Automatic bar length gauging system and method

Abstract

The application discloses an automatic lifting bar material sizing rate system and method. The automatic lifting bar material sizing rate system is composed of a self-feedback adjusting system, a negative deviation measuring system, a billet online cutting system and a self-learning system. The automatic lifting bar material sizing rate method is based on the automatic negative deviation measurement of rolling, the feedback conditions of each section multiple size, the adjustment of the cutting length of the steelmaking billet, the automatic lifting of the bar material sizing quantity, the more timely feedback of the method compared with manual supervision, the increase of the sizing yield, the increase of the sizing rate of the bar material production line from 99.35% to 99.64%, and the increase of the sizing yield by 0.29%. The automatic lifting bar material product sizing rate system can improve the rolling process control precision, effectively feed back the key parameters of the production process, reduce the labor intensity of personnel for removing short sizes, and improve the indexes such as the material yield, the operation rate and the hot charging rate of the bar material product.

Description

Technical Field

[0001] This invention belongs to the field of continuous casting technology for steel billets, specifically relating to an automatic lifting system and method for achieving a fixed length ratio of bars. Background Technology

[0002] The goal of producing bar stock in fixed length has always been the industry's pursuit. However, due to imperfect control systems and steel rolling design concepts, the improvement of the fixed length rate of bar products is greatly limited in the current technology.

[0003] The present invention aims to provide an automatic lifting system and method for achieving the correct length of bar stock. Summary of the Invention

[0004] The first objective of this invention is to provide a self-feedback adjustment system for improving the sizing rate of bar production lines, and the second objective of this invention is to provide a method for automatically improving the sizing rate of bar stock.

[0005] The first objective of this invention is achieved as follows: the automatic bar length-keeping system consists of a self-feedback adjustment system, a negative deviation measurement system, an online billet cutting system, and a self-learning system.

[0006] The self-feedback adjustment system consists of a rolling mill control system and an industrial control computer. The rolling mill control system sets the length of each multiple length on the cooling bed, and the negative deviation measurement system monitors the multiple length data of each section. When the negative deviation value is within the set target area, it determines whether the tail steel length meets the multiple length setting requirements. Based on the tail steel multiple length, the billet weight is adjusted, and the adjustment data is transmitted to the billet online cutting system until the rolling requirements are met. The billet online cutting system changes the cutting length based on the optimized billet weight for the rolling process. The negative deviation measurement system then judges the adjusted tail steel length of the rolled product, recalculates the optimal billet weight, and calculates whether to adjust the cutting length or maintain the current billet weight, and transmits the data to the billet online cutting system.

[0007] The negative deviation measurement system consists of a finishing mill exit signal detection device, a diameter measuring device, and an industrial control computer. The signal detection device measures the length of the finished sample by detecting the steel passage time through the exit signal. The diameter measuring device measures the length and diameter of the finished bar sample. The deviation between the theoretical length and the actual measured length under different diameters is compared. The system also compares the manually measured data within a specified time to obtain the corresponding correction coefficient, thereby making the system's calculated negative deviation value closer to the actual negative deviation value.

[0008] The self-learning system consists of an industrial control computer and a deep learning server. Based on the optimal data of the number of bars produced under the billet weight, it continuously accumulates the relationship between the weight and length of different steel grades under different casting speeds, accumulates the actual length obtained under different temperatures and different furnace temperatures, calculates the weight deviation value of hot and cold billets, and then guides the continuous casting process to carry out production.

[0009] The second objective of this invention is achieved by providing a method for automatically increasing the sizing rate of bar stock, which is implemented through the following steps:

[0010] 1) The self-feedback adjustment system provides the cutting value to the online billet cutting system based on the theoretical billet weight value. The continuous casting process then performs continuous casting production based on the data provided by the self-feedback adjustment system.

[0011] 2) The self-feedback adjustment system determines whether the negative deviation value of the current finished bar sample is within the control range based on the data uploaded by the negative deviation measurement system. If it is not within the control range, the system feedback indicates that manual adjustment is required and the judgment is made again. If it is within the control range and the material shape and size are stable, the tail steel length is measured. If it reaches the design value, the steelmaking process produces according to the current billet weight value of the rolling process. If the total length of the finished product or the tail steel is too long or too short, the self-feedback adjustment system determines the excess length weight or the length weight that needs to be added based on the actual weight of the billet cutting. The steelmaking casting machine modifies the cutting length of the billet online cutting system online based on the data from the self-feedback adjustment system to change the weight and supply it to the rolling process for production.

