A controlled process for making hard tungsten copper alloys

Abstract

The present application belongs to the technical field of tungsten copper alloy control process, and provides a control process for preparing hard tungsten copper alloy, comprising: in the process of preparing tungsten copper alloy, collecting powder loose bulk density data and environmental humidity data in real time, and obtaining abnormality coefficient by analyzing the data through the set first sliding window; if the abnormality coefficient exceeds the abnormality coefficient limit value, triggering the correlation judgment process of powder loose bulk density and environmental humidity; the correlation judgment process is to determine the lag order and correlation degree coefficient of the influence of environmental humidity on loose bulk density by cross-correlation analysis; if the correlation judgment result determines that the abnormality of the loose bulk density of the powder is caused by environmental humidity, then the drying compensation process is executed. Thus, the abnormality of the loose bulk density of the powder caused by humidity can be identified in time, the humidity interference can be compensated accurately, the forming pressure can be stabilized, the preparation quality of the tungsten copper alloy can be effectively improved, and the problem of unstable alloy performance caused by the fluctuation of the loose bulk density can be reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of tungsten-copper alloy control process, in particular to a control process for preparing hard tungsten-copper alloy. BACKGROUND

[0002] In the field of preparing hard tungsten-copper alloy, a powder metallurgy process is usually adopted, in which the bulk density of powder is a key parameter affecting the forming pressure, and abnormal change of the bulk density will directly lead to pressure fluctuation in the subsequent forming process, thereby affecting the density and mechanical properties of the alloy.

[0003] In the prior art, the monitoring and control means for the bulk density of powder are relatively rough, especially when the environmental humidity changes, there is a lack of analysis and dynamic compensation mechanism for the correlation between humidity and bulk density, when the environmental humidity fluctuates, the powder particles are easy to adsorb water, leading to volume expansion or gap filling of the particles, thereby causing abnormal bulk density, but the traditional process cannot accurately identify the influence path of humidity on the bulk density, and cannot adjust the process parameters in time according to the change of humidity, ultimately leading to unstable quality of alloy preparation.

[0004] In addition, when using ultra-fine powder (such as D50<1μm), the defects of the prior art are more prominent: the ultra-fine powder is easy to agglomerate due to its large specific surface area and high surface energy, which leads to a significant increase in the fluctuation range of the bulk density, and the drying process after moisture absorption is more lagging, the traditional process uses a unified monitoring window and compensation threshold, which cannot adapt to the characteristics of easy agglomeration and slow response of ultra-fine powder, and often leads to reduced production efficiency and product yield due to insufficient compensation in time or strength.

[0005] Therefore, the application provides a control process for preparing hard tungsten-copper alloy. SUMMARY

[0006] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0007] The technical solution adopted by the application to solve the technical problems is: a control process for preparing hard tungsten-copper alloy, comprising:

[0008] In the process of preparing tungsten-copper alloy, the bulk density data of powder and the environmental humidity data are collected in real time, and the abnormal coefficient is obtained by analyzing the data through the set first sliding window;

[0009] If the abnormal coefficient exceeds the abnormal coefficient limit value, the correlation judgment process of the bulk density of powder and the environmental humidity is triggered;

[0010] The correlation judgment process is to determine the lag order and correlation degree coefficient of the bulk density affected by the environmental humidity by using cross-correlation analysis;

[0011] If the correlation determination result determines that the abnormal bulk density of the powder is caused by the environmental humidity, a drying compensation process is performed.

[0012] As a further scheme of the present application, the abnormal coefficient is obtained by:

[0013] In the first sliding window, a time point at which the bulk density value is greater than the bulk density limit value is extracted as an exceeding time point, the number of exceeding time points is counted, and a ratio of the number of exceeding time points to the total number of data in the first sliding window is taken as an exceeding proportion value;

[0014] In the first sliding window, the maximum value and the minimum value corresponding to the bulk density value data are extracted, and a difference value is calculated, and a ratio of the difference value to the minimum value of the bulk density is taken as a relative fluctuation value;

[0015] The sum of the normalized exceeding proportion value and the relative fluctuation value is taken as the abnormal coefficient.

