Aluminum ingot alignment device and method for aluminum ingot continuous casting production line

Abstract

The invention relates to an aluminum ingot continuous casting production automation control technology, and discloses an aluminum ingot alignment device and method used in an aluminum ingot continuous casting production line. According to the method, the instantaneous speed and acceleration of the production line, the aluminum ingot attitude angular speed, the bottom surface temperature, the cooling water residual water film thickness, the release agent spraying amount and the aluminum ingot vibration amplitude and quality are collected in real time; a motion instability coefficient, a surface slip coefficient, a risk coupling coefficient and a system reliability coefficient are calculated in sequence according to the parameters of the PLC and the operation state data of the sensor and the execution mechanism, the delay trigger time of the target PLC is dynamically calculated based on the coefficients, and self-adaptive accurate adjustment of the alignment time is achieved. The problems that a traditional fixed delay or single parameter feedback control method cannot adapt to complex working conditions, and inaccurate alignment is easily caused are solved, and the aluminum ingot alignment accuracy, the production line operation stability and the product quality consistency are remarkably improved.

Description

Technical Field

[0001] This invention pertains to the field of automated control technology for continuous casting of aluminum ingots, and particularly relates to an aluminum ingot alignment device and method for use in a continuous casting production line for aluminum ingots. Background Technology

[0002] As a core component of aluminum smelting and processing, the geometric regularity and surface quality of the aluminum ingots produced by the continuous casting production line directly determine the effectiveness of subsequent transportation, storage, and deep processing. During continuous casting, after the high-temperature molten aluminum is cooled and shaped in the crystallizer, it needs to be transferred to the stacking or weighing station via a conveying mechanism. In actual production, aluminum ingots often experience positional deviations due to multiple dynamic factors: instantaneous speed fluctuations and acceleration changes during production line operation lead to inertial imbalance; the rotational effect caused by the ingot's angular velocity exacerbates its torsion; and uneven temperature distribution on the ingot's bottom surface, variations in the thickness of the water film formed by residual cooling water, and differences in the amount of release agent sprayed all significantly alter the frictional characteristics of the conveying interface. Furthermore, the vibration amplitude and mass differences of the aluminum ingot itself further amplify motion instability. Meanwhile, problems such as sensor signal jitter, multi-sensor reading dispersion, and actuator response time deviations in the control system make it difficult for traditional alignment mechanisms to accurately capture changes in the ingot's state.

[0003] Currently, the industry generally adopts two types of alignment control strategies. The first type is based on fixed delay or position sensor triggering, which only activates the alignment mechanism according to a preset time point or fixed position signal, completely failing to perceive real-time motion characteristics such as continuous changes in production line speed, dynamic fluctuations in acceleration, and the angular velocity of aluminum ingots. The second type is a control method that introduces simple sensor feedback. Although it can monitor single parameters such as aluminum ingot overheating or speed exceeding thresholds, it has a fundamental flaw: the control logic treats each influencing factor in isolation, failing to establish a coupling relationship model between bottom surface temperature, water film thickness, and release agent spraying amount, ignoring their comprehensive impact on surface slippage risk; at the same time, it ignores the amplification effect of aluminum ingot vibration amplitude and mass on motion inertia risk, leading to distorted risk assessment. More importantly, existing solutions lack a quantitative assessment mechanism for the reliability of the control system itself, and cannot dynamically correct control parameters based on sensor signal de-jittering time, reading standard deviation, and actuator response deviation. Therefore, under complex working conditions such as fluctuations in the cooling rate of molten aluminum, changes in ambient temperature and humidity, or equipment aging, the positive triggering timing often occurs prematurely or delayed, causing a chain of problems such as skewed aluminum ingot stacking, failure of bundling structure, and inaccurate weighing data. In severe cases, it can lead to collisions of the conveying mechanism or unplanned shutdowns of the production line, restricting overall production efficiency and product consistency.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to provide an aluminum ingot alignment device and method for use in an aluminum ingot continuous casting production line, in order to solve the above-mentioned problems.

