A high performance hot working die steel and its manufacturing process

Abstract

The present invention relates to the technical field of metal material processing, and specifically discloses a high-performance hot working die steel and a manufacturing process thereof. The process comprises obtaining dimensional data of a steel ingot, processing the data to obtain a deformation score of the steel ingot, and evaluating abnormal conditions of the steel ingot; obtaining temperature distribution data and stress distribution data during a heat treatment process; comprehensively analyzing the temperature distribution data and stress distribution data based on the abnormal conditions of the steel ingot to obtain an impact score; marking the steel ingot based on the impact score, and adjusting production process parameters accordingly; the present invention realizes dynamic optimization of the heat treatment process parameters by real-time monitoring and precise control of dimensional changes and internal stress distribution of the steel ingot during heat treatment, thereby effectively improving the dimensional accuracy of the steel ingot, reducing deformation during heat treatment, and ultimately improving the overall performance and service life of the die steel.

Description

Technical Field

[0001] The present invention relates to the technical field of metal material processing, in particular to a high-performance hot working die steel and a manufacturing process thereof. Background Art

[0002] In modern manufacturing, mold steel serves as the base material for producing various precision components, and its quality directly affects the performance and lifespan of the final product. In particular, in the automotive, aerospace, and precision machinery industries, extremely high requirements are placed on the dimensional accuracy, strength, and toughness of mold steel. However, during the heat treatment process of the mold steel ingot, factors such as uneven temperature distribution and internal stress often cause the ingot to deform to varying degrees, thus affecting product quality and service life. Traditional heat treatment processes often lack effective monitoring methods and are unable to accurately predict and control the deformation of the ingot during the heat treatment process. This has become one of the key factors restricting the improvement of mold steel quality.

[0003] At present, most heat treatment processes on the market only focus on temperature control, but pay insufficient attention to the changes in internal stress of steel ingots and their impact on deformation. In addition, traditional heat treatment process parameters are usually set based on experience and lack scientific basis. It is difficult to make personalized adjustments for different types of mold steels and it is difficult to adapt to the modern manufacturing industry's demand for high-quality mold steel. Therefore, how to accurately control the deformation of steel ingots during the heat treatment process and improve the overall performance and service life of mold steel has become a technical problem that needs to be solved urgently.

[0004] To address these issues, existing technical solutions have mostly focused on improving heat treatment equipment and process parameters, such as reducing temperature gradients by optimizing heating and cooling rates, or employing specialized cooling media to reduce thermal stress. While these methods can improve the performance of steel ingots to a certain extent, they still present several limitations in practical applications, such as the inability to accurately predict the deformation of the steel ingot during heat treatment, and the inability to detect and resolve potential heat treatment issues at an early stage. Therefore, there is an urgent need for a new technology that can monitor the dimensional changes and internal stress distribution of steel ingots in real time during heat treatment, so that timely measures can be taken to optimize the heat treatment process and ensure high-quality output of mold steel. Summary of the Invention

[0005] The purpose of the present invention is to provide a high-performance hot working die steel and its manufacturing process, which improves the overall performance and service life of the die steel by monitoring and controlling the deformation of the steel ingot during the heat treatment process, as well as the temperature and stress distribution during the heat treatment process. First, before the heat treatment of the steel ingot, its original dimensional data, including length, width, height, aperture length and slot width, are collected as reference data; then, after the heat treatment, these dimensional data are measured again and compared with the original data to calculate the dimensional deviation value of each part, further calculate the deformation ratio of each part, and perform weighted calculation according to pre-set weights to obtain the deformation score of the steel ingot. If the deformation score exceeds a preset threshold, the ingot is considered to have experienced an anomaly during the heat treatment process and requires further inspection and treatment. Temperature sensors and strain gauges are deployed inside the mold to monitor the temperature and stress distribution in different areas in real time. Through statistical analysis of this data, the temperature and stress influence factors are calculated and weighted according to a pre-set proportional factor to obtain an impact score. If the impact score exceeds the preset threshold, it indicates that the ingot has problems such as uneven heating or excessive internal stress during the heat treatment process. Ingots with an impact score exceeding the preset threshold are marked for the first time and their deformation score is continuously monitored. If the deformation score also exceeds the preset threshold, a second marking is performed. Within a heat treatment process cycle, the ratio of the number of ingots marked for the second time to the total number of marked ingots is calculated, which is the heat treatment process impact anomaly ratio. If this ratio exceeds the preset threshold, the heat treatment process parameters are considered to have a significant impact on the deformation of the ingot and need to be optimized. Optimization measures include adjusting the heating and cooling rates to reduce temperature gradients and thermal stresses while ensuring a more uniform temperature distribution. Through simulation and experimentation, the appropriate heating rate and cooling rate are found until the abnormal ratio of the heat treatment process of the steel ingot is reduced to below the preset threshold.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A manufacturing process for high-performance hot working die steel comprises the following steps:

