A method for fine-grain homogenization forging of high-chromium forgings

Abstract

The application discloses a high-chromium forge piece fine-grain homogenization forging method and relates to the field of forging control technology.The forging method comprises the following steps: collecting layered temperature time series data, loading force time series data and reduction displacement time series data of a high-chromium forge piece blank, determining deformation sections and stop sections of each heating time, and constructing a sectional thermal history set; determining equivalent compression processes, thermal exposure processes and stop switching processes of each layered evolution object under each heating time, analyzing process differences between the layered evolution objects, generating a control instruction set, identifying a recrystallization driving window of each layered evolution object, and executing a next heating time forging process on the high-chromium forge piece blank according to the control instruction set; and the application controls the heating time based on a history misalignment index, optimizes forging parameters through the control instruction set, and effectively improves the fine-grain homogenization forging effect of the high-chromium forge piece.

Description

Technical Field

[0001] This invention relates to the field of forging control technology, specifically to a forging method for refining and homogenizing high-chromium forgings. Background Technology

[0002] In the metal manufacturing process, forging is an important forming method widely used in aerospace, automotive, military, machinery and other industries, especially in the processing of high-strength alloy materials. High-chromium forgings, as a special alloy material, are often used in demanding industrial fields due to their excellent wear resistance, corrosion resistance and high-temperature strength, such as key components in mining machinery, metallurgical equipment and high-end power equipment. In order to ensure its high performance, the grain structure must be effectively controlled during the forging process to achieve the refinement and homogenization of high-chromium forgings.

[0003] Existing high-chromium forging methods typically rely on traditional forging process control. While these methods can guarantee the basic properties of forgings to a certain extent, they still have many shortcomings in terms of grain refinement and homogenization due to the lack of detailed thermal transformation process analysis and control. Specifically, existing technologies fail to fully consider the different thermal responses and deformation differences of each layer of material during the forging process when dealing with key aspects such as temperature stratification, the coupling effect of pressure and heat dissipation, and recrystallization in high-chromium forgings. This can lead to problems such as coarse grains or uneven temperature distribution in certain areas during actual forging, affecting the overall performance of the forging.

[0004] These shortcomings are closely related to the inaccurate control of the thermal transformation process in traditional forging methods, especially the failure to effectively monitor and regulate the temperature and stress state in different regions during the forging process. Uneven temperature distribution can lead to excessively large grains or microcracks, which in turn affect the mechanical properties of the forging, such as tensile strength and fatigue strength. Furthermore, the lack of coordination between the coupling effect of pressure and heat dissipation can also lead to uneven deformation, which in turn affects the quality of the forging. In addition, if the recrystallization driving window is not accurately identified and controlled, recrystallization may occur in some areas of the forging, further exacerbating the instability of material properties during the forging process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a high-chromium forging method for refining and homogenizing grains, thus solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a forging method for refining and homogenizing high-chromium forgings, comprising the following steps:

[0007] S1. Collect the stratification temperature time series data of high chromium forging billet in each heat treatment, and at the same time collect the compression displacement time series data, loading force time series data and transfer pause time series data corresponding to each heat treatment, and construct the original dataset of stratified thermal transformation process.

[0008] S2. Based on the original dataset of the layered thermal transformation history, determine the deformation segment and the pause segment of each firing cycle. Within the deformation segment of each firing cycle, determine the temperature drop inflection point based on the layered temperature time series data, and construct a segmented thermal transformation history set.

[0009] S3. Based on the segmented thermal evolution history set, determine the equivalent pressure process, thermal exposure process and shutdown transition process of each layered evolution object under each fire, analyze the process differences between each layered evolution object, obtain the process misalignment index set, and generate the alignment control command set.

[0010] S4. Based on the original dataset of the layered thermal transformation process, identify the recrystallization driving window of each layered evolution object, and execute the next forging process on the high-chromium forging billet according to the alignment control instruction set.

[0011] Preferably, a hierarchical thermal evolution history raw dataset is constructed, specifically including:

[0012] Sampling points were set according to a predetermined sampling time step in each firing cycle. Layer temperature, loading force and compression displacement values ​​were collected at each sampling point to determine the time series data of layer temperature, loading force and compression displacement. Each firing cycle included a furnace transfer stage, at least one deformation stage and at least one rest stage.

[0013] The transfer and pause time sequence data includes the exposure time from the time the furnace is unloaded to the start of the first deformation section and the inter-section pause time between adjacent deformation sections;

[0014] Based on the time-series data of layered temperature, displacement under pressure, loading force, and transport pauses, a raw dataset of layered thermal change history is constructed.

[0015] Preferably, the layered temperature time series data includes surface temperature time series data, transition layer temperature time series data and core temperature time series data; wherein, the transition layer corresponds to a depth of d1 from the surface of the billet, the core corresponds to a depth of d2 from the surface of the billet, and d2>d1.

[0016] Preferably, a segmented thermal history set is constructed, specifically including:

[0017] Within a single fire cycle, the collected loading force time-series data are sorted according to chronological order to construct a continuous sampling sequence. The values ​​at the 25th percentile of the sorted results in the continuous sampling sequence are extracted as the lower quartile values. Based on the lower quartile values, each sampling point in the loading force time-series data is compared and analyzed point by point. Sampling points with loading force values ​​not less than the lower quartile values ​​are marked as pressure-bearing points, and sampling points with loading force values ​​less than the lower quartile values ​​are marked as non-pressure-bearing points. For continuous sampling points with the same marked state in the continuous sampling sequence, interval merging is performed. The merged set of pressure-bearing points is divided into deformation segments, and the merged set of non-pressure-bearing points is divided into rest segments.

