Intelligent collaborative deformation and forging penetration control method for large-section steel ingot diameter forging four hammer heads

Abstract

The present application relates to the technical field of ingot forging, and particularly relates to a large-section ingot diameter forging four-hammer-head intelligent collaborative deformation and forging penetration control method. The method comprises the following steps: obtaining pressure data of each hammer head in the ingot forging process and hammer drop duration from falling to contacting the ingot; evaluating the symmetric hammer pressure imbalance of each staggered forging according to the pressure data difference and hammer drop duration difference of the symmetric hammer heads under the same staggered forging frequency; obtaining the forging ideal degree of each staggered forging of each hammer head according to the hammer drop duration, pressure data and symmetric hammer pressure imbalance of each staggered forging of each hammer head; determining the staggered pressure adjustment factor of each staggered forging of each hammer head according to the staggered influence between the hammer heads and the difference between the forging ideal degrees of the adjacent hammer heads; and obtaining the hammer force adjustment weight of each hammer head by using the staggered pressure adjustment factor, so as to regulate the pressure of each hammer head in the next forging. The present application improves the forging effect of the large-section ingot.

Description

Technical Field

[0001] This invention relates to the field of steel ingot forging technology, specifically to a method for intelligent coordinated deformation and forging penetration control of four hammers for large cross-section steel ingots. Background Technology

[0002] The production process of large-section steel ingots involves a solidification stage, which primarily shapes the ingots. During solidification, large-section steel ingots inevitably develop coarse and uneven as-cast structures, mainly consisting of coarse columnar and equiaxed crystals, resulting in a noticeably coarse as-cast structure in the final product. To address this issue, current methods typically utilize radial forging machines with four built-in hammers to forge large-section steel ingots. The purpose of using existing radial forging machines to forge large-section steel ingots is to utilize hot deformation to break down the coarse as-cast structure in the large-section steel ingot, forming uniform and fine equiaxed crystals, significantly improving strength, closing casting defects such as porosity and shrinkage, and increasing material density. The process is roughly as follows: the large-section steel ingot is mounted on an axial moving shaft, and then the large-section steel ingot is heated to a pre-set high temperature range. The four hammers built into the radial forging machine rotate at fixed angles to perform radial forging (i.e., rotating around the steel ingot), while the axial moving shaft moves along the axis of the large-section steel ingot itself, gradually completing the hot deformation treatment of the entire large-section steel ingot.

[0003] In real-world scenarios, the axial movement of the large-section steel ingot itself and the radial movement of the hammer along the surface of the large-section steel ingot are synchronized. Therefore, the position of each hammer on the large-section steel ingot during each drop will not be completely consistent. Between adjacent drops, the internal hammer will have some positions that are the same and some positions that are different. This results in incremental deformation of the core of the large-section steel ingot during the entire forging process (the axial deformation of the large-section steel ingot gradually increases or decreases). In addition, the large-section steel ingot itself also has a cast structure, which means that the actual thermal deformation that needs to be treated in different areas of the core of the large-section steel ingot is different, resulting in a poor forging effect of the large-section steel ingot. Summary of the Invention

[0004] To address the problem of poor forging results in existing methods for forging large-section steel ingots, the present invention aims to provide an intelligent collaborative deformation and forging penetration control method using four hammers for forging large-section steel ingots. The specific technical solution adopted is as follows:

[0005] This invention provides a method for intelligent coordinated deformation and forging penetration control of four hammers in forging large cross-section steel ingots. The method includes the following steps:

[0006] Obtain pressure data for each hammer during the steel ingot forging process, as well as the hammer drop time from falling to contacting the steel ingot;

[0007] Based on the differences in pressure data and hammer drop time of symmetrical hammers under the same number of misaligned forgings, the imbalance of symmetrical hammer pressure in each misaligned forging is evaluated; based on the hammer drop time, pressure data and imbalance of symmetrical hammer pressure in each misaligned forging of each hammer, the forging ideality of each hammer in each misaligned forging is obtained.

[0008] Based on the misalignment effect between hammers and the difference in forging ideality between adjacent hammers, the misalignment pressure adjustment factor for each hammer in each misalignment forging is determined; using the misalignment pressure adjustment factor, the hammer drop force adjustment weight of each hammer is obtained.

[0009] The weights and hammer duration of each hammer are adjusted according to the hammer's impact force to regulate the pressure of each hammer during the next forging.

[0010] Preferably, the evaluation of the symmetrical hammer pressure imbalance in each misaligned forging based on the differences in pressure data and hammer drop time of the symmetrical hammer heads under the same number of misaligned forgings includes:

[0011] Based on the mean difference in pressure data and the mean difference in hammer drop time of symmetrical hammer heads under the same number of misaligned forgings, the forging imbalance parameter for each misaligned forging is obtained. The mean difference in pressure data and the mean difference in hammer drop time are positively correlated with the forging imbalance parameter.

[0012] By combining the forging imbalance parameters of each misaligned forging, the forging imbalance parameters of the next misaligned forging, and the differences between the forging imbalance parameters of each misaligned forging and the next misaligned forging, the symmetrical hammer pressure imbalance of each misaligned forging is obtained.

[0013] Preferably, the step of synthesizing the forging imbalance parameters of each misaligned forging, the forging imbalance parameters of the next misaligned forging, and the difference between the forging imbalance parameters of each misaligned forging and the next misaligned forging to obtain the symmetrical hammer pressure imbalance of each misaligned forging includes:

[0014] For any misaligned forging, the product of the forging imbalance parameter of the misaligned forging, the forging imbalance parameter of the next misaligned forging, and the difference between the forging imbalance parameter of the misaligned forging and the next misaligned forging is taken as the symmetrical hammer pressure imbalance of the misaligned forging.