[0008] The raw materials are added to the electric arc furnace for smelting, and then passed through the ladle refining furnace and vacuum degassing furnace for secondary refining; then they are remelted by electroslag and cast into steel ingots;

[0009] The weight percentages of the elements in the raw materials are as follows: C: 0.35-0.42%, Si: 0.20-0.50%, Mn: 0.30-0.80%, Cr: 4.90-5.50%, Mo: 1.70-2.00%, V: 0.40-0.80%, Ni≤0.25;

[0010] Performing pressure processing on the steel ingot and heat treating the press-processed steel ingot to obtain die steel;

[0011] During the heat treatment process,

[0012] S1: Obtaining the dimensional data of the steel ingot, processing the dimensional data of the steel ingot to obtain the deformation score of the steel ingot, and evaluating the abnormality of the steel ingot;

[0013] Among them, the dimension data includes ingot length, ingot width, ingot height, aperture length and slot width;

[0014] S2: Acquire the temperature distribution data and stress distribution data of the steel ingot during the heat treatment process of the steel ingot;

[0015] S3: Based on the abnormal conditions of the steel ingot, the temperature distribution data and stress distribution data of the steel ingot are comprehensively analyzed to obtain the impact score and judge the abnormal conditions of the heat treatment process of the steel ingot;

[0016] S4: Based on the abnormal conditions during the heat treatment of the steel ingot, the steel ingot is marked, and according to the marking results, the abnormality ratio of the heat treatment process is obtained, and an impact degree signal is generated;

[0017] S5: Based on the impact degree signal, the production process parameters of the marked steel ingot are adjusted.

[0018] As a further solution of the present invention: the process of obtaining the deformation score εi is:

[0019] The weight of the ingot length deformation ratio Li is w1, the weight of the ingot width deformation ratio Si is w2, the weight of the ingot height deformation ratio Hi is w3, the weight of the aperture length Ai deformation ratio is w4, and the weight of the slot width deformation ratio Bi is w5.

[0020] As a further solution of the present invention: performing difference calculation on the dimensional data of the steel ingot after heat treatment and the dimensional data of the steel ingot before heat treatment, and taking the absolute value to obtain the dimensional data deviation value;

[0021] The deviation value of the dimensional data is compared with the dimensional data before heat treatment to obtain the ingot length deformation ratio Li, ingot width deformation ratio Si, ingot height deformation ratio Hi, aperture length Ai deformation ratio and slot width deformation ratio Bi, where i represents the number of collected ingots, i=1, 2,..., n.

[0022] As a further solution of the present invention: the process of obtaining the influence score γi is:

[0023] Analyze the temperature and stress data of each area of ​​the ingot to obtain the temperature influence factor τi and stress influence factor ρi;

[0024] The weight of the temperature influence factor τi is assigned as Wa, and the weight of the stress influence factor ρi is assigned as Wb.

[0025] As a further solution of the present invention: the process of obtaining the temperature influence factor τi is:

[0026] After the third preheating, obtain the temperature value of each area, which is recorded as Tij;

[0027] Wherein, j represents the number of regions divided by the i-th ingot, j = 1, 2, ..., m;

[0028] Sort the temperature values ​​Tij of all regions in order of size, and integrate the temperature values ​​Tij of all regions into a temperature value group.

[0029] As a further solution of the present invention: the process of obtaining the stress influence factor ρi is:

[0030] After the third preheating, the stress value of each area is obtained and recorded as Fij;

[0031] Sort the stress values ​​Fij of all regions in order of size, and subtract the j-1 stress value from the j-th stress value to obtain the stress deviation value ΔFi j-1 ; Integrate all stress deviation values ​​to obtain a stress deviation value group.