[0018] For any given deformation segment, the time-series temperature data of the surface layer, transition layer, and core of the segment are extracted. First-order difference processing is then performed on the temperature values ​​of adjacent sampling points according to the sampling time sequence to construct a temperature change difference sequence for each layer. The sampling point with the minimum difference value in the temperature change difference sequence of each layer is extracted as the temperature drop inflection point of the corresponding layer within the current deformation segment. The temperature drop inflection point characterizes the boundary point of the temperature change stage under the coupling effect of pressure and heat dissipation for the corresponding layer within the current deformation segment, and the minimum difference value characterizes the maximum temperature drop amplitude.

[0019] Based on the deformation section, pause section, and temperature drop transition position of each firing cycle, a segmented thermal transformation process set is constructed.

[0020] Preferably, determining the equivalent compression process of each stratified evolution object under each fire cycle specifically includes:

[0021] Establish a hierarchical evolutionary object, which includes a surface evolutionary object, a transitional layer evolutionary object, and a core evolutionary object;

[0022] For any layered evolution object, in any deformation segment of each fire, the temperature difference between the starting sampling point and the ending sampling point of the corresponding deformation segment is taken as the corresponding unit deformation thermal response. If the temperature difference is negative, the unit deformation thermal response of the corresponding deformation segment is recorded as zero.

[0023] The unit deformation thermal response of each layered evolution object in each deformation segment under each firing cycle is accumulated to obtain the total deformation thermal response of each layered evolution object under each firing cycle, which is used as the compressive thermal response of each layered evolution object in the equivalent compression process under each firing cycle.

[0024] Preferably, the thermal exposure process and rest transition process of each stratified evolution object under each fire cycle are determined, specifically including:

[0025] For any stratified evolution object, in any pause of each fire, the temperature difference between the starting and ending sampling points of the corresponding pause is taken as the corresponding unit cooling transfer amount. If the temperature difference is negative, the unit cooling transfer amount of the corresponding pause is recorded as zero.

[0026] The unit cooling transfer amount of each layered evolution object in each pause under each fire is accumulated to obtain the total cooling transfer amount of each layered evolution object under each fire, and this amount is used as the cooling transfer amount of each layered evolution object in the pause transfer process under each fire.

[0027] Based on the stratified temperature time series data and combined with the extreme value extraction method, the maximum and minimum temperature values ​​of each stratified evolution object in each fire cycle are extracted. The difference between the maximum and minimum temperature values ​​of each stratified evolution object in the same fire cycle is used as the temperature span of the stratified thermal exposure process of each stratified evolution object in each fire cycle.

[0028] Preferably, the process differences between each hierarchical evolution object are analyzed to obtain a set of process misalignment indicators, specifically including:

[0029] In the h-th fire, the surface layer, transition layer and core are respectively denoted as L1, L2 and L3;

[0030] The mathematical expression for temperature misalignment is as follows:

[0031]

[0032] In the formula, , and These represent the layer temperature values ​​at the i-th sampling point in the h-th firing cycle for the surface layer, transition layer, and core, respectively. This represents the temperature shift in the h-th firing cycle;

[0033] The mathematical expression for the amount of compressive dislocation is as follows:

[0034]

[0035] In the formula, , and These represent the compressive thermal response of the surface layer, transition layer, and core respectively during the h-th firing cycle. This represents the amount of pressure displacement during the h-th firing cycle;

[0036] The mathematical expression for the transfer misalignment is as follows:

[0037]

[0038] In the formula, , and These represent the cooling transfer amounts of the surface layer, transition layer, and core in the h-th firing cycle, respectively. This represents the transfer misalignment amount for the h-th fire cycle;

[0039] The process misalignment index set includes the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same fire cycle.

[0040] Preferably, a set of bit alignment control instructions is generated, specifically including:

[0041] The process misalignment index is concentrated on the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same forging process. The total process misalignment of the current forging process is obtained by numerically accumulating these values. The total process misalignment is then compared and analyzed with a preset misalignment threshold to determine whether there is a risk of high crystallization due to uneven energy transfer during the forging process of the current forging process, and a control signal is issued.

[0042] If the total process misalignment exceeds the misalignment threshold, it is determined that there is a risk of high crystallization due to uneven energy transfer in the current forging process, and an external control signal is sent. If the total process misalignment does not exceed the misalignment threshold, it is determined that there is no risk of high crystallization due to uneven energy transfer in the current forging process, and the current forging process continues.

[0043] After receiving the control signal, based on the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same firing cycle in the process misalignment index set, the forging temperature range, deformation section control duration, and pause section control duration for the next firing cycle are determined, and a positioning control instruction set is constructed.

