Intelligent control equipment and method for nodular cast iron pouring

Abstract

This invention relates to the field of metal casting technology, specifically to an intelligent control device and method for ductile iron casting, comprising: acquiring multi-dimensional data; obtaining a molten iron flow fluctuation index based on molten iron temperature data and ladle level data; determining a pouring flow response coefficient using pouring flow rate data and the molten iron flow fluctuation index; obtaining the tilt angle change intensity based on ladle tilt angle data; obtaining a coupling benefit ratio between pouring flow rate and tilt angle change based on the tilt angle change intensity, pouring flow rate data, and pouring flow response coefficient; determining a resistance coefficient using the tilt angle change intensity and pouring flow rate data; obtaining an adaptive compensation factor based on the coupling benefit ratio and resistance coefficient; determining a target ladle tilt angle based on the adaptive compensation factor; and adjusting the ladle tilt angle according to the target ladle tilt angle. This invention can ensure stable flow rate and uniform filling during the casting process, and reduce the generation of defects.

Description

Technical Field

[0001] This invention relates to the field of metal casting technology, specifically to an intelligent control device and method for ductile iron casting. Background Technology

[0002] Ductile iron, due to its excellent mechanical properties, high strength and toughness, as well as good wear resistance and vibration damping, is widely used in automotive parts, construction machinery, pressure pipelines, machine tool beds, and municipal infrastructure. The microstructure and final quality of ductile iron largely depend on the stability and controllability of the casting process. Key process parameters such as pouring temperature, pouring speed, liquid level, pouring time, and changes in molten iron composition directly affect the graphite spheroidization rate, shrinkage cavities, and casting density.

[0003] Existing ductile iron casting control equipment typically uses a programmable logic controller (PLC) or industrial control unit as its core, combined with hydraulic or electric actuators, to control the tilt angle of the ladle, supplemented by temperature sensors, weight sensors, or liquid level detection devices. However, it still has some shortcomings: existing control equipment only makes simple judgments on the casting process based on time or weight, lacking multi-parameter dynamic comprehensive analysis of changes in the casting state, and it is difficult to compensate for nonlinear factors such as temperature fluctuations and flow state changes in real time; moreover, existing control methods mostly use fixed thresholds or fixed curves for casting control, and cannot adaptively adjust according to the mold filling state, changes in casting resistance, etc. Summary of the Invention

[0004] This invention provides an intelligent control device and method for ductile iron casting to solve existing problems.

[0005] The present invention provides an intelligent control device and method for ductile iron casting, which adopts the following technical solution:

[0006] One embodiment of the present invention provides an intelligent control method for ductile iron casting, the method comprising the following steps:

[0007] Acquire multi-dimensional monitoring data; including molten iron temperature monitoring data from the start of pouring to the present time, liquid level monitoring data in the ladle, ladle tilt angle monitoring data, and pouring flow rate monitoring data;

[0008] Based on molten iron temperature monitoring data and ladle level monitoring data, the molten iron flow fluctuation index is obtained, specifically including:

[0009] The slope value between two adjacent data points in the molten iron temperature monitoring data is determined as the first slope value;

[0010] The absolute value of each negative slope value among all the first slope values ​​is determined as the first absolute value;

[0011] The difference between any two first absolute values ​​among all first absolute values ​​is determined as the first difference.

[0012] The product of the mean of all first absolute values ​​and the mean of the absolute values ​​of all first differences is used to determine the fluctuation of molten iron temperature.

[0013] The slope value between two adjacent data points in the liquid level monitoring data in the ladle is determined as the second slope value;

[0014] The absolute value of each negative slope value among all the second slope values ​​is determined as the second absolute value;

[0015] The difference between any two second absolute values ​​among all second absolute values ​​is defined as the second difference.

[0016] The product of the mean of all second absolute values ​​and the mean of the absolute values ​​of all second differences is used to determine the fluctuation of the liquid level in the ladle.

[0017] The product of the temperature fluctuation of molten iron and the level fluctuation in the ladle is defined as the molten iron flow fluctuation index. Using the pouring flow monitoring data and the molten iron flow fluctuation index, the pouring flow response coefficient is determined, specifically including:

[0018] Get the duration from the start of the pouring to the current time;

[0019] The absolute value of the difference between two adjacent data points in the pouring flow rate monitoring data is determined as the third absolute value;

[0020] The first ratio is determined by the ratio of the sum of all third absolute values ​​to the duration from the start of the pouring to the current time.

[0021] The normalized value of the product of the reciprocal of the molten iron flow volatility index and the first ratio is determined as the casting flow response coefficient.

[0022] Based on the ladle tilt angle monitoring data, obtain the intensity of the tilt angle change for each control action;

[0023] Based on the tilt angle change intensity, pouring flow rate monitoring data, and pouring flow rate response coefficient for each control action, the coupling benefit ratio between pouring flow rate and tilt angle change is obtained, specifically including:

[0024] The moment of the second data point among the two data points corresponding to each non-zero difference is determined as the start moment of each control action;

[0025] The average value of the pouring flow monitoring data from the start time of each control action to the start time of the next control action is determined as the first average value for each control action.

[0026] The average value of the pouring flow monitoring data from the start time of the last control action to the start time of each control action is determined as the second average value for each control action.

[0027] The normalized value of the difference between the first mean and the second mean of each control action is determined as the intensity of the change in pouring flow rate for each control action.

[0028] The product of the intensity of the tilt angle change and the intensity of the pouring flow rate change for each control action is determined as the first product;

[0029] The function value with constant e as the base and the first product as the exponent is determined as the intensity value of each control action;

[0030] The product of the mean of the intensity values ​​of all control actions and the injection flow rate response coefficient is determined as the coupling benefit ratio between injection flow rate and tilt angle change.

[0031] By utilizing the intensity of tilt angle changes and pouring flow rate monitoring data for each control action, the resistance coefficient during the current pouring process is determined, specifically including:

[0032] The time interval formed by the start time of the previous control action and the start time of the next control action is defined as the first time interval.