[0012] 3) Based on the quantity of finished bars of fixed length obtained from rolling, the self-learning system collects relevant information on cutting length and weight, as well as information on the weight of billets entering the furnace, accumulates information on the relationship between length, temperature and weight changes, optimizes the self-feedback adjustment system to change the adjustment accuracy of billet weight, and then continuously optimizes and adjusts the billet weight accuracy.

[0013] The beneficial effects of this invention are as follows: The method for automatically increasing the bar stock length ratio is an adaptive feedback adjustment method. Based on the automatic negative tolerance measurement of the rolling mill and the feedback of the length ratio of each segment, the steel billet is automatically cut, and the weight of the billet is adjusted according to the length ratio of each segment, thereby automatically increasing the number of bar stock lengths. This method is more timely than manual monitoring and feedback, thus increasing the length yield. The length ratio of this bar production line has increased from 99.35% to 99.64%, and the length yield has increased by 0.29%.

[0014] This invention's automatic bar product length-rate system improves the control precision of the rolling process, effectively feeds back key parameters of the production process, reduces the labor intensity of personnel removing short lengths, and improves the yield and hot charging rate of bar products. Specifically, the yield rate increases from 100.98% to 101.31%, an increase of 0.33%. By enabling full-length bars to be placed on the cooling bed, the labor intensity of removing short lengths is reduced, the collection capacity is improved, production is more stable, and hot billets can be fed into the furnace more quickly. The hot charging rate increases from 45% to 61%, an increase of 16%. The yield rate is improved while energy consumption is reduced. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the automatic bar length setting system of the present invention;

[0016] Figure 2 This is a path diagram for automatically improving the bar stock length accuracy of the present invention. Detailed Implementation

[0017] The present invention will be further described below with reference to embodiments, but this is not intended to limit the present invention in any way. Any modifications or substitutions made based on the teachings of the present invention shall fall within the protection scope of the present invention.

[0018] This invention discloses an automatic bar stock length-rate system, comprising a self-feedback adjustment system, a negative deviation measurement system, an online billet cutting system, and a self-learning system, such as... Figure 1 As shown;

[0019] The negative deviation measurement system consists of a finishing mill exit signal detection device, a diameter measuring device, and an industrial control computer. The signal detection device measures the length of the finished sample by detecting the steel passage time through the exit signal. The diameter measuring device measures the length and diameter of the finished bar sample. The deviation between the theoretical length and the actual measured length under different diameters is compared. The manual measurement data is compared with the calculated data for the first 5 steel billets after the change of product and early inspection. After the rolling is normal, the calculated data and manual data are compared every hour. The corresponding correction coefficient is obtained, so that the system calculates the negative deviation value closer to the actual negative deviation value.

[0020] The self-feedback adjustment system consists of a rolling mill control system and an industrial control computer. The rolling mill control system sets the length of each multiple length on the cooling bed, and the negative deviation measurement system monitors the multiple length data of each section. When the negative deviation value is within the set target area, it determines whether the tail steel length meets the multiple length setting requirements. Based on the tail steel multiple length, the billet weight is adjusted, and the adjustment data is transmitted to the billet online cutting system until the rolling requirements are met. The billet online cutting system changes the cutting length based on the optimized billet weight for the rolling process. The negative deviation measurement system then judges the adjusted tail steel length of the rolled product, recalculates the optimal billet weight, and calculates whether to adjust the cutting length or maintain the current billet weight, and transmits the data to the billet online cutting system.

[0021] When the billet is first fed, the self-feedback adjustment system has an initial set control value. After the billet enters the heating furnace, its actual weight is measured. After exiting the furnace, based on the length of the tail steel, if the tail steel is greater than or equal to 100.5 meters (with an additional set length of 1.5 meters), and the negative tolerance is within the internal control range, then the current weight is appropriate, and the online billet cutting system does not make any changes, continuing to produce billets at that length. If the tail steel length is less than 100.5 meters, the online billet cutting system appropriately increases the billet cutting length based on the weight entering the furnace. If the tail steel length is greater than 100.5 meters, the cutting length can be appropriately reduced (the reduction or increase in cutting length is controlled according to the data given by the self-learning system).