[0044] Preferably, the initial forging temperature range for the next forging pass is determined, specifically including:

[0045] Based on the stratified temperature time series data in the original dataset of stratified thermal evolution history, for any stratified evolution object L, the minimum stratified temperature T of the corresponding stratified evolution object is extracted using the extreme value extraction method. L,min With the maximum stratification temperature T L,max ;

[0046] Based on the minimum stratification temperature T of the corresponding stratified evolution object. L,min To determine the lower limit of the forging temperature range for the next forging pass, specifically: In the formula, This indicates the lower limit of the forging temperature range for the next forging pass. Indicates the maximum value extractor;

[0047] Based on the extraction of the maximum stratification temperature T of the corresponding stratified evolution object L,max Determine the upper limit of the forging temperature range for the next forging pass. Specifically: In the formula, This indicates the upper limit of the forging temperature range for the next forging pass. Indicates the minimum value extractor;

[0048] Based on the lower and upper limits of the forging temperature range for the next heat treatment, determine the forging temperature range for the next heat treatment. And write it into the bit control instruction set.

[0049] Preferably, the recrystallization driving window for identifying each hierarchical evolution object specifically includes:

[0050] The loading force time series data and the compression displacement time series data in the original dataset of the layered thermal transformation history are correlated and then processed without dimension to analyze the energy transfer flow state of the high chromium forging during the forging process and determine the discrete flow rate at each sampling point.

[0051] Based on the discrete flow rate at each sampling point, the first-order difference and second-order difference calculation processes are performed respectively to obtain the first-order difference value and the second-order difference value of each sampling point.

[0052] The earliest sampling point where the second-order difference value changes from positive to non-positive while the first-order difference value remains negative is taken as the trigger position of the recrystallization driving window. Starting from the trigger position of the recrystallization driving window, the high-chromium forging billet is subjected to the next forging process according to the alignment control instruction set.

[0053] This invention provides a forging method for refining and homogenizing high-chromium forgings, which has the following beneficial effects:

[0054] (1) By accurately collecting and analyzing the layered thermal transformation process of high chromium forgings, and combining dynamic temperature, stress and cooling transition data, a more comprehensive control of the forging process is achieved. This method can identify and adjust the temperature and stress distribution in real time during the forging process, thereby ensuring the fine grain and homogenization effect of high chromium forgings. It effectively solves the problems of uneven grains, uneven temperature distribution and recrystallization caused by over- or under-crystallization in traditional forging methods, and realizes the fine grain and homogenization forging process of high chromium forgings, effectively meeting the high quality requirements of high-end equipment for high chromium forgings.

[0055] (2) By accurately collecting the layered temperature time series data, loading force time series data, pressing displacement time series data and transfer pause time series data of high chromium forging billet in each heat treatment, the thermal transformation process and stress condition during forging can be reflected. The collection of these data is not limited to traditional surface temperature monitoring, but is conducted in detail on different regions of the surface, transition layer and core through a hierarchical approach, thereby providing a more accurate reference for the subsequent grain refinement and homogenization process. Based on the layered temperature time series data, the temperature drop transition position can be accurately identified and the stress and heat exposure process of each layered evolution object in each heat treatment can be analyzed. Through the precise analysis of temperature, stress and pause sections, timely adjustments can be made during the forging process to avoid the problem of coarse grains caused by uneven temperature control. Through its data-driven precise control, the heating condition of each layer of material during the forging process is optimized, improving the overall quality and uniformity of the forging.

[0056] (3) By analyzing the differences in temperature, pressure and cooling transfer of each layered evolution object in each firing, the temperature misalignment, pressure misalignment and transfer misalignment between each layer can be accurately obtained. The innovation of this method is that by monitoring these misalignments in real time, it can identify the uneven energy transfer and high crystallization risk that may exist in the forging process. When the misalignment exceeds the preset threshold, it can actively send control signals to adjust the temperature range, deformation section control time and pause section control time of the next firing, thereby effectively avoiding quality problems of forgings in the forging process. The introduction of this control mechanism ensures the stability and controllability of each forging process and improves the quality consistency and production efficiency of forgings. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the process for a high-chromium forging method to refine grains and homogenize the forging process of high-chromium forgings according to the present invention;

[0058] Figure 2 This is a logic diagram of a high-chromium forging fine-grained homogenization forging method for this invention. Detailed Implementation

[0059] The technical solutions of the embodiments 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, and not all embodiments. 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.

[0060] Example

[0061] Please see Figure 1 and Figure 2This invention provides a method for forging high-chromium forgings to achieve fine grain homogenization, comprising the following steps:

[0062] S1. Collect the stratification temperature time series data of high chromium forging billet in each heat treatment, and at the same time collect the compression displacement time series data, loading force time series data and transfer pause time series data corresponding to each heat treatment, and construct the original dataset of stratified thermal transformation process.

[0063] The construction of the hierarchical thermal transformation history raw dataset specifically includes:

[0064] Sampling points are set at predetermined sampling time steps in each firing cycle. Infrared temperature sensors, pressure sensors, and laser displacement sensors are deployed to collect layer temperature values, loading force values, and compression displacement values ​​at each sampling point to determine the time series data of layer temperature, loading force, and compression displacement. Each firing cycle includes a furnace transfer stage, at least one deformation stage, and at least one rest stage.