[0032] As a further solution of the present invention: if the influence score γi of the steel ingot is greater than or equal to the preset influence score threshold during the heat treatment process, the steel ingot is marked for the first time to obtain the deformation score of each steel ingot, and based on the steel ingot marked for the first time, if the deformation score εi of the steel ingot is greater than or equal to the preset deformation score threshold of the steel ingot, the steel ingot is marked for the second time.

[0033] As a further solution of the present invention: the total number of secondary marked steel ingots and the total number of all marked steel ingots are ratio-calculated to obtain the heat treatment process impact abnormality ratio;

[0034] comparing the heat treatment process impact abnormality ratio with a preset heat treatment process impact abnormality ratio threshold;

[0035] If the heat treatment process impact abnormality ratio is greater than or equal to the preset heat treatment process impact abnormality ratio threshold, a high impact signal is generated; otherwise, a low impact signal is generated.

[0036] As a further solution of the present invention: based on the influence degree signal, the heating rate is increased and the temperature gradient is reduced; the cooling rate is controlled to reduce stress concentration during the cooling process, and the stress influence factor ρi and the temperature influence factor τi are re-obtained under the new heating rate and cooling rate; and the influence score γi and the deformation score εi of the steel ingot are calculated. If the deformation score εi of the steel ingot is still greater than the threshold, the adjustment step is repeated.

[0037] A high-performance hot working die steel is prepared by the above-mentioned production process.

[0038] Beneficial effects of the present invention:

[0039] (1) The present invention introduces a comprehensive and sophisticated dimensional detection system, which can effectively monitor the dimensional changes of steel ingots during heat treatment. By accurately measuring key dimensions such as length, width, height, aperture length and slot width of the steel ingot before and after heat treatment, and using advanced data analysis technology to calculate the deformation fraction, the present invention can timely detect abnormal deformation that may occur during the heat treatment of the steel ingot. This precise dimensional control technology greatly improves the dimensional accuracy of mold steel products and avoids product failures caused by dimensional changes during heat treatment. The improvement in dimensional accuracy means that mold steel products can better meet customer requirements for accuracy in subsequent applications, which is particularly important for the manufacture of precision parts. In addition, since potential problems can be discovered and resolved at an early stage, production costs and scrap rates can be significantly reduced, thereby improving the economic and social benefits of the enterprise.

[0040] (2) The present invention realizes the effective optimization of heat treatment process parameters by accurately monitoring the temperature distribution and stress distribution during the heat treatment of steel ingots. By acquiring the temperature and stress data of each area in real time, the temperature influence factor and stress influence factor are calculated, and then the influence score is obtained by weighted calculation in combination with the preset proportional factor. This innovative method not only helps to timely discover problems such as uneven temperature and excessive stress that may exist in the heat treatment process, but also optimizes the heat treatment process by continuously adjusting parameters such as heating rate and cooling rate, ensuring that the steel ingot obtains a good temperature distribution during the heat treatment process, reducing thermal stress, and ultimately achieving the effect of reducing deformation. This optimization measure can not only improve the quality of mold steel, but also reduce energy consumption and improve production efficiency, thereby producing a dual positive impact in terms of environmental protection and economic benefits.

[0041] (3) The present invention effectively improves the heat treatment effect of steel ingots through a series of scientific detection methods and process optimization measures, thereby improving the overall performance and service life of mold steel. By strictly controlling the temperature distribution and stress distribution during the heat treatment process, not only can the deformation of the steel ingot be reduced, but also its fatigue resistance and wear resistance can be enhanced, which is directly related to the reliability and durability of the mold during actual use. The improvement of mold steel performance means that it can withstand higher workloads and more complex processing conditions, which is of great significance for extending the service life of the mold and reducing maintenance costs. In addition, the technical solution of the present invention also helps to reduce the frequency of mold replacement and reduce the risk of downtime and production interruption caused by mold failure, which is crucial to improving the overall efficiency of the production line. Therefore, the use of the technical solution of the present invention can significantly improve the overall performance and service life of mold steel, bringing long-term competitive advantages to manufacturers. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below with reference to the accompanying drawings.