[0006] This invention is implemented as follows: an aluminum ingot alignment method for use in an aluminum ingot continuous casting production line, comprising: calculating and obtaining a motion instability coefficient based on the instantaneous speed and instantaneous acceleration of the production line and the angular velocity of the aluminum ingot attitude; calculating and obtaining a surface slip coefficient based on the bottom surface temperature of the aluminum ingot, the thickness of the residual water film in the cooling water, and the amount of release agent sprayed; calculating and obtaining a risk coupling coefficient based on the motion instability coefficient, the surface slip coefficient, the vibration amplitude of the aluminum ingot, and the mass of the aluminum ingot; calculating and obtaining a system reliability coefficient based on the sensor signal de-jitter time, the standard deviation of multi-sensor readings, and the response time deviation of the actuator; and calculating and obtaining the PLC delay trigger time for target alignment judgment based on the basic delay, the risk coupling coefficient, and the system reliability coefficient, and adjusting the current PLC delay trigger time for alignment judgment to the PLC delay trigger time for target alignment judgment.

[0007] A further technical solution involves the following steps for calculating the PLC delay trigger time for target alignment judgment: obtaining the current basic delay, risk coupling coefficient, and system reliability coefficient; dynamically determining the additional delay amount within a preset maximum allowable additional delay range based on the system reliability coefficient and risk coupling coefficient; adding the basic delay and the additional delay amount to obtain the PLC delay trigger time for target alignment judgment; wherein, when the system reliability coefficient is low, a larger conservative additional delay is tended to be used; when the system reliability coefficient is high, adaptive adjustment is performed within the maximum allowable additional delay range based on the magnitude of the risk coupling coefficient.

[0008] A further technical solution involves the following steps for calculating the system reliability coefficient: obtaining the current sensor signal de-jitter time, the standard deviation of multi-sensor readings, and the actuator response time deviation; normalizing the sensor signal de-jitter time using a negative exponential decay function to calculate the signal stability factor, which decreases as the de-jitter time increases; comparing the current standard deviation of multi-sensor readings with the maximum permissible standard deviation, and using the complement of this ratio as the multi-sensor consistency factor; calculating the actuator response reliability factor using a linear decay function based on the ratio of the actuator response time deviation to the maximum permissible deviation, which decreases as the deviation increases; and taking the minimum value among the signal stability factor, the multi-sensor consistency factor, and the actuator response reliability factor as the system reliability coefficient.

[0009] A further technical solution involves the following steps for calculating the risk coupling coefficient: obtaining the current motion instability coefficient, surface slip coefficient, aluminum ingot vibration amplitude, and aluminum ingot mass; comparing the current aluminum ingot vibration amplitude with a reference vibration amplitude to obtain the aluminum ingot vibration amplitude index; performing maximum-minimum normalization on the current aluminum ingot mass to obtain the aluminum ingot mass index; combining the motion instability coefficient and the aluminum ingot mass index to form a motion risk component; combining the surface slip coefficient and the aluminum ingot vibration amplitude index to form a surface risk component; performing a weighted sum of squares on the motion risk component and the surface risk component, and taking the square root of the result to obtain a risk coupling coefficient ranging from 0 to 1; where a risk coupling coefficient of 0 indicates the lowest risk of multi-factor coupling, and a risk coupling coefficient of 1 indicates the highest risk of multi-factor coupling.

[0010] A further technical solution involves the following steps for calculating the motion instability coefficient: obtaining the instantaneous velocity and instantaneous acceleration of the current production line, as well as the aluminum ingot's attitude angular velocity; processing the ratio of the current production line's instantaneous velocity to the production line's maximum designed velocity to obtain the production line's instantaneous velocity index; calculating the production line's instantaneous acceleration index using a hyperbolic tangent function based on the ratio of the instantaneous acceleration to the reference acceleration, where the production line's instantaneous acceleration index monotonically increases with increasing instantaneous acceleration and tends to saturate; calculating the aluminum ingot's attitude angular velocity index using an exponentially decaying complementary function based on the ratio of the current aluminum ingot's attitude angular velocity to the reference angular velocity, where the aluminum ingot's attitude angular velocity index monotonically increases with increasing angular velocity and tends to saturate; weighted summing of the production line's instantaneous velocity index and the production line's instantaneous acceleration index, with the weight of the production line's instantaneous acceleration index term modulated by the aluminum ingot's attitude angular velocity index, thus amplifying the instability effect of the production line's instantaneous acceleration; and limiting the weighted sum to obtain a motion instability coefficient between 0 and 1.