[0065] It should be noted that during the forging process, each heat is an independent forging cycle, which includes a series of operations from heating the billet, forming, cooling, transferring, and finally stopping. Each heat represents a complete forging process, from the billet exiting the furnace to completing one round of deformation and cooling. Different stages in each heat have their own different process conditions, affecting the billet's temperature, stress, and deformation state. By accurately monitoring and collecting data for each heat, the changes in key parameters at each stage can be accurately captured, thus providing data for subsequent process adjustments and optimizations. Data collection for each heat helps to evaluate the impact of different stages in the forging process on the billet's properties, ensuring precise control of temperature, pressure, and deformation processes, thereby achieving better grain refinement and homogenization effects.

[0066] The transfer and pause time sequence data includes the exposure time from the time the furnace is unloaded to the start of the first deformation section and the inter-section pause time between adjacent deformation sections;

[0067] Based on the time-series data of layered temperature, displacement under pressure, loading force, and transport pauses, a raw dataset of layered thermal change history is constructed.

[0068] The layered temperature time series data includes surface temperature time series data, transition layer temperature time series data, and core temperature time series data. The transition layer corresponds to a depth of d1 from the billet surface, and the core corresponds to a depth of d2 from the billet surface, with d2>d1. The specific values ​​are set by those skilled in the art for specific billets.

[0069] S2. Based on the original dataset of the layered thermal transformation history, determine the deformation segment and the pause segment of each firing cycle. Within the deformation segment of each firing cycle, determine the temperature drop inflection point based on the layered temperature time series data, and construct a segmented thermal transformation history set.

[0070] The construction of a segmented thermal history set specifically includes:

[0071] Within a single fire cycle, the collected loading force time-series data are sorted according to chronological order to construct a continuous sampling sequence. The values ​​at the 25th percentile of the sorted results in the continuous sampling sequence are extracted as the lower quartile values. Based on the lower quartile values, each sampling point in the loading force time-series data is compared and analyzed point by point. Sampling points with loading force values ​​not less than the lower quartile values ​​are marked as pressure-bearing points, and sampling points with loading force values ​​less than the lower quartile values ​​are marked as non-pressure-bearing points. For continuous sampling points with the same marked state in the continuous sampling sequence, interval merging is performed. The merged set of pressure-bearing points is divided into deformation segments, and the merged set of non-pressure-bearing points is divided into rest segments.

[0072] It should be noted that the division between the deformation segment and the resting segment is based on statistical analysis of the loading force time series data. The lower quartile value is used to distinguish the distribution of the loading force data, thereby dividing the data into a compressive state and an uncompressive state. In the loading force time series data, the lower quartile value represents the value at the 25th position in the data sequence. Sampling points below this value belong to a lower loading force state, while sampling points above this value belong to a higher loading force state.

[0073] For any given deformation segment, the time-series temperature data of the surface layer, transition layer, and core of the segment are extracted. First-order difference processing is then performed on the temperature values ​​of adjacent sampling points according to the sampling time sequence to construct a temperature change difference sequence for each layer. The sampling point with the minimum difference value in the temperature change difference sequence of each layer is extracted as the temperature drop inflection point of the corresponding layer within the current deformation segment. The temperature drop inflection point characterizes the boundary point of the temperature change stage under the coupling effect of pressure and heat dissipation for the corresponding layer within the current deformation segment, and the minimum difference value characterizes the maximum temperature drop amplitude.

[0074] Based on the deformation section, pause section, and temperature drop transition position of each firing cycle, a segmented thermal transformation process set is constructed.

[0075] S3. Based on the segmented thermal evolution history set, determine the equivalent pressure process, thermal exposure process and shutdown transition process of each layered evolution object under each fire, analyze the process differences between each layered evolution object, obtain the process misalignment index set, and generate the alignment control command set.

[0076] Specifically, determining the equivalent compression process, thermal exposure process, and shutdown transition process of each stratified evolution object under each fire cycle includes:

[0077] Establish a hierarchical evolutionary object, which includes a surface evolutionary object, a transitional layer evolutionary object, and a core evolutionary object;

[0078] Let J be the set of deformed segments for the h-th fire. h Let K be the set of pause segments for the h-th fire. h ;

[0079] For any layered evolution object L, in any deformation segment j of the h-th fire, the temperature difference between the starting sampling point and the ending sampling point of the corresponding deformation segment is taken as the corresponding unit deformation thermal response. If the temperature difference is negative, the unit deformation thermal response of the corresponding deformation segment is recorded as zero.

[0080] The unit deformation thermal response of each layered evolution object in each deformation segment under each firing cycle is accumulated to obtain the total deformation thermal response of each layered evolution object under each firing cycle, and is used as the compressive thermal response of each layered evolution object in the equivalent compression process under each firing cycle.

[0081] The mathematical expressions for the compressive thermal response of each stratified evolution object under each fire cycle in the equivalent compressive process are as follows:

[0082]

[0083] In the formula, This represents the unit deformation thermal response of the Lth layered evolution object in the jth deformation segment under the hth fire. This represents the compressive thermal response of the Lth stratified evolution object under the equivalent compressive process in the hth fire. Let represent the set of deformed segments for the h-th fire.

[0084] For any hierarchical evolution object L, in any pause segment k of the h-th fire, the temperature difference between the starting and ending sampling points of the corresponding pause segment is taken as the corresponding unit cooling transfer amount. If the temperature difference is negative, the unit cooling transfer amount of the corresponding pause segment is recorded as zero.