[0033] For the pouring flow monitoring data in the first time period, calculate the slope value between two adjacent data points, and sort the absolute values ​​of all slope values ​​in ascending order to obtain the pouring flow data sequence;

[0034] Calculate the difference between two adjacent data points in the pouring flow rate data sequence, and use the larger of the two data points corresponding to the maximum difference as the dividing point;

[0035] The sequence consisting of the dividing point and the data to the right of the dividing point in the pouring flow data sequence is identified as the significant data sequence.

[0036] The normalized value of the difference between the time of the split point and the start time of each control action is determined as the time interval of each control action.

[0037] The ratio of the first average value of each control action to the first average value of the next control action is determined as the second ratio.

[0038] Based on the normalized value of the second ratio, the intensity of the tilt angle change for each control action, and the time interval between each control action, the resistance coefficient in the current pouring process is obtained.

[0039] Based on the coupling benefit ratio of the pouring flow rate and tilt angle change and the resistance coefficient during the current pouring process, the adaptive compensation factor at the current moment is obtained.

[0040] Based on the adaptive compensation factor at the current moment, determine the target ladle tilt angle at the current moment;

[0041] Adjust the ladle tilt angle according to the target ladle tilt angle at the current moment.

[0042] Furthermore, the specific steps for obtaining the tilt angle change intensity for each control action based on the ladle tilt angle monitoring data are as follows:

[0043] Calculate the difference between two adjacent data points in the ladle tilt angle monitoring data, and determine each non-zero difference as the tilt angle change for each control action;

[0044] The normalized value of the tilt angle change for each control action is determined as the tilt angle change intensity for each control action.

[0045] Furthermore, the specific steps for obtaining the resistance coefficient in the current pouring process based on the normalized value of the second ratio, the intensity of the tilt angle change for each control action, and the time interval between each control action are as follows:

[0046] The sum of the time interval between each control action and a preset non-zero constant is determined as the first sum.

[0047] Calculate the mean of all first sums;

[0048] The sum of the normalized value of the second ratio and the preset non-zero constant is determined as the second sum.

[0049] The sum of the tilt angle change intensity of each control action and the preset non-zero constant is determined as the third sum value;

[0050] The ratio of the second sum to the third sum is determined as the third ratio.

[0051] Calculate the mean of all third ratios;

[0052] The product of the mean of all first sums and the mean of all third ratios is determined as the resistance coefficient in the current pouring process.

[0053] Furthermore, the specific steps for obtaining the adaptive compensation factor at the current moment based on the coupling benefit ratio of the pouring flow rate and tilt angle change and the resistance coefficient during the current pouring process are as follows:

[0054] The third product is determined by multiplying the coupling benefit ratio of the pouring flow rate and the tilt angle change with the reciprocal of the resistance coefficient in the current pouring process.

[0055] The hyperbolic tangent function value of the third product is determined as the adaptive compensation factor at the current time.

[0056] Furthermore, the specific steps for determining the target ladle tilt angle at the current moment based on the adaptive compensation factor are as follows:

[0057] Obtain the target average flow rate and the actual average flow rate from the start of pouring to the current time;

[0058] The difference between the target average flow rate and the actual average flow rate is determined as the target correction amount for the ladle tilt angle.

[0059] Obtain the actual ladle tilt angle at the current moment;

[0060] The product of the current adaptive compensation factor and the target correction amount of the ladle tilt angle is determined as the second product;

[0061] The sum of the second product and the actual ladle tilt angle at the current moment is determined as the target ladle tilt angle at the current moment.

[0062] One embodiment of the present invention provides an intelligent control device for ductile iron casting, used in the intelligent control method for ductile iron casting as described above. The device includes a processor and a memory, wherein the processor processes instructions stored in the memory to implement the processing of the following modules:

[0063] The acquisition module is used to acquire multi-dimensional monitoring data, including molten iron temperature monitoring data from the start of pouring to the current time, liquid level monitoring data in the ladle, ladle tilt angle monitoring data, and pouring flow rate monitoring data.

[0064] The analysis module is used to obtain the molten iron flow fluctuation index based on molten iron temperature monitoring data and ladle level monitoring data; determine the pouring flow response coefficient using pouring flow monitoring data and the molten iron flow fluctuation index; obtain the tilt angle change intensity for each control action based on ladle tilt angle monitoring data; obtain the coupling benefit ratio between pouring flow and tilt angle change based on the tilt angle change intensity for each control action, pouring flow monitoring data, and pouring flow response coefficient; determine the resistance coefficient in the current pouring process using the tilt angle change intensity for each control action and pouring flow monitoring data; obtain the adaptive compensation factor at the current moment based on the coupling benefit ratio between pouring flow and tilt angle change and the resistance coefficient in the current pouring process; and determine the target ladle tilt angle at the current moment based on the adaptive compensation factor.

[0065] The adjustment module is used to adjust the ladle tilt angle according to the target ladle tilt angle at the current moment.

[0066] The beneficial effects of the technical solution of the present invention are as follows: The embodiments of the present invention propose an intelligent control device and method for ductile iron casting. First, the molten iron flow fluctuation index is calculated based on the change characteristics of molten iron temperature monitoring data and liquid level monitoring data in the ladle. Then, the molten iron flow response state is evaluated in combination with the rate of change of the casting flow rate. The coupling benefit ratio between the flow rate and the tilt angle is obtained based on the change trend between the flow rate and the tilt angle during the intervention of the control action. Next, the resistance during the casting process is evaluated based on the time interval between the change in the control action and the flow response, as well as the difference in data change. The adaptive compensation factor of the intelligent control device during the casting process is obtained together with the resistance and the coupling benefit ratio between the flow rate and the tilt angle. By compensating for the tilt angle of the casting device, closed-loop adaptive control is achieved in the intelligent control process of ductile iron casting, ensuring stable flow rate, uniform filling, and reducing defects during the casting process. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a flowchart illustrating the steps of an intelligent control method for ductile iron casting according to the present invention.

[0069] Figure 2 This is a schematic diagram of a module of an intelligent control device for ductile iron casting according to the present invention. Detailed Implementation

[0070] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent control device and method for ductile iron casting according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0072] The following description, in conjunction with the accompanying drawings, details the specific solution of the intelligent control device and method for ductile iron casting provided by this invention.