[0011] A further technical solution involves the following steps for calculating the surface slip coefficient: obtaining the current bottom surface temperature of the aluminum ingot, the thickness of the residual cooling water film, and the amount of mold release agent sprayed; comparing the current bottom surface temperature and the amount of mold release agent sprayed with the maximum allowable temperature and the maximum allowable amount of mold release agent sprayed on the bottom surface of the aluminum ingot, respectively, and then using a min function to limit the ratio to an upper limit of 1 to obtain the bottom surface temperature index and the mold release agent spraying amount index; calculating the residual cooling water film thickness index using a square function based on the ratio of the residual cooling water film thickness to the critical film thickness, wherein the residual cooling water film thickness index increases rapidly with the increase of the residual cooling water film thickness; superimposing and fusing the residual cooling water film thickness index and the mold release agent spraying amount index to obtain a comprehensive lubrication factor; further amplifying the comprehensive lubrication factor using the bottom surface temperature index to obtain the surface slip coefficient, wherein the surface slip coefficient monotonically increases with the increase of the bottom surface temperature index, the residual cooling water film thickness index, and the mold release agent spraying amount index.

[0012] An aluminum ingot alignment device for use in an aluminum ingot continuous casting production line includes: a memory for storing a computer program; and a processor for implementing the steps of the above-described aluminum ingot alignment method for use in an aluminum ingot continuous casting production line when executing the computer program.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Achieved dynamic adaptive adjustment of delay trigger time: By comprehensively evaluating multi-dimensional real-time data such as production line motion status, aluminum ingot surface conditions and system reliability, the optimal PLC delay trigger time is dynamically calculated, overcoming the defect of traditional fixed delay strategy inaccurate alignment when operating conditions such as speed and temperature change.

[0014] 2. Improved the comprehensiveness and accuracy of risk assessment: By establishing a multi-factor coupled risk assessment model, which comprehensively considers motion instability, surface slip risk, and the amplification effect of mass and vibration, the judgment of alignment timing is more scientific and accurate, significantly reducing alignment failure rate and product defect rate.

[0015] 3. Enhanced system robustness and reliability: The innovative introduction of a system reliability coefficient quantifies and evaluates sensor signal stability, data consistency, and actuator response accuracy, and compensates for these in delay calculations. This ensures the reliability of positive control even when the system's own state fluctuates, thereby improving the overall stability of production. Attached Figure Description

[0016] Figure 1 The present invention provides a flowchart of an aluminum ingot alignment method for use in an aluminum ingot continuous casting production line. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the alignment control of traditional aluminum ingot continuous casting production lines, factors such as fluctuations in production line speed, mechanical vibration, uneven temperature distribution of aluminum ingots, bottom cooling water film, and residual release agent can cause positional deviations or attitude twisting of aluminum ingots during transport. Existing methods rely on fixed delays or simple feedback control based on a single sensor, which cannot respond in real time to the dynamic changes in instantaneous speed, instantaneous acceleration, and aluminum ingot attitude angular velocity of the production line. Furthermore, they neglect the coupling effect between the aluminum ingot bottom surface temperature, cooling water film thickness, and release agent spraying amount on the surface friction coefficient, and fail to comprehensively consider the amplifying effect of aluminum ingot mass and vibration amplitude on the risk of motion inertia. In addition, the lack of assessment and compensation mechanisms for system reliability issues such as sensor signal jitter and actuator response deviations results in the alignment judgment delay trigger time being unable to adapt to complex operating conditions, leading to premature or delayed triggering, which in turn affects the automation level and operational stability of the production line.