[0015] Preferably, obtaining the forging ideality of each hammer head in each misaligned forging based on the hammer drop time, pressure data, and symmetrical hammer pressure imbalance in each hammer head's misaligned forging includes:

[0016] The product of the normalized value of the hammer drop time for each misaligned forging of each hammer head, the normalized value of the pressure data, and the negative correlation mapping value of the symmetrical hammer pressure imbalance is determined as the forging ideality of each misaligned forging of each hammer head.

[0017] Preferably, determining the misalignment pressure adjustment factor for each misalignment forging of each hammer head based on the misalignment effect between hammer heads and the difference in forging ideality between adjacent hammer heads includes:

[0018] For any single misaligned forging:

[0019] Calculate the first difference in forging ideality for the next misaligned forging of any two adjacent hammers (excluding the first hammer); based on the first difference and the forging ideality of any misaligned forging of the first hammer, obtain the radial misalignment pressure adjustment parameter for any misaligned forging of the first hammer. The first difference is positively correlated with the radial misalignment pressure adjustment parameter, and the forging ideality of any misaligned forging of the first hammer is negatively correlated with the radial misalignment pressure adjustment parameter.

[0020] Based on the differences between the radial misalignment pressure adjustment parameters of each hammer (excluding the first hammer) and its adjacent next hammer in any misalignment forging, the comprehensive misalignment chain interference value of the first hammer in any misalignment forging is obtained.

[0021] Based on the comprehensive misalignment chain interference value of any misalignment forging of the first hammer, the radial misalignment pressure adjustment parameter of any misalignment forging of the first hammer, and the sequence number of any misalignment forging in the corresponding steel ingot axial movement stage, the misalignment pressure adjustment factor of any misalignment forging of the first hammer is obtained.

[0022] The first hammerhead can be any hammerhead.

[0023] Preferably, the step of obtaining the misalignment pressure adjustment factor for any misalignment forging by the first hammer head based on the comprehensive misalignment chain interference value of any misalignment forging by the first hammer head, the radial misalignment pressure adjustment parameter of any misalignment forging by the first hammer head, and the sequence number of any misalignment forging within the corresponding axial movement stage of the steel ingot, includes:

[0024] Calculate the first product of the comprehensive misalignment chain interference value of any misalignment forging of the first hammer head and the radial misalignment pressure adjustment parameter of any misalignment forging of the first hammer head;

[0025] The ratio between the first product and the order of any misaligned forging within the corresponding axial movement stage of the steel ingot is determined as the misalignment pressure adjustment factor for any misaligned forging of the first hammer.

[0026] Preferably, obtaining the hammer impact force adjustment weight for each hammer head using the misalignment pressure adjustment factor includes:

[0027] For any given hammer:

[0028] Calculate the first average value of the misalignment pressure adjustment factor for any of the hammer heads in the historical reference forging;

[0029] Based on the difference between the misalignment pressure adjustment factor of the current misalignment forging of any hammer and the first average value, the hammer drop force adjustment weight of any hammer is obtained.

[0030] Preferably, obtaining the hammer drop force adjustment weight of any hammer based on the difference between the misalignment pressure adjustment factor of the current misalignment forging of any hammer and the first average value includes:

[0031] The normalized result of the difference between the misalignment pressure adjustment factor of the current misalignment forging of any hammer and the first average value is used as the hammer drop force adjustment weight of any hammer.

[0032] Preferably, the step of adjusting the weights based on the hammer drop force and the drop duration of each hammer to control the pressure of each hammer during the next forging includes:

[0033] For any given hammer:

[0034] If the drop time of the most recent misaligned forging of any hammer head is equal to the average drop time of all historical reference forgings of any hammer head, then the pressure of the hammer head will not be adjusted in the next forging.

[0035] If the drop time of the most recent misaligned forging of any hammer is less than or greater than the average drop time of all historical reference forgings of any hammer, then the pressure of the next forging of any hammer is adjusted using the drop force adjustment weight of any hammer.

[0036] Preferably, if the drop time of the most recent misaligned forging of any hammer head is less than or greater than the average drop time of all historical reference forgings of any hammer head, then the pressure of the next forging of any hammer head is adjusted using the drop force adjustment weight of any hammer head, including:

[0037] If the hammer drop time of the most recent misaligned forging of any hammer is less than the average hammer drop time of all historical reference forgings of any hammer, then calculate the first sum of constant 1 and the hammer drop force adjustment weight of any hammer, and use the product of the pressure of the most recent misaligned forging of any hammer and the first sum as the pressure of the next forging of any hammer.

[0038] If the drop duration of the most recent misaligned forging of any hammer is greater than the average drop duration of all historical reference forgings of any hammer, then the product of the pressure of the most recent misaligned forging of any hammer and the drop force adjustment weight of any hammer will be used as the pressure of the next forging of any hammer.