[0016] As a further scheme of the present application, the lag order of the bulk density affected by the environmental humidity is:

[0017] Based on the labeled abnormal state sliding window, the environmental humidity data in the abnormal state sliding window and the environmental humidity data in the previous sliding window are obtained;

[0018] The humidity data and the bulk density data are normalized, and the lag k-order cross-correlation coefficient is calculated for the humidity data and the bulk density data;

[0019] The lag order corresponding to the maximum absolute value of the calculated cross-correlation coefficient is extracted, which is taken as the lag order of the humidity affecting the bulk density.

[0020] As a further scheme of the present application, the abnormal state sliding window is:

[0021] The first sliding window in which the abnormal coefficient is greater than or equal to the abnormal coefficient limit value is marked as the abnormal state sliding window.

[0022] As a further scheme of the present application, the correlation determination result determination process is:

[0023] The maximum absolute value of the cross-correlation coefficient is taken as the correlation degree coefficient;

[0024] If the correlation degree coefficient is greater than the correlation determination threshold, the abnormal bulk density is related to the environmental humidity.

[0025] As a further scheme of the present application, the drying compensation process is:

[0026] If the current time is t, the environmental humidity corresponding to the k0 previous time points of the current time is obtained as the first lag time environmental humidity combined with the lag order k0.

[0027] The difference between the first hysteresis time environment humidity and the target environment humidity, and the ratio of the difference to the target environment humidity is taken as the first environment humidity deviation ratio;

[0028] The first reference coefficient is multiplied by the abnormal coefficient, and then summed with the first reference coefficient, and the obtained value is taken as the first compensation intensity coefficient;

[0029] The product of the first compensation intensity coefficient and the first environment humidity deviation ratio is taken as the first temperature adjustment amount, and the sum of the first temperature adjustment amount and the temperature at the current time is taken as the first temperature adjustment value.

[0030] As a further scheme of the present application: further comprising: detecting the particle size of the powder when the powder is added in production, judging whether it is superfine powder through the particle size detection result, if it is superfine powder, then performing early warning analysis on the loose bulk density abnormality, and performing early control based on the early warning analysis result.

[0031] As a further scheme of the present application: the process of judging whether it is superfine powder through the particle size detection result is:

[0032] Detecting the particle size of the powder when the powder is added in production, installing an online laser particle size instrument beside the powder conveying pipeline, making the powder uniformly dispersed through airflow or vibration device and then flowing through the detection area, if , then automatically marking it as superfine powder.

[0033] As a further scheme of the present application: the process of early warning analysis is:

[0034] Setting a second sliding window; for each second sliding window, an abnormal coefficient is calculated;

[0035] If the abnormal coefficient corresponding to the second sliding window is greater than or equal to the abnormal coefficient trigger threshold, the early control process is executed.

[0036] As a further scheme of the present application: the process of performing early control is:

[0037] The hysteresis order of the humidity data and the loose bulk density data is calculated, and the hysteresis order k1 corresponding to the superfine powder is marked; and it is judged whether the environment humidity is the cause of the loose bulk density, if yes, then dry compensation is performed;

[0038] The environment humidity corresponding to the k1 time points before the current time is taken as the second hysteresis time environment humidity;

[0039] The difference between the second hysteresis time environment humidity and the target environment humidity, and the ratio of the difference to the target environment humidity is taken as the second environment humidity deviation ratio;

[0040] The second reference coefficient, the abnormal coefficient and the reinforcement factor are multiplied, and the sum of the second reference coefficient is calculated to obtain a second compensation intensity coefficient;

[0041] The product of the second compensation intensity coefficient and the second environmental humidity deviation ratio is taken as a second temperature adjustment amount, and the sum of the second temperature adjustment amount and the temperature at the current time is taken as a second temperature adjustment value.