[0073] The scenario addressed by this invention is:

[0074] In the production of ductile iron castings, molten iron undergoes spheroidization and inoculation treatments after melting to form ductile iron that meets casting requirements. The treated molten iron is then collected in a ladle and transported to the pouring station. At the start of pouring, a drive device tilts the ladle, allowing the molten iron to flow into the mold through the gating system to complete the filling process. As pouring progresses, the molten iron level in the ladle continuously decreases, and the filling state and pouring resistance inside the mold constantly change, resulting in variations in the flow rate and flow pattern of the molten iron. In the later stages of pouring, improper control of the ladle tilting action can easily lead to instability or interruption in the pouring process. Therefore, it is necessary to rationally control the ladle tilting behavior and pouring rhythm throughout the entire pouring process to ensure the smooth completion of the mold filling process. Finally, pouring is stopped and the pouring process ends once the predetermined filling state is reached.

[0075] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligent control of ductile iron casting according to an embodiment of the present invention. The method includes the following steps:

[0076] Step S001: Obtain multi-dimensional monitoring data; wherein, the multi-dimensional monitoring data includes molten iron temperature monitoring data from the start of pouring to the current time, liquid level monitoring data in the ladle, ladle tilt angle monitoring data, and pouring flow rate monitoring data.

[0077] Specifically, during the pouring process, the temperature of the molten iron is monitored by temperature detection units (such as immersion continuous temperature measuring thermocouples or fast thermocouples) installed at the ladle or pouring channel, and the temperature monitoring data of the molten iron at each moment from the start of pouring to the current moment is obtained.

[0078] During the pouring process, the amount of molten iron remaining in the ladle is monitored by a weight detection or liquid level detection unit (such as a radar liquid level gauge or a laser liquid level gauge) installed on the ladle support structure or the ladle body, and the liquid level monitoring data in the ladle at each moment from the start of pouring to the current moment is obtained.

[0079] During the pouring process, the tilt angle of the pouring ladle is monitored by an angle detection unit (such as a tilt angle sensor) installed on the tilting mechanism of the pouring ladle, and the tilt angle monitoring data of the pouring ladle at each moment from the start of pouring to the current moment is obtained.

[0080] During the pouring process, the flow state of molten iron is monitored by flow detection or flow regime detection units (such as electromagnetic flowmeters) installed at the pouring channel or gate, and the pouring flow monitoring data at each moment from the start of pouring to the current moment is obtained.

[0081] After acquiring various monitoring data, the validity of the monitoring data is first judged, and outliers or data that obviously do not conform to the casting conditions are removed. Then, the valid monitoring data is smoothed to reduce the data jitter caused by instantaneous fluctuations or interference and improve the continuity of data changes. Finally, the monitoring data of different dimensions are time-synchronized to make various monitoring data correspond on the same time scale.

[0082] Step S002: Based on the molten iron temperature monitoring data and the liquid level monitoring data in the ladle, obtain the molten iron flow fluctuation index.

[0083] It should be noted that this step, through dynamic analysis of the changes in molten iron temperature and ladle level during the pouring process, can identify the potential impact of the temperature drop rate and level fluctuation amplitude on the pouring flow pattern and filling uniformity, providing accurate data for subsequent pouring state determination and control parameter generation.

[0084] Step S002 further includes steps S0021-S0029:

[0085] Step S0021: Determine the slope value between two adjacent data points in the molten iron temperature monitoring data as the first slope value.

[0086] Specifically, in the molten iron temperature monitoring data, the molten iron temperature monitoring data at two adjacent moments form a set of slope values, namely the first slope value. The first slope value is the ratio of the difference between two adjacent data points to their corresponding time interval.

[0087] For example, in the molten iron temperature monitoring data, two adjacent data points are 1419℃ and 1433℃, and the time interval between the two data points is 1 second. Then the first slope value is (1433-1419) / 1=14℃ / s.

[0088] Step S0022: Determine the absolute value of each negative slope value among all the first slope values ​​as the first absolute value.

[0089] Specifically, all the first slope values ​​are filtered out, all negative slope values ​​are marked, and the absolute value of the negative slope value, i.e. the first absolute value, is denoted as k.

[0090] For example, if all the first slope values ​​are -1, 14, 8, and -9, then the absolute values ​​of the two negative slope values, -1 and -9, are taken as the first absolute values.

[0091] Step S0023: Determine the difference between any two first absolute values ​​among all the first absolute values ​​as the first difference.

[0092] Specifically, Let be the first difference, where This represents the o-th first absolute value among all first absolute values; Let represent the u-th first absolute value among all first absolute values, where o and u are not equal.

[0093] For example, if all the first absolute values ​​are 1, 9, and 10, then the difference between 1 and 9 - 8 is taken as the first difference, the difference between 1 and 10 - 9 is taken as the first difference, and the difference between 9 and 10 - 1 is taken as the first difference.

[0094] Step S0024: The product of the mean of all first absolute values ​​and the mean of the absolute values ​​of all first differences is determined as the fluctuation of the molten iron temperature.

[0095] Specifically, This represents the mean of all first absolute values. The larger the mean of all first absolute values, the faster the temperature of the molten iron decreases.

[0096] This represents the mean of the absolute values ​​of all the first differences. Let represent the absolute value of the i-th first difference, and n represent the number of first differences. The larger the mean of the absolute values ​​of all first differences, the larger the local fluctuation in the temperature drop of the molten iron during the casting process.

[0097] Calculate the temperature fluctuation of molten iron during the casting process :

[0098]

[0099] The larger the value, the greater the temperature fluctuation of the molten iron during the casting process.

[0100] Step S0025: Determine the slope value between two adjacent data points in the liquid level monitoring data in the ladle as the second slope value.

[0101] Specifically, in the liquid level monitoring data inside the ladle, the liquid level monitoring data at two adjacent moments form a set of slope values, namely the second slope value. The second slope value is the ratio of the difference between two adjacent data points to their corresponding time interval.

[0102] For example, in the liquid level monitoring data inside the ladle, two adjacent data points are 1m and 1.1m, and the time interval between the two data points is 1 second. Then the second slope value is (1.1-1) / 1=0.1m / s.