[0022] The self-learning system comprises an industrial control computer and a deep learning server. Based on optimal data on the number of bar products in length under a given billet weight, it continuously accumulates data on the relationship between weight and length for different steel grades at different casting speeds, and on the actual length yield at different temperatures and furnace entry temperatures. It calculates the weight deviation between cold and hot billets, thereby guiding the continuous casting process. Under different temperature conditions, the oxide layer size and thickness of cold and hot billets differ. Under the same control conditions, the actual billet weight of cold billets will be less than that of hot billets, and the length yield of cold billets will be lower than that of hot billets. The self-learning system calculates the oxidation of hot billets during hot delivery based on the control conditions of the collected cold and hot billets and the furnace entry temperature, thus appropriately reducing billet weight and waste. It also optimizes the weight gain of billets leaving the steelmaking line, making the actual weight of cold billets closer to the control weight, increasing the length yield, and establishing different cutting length models for cold billets leaving the line and cold billets delivered hot.

[0023] This invention also provides a method for automatically improving the sizing rate of bar stock, which is implemented according to the following steps:

[0024] 1) The self-feedback adjustment system provides the cutting value to the online billet cutting system based on the theoretical billet weight value. The continuous casting process then performs continuous casting production based on the data provided by the self-feedback adjustment system.

[0025] 2) The self-feedback adjustment system determines whether the negative deviation value of the current finished bar sample is within the control range based on the data uploaded by the negative deviation measurement system. If it is not within the control range, the system feedback requires manual adjustment and re-judgment. If it is within the control range and the material shape and size are stable, the tail steel length is measured. If it reaches the design value, the steelmaking process produces according to the current billet weight value of the rolling process. If the tail steel is too long or too short, the self-feedback adjustment system determines the excess length weight or the length that needs to be supplemented based on the actual weight of the billet cutting. The steelmaking casting machine modifies the cutting length of the billet online cutting system online based on the data from the self-feedback adjustment system to change the weight and supply it to the rolling process for production.

[0026] 3) Based on the quantity of finished bars of fixed length obtained from rolling, the self-learning system collects relevant information on cutting length and weight, as well as information on the weight of billets entering the furnace, accumulates information on the relationship between length and weight changes, optimizes the self-feedback adjustment system to change the adjustment accuracy of billet weight, and then continuously optimizes and adjusts the billet weight accuracy.

[0027] The automatic improvement path of bar stock length accuracy in this invention is as follows: Figure 2 As shown.

[0028] Example 1

[0029] 1. The self-feedback adjustment system determines the inner diameter and negative deviation range of the bar stock according to the size range and negative deviation range specified in the national and industry standards (GB / T 1499.2-2018), and the negative deviation measurement system detects the negative deviation rate of a single bar stock product.

[0030] 2. The negative deviation measurement system converts the actual length of the manufactured product into a negative deviation rate. The detailed calculation method is: (actual weight / actual length - theoretical weight) / theoretical weight * 100%. For steel products, the actual measurement can be carried out according to GB / T1499.2-2018, section 8.4.2. The theoretical weight value can be obtained by using the formula of specification size and density. The weight of a single piece of fixed length needs to be verified according to the listed formula to ensure that the current negative deviation rate is within the feasible range.

[0031] 3. The negative deviation measurement system uploads the negative deviation data to the self-feedback adjustment system. Based on the required segment length (converted from weight per meter to fixed length weight), the length of all cut ends (converted from volume to end weight), and the heating loss rate of 0.08%~0.1% (converted to loss weight), the self-feedback adjustment system calculates the optimal billet weight and feeds the required weight back to the billet online cutting system to cut the required billet length. The system automatically cuts the billet to the required length based on the required billet weight and supplies it to the rolling process for production. This process involves the conversion and connection between the number of fixed lengths and the number of billets. The specific general formula is: Total fixed length weight = Billet weight - Loss weight - Cut end weight; Individual component formulas: Total fixed length weight = Single fixed length weight * Number of billets; Billet length = Billet weight / Billet weight per meter; Billet weight per meter = Density * Billet side length * Side length * 1.

[0032] 4. The finished product dimensions change significantly during the rolling process. After the parameters are modified, the negative deviation measurement system is entered for recalculation. The negative deviation system transmits the corrected value to the self-feedback adjustment system. After the parameters are given, step (3) is repeated to form a closed cycle.

[0033] 5. After one round of adjustments, the number of multiple lengths produced by the rolling process will be met according to the calculated value. Based on a cooling bed length of 120 meters and a customer-customized length of 9 meters, it can be divided into multiple lengths of 81 meters to 108 meters (i.e., 9 to 12 lengths). The length of the cut-off ends is set to 0.5 to 1 meter according to the rolling specifications. The weight of the cut-off ends is set according to the number of shears used in the rolling process. For example, the weight cut by 5 shears for high-speed bars is 24 kg. Finally, based on the weighing value, negative deviation rate, and the correlation between weight and length, the self-learning system calculates the required billet length based on the actual number of lengths of the produced bars, forming a closed-loop control system. Ultimately, through the correlation between the rolling and continuous casting processes, the bar length rate is automatically improved. (This adjustment method can be used repeatedly and is not limited by metal type or rolling specifications. It is also applicable to other types of metal bar products.)