[0042] The beneficial effects of the present application are as follows:

[0043] 1. For ordinary powder, by collecting real-time bulk density and environmental humidity data, calculating the abnormal coefficient and judging the influence of humidity on bulk density, dynamically adjusting the temperature for compensation, the abnormality of bulk density caused by humidity of ordinary powder can be identified in time, the humidity interference can be compensated accurately, the forming pressure can be stabilized, the preparation quality of tungsten-copper alloy can be effectively improved, and the problem of unstable alloy performance caused by bulk density fluctuation can be reduced.

[0044] 2. For superfine powder, after identification by particle size detection, a short sliding window is set to realize early warning, the lag order is calculated and the reinforcement factor is used to enhance the compensation strength, the problems of easy agglomeration, large bulk density fluctuation and drying response lag of superfine powder are optimized, the timeliness and effectiveness of drying compensation are improved, the stability of the production process is ensured, the production efficiency and product yield are significantly improved, and the limitations of traditional process control on superfine powder are broken through. BRIEF DESCRIPTION OF DRAWINGS

[0045] The present application will be further described below with reference to the accompanying drawings.

[0046] Figure 1 is a step flow chart of embodiment 1 of the present application;

[0047] Figure 2 is a step flow chart of embodiment 2 of the present application. DETAILED DESCRIPTION

[0048] In order to make the technical means, creative features, purposes and effects realized by the present application easy to understand, the present application will be further described below with reference to the specific embodiments.

[0049] Embodiment 1

[0050] Since the powder metallurgy process is usually adopted in the preparation of hard tungsten copper alloy, abnormal change of the bulk density of the powder will affect the forming pressure, therefore, the bulk density of the powder needs to be monitored in the preparation of the tungsten copper alloy, when the abnormal change of the bulk density of the powder occurs, we firstly think that the humidity may affect the bulk density of the powder (the increase of the humidity will make the powder particles adsorb water, the volume of the particles may expand or the water fills the gap between the particles, thus changing the bulk density), therefore, the humidity also needs to be monitored in real time in the preparation of the tungsten copper alloy, to determine whether the abnormal change of the bulk density is caused by the excessive humidity, if so, dynamic compensation control is performed;

[0051] Please refer to Figure 1 The control process for preparing the hard tungsten copper alloy comprises the following steps:

[0052] Step one: in the process of preparing the tungsten copper alloy, the bulk density data of the powder and the environmental humidity data are collected in real time, and are analyzed, if the bulk density of the powder is determined to be in an abnormal state, the correlation judgment process of the bulk density of the powder and the environmental humidity is triggered;

[0053] In this embodiment, in the process of preparing the tungsten copper alloy, the bulk density data of the powder and the environmental humidity data are collected in real time;

[0054] Further, the bulk density of the powder is obtained by real-time monitoring, which can be collected by installing an online bulk density sensor; the environmental humidity is obtained by collecting the data of a capacitive humidity sensor;

[0055] The online bulk density sensor controls the powder flow through a volumetric powder feeding device (such as a rotary feeder), and combines a weighing sensor to measure the mass of the powder in real time, to calculate the bulk density, which is the ratio of the mass in unit time to the volume in unit time; the capacitive humidity sensor uses a high polymer or metal oxide film as a humidity sensing material, the dielectric constant of which changes with the environmental humidity, and the humidity is calculated by measuring the change of the capacitance value;

[0056] A first sliding window is set, to determine whether the bulk density value is in an abnormal state in the sliding window, the first sliding window is set by the person skilled in the art according to experience and the change characteristics of the historical bulk density of the powder;

[0057] In the first sliding window, the time when the bulk density value is greater than the bulk density limit value is extracted as the exceeding time, the number of the exceeding time is counted, and the ratio of the number of the exceeding time to the total number of data in the first sliding window is taken as the exceeding ratio value;