[0103] Step S0026: Determine the absolute value of each negative slope value among all the second slope values ​​as the second absolute value.

[0104] Specifically, all the second slope values ​​are filtered out, all negative slope values ​​are marked, and the absolute value of the negative slope value, i.e. the second absolute value, is denoted as b.

[0105] For example, if all the second slope values ​​are -0.1, 0.08, 0.05, and -0.07, then the absolute values ​​of the two negative slope values, -0.1 and -0.07, are taken as the second absolute values.

[0106] Step S0027: Determine the difference between any two second absolute values ​​among all the second absolute values ​​as the second difference.

[0107] Specifically, Let be the second difference, where This represents the l-th second absolute value among all second absolute values; Let l represent the g-th second absolute value among all second absolute values, where l and g are not equal.

[0108] For example, if all the second absolute values ​​are 0.1, 0.07, and 0.06, then the difference between 0.1 and 0.07, 0.03, is taken as the second difference, the difference between 0.1 and 0.06, 0.04, is taken as the second difference, and the difference between 0.07 and 0.06, 0.01, is taken as the second difference.

[0109] Step S0028: The product of the mean of all second absolute values ​​and the mean of the absolute values ​​of all second differences is used to determine the fluctuation of the liquid level in the ladle.

[0110] Specifically, This represents the mean of all second absolute values. The larger the mean of all second absolute values, the faster the liquid level in the ladle drops.

[0111] This represents the mean of the absolute values ​​of all second differences. Let represent the absolute value of the j-th second difference, and m represent the number of second differences. The larger the mean of the absolute values ​​of all second differences, the larger the local fluctuation in the drop of liquid level in the ladle during the pouring process.

[0112] Calculate the fluctuation of liquid level in the ladle during the pouring process. :

[0113]

[0114] The larger the value, the greater the fluctuation in the liquid level inside the ladle during the pouring process.

[0115] Step S0029: The product of the temperature fluctuation of molten iron and the level fluctuation in the ladle is determined as the molten iron flow fluctuation index.

[0116] Specifically, the fluctuation index of molten iron flow is calculated. :

[0117]

[0118] During the pouring of ductile iron, a drop in temperature or liquid level means a reduction in the flow of molten iron or an energy loss due to heat dissipation. Therefore, attention should be paid to the changing characteristics of the negative slope value. The larger the value, the greater the fluctuation index of molten iron flow during the current casting process. This means that the temperature and liquid level changes have a greater potential impact on the entire casting process, posing a significant potential risk. It may cause different filling rates at different locations, leading to filling imbalances or local defects.

[0119] Step S003: Determine the pouring flow response coefficient using the pouring flow monitoring data and the molten iron flow fluctuation index.

[0120] It should be noted that the previous steps analyzed the potential unstable factors of molten iron flow, but only characterized the passive response. The causal relationship between the actual pouring action and the flow rate change has not yet been revealed. In order to achieve intelligent control of the pouring process, it is necessary to understand the response relationship between the actual control action and the molten iron flow rate.

[0121] This step, by observing the dynamic coupling relationship between the pouring flow rate and the tilt angle, can analyze the actual impact of control actions on the molten iron flow state, clarify the response relationship between the tilt angle, tilt speed and flow rate changes, and reflect the response amplitude and delay characteristics of the molten iron flow rate in real time, thereby providing data support for subsequent adaptive control logic.

[0122] Step S003 further includes steps S0031-S0034:

[0123] Step S0031: Obtain the duration from the start of the pouring to the current time.

[0124] Specifically, the duration from the start of the pouring to the current moment is denoted as t.

[0125] Step S0032: Determine the absolute value of the difference between two adjacent data points in the pouring flow rate monitoring data as the third absolute value.

[0126] Specifically, the third absolute value is denoted as . It reflects the actual flow state of molten iron in the pouring channel or gate. The larger the value, the more sensitive it is to the flow state.

[0127] Step S0033: Determine the first ratio as the ratio of the sum of all third absolute values ​​to the duration from the start of the pouring to the current time.

[0128] Specifically, Let be the first ratio, where This represents the sum of all third absolute values. This represents the average rate of change from the start of the pouring to the current time. The larger the value, the more responsive the pouring flow rate is to the control action.

[0129] Step S0034: The normalized value of the product of the reciprocal of the molten iron flow fluctuation index and the first ratio is determined as the casting flow response coefficient.

[0130] Specifically, calculate the pouring flow response coefficient. :

[0131]

[0132] Where norm represents the normalization function. This represents the fluctuation index of molten iron flow, i.e., the disturbance or instability of molten iron flow. The larger the value, the higher the flow instability; The larger the value, the greater the flow sensitivity and the better the flow stability during the casting process. In other words, the higher the casting flow response coefficient, the more effective the control action.

[0133] Step S004: Based on the ladle tilt angle monitoring data, obtain the intensity of the tilt angle change for each control action.

[0134] It should be noted that the pouring flow response coefficient only describes the sensitivity and reliability of the current pouring flow to the control action, but it lacks a characterization of the time dynamic coupling characteristics between the flow and the control action.

[0135] Step S004 further includes:

[0136] Calculate the difference between two adjacent data points in the ladle tilt angle monitoring data, and determine each non-zero difference as the tilt angle change for each control action.

[0137] Specifically, the difference between two adjacent data points in the ladle tilt angle monitoring data is calculated as: the data from the later moment minus the data from the previous moment. When the difference between the ladle tilt angle monitoring data from two adjacent moments is not zero, it indicates that the tilt angle has changed, meaning that control action has been initiated.

[0138] The normalized value of the tilt angle change for each control action is determined as the tilt angle change intensity for each control action.

[0139] Specifically, the change data of the tilt angle for each time (i.e., the execution of the control action) is recorded. The change in tilt angle (the difference between the tilt angle after the change and the tilt angle before the change) is normalized, and the normalized result of the change, i.e., the intensity of the tilt angle change for each control action, is denoted as... Normalization of changes (such as min-max normalization) is a well-known technique and will not be elaborated upon here.