[0039] The present invention has at least the following beneficial effects:

[0040] This invention first evaluates the imbalance of symmetrical hammer pressure in each misaligned forging based on the differences in pressure data and hammer drop time of symmetrical hammers during the same number of misaligned forgings in the forging process of large-section steel ingots. It then quantifies the forging effect of each misaligned forging by combining the hammer drop time and pressure data of each hammer in each misaligned forging, obtaining the forging ideality. Considering the mutual influence between different hammers during the forging process, this feature is combined with the difference in forging ideality between adjacent hammers to comprehensively judge the radial rotation state of the built-in hammers and the axial movement state of the large-section steel ingot, jointly affecting the misalignment space generated during the forging of the large-section steel ingot. The degree of interference of the misalignment space on the forging effect is analyzed, and the hammer drop force adjustment weight is determined. This allows for real-time control of the forging pressure of the hammers, making the four hammers built into the radial forging machine more intelligent and coordinated when forging steel ingots, strengthening the control of the radial forging machine over the forging process, and improving the forging effect of large-section steel ingots. Attached Figure Description

[0041] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 The flowchart shows the intelligent collaborative deformation and forging penetration control method of four hammers for large cross-section steel ingots provided in the embodiment of the present invention.

[0043] Figure 2 The diagram shows the structural block of the intelligent collaborative deformation and forging penetration control system for large cross-section steel ingots with four hammers, as provided in an embodiment of the present invention. Detailed Implementation

[0044] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description of the intelligent collaborative deformation and forging penetration control method of large cross-section steel ingot diameter forging four hammers proposed according to the present invention is provided in conjunction with the accompanying drawings and preferred embodiments.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0046] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent collaborative deformation and forging penetration control method for large cross-section steel ingot diameter forging with four hammers provided by the present invention.

[0047] An example of an intelligent collaborative deformation and forging penetration control method for large cross-section steel ingots using four hammers:

[0048] The specific scenario addressed in this embodiment is as follows: During the forging process of large-section steel ingots, when using radial forging four hammers for forging, the core of the large-section steel ingot may experience incremental deformation throughout the forging process, affecting the processing quality of the steel ingot forging. Therefore, during the processing of large-section steel ingots, it is necessary to adjust the pressure of each hammer in a timely manner according to the real-time forging situation to ensure the forging quality of the large-section steel ingot.

[0049] This embodiment proposes a method for intelligent coordinated deformation and forging penetration control of four hammers in forging large cross-section steel ingots, such as... Figure 1 As shown, the intelligent collaborative deformation and forging penetration control method for large cross-section steel ingot diameter forging with four hammers in this embodiment includes the following steps:

[0050] Step S1: Obtain the pressure data of each hammer during the steel ingot forging process and the hammer drop time from falling to contacting the steel ingot.

[0051] This embodiment uses a 550mm diameter steel ingot, heated to 1150℃, and a radial rotation angle of 1° (clockwise) for the built-in hammers of the radial forging machine. It describes a high-frequency forging process of 80 times per minute, with the axial movement axis moving axially at a frequency of 5mm per minute. The embodiment records the pressure data detected by each hammer during each forging operation and the hammer's contact time with the ingot. The set of all historical moments with a time interval less than or equal to a preset duration is considered the current axial movement stage of the ingot. In this embodiment, the preset duration is one minute; however, the implementer can adjust this setting according to specific circumstances. The radial forging machine in this embodiment has four hammers, symmetrically distributed in pairs. It should be noted that at the beginning of forging, the hammer pressure of each hammer is set to a preset hammer pressure; in this embodiment, the preset hammer pressure is 50N, but this can be adjusted according to specific circumstances.

[0052] Thus, the pressure data of each hammer head during each forging stage of the current axial movement of the steel ingot and the hammer drop time from falling to contacting the steel ingot have been collected.

[0053] Step S2: Based on the differences in pressure data and hammer drop time of symmetrical hammers under the same number of misaligned forgings, evaluate the symmetrical hammer pressure imbalance of each misaligned forging; based on the hammer drop time, pressure data and symmetrical hammer pressure imbalance of each hammer in each misaligned forging, obtain the forging ideality of each hammer in each misaligned forging.

[0054] Because the four hammers inside the radial forging mill are symmetrically distributed in pairs, during the actual rotary forging process, the radial forging mill will use the hammers in symmetrical positions alternately for forging. At this time, since the large-section steel ingot is a heated steel material, it is easily deformed by the extrusion of the hammers. In addition, the large-section steel ingot is a regular column, so the extrusion force exerted by the hammers in symmetrical positions on the large-section steel ingot is balanced during a single forging. However, due to the as-cast structure of the radial forging mill itself and the overlap of the front and rear hammer positions, the extrusion force balance exerted by the hammers in symmetrical positions on the large-section steel ingot during a single forging will be disrupted, thus affecting the forging of the core of the large-section steel ingot during a single forging. Furthermore, because the hammers at symmetrical positions are activated during a single forging operation of the radial forging mill, the two sides of the hammers will bulge to varying degrees due to the actual extrusion conditions, resulting in different degrees of shortening of the subsequent hammer drop time. In summary, the extrusion force balance can be demonstrated through pressure data and hammer drop time. Therefore, based on the pressure data and hammer drop time of the hammers at symmetrical positions within the radial forging mill, we can analyze the extrusion conditions on both sides of the steel ingot when the hammers at symmetrical positions forge the ingot, and the imbalance of symmetrical hammer pressure during each forging operation of the radial forging mill.

[0055] In practical scenarios, during the first forging of a radial forging mill, since there is no hammer forging beforehand, there is no radial misalignment space on the surface of the large-section steel ingot during the first forging. Radial misalignment space only appears from the second forging onwards. Therefore, each forging during the current axial movement phase of the steel ingot, except for the first forging, is considered a misalignment forging.