[0043] Figure 1 This is a flowchart of the specific steps of the manufacturing process of a high-performance hot working die steel of the present invention;

[0044] Figure 2 This is a flowchart of the quality judgment standard of mold steel in the manufacturing process of high-performance hot working mold steel of the present invention;

[0045] Figure 3 The present invention provides a flow chart of a standard for determining heat treatment signals in a manufacturing process for high-performance hot working die steel. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0047] See also Figure 1 As shown, the present invention is a manufacturing process for high-performance hot working die steel, comprising the following steps:

[0048] The raw materials are added to the electric arc furnace for smelting, and then passed through the ladle refining furnace and vacuum degassing furnace for secondary refining; then electroslag remelting is performed and the steel ingots are cast;

[0049] Performing pressure processing on the steel ingot and heat treating the press-processed steel ingot to obtain die steel;

[0050] During the heat treatment process, the deformation of the steel ingot has a great influence on the quality of the product. Based on this:

[0051] During the heat treatment process, the dimensional data of the steel ingot is obtained, the dimensional data of the steel ingot is processed to obtain the deformation score of the steel ingot, and the abnormality of the steel ingot is evaluated;

[0052] Among them, the dimension data includes ingot length, ingot width, ingot height, aperture length and slot width;

[0053] Obtaining temperature distribution data and stress distribution data of steel ingots during heat treatment of steel ingots;

[0054] Based on the abnormal situation of the steel ingot, the temperature distribution data and stress distribution data of the steel ingot are comprehensively analyzed to obtain the influence score, and the degree to which the deformation score of the steel ingot is affected by the temperature distribution data and stress distribution number is evaluated;

[0055] Based on the degree of influence, the steel ingots are marked, and according to the marking results, the production process parameters of the marked steel ingots are adjusted.

[0056] The weight percentages of the elements in the mold steel are as follows: C: 0.35-0.42%, Si: 0.20-0.50%, Mn: 0.30-0.80%, Cr: 4.90-5.50%, Mo: 1.70-2.00%, V: 0.40-0.80%, Ni≤0.25;

[0057] Example 2

[0058] See also Figure 2 As shown, based on Example 1, under the same mold steel production process, within the mold steel production cycle, the dimensional data of all steel ingots are collected and analyzed to obtain the ingot deformation abnormality value, and the qualification of the mold steel production is identified;

[0059] The dimensional data include ingot length, ingot width, ingot height, aperture length and slot width;

[0060] Specifically:

[0061] Collect the pre-heat treatment dimensional data of all steel ingots before the heat treatment process, including original ingot length, original ingot width, original ingot height, original aperture length and original slot width;

[0062] Collect the dimensional data of all steel ingots after heat treatment, including final ingot length, final ingot width, final ingot height, final aperture length and final slot width;

[0063] Calculate the difference between the size data of the steel ingot after treatment and the size data of the steel ingot before heat treatment, and take the absolute value to obtain the size data deviation value, including the steel ingot length deviation value, the steel ingot width deviation value, the steel ingot height deviation value, the aperture length deviation value and the slot width deviation value;

[0064] The deviation value of the dimensional data is compared with the dimensional data before heat treatment to obtain the ingot length deformation ratio Li, ingot width deformation ratio Si, ingot height deformation ratio Hi, aperture length deformation ratio Ai, and slot width deformation ratio Bi, where i represents the number of collected ingots, i=1, 2, ..., n;

[0065] The weight of the ingot length deformation ratio Li is assigned to w1, the weight of the ingot width deformation ratio Si is assigned to w2, the weight of the ingot height deformation ratio Hi is assigned to w3, the weight of the aperture length Ai deformation ratio is assigned to w4, and the weight of the slot width deformation ratio Bi is assigned to w5;

[0066] Among them, w1+w2+w3+w4+w5=1;

[0067] The deformation fraction εi of the steel ingot is calculated by the formula εi=Li*w1+Si*w2+Hi*w3+Ai*w4+Bi*w5;

[0068] Comparing the deformation score εi of the steel ingot with a preset deformation score threshold of the steel ingot;