[0058] In the first sliding window, the maximum value and the minimum value corresponding to the bulk density value data are extracted, and the difference value is calculated, and the ratio of the difference value to the minimum value of the bulk density is taken as the relative fluctuation value;

[0059] The exceeding proportion value and the relative fluctuation value are normalized, and the range of all values is mapped in the interval [0, 1], wherein the normalization method can be maximum-minimum normalization;

[0060] The sum of the normalized exceeding proportion value and the relative fluctuation value is taken as the abnormal coefficient;

[0061] Specifically, for the calculation method of the abnormal coefficient: the exceeding proportion value represents the proportion of the time when the bulk density exceeds the limit value in the sliding window, and the physical meaning is the time distribution characteristic of the bulk density exceeding the limit value, for example, the exceeding proportion value is 0.6, which means that 60% of the time in the window the bulk density exceeds the limit value, directly reflecting the time persistence of the abnormality; the relative fluctuation value reflects the relative relationship between the maximum change amplitude and the minimum value of the bulk density in the sliding window, and the physical meaning is the stability characteristic of the bulk density, for example, the relative fluctuation value is 0.5, which means that the maximum value of the bulk density is 50% higher than the minimum value, reflecting the degree of data fluctuation; the abnormal coefficient as the sum of the two, comprehensively reflects the abnormal degree of the bulk density in the sliding window, the larger the value, the more significant the abnormal performance of the bulk density in frequency and amplitude, and the more sufficient the basis for triggering the associated judgment process, so as to timely investigate the influence of humidity and other factors on the bulk density and ensure the stability of the tungsten-copper alloy preparation process;

[0062] The abnormal coefficient corresponding to the first sliding window is compared with the abnormal coefficient limit value;

[0063] If the abnormal coefficient is greater than or equal to the abnormal coefficient limit value, it means that the powder bulk density in the sliding window is abnormal, which may affect the forming pressure, so the sliding window is marked as an abnormal state sliding window, and the powder bulk density and environmental humidity associated judgment process is triggered;

[0064] If the abnormal coefficient is less than the abnormal coefficient limit value, it means that the powder bulk density in the sliding window is normal, so the sliding window is marked as a normal state sliding window;

[0065] The specific implementation process of the powder bulk density and environmental humidity associated judgment process is as follows:

[0066] Based on the marked abnormal state sliding window, the corresponding environmental humidity data in the abnormal state sliding window and the environmental humidity data in the previous sliding window are obtained, and the correlation between the powder bulk density and the environmental humidity is analyzed;

[0067] It should be noted that the change in humidity may take a certain time to affect the bulk density (such as the time required for the powder to absorb moisture and diffuse), and the lag order of the environmental humidity and the bulk density needs to be determined through cross-correlation analysis (such as the change in environmental humidity leading the change in bulk density by k time points), so as to avoid misjudging the lag correlation as immediate correlation;

[0068] Optionally, the process of cross-correlation analysis is as follows:

[0069] First of all, it should be noted that the sampling frequencies of the humidity sensor and the bulk density sensor are consistent, and the time stamps are aligned;

[0070] If the sampling frequencies of the humidity sensor and the bulk density sensor are not consistent, the data needs to be supplemented through interpolation;

[0071] The humidity data and the bulk density data are normalized so that their value ranges are mapped in the interval [0, 1];

[0072] For the humidity data and the bulk density data, the cross-correlation coefficient CFF(k) of lag k is:

[0073] ;

[0074] Wherein, k is the lag order of the environmental humidity leading the density (k>0, indicating that the humidity changes first and the density changes later), N represents the length of the abnormal state time window, t represents the time, H represents the humidity, and D represents the bulk density;

[0075] The maximum lag order of k is set to N / 2; this setting can reduce the calculation redundancy caused by too large a range;

[0076] Extract the lag order k0 corresponding to the absolute maximum value of the calculated cross-correlation coefficient CFF(k), and take it as the lag order of the influence of humidity on bulk density;