[0056] Then, the mean values ​​of the pressure data differences and the hammer drop time differences of the symmetrical hammer heads under the same number of misaligned forgings are calculated respectively. Based on the mean values ​​of the pressure data differences and the hammer drop time differences of the symmetrical hammer heads under the same number of misaligned forgings, the forging imbalance parameters for each misaligned forging are obtained. The mean values ​​of the pressure data differences and the hammer drop time differences are positively correlated with the forging imbalance parameters.

[0057] Among them, a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. It can be an additive relationship, a multiplicative relationship, etc., which is determined by the actual application.

[0058] As a specific implementation, for any misaligned forging, the mean value of the pressure data difference of the symmetrical hammers during that misaligned forging is normalized, and the normalized result is recorded as the first normalized value; the mean value of the hammer drop time difference of the symmetrical hammers during that misaligned forging is normalized, and the normalized result is recorded as the second normalized value; the product of the first normalized value and the second normalized value is used as the forging imbalance parameter for that misaligned forging. The larger the forging imbalance parameter, the more unbalanced the forging state of the symmetrical hammers on the core of the large cross-section steel ingot is during the misaligned forging process, reflecting that the effect of the symmetrical hammers on the core of the large cross-section steel ingot is less than ideal. There are many methods for normalizing data. In this embodiment, the maximum-minimum normalization method is used to normalize the pressure data difference and the hammer drop time difference respectively to eliminate the influence of dimensions. The data normalization method is existing technology and will not be described in detail here.

[0059] It should be noted that in this embodiment, the method for calculating the pressure data difference is as follows: calculate the absolute value of the difference between two corresponding pressure data, and take the absolute value as the pressure data difference; the method for calculating the hammer drop duration difference is as follows: calculate the absolute value of the difference between two corresponding hammer drop durations, and take the absolute value as the hammer drop duration difference.

[0060] For a single forging process, the greater the forging imbalance, the greater the difference in the degree of protrusion formed by the extrusion on both sides of the large cross-section steel ingot after a single forging by the radial forging machine.

[0061] Next, for any misaligned forging, the product of the forging imbalance parameter of this misaligned forging, the forging imbalance parameter of the next misaligned forging, and the difference between the forging imbalance parameter of this misaligned forging and its adjacent next misaligned forging is taken as the symmetrical hammer pressure imbalance of this misaligned forging. The method for obtaining the difference between the forging imbalance parameter of this misaligned forging and its adjacent next misaligned forging is as follows: calculate the absolute value of the difference between the forging imbalance parameter of this misaligned forging and its adjacent next misaligned forging, and take this absolute value as the difference between the forging imbalance parameter of this misaligned forging and its adjacent next misaligned forging.

[0062] The greater the imbalance of symmetrical hammer pressure, the greater the disruption to the equilibrium stress state during the actual forging of the core of a large-section steel ingot by the corresponding misaligned forging, reflecting a poorer forging effect. Using the above method, the symmetrical hammer pressure imbalance of each misaligned forging can be obtained.

[0063] The hammer in a radial forging mill is a rectangular object with a certain volume. When it contacts and forges a large-section steel ingot, the hammer contacts not just a single point, but an entire area. Furthermore, in practice, to ensure the final radial surface of the steel ingot is machined as precisely as possible, the hammer is typically rotated by a small angle after each forging, allowing the next set of hammers to repeat the forging of a portion of the previous area. Therefore, for two adjacent forging operations, the two sets of hammers in symmetrical positions within the radial forging mill will have a certain radial misalignment (overlap) space between adjacent hammers. Therefore, based on the symmetrical hammer pressure imbalance, we can analyze the misalignment of the hammers on the radial surface of the steel ingot during forging of a large-section steel ingot, and analyze the radial misalignment pressure adjustment of each hammer in the radial forging mill during each forging operation.

[0064] When the drop hammer duration and pressure data for misaligned forging are both large, it indicates that the corresponding hammer head has already forged more of the large-section steel ingot during the corresponding misaligned forging process. The smaller the imbalance of symmetrical hammer pressure, the better the forging effect when forging the core of the large-section steel ingot.

[0065] Based on the above characteristics, the hammer drop time and pressure data for each misaligned forging operation of each hammer head were normalized. The exponential function value, with the natural constant as the base and negative symmetric hammer pressure imbalance as the exponent, was used as the negative correlation mapping value for symmetric hammer pressure imbalance. Then, the product of the normalized hammer drop time, the normalized pressure data, and the negative correlation mapping value for symmetric hammer pressure imbalance for each misaligned forging operation of each hammer head was determined as the forging ideality for each misaligned forging operation. A higher forging ideality indicates a better forging effect on the steel ingot by the corresponding built-in hammer head during the corresponding misaligned forging operation.

[0066] Any one of the four hammers built into the radial forging machine is designated as the first hammer. The other hammers are designated as the second, third, and fourth hammers in a clockwise order. The first and third hammers are symmetrically positioned, and the second and fourth hammers are symmetrically positioned.

[0067] After the first hammer performs a misaligned forging, the four built-in hammers will move to the same angle position during the next misaligned forging. Therefore, the other three hammers will reflect the chain effect of the first hammer on the forging of the large cross-section steel ingot, that is, the first hammer affects the second hammer, the second hammer affects the third hammer, and the third hammer affects the fourth hammer.