[0069] If the deformation fraction εi of the steel ingot is greater than or equal to the preset deformation fraction threshold of the steel ingot, it means that an abnormality occurs during the heat treatment of the corresponding steel ingot, and a steel ingot abnormality signal is generated;

[0070] If the deformation fraction εi of the steel ingot is less than the preset deformation fraction threshold of the steel ingot, it means that the corresponding steel ingot heat treatment process is normal, and a steel ingot normal signal is generated;

[0071] It should be noted that: before and after heat treatment, ensure that the same measurement methods and tools are used to ensure the accuracy of the data; the deformation fraction εi of the steel ingot is a factor in judging the quality of the steel ingot. The larger the deformation fraction εi of the steel ingot, the more serious the deformation of the steel ingot.

[0072] Among them, the heat treatment process includes:

[0073] Stress relief tempering: a vacuum stress relief tempering treatment is carried out in a vacuum tempering furnace to eliminate processing stress;

[0074] Vacuum quenching: According to the complexity and size of the steel ingot, 2 to 3 preheating steps are selected, followed by austenitization and heating, and finally cooling. When the surface temperature of the steel ingot cools to 120°C, it is taken out of the furnace and air-cooled.

[0075] Tempering: The number of tempering times is greater than or equal to 4 times. The tempering temperature is 570℃ for the first time. The subsequent tempering temperatures are determined according to the required hardness. Before the second, third and fourth tempering, the steel ingot must be cooled to room temperature before entering the furnace for tempering.

[0076] Example 3

[0077] See also Figure 3 As shown, based on Example 2, during the heat treatment process of the steel ingot, due to the uneven heating and large internal stress of the steel ingot during heating, the final mold steel will be deformed. A comprehensive analysis of the uneven heating and large internal stress during the heat treatment process of the steel ingot is performed to facilitate the subsequent control of temperature and stress in the heat treatment process of the steel ingot, thereby optimizing the heat treatment production process, thereby improving the overall performance and service life of the mold steel; in the same heat treatment process, the same batch of mold steel is produced, and the temperature data and stress data of this batch of mold steel are collected, and the temperature influence factor and stress influence factor are obtained based on the temperature data and stress data to evaluate the internal heating condition and stress degree of the steel ingot during the heat treatment process;

[0078] Specifically:

[0079] During a heat treatment process, the temperature and stress data of the steel ingots of the same batch of mold steel are collected;

[0080] The temperature data of the hot steel ingot is monitored in real time through temperature sensors, and each temperature sensor is calibrated to ensure the accuracy of the collected data. The stress changes inside the steel ingot are monitored in real time and recorded;

[0081] Divide each steel ingot into several identical areas;

[0082] Collect temperature data for each area separately;

[0083] After the third preheating, obtain the temperature value of each area, which is recorded as Tij;

[0084] Wherein, j represents the number of regions divided by the i-th ingot, j = 1, 2, ..., m;

[0085] Sort the temperature values ​​Tij of all regions in order of size, and integrate the temperature values ​​Tij of all regions into a temperature value group;

[0086] The average temperature value Tiy in the temperature value group is calculated by the mean calculation formula;

[0087] The standard deviation value αi within the temperature value group is calculated using the standard deviation calculation formula;

[0088] By formula The temperature influence factor τi is calculated;

[0089] Wherein, b1 and b2 are preset scaling factors, and both b1 and b2 are greater than 0;

[0090] Obtain the stress value of each area separately;

[0091] After the third preheating, the stress value of each area is obtained and recorded as Fij;

[0092] Sort the stress values ​​Fij of all regions in order of size, and subtract the j-1 stress value from the j-th stress value to obtain the stress deviation value ΔFi j-1 ; Integrate all stress deviation values ​​to obtain a stress deviation value group;

[0093] The average value ΔFiy within the stress deviation value group is calculated using the mean calculation formula;

[0094] The standard deviation value βi within the stress deviation value group is calculated using the standard deviation calculation formula;

[0095] By formula The stress influence factor ρi is calculated;

[0096] Wherein, b3 and b4 are preset scaling factors, and both b3 and b4 are greater than 0;

[0097] Process the temperature influence factor τi and stress influence factor ρi;