[0077] For example, if k0=6, it means that the bulk density starts to respond significantly at the 6th time point after the change in humidity;

[0078] Take the absolute maximum value of the cross-correlation coefficient CFF(k) as the correlation degree coefficient;

[0079] Compare the correlation degree coefficient with the correlation judgment threshold value, if the correlation degree coefficient is greater than the correlation judgment threshold value, then the bulk density abnormality is related to the environmental humidity, and if the correlation degree coefficient is less than or equal to the correlation judgment threshold value, then the bulk density abnormality is not related to the environmental humidity;

[0080] Step two: if the association judgment result determines that the bulk density abnormality of the powder is caused by the environmental humidity, then execute the drying compensation process;

[0081] In the present embodiment, based on the judgment result of the aforementioned step one, if the abnormal bulk density of the powder is caused by the ambient humidity, a drying compensation process is triggered;

[0082] The specific implementation process of the drying compensation process is as follows:

[0083] If the current time is t, the ambient humidity corresponding to the k0 previous time points is obtained as the first lag time ambient humidity in combination with the lag order k0;

[0084] The difference between the first lag time ambient humidity and the target ambient humidity is obtained, and the ratio of the difference to the target ambient humidity is taken as the first ambient humidity deviation ratio;

[0085] The first reference coefficient is multiplied by the abnormal coefficient, and then summed with the first reference coefficient, and the obtained value is taken as the first compensation intensity coefficient;

[0086] The first reference coefficient is a basic parameter for the first compensation intensity calculation, representing the reference compensation intensity corresponding to a unit humidity deviation or other related factors when the abnormal coefficient S=0 (i.e. no abnormality in bulk density), which is summarized and set by process experiments and the characteristics of the equipment. For example, if the humidity deviates from the target value by 1%, the reference drying temperature compensation is 1.2°C, then the first reference coefficient is 1.2;

[0087] The meaning represented by the first compensation intensity coefficient is that the larger the first abnormal coefficient, the more serious the abnormality of the bulk density of the powder, and the compensation intensity needs to be enhanced;

[0088] When performing drying compensation, the ambient humidity needs to be compensated by dynamic adjustment of the temperature;

[0089] The product of the first compensation intensity coefficient and the first ambient humidity deviation ratio is taken as the first temperature adjustment amount, and the sum of the first temperature adjustment amount and the temperature at the current time is taken as the first temperature adjustment value;

[0090] For the first temperature adjustment value calculated in this example, it needs to be explained that the first temperature adjustment value is the temperature adjustment amount set for dynamic compensation of the influence of environmental humidity on the bulk density of the powder. By changing the temperature process parameter, the disturbance of the change of environmental humidity on the bulk density of the powder is offset, so that the physical state of the powder remains stable during the preparation process, thereby maintaining the consistency of the subsequent forming process, and ultimately ensuring the preparation quality of the hard tungsten-copper alloy; the calculation of this value is based on real-time monitoring of environmental humidity, powder bulk density and other data, the correlation degree of humidity and bulk density is identified through abnormal coefficient calculation, the lag order of humidity influence is determined by cross-correlation analysis, and the humidity deviation ratio, reference coefficient and other parameters are combined to comprehensively deduce. This calculation method is based on actual monitoring data, quantifies the influence relationship between factors by means of mathematical model, dynamically responds to the effect of humidity change on bulk density, and can adjust the temperature to offset the humidity disturbance,

[0091] In the embodiment, when preparing the hard tungsten-copper alloy, step one needs to collect powder bulk density and environmental humidity data in real time, calculate the exceeding proportion value and the relative fluctuation value through the first sliding window, add the normalized values to obtain the abnormal coefficient, and if the abnormal coefficient exceeds the limit value, the correlation judgment process is triggered. The lag order and the correlation degree coefficient of the influence of humidity on bulk density are determined by cross-correlation analysis, and compared with the judgment threshold to determine whether the abnormality is caused by humidity; step two is to execute the drying compensation process when the correlation judgment determines that the abnormality is caused by humidity, obtain the environmental humidity at the corresponding time according to the lag order, calculate the deviation ratio with the target humidity, combine the reference coefficient and the abnormal coefficient to obtain the compensation intensity coefficient, and then calculate the temperature adjustment value to dynamically compensate the influence of humidity.