[0019] For example, on an aluminum ingot continuous casting production line, when the production line speed increases due to adjustments in upstream processes, the temperature of the aluminum ingot's bottom surface rises and the thickness of the cooling water film increases, while the amount of release agent sprayed also increases. In this scenario, the coefficient of friction between the aluminum ingot and the conveying surface decreases, and coupled with the increased instantaneous acceleration of the production line, the fluctuation of the aluminum ingot's attitude angular velocity intensifies, causing the aluminum ingot to slip and rotate during transport. Existing alignment methods only rely on a preset fixed delay to trigger the alignment mechanism, failing to dynamically adjust the trigger time according to real-time operating conditions. As a result, the alignment mechanism is triggered too early, interfering with the aluminum ingot before it reaches the predetermined position, causing inaccurate alignment and the mechanism to be unloaded; or it is triggered too late, with the aluminum ingot already deviating from its position, causing stacking skew and bundling failure. Stacking skew further leads to aluminum ingot collapse, and bundling failure causes aluminum ingots to scatter, interfering with the weighing process and affecting quality control.

[0020] If the above problems are not resolved, the positional deviation and orientation twisting during the aluminum ingot alignment process cannot be effectively corrected, significantly increasing the risk of failure in subsequent stacking, bundling, and weighing operations. Emergency shutdowns of the production line are triggered, increasing the likelihood of collisions between the alignment mechanism and the aluminum ingots, causing equipment damage and production interruptions. Consequently, the automation level and operational reliability of the production line are weakened, affecting the geometric regularity, surface quality, and internal structure of the aluminum ingot products, thus restricting the efficiency of subsequent transportation, warehousing, and deep processing, as well as the consistency of finished product quality.

[0021] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a method for aligning aluminum ingots in an aluminum ingot continuous casting production line, comprising: Based on the instantaneous speed and acceleration of the production line, as well as the angular velocity of the aluminum ingot, a motion instability coefficient is calculated. This coefficient is a quantitative indicator used to assess the instability of the aluminum ingot's motion during transport due to factors such as production line speed, acceleration, and the ingot's own angular velocity. A higher coefficient indicates greater instability in the ingot's motion, making it more prone to positional shifts or torsional twisting.

[0023] The surface slip coefficient is calculated based on the bottom surface temperature of the aluminum ingot, the thickness of the residual cooling water film, and the amount of release agent applied. The surface slip coefficient is a quantitative indicator used to assess the risk of relative slippage between the bottom of the aluminum ingot and the conveying surface. This coefficient comprehensively considers the influence of factors such as the bottom surface temperature of the aluminum ingot, the thickness of the cooling water film, and the amount of release agent applied on friction. A higher coefficient indicates a greater risk of surface slippage on the aluminum ingot.

[0024] A risk coupling coefficient is calculated based on the motion instability coefficient, surface slip coefficient, aluminum ingot vibration amplitude, and aluminum ingot mass. This risk coupling coefficient is a comprehensive indicator used to quantitatively assess the overall motion risk faced by the aluminum ingot during transport. By coupling multiple factors such as the motion instability coefficient, surface slip coefficient, aluminum ingot vibration amplitude, and aluminum ingot mass, this coefficient reflects the likelihood of severe displacement or attitude problems in the aluminum ingot.

[0025] The system reliability coefficient is calculated based on the sensor signal de-jitter time, the standard deviation of multi-sensor readings, and the actuator response time deviation. The system reliability coefficient is a quantitative indicator used to evaluate the operational reliability of the entire alignment control system (including sensors, controllers, and actuators). This coefficient comprehensively considers the stability of sensor signals, the consistency of multi-sensor data, and the accuracy of actuator response. A higher coefficient indicates stronger system reliability.

[0026] Based on the base delay, risk coupling coefficient, and system reliability coefficient, the PLC delay trigger time for target alignment judgment is calculated and obtained. The current PLC delay trigger time for alignment judgment is then adjusted to match the target alignment trigger time. The base delay refers to the alignment mechanism trigger time determined empirically or by a preset value under ideal, stable operating conditions. The PLC delay trigger time refers to the time it takes for the programmable logic controller (PLC) to trigger the alignment mechanism after receiving a specific signal and waiting for a preset or calculated delay. This time is a key parameter for achieving accurate alignment.