[0085] The unit cooling transfer amount of each layered evolution object in each pause under each fire is accumulated to obtain the total cooling transfer amount of each layered evolution object under each fire, and this amount is used as the cooling transfer amount of each layered evolution object in the pause transfer process under each fire.

[0086] The mathematical expressions for the cooling transfer amount of each layered evolution object during the pause and transfer process under each fire are as follows:

[0087]

[0088] In the formula, This represents the unit cooling transfer amount of the Lth layered evolution object in the jth deformation segment under the hth fire. This represents the cooling transfer amount of the Lth layered evolution object during the pause and transfer process in the hth fire. This represents the set of pause segments for the h-th fire.

[0089] Based on the stratified temperature time series data and combined with the extreme value extraction method, the maximum and minimum temperature values ​​of each stratified evolution object in each fire cycle are extracted. The difference between the maximum and minimum temperature values ​​of each stratified evolution object in the same fire cycle is used as the temperature span of the stratified thermal exposure process of each stratified evolution object in each fire cycle.

[0090] It should be noted that the temperature span measure refers to the difference between the maximum and minimum temperature values ​​of any layer within a single forging cycle. It reflects the range of temperature changes experienced by that layer during forging and quantifies the degree of heat exposure of the billet during the forging process. By using the extreme value extraction method, the maximum and minimum temperature values ​​of each layer are extracted, and the difference between them is calculated, thus obtaining the temperature span measure. This value is used to assess the range of temperature changes experienced by each layer of the billet throughout the forging process, and directly affects the thermal stress, thermal deformation, and recrystallization process of the billet, thereby influencing the grain structure and properties of the forging. Measuring the temperature span measure can help engineering... The forging engineer understands the heat exposure of the billet during the heat treatment process. If the temperature span is too large, it indicates that the billet has been heated or cooled unevenly, resulting in inconsistent temperature changes in different layers, thus generating internal stress or grain inhomogeneity. On the other hand, a smaller temperature span indicates a more uniform temperature change, which helps to ensure the quality and performance of the billet. The difference between the maximum and minimum temperature values ​​is used to determine the temperature span, which can directly quantify the heat exposure range of each layer during the forging process and reflect the degree of heat change of each layer in each heat treatment. By comparing the temperature span of different heat treatments or different layers, the forging process can be effectively analyzed and adjusted to ensure the uniformity of the heat treatment process, thereby obtaining better forging quality.

[0091] This includes analyzing the process differences between various hierarchical evolutionary objects to obtain a set of process misalignment indicators, specifically including:

[0092] In the h-th fire, the surface layer, transition layer and core are respectively denoted as L1, L2 and L3;

[0093] The mathematical expression for temperature misalignment is as follows:

[0094]

[0095] In the formula, , and These represent the layer temperature values ​​at the i-th sampling point in the h-th firing cycle for the surface layer, transition layer, and core, respectively. This represents the temperature shift in the h-th firing cycle;

[0096] The mathematical expression for the amount of compressive dislocation is as follows:

[0097]

[0098] In the formula, , and These represent the compressive thermal response of the surface layer, transition layer, and core respectively during the h-th firing cycle. This represents the amount of pressure displacement during the h-th firing cycle;

[0099] The mathematical expression for the transfer misalignment is as follows:

[0100]

[0101] In the formula, , and These represent the cooling transfer amounts of the surface layer, transition layer, and core in the h-th firing cycle, respectively. This represents the transfer misalignment amount for the h-th fire cycle;

[0102] The process misalignment index set includes the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same fire cycle.

[0103] Specifically, the generation of the alignment control instruction set includes:

[0104] The process misalignment index is concentrated on the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same forging process. The total process misalignment of the current forging process is obtained by numerically accumulating these values. The total process misalignment is then compared and analyzed with a preset misalignment threshold to determine whether there is a risk of high crystallization due to uneven energy transfer during the forging process of the current forging process, and a control signal is issued.

[0105] If the total process misalignment exceeds the misalignment threshold, it is determined that there is a risk of high crystallization due to uneven energy transfer in the current forging process, and an external control signal is sent. If the total process misalignment does not exceed the misalignment threshold, it is determined that there is no risk of high crystallization due to uneven energy transfer in the current forging process, and the current forging process continues.

[0106] It should be noted that the misalignment threshold is a reference value set based on historical data. It is used to determine whether the energy transfer is uniform during a certain forging process and whether there is energy imbalance caused by factors such as temperature, pressure, or cooling rate, which may lead to a high crystallization risk. This threshold is usually determined based on historical data and practical experience of temperature misalignment, pressure misalignment, and transition misalignment in different layers during a large number of experiments and process optimizations. The preset misalignment threshold is used to identify and predict the high crystallization risk that may exist in the forging process in a timely manner. When the total misalignment exceeds this threshold, it indicates that there may be uneven energy transfer, which may lead to local overcooling or overheating of the billet, thereby generating the risk of uneven crystallization. A control signal is issued to trigger the adjustment of process parameters (hot temperature range, deformation section control time, and pause section control time) to ensure the uniformity of temperature and pressure distribution during the forging process, reduce the risk of poor crystallization, and ensure the quality of the final forging.