[0092] The effects mainly lie in the following aspects: through real-time monitoring and dynamic analysis, the correlation between powder bulk density abnormality and environmental humidity can be accurately identified, drying compensation can be triggered in time, forming pressure control can be effectively optimized, the adverse effects of humidity on powder bulk density can be reduced, the stability and reliability of the tungsten-copper alloy preparation process can be improved, the uniformity of alloy performance can be ensured, the automation and intelligence level of the production process can be improved, the production loss caused by process abnormalities can be reduced, and the production efficiency can be improved.

[0093] Embodiment 2

[0094] Based on the steps in the foregoing embodiment 1, there are also extreme working conditions. If the powder used is superfine powder (such as D50<1μm), it is more prone to agglomeration, the bulk density fluctuates greatly, and then the response is slow, the drying control process lags behind, and the production efficiency is affected;

[0095] Please refer to Figure 2 The control process for preparing a hard tungsten-copper alloy according to the embodiment of the present application comprises the following steps:

[0096] The particle size of the powder is detected when the powder is added in production, whether it is superfine powder is determined by the particle size detection result, if it is superfine powder, the early warning analysis is carried out in advance when the apparent density is abnormal, and the early control is carried out based on the early warning analysis result;

[0097] In this embodiment, the particle size of the powder is detected when the powder is added in production, and the particle size of the powder is detected by a laser diffraction method;

[0098] The implementation process of the laser diffraction method is as follows: an online laser particle size instrument is installed beside the powder feeding pipe, the powder is uniformly dispersed by airflow or vibration device and then flows through the detection area, the built-in software of the instrument processes data in real time, if , the powder is automatically marked as superfine powder, otherwise, no marking is performed;

[0099] Wherein, is an important parameter for measuring particle size distribution, also known as median diameter or median particle size, which refers to the particle size corresponding to the cumulative particle size distribution percentage of 50% in the particle size distribution, and its physical meaning is that 50% of the particles have a particle size smaller than this value, and the other 50% of the particles have a particle size larger than this value, which reflects the average particle size level of the particle group;

[0100] For the batch marked as superfine powder, the specific process of performing early warning analysis is as follows:

[0101] A second sliding window is set, which is shorter than the first sliding window described in step one of the foregoing embodiment, and is set for the working condition of superfine powder;

[0102] For each second sliding window, an abnormality coefficient is calculated, wherein the abnormality coefficient is calculated in the same way as in step one of the foregoing embodiment, and will not be described here;

[0103] The abnormality coefficient corresponding to the current second sliding window is compared with an abnormality coefficient trigger threshold; wherein the abnormality coefficient trigger threshold is less than the abnormality coefficient limit value, and is used for early warning and further early control compensation;

[0104] If the abnormality coefficient corresponding to the second sliding window is greater than or equal to the abnormality coefficient trigger threshold, the early control process is performed;

[0105] The specific process of performing the early control process is as follows:

[0106] For the case of superfine powder, the lag order of humidity data and apparent density data is calculated, and the lag order k1 corresponding to the superfine powder is marked;

[0107] It is judged whether the environmental humidity is the cause of the apparent density, if yes, drying compensation is performed;

[0108] Obtaining the environment humidity corresponding to the k1 time points before the current time as the second lag time environment humidity;

[0109] The difference between the second lag time environment humidity and the target environment humidity, and the ratio of the difference to the target environment humidity, are taken as the second environment humidity deviation ratio;

[0110] The second reference coefficient, the abnormal coefficient, and the reinforcement factor are multiplied and summed with the second reference coefficient to obtain the second compensation intensity coefficient;