[0140] Step S005: Based on the tilt angle change intensity, pouring flow monitoring data, and pouring flow response coefficient for each control action, obtain the coupling benefit ratio between pouring flow and tilt angle change.

[0141] Step S005 further includes steps S0051-S0057:

[0142] Step S0051: Determine the time of the second data among the two data corresponding to each non-zero difference as the start time of each control action.

[0143] For example, in the ladle tilt angle monitoring data, two adjacent data points are 45° and 50°. A non-zero difference of 5° is calculated, and the time corresponding to 50° is determined as the start time of this control action.

[0144] Step S0052: Determine the average value of the pouring flow monitoring data from the start time of each control action to the start time of the next control action as the first average value for each control action.

[0145] For example, the current control action is to change the tilt angle from 45° to 50°, and the time corresponding to 50° is determined as the start time y1 of this control action; the next control action is to change the tilt angle from 50° to 55°, and the time corresponding to 55° is determined as the start time y2 of the next control action. The average value of all pouring flow monitoring data from time y1 to time y2 is calculated, which is the first average value of this control action.

[0146] Step S0053: The average value of the pouring flow monitoring data from the start time of the previous control action to the start time of each control action is determined as the second average value for each control action.

[0147] For example, the current control action is to change the tilt angle from 45° to 50°, and the time corresponding to 50° is determined as the start time y1 of the current control action; the previous control action was to change the tilt angle from 43° to 45°, and the time corresponding to 45° is determined as the start time y3 of the previous control action. The average value of all pouring flow monitoring data from time y3 to time y1 is calculated, which is the second average value of the current control action.

[0148] Step S0054: The normalized value of the difference between the first mean and the second mean of each control action is determined as the intensity of the change in pouring flow rate for each control action.

[0149] Specifically, the normalized result of the difference (including positive and negative) between the average flow rate after the current tilt angle change (and before the next tilt angle change) and the average flow rate before the tilt angle change, i.e., the intensity of the change in pouring flow rate for each control action, is denoted as E. The normalization of the difference (such as minimum-maximum normalization) is a well-known technique and will not be elaborated on here.

[0150] Step S0055: The product of the tilt angle change intensity and the pouring flow rate change intensity of each control action is determined as the first product.

[0151] Specifically, It is denoted as the first product.

[0152] Step S0056: Set the constant The base is the function value with the first product being the exponent, which is determined as the intensity value of each control action.

[0153] Specifically, , which is recorded as the intensity value of each control action. exp() is equivalent to function.

[0154] Step S0057: The product of the mean of the intensity values ​​of all control actions and the pouring flow response coefficient is determined as the coupling benefit ratio between pouring flow and tilt angle change.

[0155] Specifically, the coupling benefit ratio of calculating the pouring flow rate and the change in tilt angle is... :

[0156]

[0157] in, This represents the average intensity value of all control actions. Since there may be multiple control actions from the start of the pouring process to the current time, the average level of all control actions is calculated.

[0158] This indicates the input intensity of each control action. The larger the value, the greater the change in tilt angle. During the casting process, the flow rate of molten iron should increase in the same direction, meaning the value of E also needs to be larger. The data changes of both should be in the same direction.

[0159] The larger the value, the more stable the response of the pouring flow rate is during the casting process. Furthermore, the trends of the changes in the pouring flow rate and the changes in the tilt angle are more consistent during this process. In other words, the coupling benefit between the changes in the pouring flow rate and the changes in the tilt angle is relatively high, meaning that the system input and output are in the same direction, and the system's response to the control input is more stable.

[0160] Step S006: Using the tilt angle change intensity and pouring flow monitoring data of each control action, determine the resistance coefficient in the current pouring process.

[0161] It should be noted that the coupling between the pouring flow rate and the change in tilt angle indicates whether the change in tilt angle can drive the flow rate. However, the rationality of the pouring flow rate is not only related to the control action, but also to the filling state inside the mold. Even if the coupling relationship remains stable, excessive or mismatched flow rates may still cause filling defects.

[0162] This step analyzes the relationship between the mold filling process and the pouring resistance during the casting process. This allows the control system to move beyond simply relying on flow rate changes for adjustment. Introducing the state of the controlled object can prevent casting quality abnormalities caused by misjudgment of a single indicator.

[0163] Step S006 further includes steps S0061-S0067:

[0164] Step S0061: The time period formed by the start time of the previous control action and the start time of the next control action is defined as the first time period.

[0165] For example, the current control action is to change the tilt angle from 45° to 50°, and the time corresponding to 50° is determined as the start time y1 of this control action; the previous control action was to change the tilt angle from 43° to 45°, and the time corresponding to 45° is determined as the start time y3 of the previous control action; the next control action is to change the tilt angle from 50° to 55°, and the time corresponding to 55° is determined as the start time y2 of the next control action. The time period from time y3 to time y2 is denoted as the first time period.

[0166] Step S0062: For the pouring flow monitoring data of the first time period, calculate the slope value between two adjacent data points, and sort the absolute values ​​of all slope values ​​in ascending order to obtain the pouring flow data sequence.

[0167] Specifically, the slope value is the ratio of the difference between two adjacent data points to the time interval between the two data points. For the first time interval, the data points between two adjacent data points form a set of slope values. Then, all the absolute values ​​of the slope values ​​are sorted in ascending order to obtain a new data sequence, namely the injection flow rate data sequence.

[0168] For example, the pouring flow monitoring data for the first time period are 7.5, 8.1, 8.0, 8.5, and 7.6. The slope values ​​between two adjacent data points are 0.6, -0.1, 0.5, and -0.9. The absolute values ​​of all slope values ​​are sorted in ascending order as 0.1, 0.5, 0.6, and 0.9. The pouring flow data sequence is [0.1, 0.5, 0.6, 0.9].

[0169] Step S0063: Calculate the difference between two adjacent data in the pouring flow rate data sequence, and take the larger of the two data corresponding to the maximum difference as the split point.

[0170] For example, the pouring flow rate data sequence is [0.1, 0.5, 0.6, 0.9], the difference between two adjacent data is 0.4, 0.1, 0.3, the maximum difference is 0.4, and the two corresponding data are 0.1 and 0.5. Then the larger data 0.5 is used as the dividing point.