[0107] After receiving the control signal, based on the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same firing cycle in the process misalignment index set, the forging temperature range, deformation section control duration, and pause section control duration for the next firing cycle are determined, and a positioning control instruction set is constructed.

[0108] Furthermore, the initial forging temperature range for the next heat treatment is determined, specifically including:

[0109] Based on the stratified temperature time series data in the original dataset of stratified thermal evolution history, for any stratified evolution object L, the minimum stratified temperature T of the corresponding stratified evolution object is extracted using the extreme value extraction method. L,min With the maximum stratification temperature T L,max ;

[0110] Based on the minimum stratification temperature T of the corresponding stratified evolution object. L,min To determine the lower limit of the forging temperature range for the next forging pass, specifically: In the formula, This indicates the lower limit of the forging temperature range for the next forging pass. Indicates the maximum value extractor;

[0111] Based on the extraction of the maximum stratification temperature T of the corresponding stratified evolution object L,max Determine the upper limit of the forging temperature range for the next forging pass. Specifically: In the formula, This indicates the upper limit of the forging temperature range for the next forging pass. Indicates the minimum value extractor;

[0112] Based on the lower and upper limits of the forging temperature range for the next heat treatment, determine the forging temperature range for the next heat treatment. And write it into the bit control instruction set.

[0113] It should be noted that the determination of the initial forging temperature range is based on the uniformity and accuracy of temperature control during the forging process. By extracting the extreme values ​​of the stratified temperature time series data, it is ensured that the next forging process is carried out within a reasonable temperature range, thereby optimizing forging quality and avoiding overheating or underheating. The minimum stratification temperature T for any stratified evolution object is extracted. L,min With the maximum stratification temperature T L,max The temperature fluctuation range experienced by the layer in the current forging cycle is determined. To ensure that the forging process in the next cycle is carried out within a controllable temperature range, the lower limit of the forging temperature range is set by the maximum temperature to ensure that it is not lower than the lowest temperature in the current cycle, thereby avoiding material property problems caused by excessively low temperatures. At the same time, the minimum temperature is used to determine the upper limit of the forging temperature range to ensure that it does not exceed the highest temperature in the current cycle, thereby avoiding thermal damage or grain coarsening caused by overheating. In this way, the setting of the temperature range helps to maintain temperature stability and uniformity, optimize the subsequent forging process, and improve the mechanical properties and grain refinement effect of the material.

[0114] S4. Based on the original dataset of the layered thermal transformation process, identify the recrystallization driving window of each layered evolution object, and execute the next forging process on the high-chromium forging billet according to the alignment control instruction set.

[0115] Specifically, the recrystallization driving window for identifying each hierarchical evolution object includes:

[0116] The loading force time series data and compression displacement time series data in the original dataset of the layered thermal transformation history are correlated and, after dimensionless processing, the energy transfer flow state of the high-chromium forging during the forging process is analyzed to determine the discrete flow rate at each sampling point, specifically: In the formula, This represents the applied force value at the i-th sampling point. This represents the compression displacement value at the i-th sampling point. This represents the discrete flow rate at the i-th sampling point;

[0117] It should be noted that this formula is designed to quantify the energy transfer flow state of high-chromium forgings during the forging process, particularly how to describe the energy change and transfer process through the interaction of loading force and compression displacement. In the background art, analyzing the stress and deformation of each layered evolution object is crucial, because uneven energy transfer during forging can lead to undesirable physical phenomena, such as uneven crystallization or excessive internal stress. Therefore, by introducing discrete flow rate, the interaction effect of loading force and displacement changes at each sampling point is quantitatively described, reflecting the energy transfer process in each instantaneous step. This facilitates precise control and optimization of energy distribution during the forging process, thereby achieving more refined process control. The loading force value at the i-th sampling point is a direct factor affecting forging deformation; the magnitude of the loading force affects the deformation capacity of the billet and its final quality. The value of the compression displacement at the i-th sampling point is used to characterize the compression or tension process experienced by the billet during deformation. The discrete flow rate is quantified by the difference between the loading force and the compression displacement at each sampling point, reflecting the energy change in local areas during forging. This quantification of energy flow helps to reveal the distribution of energy during forging, i.e., whether the energy is uniformly transferred to all parts of the billet. By calculating the discrete flow rate, the energy transfer status can be monitored in real time, and the non-uniformity of energy transfer can be detected in a timely manner, thus providing a basis for adjusting process parameters.

[0118] Based on the discrete flow rate at each sampling point, the first-order difference and second-order difference calculation processes are performed respectively to obtain the first-order difference value and the second-order difference value of each sampling point.

[0119] The mathematical expressions for the first-order difference values ​​of each sampling point are as follows:

[0120]

[0121] In the formula, This represents the first-order difference value of the i-th sampling point. and Let represent the discrete flow rates at the i-th sampling point and the (i-1)-th sampling point, respectively;

[0122] The mathematical expressions for the second-order difference values ​​of each sampling point are as follows:

[0123]

[0124] In the formula, This represents the second-order difference value of the i-th sampling point. and Let represent the first-order difference values ​​of the i-th sampling point and the (i-1)-th sampling point, respectively;

[0125] The earliest sampling point where the second-order difference value changes from positive to non-positive while the first-order difference value remains negative is taken as the trigger position of the recrystallization driving window. Starting from the trigger position of the recrystallization driving window, the high-chromium forging billet is subjected to the next forging process according to the alignment control instruction set.