[0111] The reinforcement factor is set by a person skilled in the art according to the process characteristics of the superfine powder, and the reinforcement factor is greater than 1;

[0112] When performing drying compensation, the environment humidity needs to be compensated through dynamic adjustment of the temperature;

[0113] The product of the second compensation intensity coefficient and the second environment humidity deviation ratio is taken as the second temperature adjustment amount, and the sum of the second temperature adjustment amount and the temperature at the current time is taken as the second temperature adjustment value;

[0114] The second temperature adjustment value is used to offset the influence of humidity changes on powder particle agglomeration and gap filling, maintain the stability of the loose bulk density, and thus ensure the consistency of the forming pressure,

[0115] If the abnormal coefficient corresponding to the second sliding window is less than the abnormal coefficient trigger threshold, the monitoring continues;

[0116] In Example 2, when preparing the hard tungsten-copper alloy, the particle size of the added powder is first detected by the laser diffraction method. If it is determined that the powder is a superfine powder, a shorter second sliding window is set, the abnormal coefficient is calculated in real time and compared with a lower abnormal coefficient trigger threshold. When the abnormal coefficient exceeds the standard, the lag order k1 of the influence of humidity on the loose bulk density is calculated. If it is determined that the environment humidity is the cause of the loose bulk density abnormality, the second compensation intensity coefficient is calculated according to the deviation ratio of the second lag time environment humidity and the target humidity, combined with the second reference coefficient, the abnormal coefficient, and the reinforcement factor greater than 1, and then the second temperature adjustment value is obtained to dynamically adjust the temperature for drying compensation. If the abnormal coefficient does not exceed the standard, the monitoring continues;

[0117] The beneficial effects are that through early warning analysis and reinforcement compensation mechanism, the problem of large loose bulk density fluctuation and drying control lag caused by easy agglomeration of superfine powder can be optimized, the timeliness and effectiveness of drying compensation are improved, the adverse effects of humidity on the loose bulk density of superfine powder are reduced, the stability of the forming pressure is ensured, and thus the stability and reliability of the tungsten-copper alloy preparation process are improved, the production efficiency is improved, and the production loss is reduced;

[0118] The powder particle size detection is introduced, a special short sliding window and an early warning threshold are set for the working condition of superfine powder, the lag order is determined by combining the cross-correlation analysis, and the compensation strength is enhanced by the reinforcement factor, so that the differential and advanced dynamic regulation of the superfine characteristic powder is realized, and an intelligent control system integrating particle size identification, lag analysis and reinforcement compensation is constructed, thereby providing a more accurate and efficient process control scheme for the preparation of hard tungsten-copper alloy.