[0098] The weight of the temperature influence factor τi is assigned to Wa, and the weight of the stress influence factor ρi is assigned to Wb, where Wa+Wb=1;

[0099] The influence score γi is calculated by the formula γi=τi*Wa+ρi*Wb;

[0100] Compare the influence score γi with the preset influence score threshold;

[0101] If the influence score γi is greater than or equal to the preset influence score threshold, it means that the internal stress level of the corresponding steel ingot during the heat treatment process is abnormal, which may cause the steel ingot to deform; a heat treatment abnormality signal is generated;

[0102] If the influence score γi is greater than or equal to the preset influence score threshold, it means that the internal heat stress level of the corresponding steel ingot is normal during the heat treatment process, and the steel ingot will not be deformed; a heat treatment normal signal is generated;

[0103] It should be noted that the influence fraction γi is a criterion for judging whether the heating of the steel ingot is uneven and the internal stress is large during heating. The larger the influence fraction γi is, the greater the influence of the heating and stress conditions during the heat treatment of the steel ingot on the quality of the steel ingot.

[0104] Example 4

[0105] On the basis of Example 3, during the heat treatment process, if the ingot influence score γi is greater than or equal to the preset influence score threshold, the ingot is marked for the first time, and the deformation score of each ingot is obtained. Based on the ingot marked for the first time, if the deformation score εi of the ingot is greater than or equal to the preset deformation score threshold of the ingot, the ingot is marked for the second time;

[0106] In one heat treatment production process cycle, the total number of secondary marked steel ingots is obtained, and the total number of all marked steel ingots is obtained;

[0107] The heat treatment process influence abnormality ratio is calculated by calculating the ratio of the total number of secondary marked steel ingots to the total number of all marked steel ingots;

[0108] comparing the heat treatment process impact abnormality ratio with a preset heat treatment process impact abnormality ratio threshold;

[0109] If the heat treatment process influence abnormality ratio is greater than or equal to the preset heat treatment process influence abnormality ratio threshold, it indicates that the heat treatment process parameter abnormality has a high influence on the deformation degree of the steel ingot in the corresponding heat treatment process, and a high influence signal is generated;

[0110] If the heat treatment process influence abnormality ratio is less than the preset treatment process influence abnormality ratio threshold, it indicates that the heat treatment process parameter abnormality has a low influence on the deformation degree of the steel ingot in the corresponding heat treatment process, and a low influence signal is generated;

[0111] It should be noted that heat treatment process parameters are the temperature control process and stress control process during the heat treatment process; adjusting the heat treatment process parameters and finding the best heat treatment working environment can effectively improve the quality of mold steel, improve production efficiency and save raw materials.

[0112] Based on the high-influence signals, the heat treatment process parameters are optimized;

[0113] Specifically:

[0114] Increasing the heating rate can reduce temperature gradients and help achieve a more uniform temperature distribution. However, excessively fast heating rates can increase internal stress, so a balance needs to be found. The original heating rate of X degrees per minute is adjusted to Y degrees per minute (Y>X). The temperature rise process is adjusted based on historical normal temperature data and multiple simulations within the normal temperature data. After the temperature adjustment process, controlling the cooling rate can reduce stress concentration during the cooling process. A slower cooling rate helps reduce thermal stress, but may extend the entire heat treatment cycle. For example, different cooling media (such as oil cooling, air cooling, etc.) or adjusting the cooling air speed can achieve different cooling rates.

[0115] The stress influence factor ρi and temperature influence factor τi are re-obtained under the new heating rate and cooling rate; and the influence fraction γi and the deformation fraction εi of the steel ingot are calculated. If the deformation fraction εi of the steel ingot is still greater than the threshold, the adjustment steps are repeated until the situation is found where the abnormal ratio of the influence of the heat treatment process of the steel ingot is less than the preset abnormal ratio threshold of the influence of the treatment process.

[0116] It should be noted that by adjusting the heat treatment process parameters and repeating the adjustment operation steps, the heat treatment process can be gradually optimized until the optimal heat treatment process is found, ensuring that the steel ingot obtains better temperature distribution during the heat treatment process, reducing thermal stress and deformation, thereby improving the overall performance and service life of the mold.