[0068] For any single misaligned forging:

[0069] The difference in forging ideality between two adjacent hammers (excluding the first hammer) in the next misaligned forging is calculated and denoted as the first difference. Based on the first difference and the forging ideality of the first hammer in this misaligned forging, the radial misalignment pressure adjustment parameter of the first hammer in this misaligned forging is obtained. The first difference is positively correlated with the radial misalignment pressure adjustment parameter, and the forging ideality of the first hammer in this misaligned forging is negatively correlated with the radial misalignment pressure adjustment parameter.

[0070] Among them, a positive correlation means that the dependent variable increases as the independent variable increases, and the dependent variable decreases as the independent variable decreases. It can be an additive relationship, a multiplicative relationship, etc., which is determined by practical application. A negative correlation means that the dependent variable decreases as the independent variable increases, and the dependent variable increases as the independent variable decreases. It can be a subtractive relationship, a division relationship, etc., which is determined by practical application.

[0071] In this embodiment, a specific calculation formula for the radial misalignment pressure adjustment parameter is given. The radial misalignment pressure adjustment parameter for the first hammer head in this misalignment forging can be expressed as:

[0072]

[0073] Where R represents the radial misalignment pressure adjustment parameter of the first hammer in this misalignment forging, and I represents the total number of hammers; E i E represents the forging ideality of the i-th hammer (excluding the first hammer) in the next misaligned forging process; i+1 E1 represents the forging ideality of the (i+1)th hammer (excluding the first hammer) in the next misaligned forging; E1 represents the forging ideality of the first hammer in the misaligned forging; λ represents the zero-prevention parameter; and || represents the absolute value sign.

[0074] In this embodiment, a zero-prevention parameter is introduced into the calculation formula of the radial misalignment pressure adjustment parameter to prevent the denominator from being 0. In this embodiment, the zero-prevention parameter is 0.01. In specific applications, the implementer can set it according to the specific situation.

[0075] |E i -E i+1 | indicates the first difference. This value reflects the chain reaction effect of the first hammer during the misaligned forging. The larger the value, the greater the impact of the first hammer's misaligned forging on subsequent hammers, and the more obvious the chain reaction effect of the first hammer on other hammers under the misaligned forging. If the radial misalignment pressure adjustability is greater, it means that the corresponding hammer is subject to a larger proportion of radial misalignment overlap with the large cross-section steel ingot, and the more obvious the chain interference on forging at other positions, reflecting the greater degree to which the corresponding hammer needs to adjust the hammer pressure state in the next forging.

[0076] It should be noted that when calculating the radial misalignment pressure adjustment parameters for the remaining second, third, and fourth hammers, it is only necessary to treat the corresponding hammers as the first hammers again and use the above method to calculate the radial misalignment pressure adjustment parameters for each hammer in each misalignment forging.

[0077] Step S3: Based on the misalignment effect between hammers and the difference in forging ideality between adjacent hammers, determine the misalignment pressure adjustment factor for each hammer in each misalignment forging; use the misalignment pressure adjustment factor to obtain the hammer drop force adjustment weight for each hammer.

[0078] In the actual forging process of large-section steel ingots by the built-in hammers in the radial forging mill, the radial rotation of the hammers along the large-section steel ingot and the axial movement of the steel ingot itself are carried out synchronously. Therefore, when adjacent hammers rotate radially, the misalignment overlap space between them changes not only in the radial direction but also in the axial direction, so that the large-section steel ingot can be forged into a complete whole component. Therefore, based on the radial misalignment pressure adjustment, the positional influence of the large-section steel ingot on the hammer forging when it moves axially can be analyzed. Furthermore, combined with the misalignment space generated when the steel ingot moves axially, the misalignment pressure adjustment factor of each hammer in the radial forging mill for each forging can be calculated.

[0079] Then, based on the differences in radial misalignment pressure adjustment parameters between each hammer (excluding the first hammer) and its adjacent next hammer in this misalignment forging, the comprehensive misalignment chain interference value of the first hammer in this misalignment forging is obtained. Specifically, the absolute value of the difference between the radial misalignment pressure adjustment parameters of each hammer (excluding the first hammer) and its adjacent next hammer in this misalignment forging is calculated. For each pair of adjacent hammers (excluding the first hammer), there is a corresponding absolute value in this misalignment forging. This absolute value characterizes the difference between the corresponding radial misalignment pressure adjustment parameters. The sum of all the calculated absolute values ​​is taken as the comprehensive misalignment chain interference value of the first hammer in this misalignment forging. The larger the comprehensive misalignment chain interference value, the more significant the interference of the misalignment space generated by the radial and axial movements of the first hammer in this misalignment forging on the forging effect of subsequent hammers, and the more significant the chain effect.

[0080] Furthermore, by combining the comprehensive misalignment chain interference value of the first hammer for this misalignment forging, the radial misalignment pressure adjustment parameter of the first hammer for this misalignment forging, and the sequence number of the misalignment forging in the corresponding steel ingot axial movement stage, the misalignment pressure adjustment factor of the first hammer for this misalignment forging is obtained.

[0081] As a specific example, the product of the comprehensive misalignment chain interference value of the first hammer for this misalignment forging and the radial misalignment pressure adjustment parameter of the first hammer for this misalignment forging is calculated, and this product is recorded as the first product; the ratio between the first product and the sequence number of the misalignment forging in the corresponding axial movement stage of the steel ingot is determined as the misalignment pressure adjustment factor of the first hammer for this misalignment forging.