[0171] Step S0064: The sequence consisting of the dividing point and the data to the right of the dividing point in the casting flow rate data sequence is determined as the significant data sequence.

[0172] Specifically, in the new data sequence, there is a set of differences between two adjacent data points. Then, all the differences are traversed, and the position of the largest difference divides the new data sequence into two parts. The data set on the right is considered to be the data set where the injection flow rate changes significantly.

[0173] For example, the pouring flow rate data sequence is [0.1, 0.5, 0.6, 0.9], with 0.5 as the dividing point, then the significant data sequence is [0.5, 0.6, 0.9].

[0174] Step S0065: Determine the normalized value of the difference between the time of the split point and the start time of each control action as the time interval of each control action.

[0175] Specifically, the start time of each control action is denoted as c. The time of the split point is denoted as c'. The normalized result of the time interval between the time corresponding to the first set of slope values ​​in the salient event set and the start time of this control action, i.e., the time interval of each control action, is denoted as c'. Normalization of time intervals (such as minimum-maximum normalization) is a well-known technique and will not be elaborated upon here.

[0176] For example, the dividing point in the significant data sequence [0.5, 0.6, 0.9] is 0.5, and 0.5 corresponds to 8.0 and 8.5 in the first time period of the pouring flow monitoring data 7.5, 8.1, 8.0, 8.5, 7.6. Then the time corresponding to the pouring flow monitoring data 8.0 is denoted as c'.

[0177] Step S0066: Determine the second ratio as the ratio of the first average value of each control action to the first average value of the next control action.

[0178] For example, the current control action is to change the tilt angle from 45° to 50°, and the time corresponding to 50° is determined as the start time y1 of this control action; the next control action is to change the tilt angle from 50° to 55°, and the time corresponding to 55° is determined as the start time y2 of the next control action. The control action after that is to change the tilt angle from 55° to 58°, and the time corresponding to 58° is determined as the start time y4 of the next control action.

[0179] Calculate the average value of all pouring flow monitoring data from time y1 to time y2, which is the first average value D1 of this control action.

[0180] Calculate the average of all pouring flow rate monitoring data from time y2 to time y4, which is the first average value D2 for the next control action. Record D1 / D2 as the second ratio.

[0181] Step S0067: Based on the normalized value of the second ratio, the intensity of the tilt angle change for each control action, and the time interval for each control action, obtain the resistance coefficient in the current pouring process.

[0182] Specifically, the normalized result of the ratio of the average pouring flow rate after two control inputs (the ratio of the current control action to the next control action), i.e., the normalized value of the second ratio, is denoted as: Ratio normalization (such as minimum-maximum normalization) is a well-known technique and will not be elaborated upon here.

[0183] Step S0067 further includes steps S0671-S0677:

[0184] Step S0671: Determine the sum of the time interval of each control action and the preset non-zero constant as the first sum value.

[0185] Specifically, the preset nonzero constant This represents a non-zero constant between 0 and 0.1, used to ensure that the calculation is meaningful. It can be set according to specific circumstances, and in this embodiment, it is preferably 0.05. , which is denoted as the first sum.

[0186] Step S0672: Calculate the mean of all first sums.

[0187] Specifically, Let be the mean of all first sums.

[0188] Step S0673: Determine the second sum value as the sum of the normalized value of the second ratio and the preset non-zero constant.

[0189] Specifically, , which is denoted as the second sum.

[0190] Step S0674: Determine the sum of the tilt angle change intensity of each control action and the preset non-zero constant as the third sum value.

[0191] Specifically, , denoted as the third sum.

[0192] Step S0675: Determine the ratio of the second sum to the third sum as the third ratio.

[0193] Specifically, This is denoted as the third ratio.

[0194] Step S0676: Calculate the mean of all third ratios.

[0195] Specifically, Let be the mean of all third ratios.

[0196] Step S0677: The product of the mean of all first sums and the mean of all third ratios is determined as the resistance coefficient in the current pouring process.

[0197] Specifically, calculate the resistance coefficient during the current pouring process. :

[0198]

[0199] in This indicates the intensity of the tilt angle change for each control action. If, for two consecutive control inputs, the tilt angle change is roughly the same, but the resulting change in pouring flow rate decreases (i.e., the larger the third ratio), it indicates that the resistance during the pouring process is increasing. Assuming there are multiple control inputs during the current pouring period, i.e. To calculate the average level; This indicates the response delay of the actual pouring flow rate after the control action is input. If the control action occurs but the flow rate changes after a while, the larger the value, the more restricted the flow path and the more resistance dominates. The larger the value, the greater the resistance coefficient in the current cast iron pouring process. This means that the input required to obtain a unit flow rate is greater and the response is slower, i.e., the stronger the pouring resistance.

[0200] Step S007: Obtain the adaptive compensation factor at the current moment based on the coupling benefit ratio of the pouring flow rate and tilt angle change and the resistance coefficient during the current pouring process.

[0201] It should be noted that this step mainly combines all the above analysis results to obtain the adaptive control correction amount in the intelligent control process of ductile iron casting during the current casting stage, so as to better cope with the actual response requirements in the casting process and ensure the reliability and stability of the cast iron casting process.

[0202] Step S007 further includes:

[0203] The third product is determined by multiplying the coupling benefit ratio of the pouring flow rate and the tilt angle change with the reciprocal of the resistance coefficient in the current pouring process.

[0204] Specifically, This is denoted as the third product.

[0205] The hyperbolic tangent function value of the third product is determined as the adaptive compensation factor at the current time.

[0206] Specifically, the adaptive compensation factor of the intelligent control equipment during the final calculation of the pouring process is... :

[0207]

[0208] Where th() represents the hyperbolic tangent function, This indicates whether the matching between the tilting action and the flow rate change is effective during the cast iron pouring process. The larger the value, the more effective the control action is at the current stage; This represents the input cost required to achieve a unit change in flow rate during the pouring process. The larger the value, the more restricted the flow.

[0209] The larger the value, the safer the control input can be during the casting process, allowing the casting flow rate to more fully match the target filling curve, thereby achieving the goals of rapid filling, improved flow stability, and casting uniformity.