[0117] Example 5

[0118] A high-performance hot working die steel is prepared by the above-mentioned production process.

[0119] The working principle of the present invention is to improve the overall performance and service life of the steel ingot by monitoring and controlling the deformation of the steel ingot during the heat treatment process, as well as the temperature and stress distribution during the heat treatment process. First, before the heat treatment of the steel ingot, its original dimensional data, including length, width, height, aperture length and slot width, are collected as benchmark data. Subsequently, after the heat treatment, these dimensional data are measured again and compared with the original data to calculate the dimensional deviation value of each part, and further calculate the deformation ratio of each part, and perform weighted calculation according to the pre-set weights to obtain the deformation score of the steel ingot. If the deformation score exceeds the preset threshold, it is considered that the steel ingot has an abnormality during the heat treatment process and requires further inspection and processing.

[0120] In order to more accurately grasp the temperature and stress changes during the heat treatment of the steel ingot, the present invention also deploys temperature sensors and strain gauges inside the mold to monitor the temperature and stress distribution in different areas in real time. Through statistical analysis of these data, the temperature influence factor and stress influence factor are calculated, and a weighted calculation is performed according to a pre-set proportional factor to obtain an impact score. If the impact score exceeds the preset threshold, it means that the steel ingot has problems such as uneven heating or excessive internal stress during the heat treatment process, which may cause the steel ingot to deform.

[0121] Ingots with an impact score exceeding a preset threshold are initially flagged, and their deformation scores are continuously monitored. If the deformation score also exceeds the preset threshold, they are flagged again. Within a heat treatment cycle, the ratio of the number of ingots with secondary flagging to the total number of flagged ingots is calculated, representing the heat treatment process impact anomaly ratio. If this ratio exceeds a preset threshold, it is assumed that the heat treatment process parameters have a significant impact on the ingot's deformation and require optimization.

[0122] Optimization measures include adjusting the heating and cooling rates to reduce temperature gradients and thermal stresses while ensuring a more uniform temperature distribution. Through simulation and testing, the appropriate heating and cooling rates are found until the abnormality ratio of the ingot's heat treatment process is reduced to below a preset threshold. This not only effectively controls the deformation of the ingot but also improves its overall performance and service life, thereby achieving the goal of improving the ingot's quality. The application of this manufacturing process can significantly improve the product quality and production efficiency of hot-working steel ingots, and has important practical significance for the precision mold manufacturing industry.