[0082] The sequence number of this misaligned forging within the corresponding ingot axial movement stage is used to characterize the distance the ingot moves axially during this misaligned forging by the radial forging mill. A larger misalignment pressure adjustment factor indicates a more significant interference between the incremental deformation of the first hammer during this misaligned forging, caused by the combined axial movement of the ingot and the radial rotation of the hammer, and the forging effect formed by the first hammer during this misaligned forging. This reflects the greater need to adjust the pressure state of the first hammer during the rear-end forging stage.

[0083] It should be noted that: if the misaligned forging is the 3rd misaligned forging within the axial movement stage of the ingot, its sequence number is 3; if the misaligned forging is the 5th misaligned forging within the axial movement stage of the ingot, its sequence number is 5.

[0084] To calculate the misalignment pressure adjustment factor for the remaining second, third, and fourth hammers, simply treat the corresponding hammers as the first hammers again and calculate using the method described above to obtain the misalignment pressure adjustment factor for each hammer in each misalignment forging.

[0085] Using the method provided in this embodiment, the misalignment pressure adjustment factor for each hammer head in each misalignment forging was obtained.

[0086] The following explanation uses a hammerhead as an example. The method provided in this embodiment can be used to process other hammerheads as well.

[0087] Specifically, for any given hammerhead:

[0088] In this embodiment, the last misaligned forging within the current axial movement stage of the steel ingot is recorded as the current misaligned forging, and all misaligned forgings within the current axial movement stage other than the current misaligned forging are recorded as historical reference forgings. The average value of the misalignment pressure adjustment factor of the hammer head in all historical reference forgings is calculated, and this average value is recorded as the first average value. The absolute value of the difference between the misalignment pressure adjustment factor of the current misaligned forging and the first average value is calculated, and the normalized result of this absolute value is used as the hammer's drop force adjustment weight. There are many existing data normalization methods, and implementers can choose according to specific circumstances. The value range of the drop force adjustment weight is (0, 1).

[0089] If the weight of the hammer force adjustment is greater, it means that when the radial forging machine forges a large cross-section steel ingot to real-time forging, the change in the amount of steel nail deformation between the corresponding hammer head and the corresponding forged steel ingot area is more obvious, and the forging effect formed after forging with the current force is less ideal.

[0090] Using the above method, the adjustment weight of the hammer's falling force can be obtained.

[0091] Step S4: Adjust the weights and hammering duration of each hammer head according to the hammering force to control the pressure of each hammer head during the next forging.

[0092] In this embodiment, the hammer force adjustment weight of each hammer head is obtained in step S3, and then the pressure of each hammer head will be adjusted in real time based on the hammer force adjustment weight.

[0093] Specifically, for any given hammerhead:

[0094] If the drop time of the most recent misaligned forging is equal to the average drop time of all historical reference forgings of that hammer, it means that the diameter of the ingot during the real-time forging cycle is the same as the diameter of the ingot that has been forged previously. In this case, there is no need to adjust the drop pressure for the next forging cycle. Therefore, the pressure of the hammer will not be adjusted for the next forging cycle.

[0095] If the drop time of the hammer in the most recent misaligned forging is less than the average drop time of all historical reference forgings of the hammer, it means that the diameter of the ingot during real-time forging is larger than the diameter of previously forged ingots. To ensure that the overall ingot diameter is as balanced as possible and the surface is flat, the drop pressure needs to be increased and the ingot diameter reduced in the next forging. Therefore, the first sum of constant 1 and the hammer drop force adjustment weight is calculated, and the product of the pressure of the most recent misaligned forging and the first sum is used as the pressure for the next forging.

[0096] If the drop duration of the hammer in the most recent misaligned forging is greater than the average drop duration of all historical reference forgings of the hammer, it indicates that the diameter of the ingot during the real-time forging cycle is smaller than the diameter of previously forged ingots. To ensure a more balanced overall ingot diameter and a smooth surface, the drop pressure needs to be reduced and the ingot diameter increased during the next forging cycle. Therefore, in this case, the product of the pressure of the hammer's most recent misaligned forging and the weighting of the hammer's drop force adjustment is used as the pressure for the hammer's next forging cycle.

[0097] In this embodiment, a specific formula for calculating the pressure during the next forging of the hammerhead is given:

[0098]

[0099] Where H represents the pressure during the next forging of the hammer, H0 represents the pressure during the most recent misaligned forging of the hammer, f represents the weighting of the hammer's falling force adjustment, and t1 represents the falling time of the most recent misaligned forging of the hammer. This indicates the average hammer drop time across all historical reference forgings of this hammerhead.

[0100] If the ingot diameter is larger than that of a previously forged ingot during real-time forging, it indicates that the hammer pressure needs to be increased and the ingot diameter reduced during the next forging. If the ingot diameter is smaller than that of a previously forged ingot during the current forging cycle, the hammer pressure should be reduced and the ingot diameter increased during the next forging cycle. At this time, there is no need to adjust the hammer pressure for the next forging.

[0101] Thus, by using the method provided in this embodiment, the pressure of the four hammers can be adjusted in real time during the forging process of large cross-section steel ingots, thereby achieving precise control of the forging process.