[0210] Step S008: Determine the target ladle tilt angle at the current moment based on the adaptive compensation factor at the current moment.

[0211] Step S008 further includes steps S0081-S0085:

[0212] Step S0081: Obtain the target average flow rate and the actual average flow rate from the start time of pouring to the current time.

[0213] It should be noted that during the casting process of ductile iron, a target average casting flow rate is usually set in advance: the ratio of the target total casting flow rate at the current moment to the duration from the start of casting to the current moment.

[0214] Actual average pouring flow rate: The ratio of the total pouring flow rate at the current moment to the duration from the start of pouring to the current moment.

[0215] Step S0082: The difference between the target average flow rate and the actual average flow rate is determined as the target correction amount for the ladle tilt angle.

[0216] Specifically, the difference between the target average pouring flow rate and the actual average pouring flow rate is converted into a target correction amount for the ladle tilt angle, denoted as Q.

[0217] Step S0083: Obtain the actual ladle tilt angle at the current moment.

[0218] Specifically, obtain the actual ladle tilt angle at the current moment, denoted as . .

[0219] Step S0084: The product of the current adaptive compensation factor and the target correction amount of the ladle tilt angle is determined as the second product.

[0220] Specifically, This is denoted as the second product.

[0221] Step S0085: The sum of the second product and the actual ladle tilt angle at the current moment is determined as the target ladle tilt angle at the current moment.

[0222] Specifically, The corrected target tilt angle is the target ladle tilt angle at the current moment. Then, the target ladle tilt angle at the current moment is sent to the tilting actuator to drive the ladle to complete the corresponding angle change.

[0223] Step S009: Adjust the ladle tilt angle according to the target ladle tilt angle at the current moment.

[0224] Specifically, during the casting process, the pouring flow rate, tilt angle, and resistance status are collected to calculate the adaptive compensation factor and target tilt angle for the next moment. The target tilt angle is dynamically updated based on the feedback data and the recalculated compensation factor to achieve closed-loop adaptive control, ensuring stable flow rate and uniform filling during the pouring process, and reducing the generation of defects.

[0225] Please see Figure 2 , Figure 2 This is a schematic diagram of a module of an intelligent control device for ductile iron casting according to the present invention, including a processor and a memory. The processor is used to process instructions stored in the memory to realize the monitoring process of the following modules:

[0226] The acquisition module 100 is used to acquire multi-dimensional monitoring data, including molten iron temperature monitoring data from the start of pouring to the current time, liquid level monitoring data in the ladle, ladle tilt angle monitoring data, and pouring flow rate monitoring data.

[0227] The analysis module 200 is used to: obtain the molten iron flow fluctuation index based on molten iron temperature monitoring data and ladle level monitoring data; determine the pouring flow response coefficient using pouring flow monitoring data and the molten iron flow fluctuation index; obtain the tilt angle change intensity for each control action based on ladle tilt angle monitoring data; obtain the coupling benefit ratio between pouring flow and tilt angle change based on the tilt angle change intensity for each control action, pouring flow monitoring data, and pouring flow response coefficient; determine the resistance coefficient in the current pouring process using the tilt angle change intensity for each control action and pouring flow monitoring data; obtain the adaptive compensation factor at the current moment based on the coupling benefit ratio between pouring flow and tilt angle change and the resistance coefficient in the current pouring process; and determine the target ladle tilt angle at the current moment based on the adaptive compensation factor at the current moment.

[0228] The adjustment module 300 is used to adjust the tilt angle of the ladle according to the target tilt angle of the ladle at the current moment.

[0229] In summary, in this embodiment of the invention, the molten iron flow fluctuation index is first calculated based on the changing characteristics of molten iron temperature monitoring data and ladle level monitoring data. Then, the molten iron flow response state is evaluated in conjunction with the rate of change of the molten iron flow rate. The coupling benefit ratio between the flow rate and tilt angle changes is obtained based on the changing trend between the flow rate and the tilt angle during the intervention of the control action. Next, the resistance during the molten iron process is evaluated based on the time interval between the change in the control action and the flow response, as well as the differences in data changes. The adaptive compensation factor of the intelligent control equipment during the molten iron process is obtained based on the resistance and the coupling benefit ratio between the flow rate and the tilt angle changes. By compensating for the tilt angle of the molten iron equipment, closed-loop adaptive control is achieved in the intelligent control process of ductile iron molten ...