[0034] 6. The self-learning system compares the quantity of billets received in length with the design value based on different billet weights and corresponding lengths, continuously accumulating data on the length received of both hot and cold billets. It also accumulates data on the length received rate and whether the actual value reaches the set value, thereby obtaining the optimal value for weight control and continuously optimizing this value to comprehensively improve the length received rate during hot delivery; it also continuously improves the length received rate of cold billets after they enter the furnace; the online steelmaking cutting system implements different controls for hot billets and finished billets, thereby improving the overall length received rate of bar products (Note: The oxidation conditions of cold and hot billets differ; cold billets are relatively more severely oxidized, resulting in a lower metal yield than hot billets).

[0035] In 2022, the yield rate of a certain bar production line was 100.98%, and the non-length rate was 0.53%. In 2023, after the system of this invention was put into use, the yield rate was 101.31% and the non-length rate was 0.36% as of July. The yield rate increased by 0.33%, and the non-length rate decreased by 0.18%.

Claims

1. An automatic lifting system for achieving a fixed length rate of bar stock, characterized in that, The automatic bar length-rate system consists of a negative deviation measurement system, a self-feedback adjustment system, an online billet cutting system, and a self-learning system. The negative deviation measurement system consists of a finishing mill exit signal detection device, a diameter measuring device, and an industrial control computer. The signal detection device measures the length of the finished sample by detecting the steel passage time through the exit signal. The diameter measuring device measures the length and diameter of the finished bar sample. The deviation between the theoretical length and the actual measured length under different diameters is then compared. The system also compares the data measured manually within a specified time to obtain the corresponding correction coefficient, thereby making the negative deviation value calculated by the system closer to the actual negative deviation value. The self-feedback adjustment system consists of a rolling line control system and an industrial control computer. The rolling line control system sets the length of each multiple length on the cooling bed, and the negative deviation measurement system monitors the multiple length data of each section. When the negative deviation value is within the set target area, it determines whether the tail steel length meets the multiple length setting requirements. Based on the tail steel multiple length, the billet weight is adjusted, and the adjustment data is transmitted to the billet online cutting system until the rolling requirements are met. The billet online cutting system changes the cutting length according to the optimized billet weight for the rolling process. The negative deviation measurement system then judges the adjusted length of the rolled product tail steel, recalculates the optimal billet weight, and calculates whether to adjust the cutting length or maintain the current billet weight, and transmits the data to the billet online cutting system. The self-learning system consists of an industrial control computer and a deep learning server. Based on the optimal data of the number of bars produced under the billet weight, it continuously accumulates the relationship between the weight and length of different steel grades under different casting speeds, accumulates the actual length obtained under different temperatures and different furnace temperatures, calculates the weight deviation value of hot and cold billets, and then guides the continuous casting process to carry out production.

2. The system for automatically increasing the bar length accuracy according to claim 1, characterized in that, The length of the cut ends is 0.5 to 1 meter, and the weight of the cut ends is set according to the number of shears used in the rolling process.

3. A method for automatically improving the length accuracy of bar stock, characterized in that, Follow these steps to achieve the following: 1) The self-feedback adjustment system provides the cutting value to the online billet cutting system based on the theoretical billet weight value. The continuous casting process then performs continuous casting production based on the data provided by the self-feedback adjustment system. 2) The self-feedback adjustment system determines whether the negative deviation value of the current finished bar sample is within the control range based on the data uploaded by the negative deviation measurement system. If it is not within the control range, the system feedback requires manual adjustment and re-judgment. If it is within the control range and the material shape and size are stable, the tail steel length is measured. If it reaches the design value, the steelmaking process produces according to the current billet weight value of the rolling process. If the tail steel is too long or too short, the self-feedback adjustment system determines the excess length weight or the length that needs to be supplemented based on the actual weight of the billet cutting. The steelmaking casting machine modifies the cutting length of the billet online cutting system online based on the data from the self-feedback adjustment system to change the weight and supply it to the rolling process for production. 3) Based on the quantity of finished bars of fixed length obtained from rolling, the self-learning system collects relevant information on cutting length and weight, as well as information on the weight of billets entering the furnace, accumulates information on the relationship between length, temperature and weight changes, optimizes the self-feedback adjustment system to change the adjustment accuracy of billet weight, and then continuously optimizes and adjusts the billet weight accuracy.

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