[0027] Compared to existing technologies that rely on fixed delays or fixed position signals for trigger control, this method can respond in real time to the dynamic changes in the instantaneous speed, instantaneous acceleration, and angular velocity of the aluminum ingot, avoiding alignment misalignment caused by fluctuations in the production line's operating conditions. For example, in the case of production line acceleration, existing fixed delay schemes may cause the alignment mechanism to trigger prematurely, resulting in no-load or collision; while this method, by calculating the motion instability coefficient, can predict and compensate for the impact of such dynamic changes.

[0028] This method achieves refined control of the alignment process in aluminum ingot continuous casting production lines by constructing a multi-dimensional, dynamic, and adaptive alignment judgment model. This method effectively solves problems such as inaccurate alignment, mechanical idling, or collisions in existing technologies, significantly improving the automation level, operational reliability, and product quality consistency of the production line.

[0029] This application further proposes the following steps for calculating and obtaining the PLC delay trigger time for target alignment judgment: The system acquires the current base delay, risk coupling coefficient, and system reliability coefficient. Base delay refers to the basic time delay required from triggering the alignment command to the actual response of the actuator under ideal or stable conditions. This base delay can be determined through pre-calibration of the system or statistical analysis based on historical operating data, or it can be set using empirical values. The risk coupling coefficient comprehensively reflects the degree of coupling of various risk factors that aluminum ingots may face during continuous casting production, such as motion instability, surface slippage, vibration, and quality. A higher value indicates a greater risk. This risk coupling coefficient can be calculated using a preset mathematical model combined with real-time collected parameters such as the production line's instantaneous speed, instantaneous acceleration, aluminum ingot attitude angular velocity, aluminum ingot bottom surface temperature, residual water film thickness in cooling water, release agent spraying amount, aluminum ingot vibration amplitude, and aluminum ingot quality. Alternatively, it can be obtained by training and predicting historical data using a machine learning model. The system reliability coefficient measures the overall reliability of the alignment system in terms of sensor signal processing, multi-sensor data consistency, and actuator response. A higher value indicates a more reliable system. The system's reliability coefficient can be calculated by comprehensively evaluating parameters such as sensor signal de-jitter time, standard deviation of multi-sensor readings, and actuator response time deviation. Alternatively, the reliability status of each component can be assessed in real time through the system's self-diagnosis and health monitoring modules. The maximum permissible additional delay serves as an adjustment factor, limiting the upper limit of the additional delay and ensuring that delay adjustments remain within an acceptable range.

[0030] Based on the system reliability coefficient and risk coupling coefficient, the additional delay is dynamically determined within a preset maximum allowable additional delay range. The basic delay and the additional delay are added together to obtain the PLC delay trigger time for target alignment judgment. The determination of the additional delay follows the following principle: when the system reliability coefficient is low, a larger conservative additional delay is tended to be used; when the system reliability coefficient is high, adaptive adjustment is made within the maximum allowable additional delay range based on the magnitude of the risk coupling coefficient. Specifically, the calculation method involves importing the basic delay, risk coupling coefficient, and system reliability coefficient into a formula... Obtain the PLC delay trigger time for target alignment judgment. ,in, Based on delay, To the maximum permissible additional delay, The system reliability coefficient, This represents the risk coupling coefficient. This formula provides a quantitative and dynamic mechanism to adaptively adjust the PLC delay trigger time by comprehensively considering the base delay, system reliability, and risk coupling. Specifically, when the system reliability coefficient... When it is high (close to 1), the formula in When the term approaches zero, the additional delay is mainly affected by... The impact of the term, i.e., mainly due to the risk coupling coefficient. Decision. This means that under conditions of high system reliability, if the risk coupling coefficient... A higher reliability coefficient will increase additional latency, allowing the system more response time to handle high-risk situations. Conversely, a lower reliability coefficient will increase latency. When the value is low (close to 0), the formula in When the system is in a dominant state, the additional delay increases significantly to compensate for the inherent unreliability of the system, thus providing a greater safety margin when the system itself is in poor condition. In this way, the solution of this application can intelligently calculate the optimal PLC delay trigger time based on the real-time operating conditions of the production line and the reliability level of the system itself, thereby solving the problem of fixed or inaccurate delay settings in traditional methods, and significantly improving the accuracy of aluminum ingot alignment and the robustness of the system.