[0102] This embodiment first evaluates the imbalance of symmetrical hammer pressure in each misaligned forging based on the differences in pressure data and hammer drop time of symmetrical hammers under the same number of misaligned forgings during the forging process of large cross-section steel ingots. It then quantifies the forging effect of each misaligned forging by combining the hammer drop time and pressure data of each hammer in each misaligned forging, obtaining the forging ideality. Considering the mutual influence between different hammers during the forging process, this characteristic and the difference in forging ideality between adjacent hammers are combined to comprehensively judge the radial rotation state of the built-in hammers and the axial movement state of the large cross-section steel ingot, jointly affecting the misalignment space generated during the forging of the large cross-section steel ingot. The degree of interference of the misalignment space on the forging effect is analyzed, and the hammer drop force adjustment weight is determined. This allows for real-time control of the forging pressure of the hammers, making the four hammers built into the radial forging machine more intelligent and coordinated when forging steel ingots, strengthening the control of the radial forging machine over the forging process, and improving the forging effect of large cross-section steel ingots.

[0103] An embodiment of an intelligent collaborative deformation and forging penetration control system for large cross-section steel ingots with four hammers:

[0104] See Figure 2 The diagram shows a structural block diagram of a large cross-section steel ingot diameter forging four-hammer intelligent collaborative deformation and forging penetration control system provided in an embodiment of the present invention. The system may include a data acquisition module, an effect evaluation module, a weight determination module, and a control module.

[0105] Among them, the data acquisition module is used to acquire the pressure data of each hammer during the steel ingot forging process and the hammer drop time from falling to contacting the steel ingot;

[0106] The effect evaluation module is used to evaluate the symmetrical hammer pressure imbalance of each misaligned forging based on the differences in pressure data and hammer drop time of the symmetrical hammer heads under the same number of misaligned forgings; and to obtain the forging ideality of each hammer head in each misaligned forging based on the hammer drop time, pressure data and symmetrical hammer pressure imbalance of each hammer head in each misaligned forging.

[0107] The weight determination module is used to determine the misalignment pressure adjustment factor for each hammer in each misalignment forging based on the misalignment effect between hammers and the difference in forging ideality between adjacent hammers; and to obtain the hammer drop force adjustment weight for each hammer using the misalignment pressure adjustment factor.

[0108] The control module is used to adjust the weights and the falling time of each hammer head according to the hammer force, and to control the pressure of each hammer head during the next forging.

[0109] It should be understood that Figure 2 The structural block diagram and modules of the intelligent collaborative deformation and forging penetration control system for large cross-section steel ingot diameter forging four hammers shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described methods and systems can be implemented using computer-executable instructions and / or included in processor control code, for example, on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the above-described hardware circuits and software (e.g., firmware).

[0110] For more details about the above modules, please refer to other parts of this manual; they will not be repeated here.

[0111] In other embodiments, a large-section steel ingot diameter forging four-hammer intelligent collaborative deformation and forging penetration control device is also provided, including a memory and a processor. The memory stores executable program code, and the processor calls and runs the executable program code from the memory, causing the device to execute the aforementioned large-section steel ingot diameter forging four-hammer intelligent collaborative deformation and forging penetration control method. Specifically, the device may be a chip, component, or module. The chip may include a connected processor and memory; wherein, the memory stores instructions, and when the processor calls and executes the instructions, it enables the chip to execute the large-section steel ingot diameter forging four-hammer intelligent collaborative deformation and forging penetration control method provided in the above embodiments.

[0112] In other embodiments, a computer program product is also provided, which, when run on a computer, causes the computer to perform the aforementioned related steps to realize the intelligent collaborative deformation and forging penetration control method for large cross-section steel ingot diameter forging four hammers provided in the above embodiments.

[0113] In other embodiments, a computer-readable storage medium is also provided, which stores computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the intelligent collaborative deformation and forging penetration control method for large cross-section steel ingot diameter forging four hammers provided in the above embodiments.

[0114] The systems, electronic devices, computer program products, and computer-readable storage media provided are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0115] It should be noted that 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent coordinated deformation and forging penetration control of four hammers in forging large cross-section steel ingots, characterized in that, The method includes the following steps: Obtain pressure data for each hammer during the steel ingot forging process, as well as the hammer drop time from falling to contacting the steel ingot; Based on the differences in pressure data and hammer drop time of symmetrical hammers under the same number of misaligned forgings, the imbalance of symmetrical hammer pressure in each misaligned forging is evaluated; based on the hammer drop time, pressure data and imbalance of symmetrical hammer pressure in each misaligned forging of each hammer, the forging ideality of each hammer in each misaligned forging is obtained. Based on the misalignment effect between hammers and the difference in forging ideality between adjacent hammers, the misalignment pressure adjustment factor for each hammer in each misalignment forging is determined; using the misalignment pressure adjustment factor, the hammer drop force adjustment weight of each hammer is obtained. The weights and hammer duration of each hammer are adjusted according to the hammer's impact force to regulate the pressure of each hammer during the next forging.

2. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingots with four hammers as described in claim 1, characterized in that, The evaluation of the symmetrical hammer pressure imbalance in each misalignment forging process, based on the differences in pressure data and hammer drop time of the symmetrical hammer heads under the same number of misalignment forging operations, includes: Based on the mean difference in pressure data and the mean difference in hammer drop time of symmetrical hammer heads under the same number of misaligned forgings, the forging imbalance parameter for each misaligned forging is obtained. The mean difference in pressure data and the mean difference in hammer drop time are positively correlated with the forging imbalance parameter. By combining the forging imbalance parameters of each misaligned forging, the forging imbalance parameters of the next misaligned forging, and the differences between the forging imbalance parameters of each misaligned forging and the next misaligned forging, the symmetrical hammer pressure imbalance of each misaligned forging is obtained.

3. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingots with four hammers as described in claim 2, characterized in that, The forging imbalance parameters of each misaligned forging, the forging imbalance parameters of the next misaligned forging, and the differences between the forging imbalance parameters of each misaligned forging and the next misaligned forging are used to obtain the symmetrical hammer pressure imbalance of each misaligned forging, including: For any misaligned forging, the product of the forging imbalance parameter of the misaligned forging, the forging imbalance parameter of the next misaligned forging, and the difference between the forging imbalance parameter of the misaligned forging and the next misaligned forging is taken as the symmetrical hammer pressure imbalance of the misaligned forging.

4. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingots with four hammers as described in claim 1, characterized in that, The process of obtaining the forging ideality of each hammer head in each misaligned forging based on the hammer drop time, pressure data, and symmetrical hammer pressure imbalance in each hammer head's misaligned forging includes: The product of the normalized value of the hammer drop time for each misaligned forging of each hammer head, the normalized value of the pressure data, and the negative correlation mapping value of the symmetrical hammer pressure imbalance is determined as the forging ideality of each misaligned forging of each hammer head.

5. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingots with four hammers as described in claim 1, characterized in that, The process of determining the misalignment pressure adjustment factor for each hammer in each misalignment forging based on the misalignment effect between hammers and the difference in forging ideality between adjacent hammers includes: For any single misaligned forging: Calculate the first difference in forging ideality for the next misaligned forging of any two adjacent hammers (excluding the first hammer); based on the first difference and the forging ideality of any misaligned forging of the first hammer, obtain the radial misalignment pressure adjustment parameter for any misaligned forging of the first hammer. The first difference is positively correlated with the radial misalignment pressure adjustment parameter, and the forging ideality of any misaligned forging of the first hammer is negatively correlated with the radial misalignment pressure adjustment parameter. Based on the differences between the radial misalignment pressure adjustment parameters of each hammer (excluding the first hammer) and its adjacent next hammer in any misalignment forging, the comprehensive misalignment chain interference value of the first hammer in any misalignment forging is obtained. Based on the comprehensive misalignment chain interference value of any misalignment forging of the first hammer, the radial misalignment pressure adjustment parameter of any misalignment forging of the first hammer, and the sequence number of any misalignment forging in the corresponding steel ingot axial movement stage, the misalignment pressure adjustment factor of any misalignment forging of the first hammer is obtained. The first hammerhead can be any hammerhead.

6. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingots with four hammers as described in claim 5, characterized in that, The method of obtaining the misalignment pressure adjustment factor for any misalignment forging by the first hammer head based on the comprehensive misalignment chain interference value of any misalignment forging by the first hammer head, the radial misalignment pressure adjustment parameter of any misalignment forging by the first hammer head, and the sequence number of any misalignment forging within the corresponding axial movement stage of the steel ingot, includes: Calculate the first product of the comprehensive misalignment chain interference value of any misalignment forging of the first hammer head and the radial misalignment pressure adjustment parameter of any misalignment forging of the first hammer head; The ratio between the first product and the order of any misaligned forging within the corresponding axial movement stage of the steel ingot is determined as the misalignment pressure adjustment factor for any misaligned forging of the first hammer.

7. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingot diameter forging with four hammers according to claim 5, characterized in that, The step of using the misaligned pressure adjustment factor to obtain the hammer impact force adjustment weight for each hammer includes: For any given hammer: Calculate the first average value of the misalignment pressure adjustment factor for any of the hammer heads in the historical reference forging; Based on the difference between the misalignment pressure adjustment factor of the current misalignment forging of any hammer and the first average value, the hammer drop force adjustment weight of any hammer is obtained.

8. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingot diameter forging with four hammers according to claim 7, characterized in that, The step of obtaining the hammer drop force adjustment weight for any hammer based on the difference between the misalignment pressure adjustment factor of the current misalignment forging of any hammer and the first average value includes: The normalized result of the difference between the misalignment pressure adjustment factor of the current misalignment forging of any hammer and the first average value is used as the hammer drop force adjustment weight of any hammer.

9. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingots using four hammers as described in claim 1, characterized in that, The method of adjusting the weights based on the hammer drop force and the drop duration of each hammer to control the pressure of each hammer during the next forging includes: For any given hammer: If the drop time of the most recent misaligned forging of any hammer head is equal to the average drop time of all historical reference forgings of any hammer head, then the pressure of the hammer head in the next forging will not be adjusted. If the drop time of the most recent misaligned forging of any hammer is less than or greater than the average drop time of all historical reference forgings of any hammer, then the pressure of the next forging of any hammer is adjusted using the drop force adjustment weight of any hammer.

10. The intelligent coordinated deformation and forging penetration control method for large cross-section steel ingot diameter forging with four hammers according to claim 9, characterized in that, If the drop time of the most recent misaligned forging of any hammer head is less than or greater than the average drop time of all historical reference forgings of any hammer head, then the pressure of the next forging of any hammer head is adjusted using the drop force adjustment weight of any hammer head, including: If the hammer drop time of the most recent misaligned forging of any hammer is less than the average hammer drop time of all historical reference forgings of any hammer, then calculate the first sum of constant 1 and the hammer drop force adjustment weight of any hammer, and use the product of the pressure of the most recent misaligned forging of any hammer and the first sum as the pressure of the next forging of any hammer. If the drop duration of the most recent misaligned forging of any hammer is greater than the average drop duration of all historical reference forgings of any hammer, then the product of the pressure of the most recent misaligned forging of any hammer and the drop force adjustment weight of any hammer will be used as the pressure of the next forging of any hammer.

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