[0230] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent control of ductile iron casting, characterized in that, The method includes the following steps: Acquire multi-dimensional monitoring data; including molten iron temperature monitoring data from the start of pouring to the present time, liquid level monitoring data in the ladle, ladle tilt angle monitoring data, and pouring flow rate monitoring data; Based on molten iron temperature monitoring data and ladle level monitoring data, the molten iron flow fluctuation index is obtained, specifically including: The slope value between two adjacent data points in the molten iron temperature monitoring data is determined as the first slope value; The absolute value of each negative slope value among all the first slope values ​​is determined as the first absolute value; The difference between any two first absolute values ​​among all first absolute values ​​is determined as the first difference. The product of the mean of all first absolute values ​​and the mean of the absolute values ​​of all first differences is used to determine the fluctuation of molten iron temperature. The slope value between two adjacent data points in the liquid level monitoring data in the ladle is determined as the second slope value; The absolute value of each negative slope value among all the second slope values ​​is determined as the second absolute value; The difference between any two second absolute values ​​among all second absolute values ​​is defined as the second difference. The product of the mean of all second absolute values ​​and the mean of the absolute values ​​of all second differences is used to determine the fluctuation of the liquid level in the ladle. The product of the temperature fluctuation of molten iron and the level fluctuation in the ladle is defined as the molten iron flow fluctuation index. Using the pouring flow monitoring data and the molten iron flow fluctuation index, the pouring flow response coefficient is determined, specifically including: Get the duration from the start of the pouring to the current time; The absolute value of the difference between two adjacent data points in the pouring flow rate monitoring data is determined as the third absolute value; The first ratio is determined by the ratio of the sum of all third absolute values ​​to the duration from the start of the pouring to the current time. The normalized value of the product of the reciprocal of the molten iron flow volatility index and the first ratio is determined as the casting flow response coefficient. Based on the ladle tilt angle monitoring data, obtain the intensity of the tilt angle change for each control action; Based on the tilt angle change intensity, pouring flow rate monitoring data, and pouring flow rate response coefficient for each control action, the coupling benefit ratio between pouring flow rate and tilt angle change is obtained, specifically including: The moment of the second data point among the two data points corresponding to each non-zero difference is determined as the start moment of each control action; The average value of the pouring flow monitoring data from the start time of each control action to the start time of the next control action is determined as the first average value for each control action. The average value of the pouring flow monitoring data from the start time of the last control action to the start time of each control action is determined as the second average value for each control action. The normalized value of the difference between the first mean and the second mean of each control action is determined as the intensity of the change in pouring flow rate for each control action. The product of the intensity of the tilt angle change and the intensity of the pouring flow rate change for each control action is determined as the first product; The function value with constant e as the base and the first product as the exponent is determined as the intensity value of each control action; The product of the average intensity values ​​of all control actions and the pouring flow response coefficient is determined as the coupling benefit ratio between pouring flow and tilt angle change; using the tilt angle change intensity and pouring flow monitoring data for each control action, the resistance coefficient of the current pouring process is determined, specifically including: The time interval formed by the start time of the previous control action and the start time of the next control action is defined as the first time interval. For the pouring flow monitoring data in the first time period, calculate the slope value between two adjacent data points, and sort the absolute values ​​of all slope values ​​in ascending order to obtain the pouring flow data sequence; Calculate the difference between two adjacent data points in the pouring flow rate data sequence, and use the larger of the two data points corresponding to the maximum difference as the dividing point; The sequence consisting of the dividing point and the data to the right of the dividing point in the pouring flow data sequence is identified as the significant data sequence. The normalized value of the difference between the time of the split point and the start time of each control action is determined as the time interval of each control action. The ratio of the first average value of each control action to the first average value of the next control action is determined as the second ratio. Based on the normalized value of the second ratio, the intensity of the tilt angle change for each control action, and the time interval between each control action, the resistance coefficient in the current pouring process is obtained; based on the coupling benefit ratio of pouring flow rate and tilt angle change and the resistance coefficient in the current pouring process, the adaptive compensation factor at the current moment is obtained. Based on the adaptive compensation factor at the current moment, determine the target ladle tilt angle at the current moment; Adjust the ladle tilt angle according to the target ladle tilt angle at the current moment.

2. The intelligent control method for ductile iron casting according to claim 1, characterized in that, The specific steps for obtaining the intensity of tilt angle change for each control action based on ladle tilt angle monitoring data are as follows: Calculate the difference between two adjacent data points in the ladle tilt angle monitoring data, and determine each non-zero difference as the tilt angle change for each control action; The normalized value of the tilt angle change for each control action is determined as the tilt angle change intensity for each control action.

3. The intelligent control method for ductile iron casting according to claim 1, characterized in that, The specific steps for obtaining the resistance coefficient during the current pouring process based on the normalized value of the second ratio, the intensity of the tilt angle change for each control action, and the time interval between each control action are as follows: The sum of the time interval between each control action and a preset non-zero constant is determined as the first sum. Calculate the mean of all first sums; The sum of the normalized value of the second ratio and the preset non-zero constant is determined as the second sum. The sum of the tilt angle change intensity of each control action and the preset non-zero constant is determined as the third sum value; The ratio of the second sum to the third sum is determined as the third ratio. Calculate the mean of all third ratios; The product of the mean of all first sums and the mean of all third ratios is determined as the resistance coefficient in the current pouring process.

4. The intelligent control method for ductile iron casting according to claim 1, characterized in that, The specific steps for obtaining the adaptive compensation factor at the current moment based on the coupling benefit ratio of the change in pouring flow rate and tilt angle and the resistance coefficient during the current pouring process are as follows: The third product is determined by multiplying the coupling benefit ratio of the pouring flow rate and the tilt angle change with the reciprocal of the resistance coefficient in the current pouring process. The hyperbolic tangent function value of the third product is determined as the adaptive compensation factor at the current time.

5. The intelligent control method for ductile iron casting according to claim 1, characterized in that, The specific steps for determining the target ladle tilt angle at the current moment based on the adaptive compensation factor are as follows: Obtain the target average flow rate and the actual average flow rate from the start of pouring to the current time; The difference between the target average flow rate and the actual average flow rate is determined as the target correction amount for the ladle tilt angle. Obtain the actual ladle tilt angle at the current moment; The product of the current adaptive compensation factor and the target correction amount of the ladle tilt angle is determined as the second product; The sum of the second product and the actual ladle tilt angle at the current moment is determined as the target ladle tilt angle at the current moment.

6. A smart control device for ductile iron casting, used in the smart control method for ductile iron casting as described in claim 1, characterized in that, Includes a processor and a memory, the processor being used to process instructions stored in the memory to implement the processing of the following modules: The acquisition module is used to acquire multi-dimensional monitoring data, including molten iron temperature monitoring data from the start of pouring to the current time, liquid level monitoring data in the ladle, ladle tilt angle monitoring data, and pouring flow rate monitoring data. The analysis module is used to obtain the molten iron flow fluctuation index based on molten iron temperature monitoring data and ladle level monitoring data; determine the pouring flow response coefficient using pouring flow monitoring data and the molten iron flow fluctuation index; obtain the tilt angle change intensity for each control action based on ladle tilt angle monitoring data; obtain the coupling benefit ratio between pouring flow and tilt angle change based on the tilt angle change intensity, pouring flow monitoring data, and pouring flow response coefficient for each control action; determine the resistance coefficient in the current pouring process using the tilt angle change intensity and pouring flow monitoring data for each control action; obtain the adaptive compensation factor at the current moment based on the coupling benefit ratio between pouring flow and tilt angle change and the resistance coefficient in the current pouring process; and determine the target ladle tilt angle at the current moment based on the adaptive compensation factor at the current moment. The adjustment module is used to adjust the ladle tilt angle according to the target ladle tilt angle at the current moment.

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