[0031] The following is a concrete example to illustrate this. Suppose that in a certain aluminum ingot continuous casting production line, after system calibration, the basic delay... Set to 50 milliseconds, maximum allowed additional delay The interval is set to 100 milliseconds. At a certain moment, the system monitors and calculates the risk coupling coefficient in real time. The system reliability coefficient is 0.2. The value is 0.8. Substitute these values ​​into the formula. Calculation 76.8 milliseconds. At this point, the PLC delay trigger time for target alignment detection is... This is calculated to be approximately 76.8 milliseconds. Based on this calculation, the PLC control system will adjust the current PLC delay trigger time for the positive judgment to 76.8 milliseconds. In another scenario, if the production line is in a high-risk state, such as a high risk coupling coefficient... The reliability coefficient is 0.7, and the system reliability has decreased. The value is 0.5. Therefore, the calculation is... The delay time is set to 120.3 milliseconds. At this point, the PLC delay trigger time will be adjusted to approximately 120.3 milliseconds to address the higher risk and lower system reliability.

[0032] Through the above technical solution, this application provides a precise and adaptive PLC delay trigger time calculation method. This method organically combines the basic delay, system reliability coefficient, and risk coupling coefficient, dynamically calculating the PLC delay trigger time for target alignment judgment through a quantitative mathematical model. This effectively solves the technical challenge of accurately and scientifically setting the alignment delay to cope with different working conditions in the complex and ever-changing continuous casting production environment. By adjusting the delay in real time, the problems of untimely alignment due to excessively short delays or decreased production efficiency due to excessively long delays can be avoided, thereby significantly improving the accuracy and stability of aluminum ingot alignment and the overall operating efficiency of the production line, while also enhancing the system's ability to cope with sudden risks.

[0033] This application further proposes the following steps for calculating and obtaining the system reliability coefficient: This system acquires the current sensor signal de-jitter time, multi-sensor reading standard deviation, and actuator response time deviation. Sensor signal de-jitter time refers to the time required for the sensor output signal to stabilize after a change, or the time required for filtering to eliminate noise. It reflects the real-time performance and stability of sensor data acquisition. This time can be defined through real-time analysis of raw sensor data, for example, by setting parameters of digital filters (such as moving average filters or median filters), or by monitoring the fluctuation amplitude of the signal within a specific time window using statistical methods. When the fluctuation is less than a preset threshold, the time required to reach stability is recorded. Multi-sensor reading standard deviation refers to the dispersion between readings from multiple sensors of the same type measuring the same physical quantity. It is used to assess the consistency and redundancy of the sensor system. This can be achieved by deploying multiple redundant sensors, simultaneously acquiring data, and calculating their statistical standard deviation. This parameter dynamically quantifies the consistency and reliability of the redundant sensor system itself, preventing risk misjudgments due to single sensor failure or measurement distortion. Its importance lies in enabling the control system to perceive the reliability of data acquisition in real time and compensate for system uncertainties in advance when calculating the delay trigger time, thereby achieving adaptive robust control at the right moment. Actuator response time deviation refers to the difference between the actual start or completion time of an actuator after receiving a control command and the theoretically expected time. It is used to measure the accuracy and timeliness of the actuator. This deviation can be obtained by measuring the time from the issuance of the command to the completion of the actuator's action using a high-precision timer and comparing it with the ideal response time.

[0034] The sensor signal de-jitter time is normalized using a negative exponential decay function, and a signal stability factor is calculated. The signal stability factor decreases as the de-jitter time increases. Specifically, the calculation method involves importing the current sensor signal de-jitter time into the formula... Obtain the signal stability factor ,in, The current sensor signal de-jitter time. This step serves as a reference settling time; it aims to quantify the stability of the sensor signal. The exponential decay function will determine the dejitter time. Mapped to factors between 0 and 1, where The settling time is used as a reference value. The shorter the dejittering time, the closer the signal stability factor is to 1, indicating a more stable signal; conversely, the smaller the factor value, the worse the stability.