[0126] It should be noted that the logic for determining the trigger position is based on the analysis of changes in discrete flow rates. The trigger moment of the recrystallization driving window is determined by monitoring the changing trends of the first-order and second-order difference values. Specifically, when the second-order difference value changes from positive to negative, while the first-order difference value remains negative, it indicates that a significant change in the energy transfer state has occurred before this sampling point, and the energy flow gradually weakens and tends to stabilize. This change often foreshadows the material entering an important stage, namely, when the temperature and stress conditions are suitable for the recrystallization process. Therefore, the earliest sampling point, where the first-order difference value is negative and the second-order difference value changes from positive to negative, is the point of recrystallization. The trigger position of the driving window; during the forging process, recrystallization usually occurs after the temperature and stress conditions reach a certain critical value, when the material begins to recover from the original plastic deformation and form new grains; by changing the first-order and second-order difference values, the minute changes in energy transfer can be accurately captured, especially when the second-order difference value changes from positive to negative, which means that the energy transfer process has begun to slow down and the material is close to the suitable conditions for recrystallization; while the first-order difference value is always negative, which indicates that the energy change trend is stable and no longer shows abrupt changes, further confirming the timing of the recrystallization window; therefore, using this sampling point position as the trigger position ensures the optimization process of forging recrystallization.

[0127] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0128] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0129] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0130] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0131] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0132] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method of fine grain homogenization forging of high chromium forgings, characterized by: Includes the following steps: S1. Collect the stratification temperature time series data of high chromium forging billet in each heat treatment, and at the same time collect the compression displacement time series data, loading force time series data and transfer pause time series data corresponding to each heat treatment, and construct the original dataset of stratified thermal transformation process. S2. Based on the original dataset of the layered thermal transformation history, determine the deformation segment and the pause segment of each firing cycle. Within the deformation segment of each firing cycle, determine the temperature drop inflection point based on the layered temperature time series data, and construct a segmented thermal transformation history set. S3. Based on the segmented thermal evolution history set, determine the equivalent pressure process, thermal exposure process and shutdown transition process of each layered evolution object under each fire, analyze the process differences between each layered evolution object, obtain the process misalignment index set, and generate the alignment control command set. S4. Based on the original dataset of the layered thermal transformation process, identify the recrystallization driving window of each layered evolution object, and execute the next forging process on the high-chromium forging billet according to the alignment control instruction set.

2. The method of claim 1, wherein: Constructing a hierarchical thermal evolution history dataset, specifically including: Sampling points were set according to a predetermined sampling time step in each firing cycle. Layer temperature, loading force and compression displacement values ​​were collected at each sampling point to determine the time series data of layer temperature, loading force and compression displacement. Each firing cycle included a furnace transfer stage, at least one deformation stage and at least one rest stage. The transfer and pause time sequence data includes the exposure time from the time the furnace is unloaded to the start of the first deformation section and the inter-section pause time between adjacent deformation sections; Based on the time series data of layered temperature, displacement under pressure, loading force, and transport pause, a raw dataset of layered thermal change history is constructed.

3. The method of claim 2, wherein: The stratified temperature time series data includes surface temperature time series data, transition layer temperature time series data, and core temperature time series data; wherein, the transition layer corresponds to a depth of d1 from the billet surface, the core corresponds to a depth of d2 from the billet surface, and d2>d1.

4. The method of claim 1, wherein: Constructing a segmented thermal history set, specifically including: Within a single fire cycle, the collected loading force time-series data are sorted according to chronological order to construct a continuous sampling sequence. The values ​​at the 25th percentile of the sorted results in the continuous sampling sequence are extracted as the lower quartile values. Based on the lower quartile values, each sampling point in the loading force time-series data is compared and analyzed point by point. Sampling points with loading force values ​​not less than the lower quartile values ​​are marked as pressure-bearing points, and sampling points with loading force values ​​less than the lower quartile values ​​are marked as non-pressure-bearing points. For continuous sampling points with the same marked state in the continuous sampling sequence, interval merging is performed. The merged set of pressure-bearing points is divided into deformation segments, and the merged set of non-pressure-bearing points is divided into rest segments. For any given deformation segment, the time-series temperature data of the surface layer, transition layer, and core of the segment are extracted. First-order difference processing is then performed on the temperature values ​​of adjacent sampling points according to the sampling time sequence to construct a temperature change difference sequence for each layer. The sampling point with the minimum difference value in the temperature change difference sequence of each layer is extracted as the temperature drop inflection point of the corresponding layer within the current deformation segment. The temperature drop inflection point characterizes the boundary point of the temperature change stage under the coupling effect of pressure and heat dissipation for the corresponding layer within the current deformation segment, and the minimum difference value characterizes the maximum temperature drop amplitude. Based on the deformation section, pause section, and temperature drop transition position of each firing cycle, a segmented thermal transformation process set is constructed.

