under the corresponding rotation conditions, after dimensionless transformation, the component segregation coefficient Pxs is obtained, specifically: Where, and They are all weight values, and the specific values are set by the user according to the situation; S3: During the solidification process of the molten metal, the timely feeding of the casting pipe production line is analyzed. Combined with relevant process data, the hole defect risk coefficient Kfx is constructed. Deep learning technology is used to build a loss minimization prediction model, which is fitted and output as the production selection conditions for centrifugal casting. S3 includes, S31: query the solidification shrinkage rate data of the corresponding metal material based on the material manual and database, and extract the initial liquid volume TJ in the process parameters. During the solidification process of the molten metal, collect the actual replenishment liquid metal volume Vb at the riser, analyze the timeliness of the shrinkage feeding of the cast pipe production line, and generate the timeliness function under different rotation conditions. , specifically: Where, Indicates the solidification shrinkage volume. If the actual volume of liquid metal added Vb is less than the solidification shrinkage volume ,but =1, indicating that the current casting pipe production line is not feeding in time; if the actual replenishment liquid metal volume Vb ≥ solidification shrinkage volume ,but =0, indicating that the current casting pipe production line is in a timely state of shrinkage feeding; S32: Before trial production, collecting supplementary data in the corresponding mold on the casting pipe production line under different rotation conditions, the supplementary data including the riser content Hm in the corresponding mold on the casting pipe production line under different rotation conditions; S33: Obtain the hole defect risk coefficient Kfx, specifically: Where, Indicates the width of the solidification temperature range, 、 and All are weight values; S34: The hole risk coefficient Kfx and the component segregation coefficient Pxs are used as the input values of the loss minimization prediction model. After dimensionless processing, the failure assessment index DPzs under the corresponding rotation conditions is fitted and output, which is specifically: ； Where, and are all influence coefficients, which are used to adjust the influence of pore defects and component segregation on failure assessment. e is the Euler number. S35: Based on the failure assessment index DPzs under the corresponding rotation conditions obtained in S34, after feature extraction, the failure assessment index DPzs with the smallest value is extracted, and the rotation condition corresponding to the failure assessment index DPzs with the smallest value is used as the production condition of centrifugal casting.

2. The intelligent management method for a cast pipe production line according to claim 1, characterized in that: The specific steps of S1 also include: S12: According to the casting requirements, a corresponding mold is made and a mold geometry model is obtained. The casting requirements include the specifications and shape of the cast pipe to be cast. The mold type is selected based on the company's production batch requirements, production cycle plan, and production cost control. The mold types include metal molds and sand molds.

3. The intelligent management method for a cast pipe production line according to claim 2, characterized in that: The specific steps of S2 include: S21: Based on CFD simulation technology, the mold geometry model, production line equipment operation information, and material property information in S12 are input into the CFD software. The process parameters are set as boundary conditions, and different rotation conditions are set to conduct conditional trial production of cast pipes. During the trial production process, the sequence of solidification zones under different rotation conditions is identified. The specific identification steps are as follows: S211: Based on numerical simulation technology, the finite difference method is used to discretize time and space, and the heat conduction equation is numerically solved to obtain the temperature field T at each position in the mold under different rotation conditions; S212: Based on the temperature field T of each area in the mold and combined with the material property information, obtain the solidification index at each position in the mold under different rotation conditions , specifically: ； Where, It represents the solidification index at the position r in the mold at the tth time point, represents a unit step function, if ,but =1, otherwise =0; Indicates the current temperature value at position r in the mold, represents the solidus temperature, Indicates adjustment parameters, value range ; represents the temperature gradient at position r in the mold.

4. The intelligent management method for a cast pipe production line according to claim 3, characterized in that: The specific identification steps also include: S213: Solidification index at various positions in the mold under different rotation conditions , set the coagulation threshold, and compare the coagulation threshold with the coagulation index at each position under different rotation conditions. Comparisons were performed to identify the sequence of solidification zones under different rotation conditions: S2131: If the solidification index at the corresponding position under the corresponding rotation conditions If the solidification threshold is exceeded, it is determined that the corresponding position under the corresponding rotation condition is in a solidification state, and statistics are performed to obtain the solidification area under the corresponding rotation condition, and the solidification area is marked as the first solidification area; S2132: If the solidification index at the corresponding position under the corresponding rotation conditions If the solidification threshold is not exceeded, it is determined that the corresponding position is not in a solidification state under the corresponding rotation condition. Statistics are performed to obtain the unsolidified area under the corresponding rotation condition, and the unsolidified area is marked as a post-solidification area.

5. The intelligent management method for a cast pipe production line according to claim 4, characterized in that: The specific steps of S2 also include: S22: Based on the first solidification region and the last solidification region identified in S21, the relative contents of different melting point phases in the first solidification region and the last solidification region at different solidification stages within each rotation condition are analyzed to obtain the mass fraction of the high melting point phase in the first solidification region. and the mass fraction of high melting point phase in the post-solidification region , specifically: Where, It represents the content of high melting point phase in the first solidification region at solidification stage d, It represents the content of high melting point phase in the post-solidification region at solidification stage d, Indicates the initial content of metal materials. represents the mass fraction of the high melting point phase in the first solidification region at solidification stage d, represents the mass fraction of the high melting point phase in the post-solidification region at solidification stage d; S23: The mass fraction of the high melting point phase in the first solidification region and the mass fraction of high melting point phase in the post-solidification region Perform subtraction to obtain the mass score difference , specifically: S24: Based on mass score difference The acquisition method is to extract the maximum mass fraction difference from multiple solidification stages under the corresponding rotation conditions. As an indicator of the difference in composition distribution between the first solidification area and the last solidification area , used to measure the uniformity of mechanical properties at different positions of the cast pipe.

6. An intelligent management system for a cast pipe production line, used to implement the intelligent management method for a cast pipe production line according to any one of claims 1 to 5, characterized in that: Including preparation subsystem, trial production subsystem and production management subsystem; The preparation subsystem extracts relevant process data from the pipe casting production line and determines the mold making according to the casting requirements; The trial production subsystem will conduct conditional trial production for cast pipe production based on the mold determination. During the trial production process, different rotation conditions are set and combined with CFD simulation to identify the sequence of solidification zones under different rotation conditions. Based on the identification results under different rotation conditions, the composition differences and solute element distribution in different solidification zones are analyzed to obtain the composition segregation coefficient Pxs. The production management subsystem is used to analyze the timeliness of shrinkage feeding in the casting pipe production line during the solidification process of the molten metal, and to construct the hole defect risk coefficient Kfx based on relevant process data. It uses deep learning technology to build a loss minimization prediction model and fit the output of the production selection conditions for centrifugal casting.

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