[0035] The standard deviation of the current multi-sensor readings is compared with the maximum permissible standard deviation, and the complement of this ratio is taken as the multi-sensor consistency factor. This step is used to evaluate the consistency of multi-sensor data. The smaller the standard deviation, the more consistent the sensor readings, and the closer the consistency factor is to 1.

[0036] The actuator response reliability factor is calculated using a linear decay function based on the ratio of the actuator response time deviation to the maximum permissible deviation. The actuator response reliability factor decreases as the deviation increases. Specifically, the calculation method involves importing the current actuator response time deviation into the formula... Obtain the reliability factor of the actuator response. ,in, The current response time deviation of the implementing agency This represents the maximum permissible deviation; this step quantifies the reliability of the actuator's response. The smaller the response time deviation, i.e., the more precise the actuator response, the closer the reliability factor is to 1.

[0037] The minimum value among the signal stability factor, multi-sensor consistency factor, and actuator response reliability factor is taken as the system reliability coefficient. This method of taking the minimum value ensures the conservatism of the overall system reliability assessment, meaning that the system reliability will not exceed the reliability of any single component, thus effectively reflecting the reliability level of the weakest link in the system. This application's solution acquires sensor signal dejitter time, multi-sensor reading standard deviation, and actuator response time deviation in real time, and converts them into signal stability factor, multi-sensor consistency factor, and actuator response reliability factor, respectively. These factors quantify the system's reliability from three dimensions: signal quality, data redundancy, and execution accuracy. By taking the minimum value among these factors as the system reliability coefficient, a conservative and comprehensive assessment of the overall system reliability is ensured. This system reliability coefficient is then used to adjust the PLC delay trigger time for target alignment judgment, allowing the delay trigger time to dynamically adapt to the actual reliability conditions of the production line. For example, when system reliability is low, the calculated PLC delay trigger time will be adjusted accordingly to provide a longer margin or more cautious operation, thereby avoiding alignment errors caused by system uncertainties. This dynamic adjustment mechanism enables the aluminum ingot alignment method to maintain high accuracy and robustness even when facing complex production environments and equipment performance fluctuations.

[0038] The following is a concrete example to illustrate this. Suppose that at a certain moment, the system detects the dejitter time of the sensor signal. The reference settling time is 0.05 seconds. Set to 0.1 seconds; multi-sensor reading standard deviation is 0.2 units, maximum permissible standard deviation is set to 0.5 units; actuator response time deviation. The maximum permissible deviation is 0.02 seconds. Set to 0.05 seconds. According to the formula, the signal stability factor... The calculated value is 0.6065. The multi-sensor consistency factor is calculated to be 0.6. The actuator response reliability factor... The calculated value is 0.6. Ultimately, the system reliability coefficient will be the minimum of these three factors, which is 0.6. This calculated system reliability coefficient of 0.6 will be substituted into the calculation formula for the PLC delay trigger time of target alignment judgment to reflect the overall reliability level of the current system in terms of signal stability, sensor consistency, and actuator response.

[0039] The above technical solution enables precise quantification of sensor signal stability, multi-sensor data consistency, and actuator response accuracy, thereby obtaining a comprehensive and conservative system reliability coefficient. This system reliability coefficient accurately reflects the uncertainties in real-time production line operation and incorporates them into the calculation of the PLC delay trigger time for target alignment judgment. This effectively solves the problem of decreased alignment accuracy caused by system reliability fluctuations, ensuring the accuracy and reliability of aluminum ingot alignment, and thus improving the operating efficiency and product quality of the continuous casting production line.