[0119] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A controlled process for preparing hard tungsten copper alloy, characterized in that: include: During the preparation of tungsten-copper alloy, real-time data on powder bulk density and ambient humidity are collected, and anomaly coefficients are obtained by analyzing the data through a set first sliding window. The abnormality coefficient is obtained through the following process: Within the first sliding window, the moments when the loose density value is greater than the loose density limit are extracted as the moments exceeding the standard. The number of moments exceeding the standard is counted, and the ratio of the number of moments exceeding the standard to the total number of data in the first sliding window is taken as the percentage of exceeding the standard. Within the first sliding window, extract the maximum and minimum values ​​corresponding to the loose density data, calculate the difference, and use the ratio of the difference to the minimum loose density as the relative fluctuation value. The sum of the normalized excess percentage and the relative fluctuation value is used as the anomaly coefficient; If the abnormality coefficient exceeds the standard, the correlation judgment process between powder loose density and ambient humidity will be triggered. The correlation judgment process involves using cross-correlation analysis to determine the lag order and correlation coefficient of the loose packing density affected by environmental humidity. The hysteresis order of the loose bulk density affected by the ambient humidity is: Based on the labeled abnormal state sliding window, obtain the corresponding environmental humidity data in the abnormal state sliding window and the environmental humidity data in the previous sliding window. Normalize the humidity data and loose packing density data; calculate the cross-correlation coefficient with lag order k for the humidity data and loose packing density data; The cross-correlation coefficient CFF(k) with lag order k is: ； Where k is the lag order of ambient humidity leading density (k>0, indicating that humidity changes precede density changes), N represents the length of the abnormal state time window, t represents the time, H represents humidity, and D represents loose density. Extract the hysteresis order corresponding to the maximum absolute value of the calculated cross-correlation coefficient, and use it as the hysteresis order of the effect of humidity on loose density; The process for determining the association judgment result is as follows: The maximum absolute value of the cross-correlation coefficient is used as the correlation coefficient; If the correlation coefficient is greater than the correlation judgment threshold, then the abnormal loose packing density is related to the ambient humidity. If the correlation judgment result determines that the abnormal loose density of the powder is caused by environmental humidity, then the drying compensation process is executed, and the process is as follows: If the current time is t, then, based on the lag order k0, obtain the environmental humidity corresponding to the k0 times before the current time as the environmental humidity of the first lag time. The difference between the ambient humidity at the first lag time and the target ambient humidity is used as the ratio of the difference to the target ambient humidity as the first ambient humidity deviation ratio. The first benchmark coefficient and the anomaly coefficient are multiplied together, and then summed with the first benchmark coefficient. The resulting value is used as the first compensation strength coefficient. The product of the first compensation strength coefficient and the first ambient humidity deviation ratio is used as the first temperature adjustment amount, and the sum of the first temperature adjustment amount and the current temperature is used as the first temperature adjustment value.

2. The controlled process for preparing hard tungsten copper alloy according to claim 1, characterized in that: The abnormal state sliding window is: The first sliding window with an abnormal coefficient greater than or equal to the abnormal coefficient limit is marked as an abnormal state sliding window.

3. The controlled process for preparing hard tungsten copper alloy according to any one of claims 1-2, characterized in that: Also includes: When adding powder during production, the particle size of the powder is detected. The particle size detection results are used to determine whether it is an ultrafine powder. If it is an ultrafine powder, an early warning analysis is performed for abnormal bulk density, and early control is carried out based on the early warning analysis results.

4. The controlled process for preparing hard tungsten copper alloy according to claim 3, characterized in that: The process of determining whether a powder is ultrafine based on particle size detection results is as follows: The particle size of the powder is measured during production. An online laser particle size analyzer is installed next to the powder feeding pipeline. The powder is uniformly dispersed by airflow or vibration before flowing through the detection area. If it is, it will be automatically marked as an ultrafine powder.

5. The controlled process for preparing hard tungsten copper alloy according to claim 3, characterized in that: The process of the advance warning analysis is as follows: Set a second sliding window; for each second sliding window, calculate the anomaly coefficient; If the abnormal coefficient corresponding to the second sliding window is greater than or equal to the abnormal coefficient trigger threshold, then the advance control process is executed.

6. The controlled process for preparing hard tungsten copper alloy according to claim 3, characterized in that: The process of proactive regulation is as follows: Calculate the hysteresis order of humidity data and loose density data, and mark it as the hysteresis order k1 corresponding to ultrafine powder; determine whether the ambient humidity is the cause of loose density, and if so, perform drying compensation; Obtain the ambient humidity corresponding to the k1 times before the current time as the ambient humidity at the second lag time. The difference between the ambient humidity at the second lag time and the target ambient humidity is used as the ratio of the difference to the target ambient humidity as the second ambient humidity deviation ratio. The second benchmark coefficient, the anomaly coefficient, and the strengthening factor are multiplied together, and then summed with the second benchmark coefficient. The resulting value is used as the second compensation strength coefficient. The product of the second compensation intensity coefficient and the second ambient humidity deviation ratio is used as the second temperature adjustment amount, and the sum of the second temperature adjustment amount and the current temperature is used as the second temperature adjustment value.

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