5. The method of claim 1, wherein: Determine the equivalent compression process of each hierarchical evolution object under each fire cycle, specifically including: Establish a hierarchical evolutionary object, which includes a surface evolutionary object, a transitional layer evolutionary object, and a core evolutionary object; For any layered evolution object, in any deformation segment of each fire, the temperature difference between the starting sampling point and the ending sampling point of the corresponding deformation segment is taken as the corresponding unit deformation thermal response. If the temperature difference is negative, the unit deformation thermal response of the corresponding deformation segment is recorded as zero. The unit deformation thermal response of each layered evolution object in each deformation segment under each firing cycle is accumulated to obtain the total deformation thermal response of each layered evolution object under each firing cycle, which is used as the compressive thermal response of each layered evolution object in the equivalent compression process under each firing cycle.

6. The forging method for refining and homogenizing high-chromium forgings according to claim 1, characterized in that: Determine the thermal exposure process and rest transition process of each stratified evolution object under each fire, specifically including: For any stratified evolution object, in any pause of each fire, the temperature difference between the starting and ending sampling points of the corresponding pause is taken as the corresponding unit cooling transfer amount. If the temperature difference is negative, the unit cooling transfer amount of the corresponding pause is recorded as zero. The unit cooling transfer amount of each layered evolution object in each pause under each fire is accumulated to obtain the total cooling transfer amount of each layered evolution object under each fire, and this amount is used as the cooling transfer amount of each layered evolution object in the pause transfer process under each fire. Based on the stratified temperature time series data and combined with the extreme value extraction method, the maximum and minimum temperature values ​​of each stratified evolution object in each fire cycle are extracted. The difference between the maximum and minimum temperature values ​​of each stratified evolution object in the same fire cycle is used as the temperature span of the stratified thermal exposure process of each stratified evolution object in each fire cycle.

7. The method of claim 1, wherein: Analyze the process differences between various hierarchical evolutionary objects to obtain a set of process misalignment indicators, specifically including: In the h-th fire, the surface layer, transition layer and core are respectively denoted as L1, L2 and L3; The mathematical expression for temperature misalignment is as follows: In the formula, , and These represent the layer temperature values ​​at the i-th sampling point in the h-th firing cycle for the surface layer, transition layer, and core, respectively. This represents the temperature shift in the h-th firing cycle; The mathematical expression for the amount of compressive dislocation is as follows: In the formula, , and These represent the compressive thermal response of the surface layer, transition layer, and core respectively during the h-th firing cycle. This represents the amount of pressure displacement during the h-th firing cycle; The mathematical expression for the transfer misalignment is as follows: In the formula, , and These represent the cooling transfer amounts of the surface layer, transition layer, and core in the h-th firing cycle, respectively. This represents the transfer misalignment amount for the h-th fire cycle; The process misalignment index set includes the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same fire cycle.

8. The method of claim 1, wherein: Generate a set of position control instructions, specifically including: The process misalignment index is concentrated on the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same forging process. The total process misalignment of the current forging process is obtained by numerically accumulating these values. The total process misalignment is then compared and analyzed with a preset misalignment threshold to determine whether there is a risk of high crystallization due to uneven energy transfer during the forging process of the current forging process, and a control signal is issued. If the total process misalignment exceeds the misalignment threshold, it is determined that there is a risk of high crystallization due to uneven energy transfer in the current forging process, and an external control signal is sent. If the total process misalignment does not exceed the misalignment threshold, it is determined that there is no risk of high crystallization due to uneven energy transfer in the current forging process, and the current forging process continues. After receiving the control signal, based on the temperature misalignment, pressure misalignment, and transition misalignment between different stratified evolution objects within the same firing cycle in the process misalignment index set, the forging temperature range, deformation section control duration, and pause section control duration for the next firing cycle are determined, and a positioning control instruction set is constructed.

9. The method of claim 8, wherein: Determine the initial forging temperature range for the next heat treatment, specifically including: Based on the stratified temperature time series data in the original dataset of stratified thermal evolution history, for any stratified evolution object L, the minimum stratified temperature T of the corresponding stratified evolution object is extracted using the extreme value extraction method. L,min With the maximum stratification temperature T L,max ; Based on the minimum stratification temperature T of the corresponding stratified evolution object. L,min To determine the lower limit of the forging temperature range for the next forging pass, specifically: In the formula, This indicates the lower limit of the forging temperature range for the next forging pass. Indicates the maximum value extractor; Based on the extraction of the maximum stratification temperature T of the corresponding stratified evolution object L,max Determine the upper limit of the forging temperature range for the next forging pass. Specifically: In the formula, This indicates the upper limit of the forging temperature range for the next forging pass. Indicates the minimum value extractor; According to the lower limit value and the upper limit value of the open forging temperature range of the next heating, the open forging temperature range of the next heating is determined and write the alignment control instruction set.

10. The method of claim 1, wherein: Identify the recrystallization driving window for each hierarchical evolution object, specifically including: The loading force time series data and the compression displacement time series data in the original dataset of the layered thermal transformation history are correlated and then processed without dimension to analyze the energy transfer flow state of the high chromium forging during the forging process and determine the discrete flow rate at each sampling point. Based on the discrete flow rate at each sampling point, the first-order difference and second-order difference calculation processes are performed respectively to obtain the first-order difference value and the second-order difference value of each sampling point. The earliest sampling point where the second-order difference value changes from positive to non-positive while the first-order difference value remains negative is taken as the trigger position of the recrystallization driving window. Starting from the trigger position of the recrystallization driving window, the high-chromium forging billet is subjected to the next forging process according to the alignment control instruction set.

Images