[0123] The formulas in the present invention are all dimensionless and numerically calculated. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0124] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A manufacturing process for high performance hot working die steel, characterized in that: The following steps are involved: The raw materials are added to the electric arc furnace for melting, and then passed through the ladle refining furnace and vacuum degassing furnace for secondary refining; Then it is electroslag remelted and cast into steel ingots; The weight percentages of the elements in the raw materials are as follows: C: 0.35-0.42%, Si: 0.20-0.50%, Mn: 0.30-0.80%, Cr: 4.90-5.50%, Mo: 1.70-2.00%, V: 0.40-0.80%, Ni≤0.25; Performing pressure processing on the steel ingot and heat treating the press-processed steel ingot to obtain die steel; During the heat treatment process, the dimensional data of the steel ingot is obtained, and the dimensional data of the steel ingot is processed to obtain the deformation score of the steel ingot, and the abnormality of the steel ingot is evaluated; the process of obtaining the deformation score εi is as follows: The weight of the ingot length deformation ratio Li is assigned to w1, the weight of the ingot width deformation ratio Si is assigned to w2, the weight of the ingot height deformation ratio Hi is assigned to w3, the weight of the aperture length Ai deformation ratio is assigned to w4, and the weight of the slot width deformation ratio Bi is assigned to w5; Among them, w1+w2+w3+w4+w5=1; The deformation fraction εi of the steel ingot is calculated by the formula εi=Li*w1+Si*w2+Hi*w3+Ai*w4+Bi*w5; Among them, the dimension data includes ingot length, ingot width, ingot height, aperture length and slot width; Obtaining temperature distribution data and stress distribution data of steel ingots during heat treatment of steel ingots; Based on the abnormal conditions of the steel ingot, the temperature distribution data and stress distribution data of the steel ingot are comprehensively analyzed to obtain the impact score and judge the abnormal conditions of the steel ingot heat treatment process; The process of obtaining the influence score γi is: Analyze the temperature and stress data of each area of ​​the ingot to obtain the temperature influence factor τi and stress influence factor ρi; The weight of the temperature influence factor τi is assigned as Wa, and the weight of the stress influence factor ρi is assigned as Wb, where Wa+Wb=1; The influence score γi is calculated by the formula γi=τi*Wa+ρi*Wb; The process of obtaining the temperature influence factor τi is: After the third preheating, obtain the temperature value of each area, which is recorded as Tij; Wherein, j represents the number of regions divided by the i-th ingot, j = 1, 2, ..., m; Sort the temperature values ​​Tij of all regions in order of size, and integrate the temperature values ​​Tij of all regions into a temperature value group; The average temperature value Tiy in the temperature value group is calculated by the mean calculation formula; the standard deviation value αi in the temperature value group is calculated by the standard deviation calculation formula; By formula The temperature influence factor τi is calculated; Wherein, αi is the standard deviation value, Tiy is the average temperature value, b1 and b2 are preset scale factors, and b1 and b2 are both greater than 0; The process of obtaining the stress influence factor ρi is: After the third preheating, the stress value of each area is obtained and recorded as Fij; Sort the stress values ​​Fij of all regions in order of size, and subtract the j-1 stress value from the j-th stress value to obtain the stress deviation value ΔFi j-1 ; Integrate all stress deviation values ​​to obtain a stress deviation value group; The mean value ΔFiy within the stress deviation value group is calculated by the mean calculation formula; the standard deviation value βi within the stress deviation value group is calculated by the standard deviation calculation formula; By formula The stress influence factor ρi is calculated; Wherein, ΔFiy is the average value, βi is the standard deviation, b3 and b4 are preset proportional factors, and both b3 and b4 are greater than 0; Based on the abnormal conditions during the heat treatment of the steel ingot, the steel ingot is marked. According to the marking results, the abnormality ratio of the heat treatment process is obtained and an impact degree signal is generated. If the influence score γi of the steel ingot is greater than or equal to the preset influence score threshold during the heat treatment process, the steel ingot is marked for the first time to obtain the deformation score of each steel ingot. If the deformation score εi of the steel ingot is greater than or equal to the preset deformation score threshold of the steel ingot based on the first marked steel ingot, the steel ingot is marked for the second time; The heat treatment process influence abnormality ratio is calculated by calculating the ratio of the total number of secondary marked steel ingots to the total number of all marked steel ingots; S5: Based on the impact degree signal, the production process parameters of the marked steel ingot are adjusted.

2. The manufacturing process of a high performance hot working die steel according to claim 1, characterized in that: Calculate the difference between the dimensional data of the steel ingot after heat treatment and the dimensional data of the steel ingot before heat treatment, and take the absolute value to obtain the dimensional data deviation value; The deviation value of the dimensional data is compared with the dimensional data before heat treatment to obtain the ingot length deformation ratio Li, ingot width deformation ratio Si, ingot height deformation ratio Hi, aperture length Ai deformation ratio and slot width deformation ratio Bi, where i represents the number of collected ingots, i=1, 2,..., n.

3. The manufacturing process of a high performance hot working die steel according to claim 1, characterized in that: comparing the heat treatment process impact abnormality ratio with a preset heat treatment process impact abnormality ratio threshold; If the heat treatment process impact abnormality ratio is greater than or equal to the preset heat treatment process impact abnormality ratio threshold, a high impact signal is generated; otherwise, a low impact signal is generated.

4. The manufacturing process of a high performance hot working die steel according to claim 1, characterized in that: Based on the high-influence signal, the heating rate is increased and the temperature gradient is reduced; the cooling rate is controlled to reduce stress concentration during the cooling process, and the stress influence factor ρi and temperature influence factor τi are re-obtained under the new heating and cooling rates; and the influence score γi and the deformation score εi of the ingot are calculated. If the deformation score εi of the ingot is still greater than the threshold, the adjustment steps are repeated.

5. A high performance hot working die steel, characterized in that: The high-performance hot working die steel is prepared by the production process described in any one of claims 1 to 4.

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