[0040] This application further proposes the following steps for calculating and obtaining the risk coupling coefficient: The system acquires current motion instability coefficient, surface slip coefficient, aluminum ingot vibration amplitude, and aluminum ingot mass. The motion instability coefficient is a comprehensive indicator reflecting the stability of the aluminum ingot during motion. It can be calculated based on parameters such as production line speed, acceleration, and aluminum ingot attitude angular velocity. For example, these parameters can be monitored in real time by sensors, and calculations can be performed using preset models or algorithms; alternatively, historical data analysis and machine learning models can be used to predict the instability of the current motion state. The surface slip coefficient is an indicator assessing the risk of surface friction or slippage between the aluminum ingot and the mold or conveyor belt. It can be calculated based on factors such as the bottom surface temperature of the aluminum ingot, the thickness of the residual water film in the cooling water, and the amount of release agent sprayed. For example, relevant data can be acquired using sensors such as infrared thermometers and ultrasonic thickness gauges, and calculated using physical models; alternatively, the surface condition of the aluminum ingot can be analyzed using a vision system, and the slippage risk can be assessed using image processing technology. The vibration amplitude of an aluminum ingot refers to the intensity of the mechanical vibration generated during its movement. It can be measured directly using accelerometers or displacement sensors, for example, by mounting sensors on the ingot or its supporting structure to collect vibration data in real time and calculate the amplitude; or by using a non-contact laser vibration meter. The mass of an aluminum ingot refers to the weight of a single ingot. It can be measured using load cells before the ingot enters the alignment area, for example, by installing a weighing module on the conveyor line to automatically acquire its mass data as the ingot passes by; or indirectly by measuring volume and estimating density.

[0041] The vibration amplitude of the aluminum ingot is obtained by comparing the current vibration amplitude with a reference vibration amplitude. This step aims to standardize the actual measured vibration amplitude, making it a dimensionless index for easier subsequent calculation and comparison. The ratio can be a simple division, such as dividing the current vibration amplitude by a preset maximum allowable vibration amplitude or average vibration amplitude; or, a piecewise linear function or nonlinear function (such as the sigmoid function) can be used to map the ratio to the range of 0-1 to better reflect the nonlinear characteristics of vibration risk.

[0042] The current aluminum ingot quality is subjected to maximum-min normalization to obtain the aluminum ingot quality index. This step aims to standardize the actual quality of the aluminum ingot, making it a dimensionless index for easier subsequent calculations. Maximum-min normalization typically involves linearly scaling the data to a fixed range (e.g., 0-1), where the maximum and minimum values ​​can be the minimum and maximum values ​​from historical data, or theoretical minimum and maximum values ​​set according to process requirements. Alternatively, Z-score normalization (mean-standard deviation normalization) can be used to convert the quality data into a standard normal distribution to reflect its deviation from the average level.

[0043] The motion instability coefficient is combined with the aluminum ingot quality index to form the motion risk component; the surface slip coefficient is combined with the aluminum ingot vibration amplitude index to form the surface risk component. A weighted sum of squares is performed on the motion risk component and the surface risk component, and the result is square-rooted to obtain a risk coupling coefficient ranging from 0 to 1. A risk coupling coefficient of 0 indicates the lowest risk of multi-factor coupling, while a risk coupling coefficient of 1 indicates the highest risk of multi-factor coupling. Specifically, the motion instability coefficient, surface slip coefficient, aluminum ingot vibration amplitude index, and aluminum ingot quality index are imported into the formula. Obtain the risk coupling coefficient , Output range 0-1, when When the motion is completely stable, there is no risk of surface slippage, the mass is small and there is no vibration, that is, the risk of multi-factor coupling is the lowest. At this time, it indicates that the motion is extremely unstable, the risk of surface slippage is extremely high, the mass is large and the vibration is violent, that is, the risk of multi-factor coupling is the highest, among which, The risk weight coefficient has a value range of 0-1. The instability coefficient is the coefficient of motion. This refers to the quality index of aluminum ingots. The surface slip coefficient, This is the vibration amplitude index for aluminum ingots. This step is the core calculation, which couples multiple independent risk factors through a mathematical model to generate a comprehensive risk index. The formula uses the square root of a weighted sum of squares to cross-couple motion instability with mass, surface slip, and vibration amplitude, and introduces weighting coefficients. To balance the impact of the two types of risks, the weighting coefficient Specifically, values ​​can be assigned through preset strategies or dynamic algorithms. This coupling method can more comprehensively reflect the interaction between different risk factors, avoiding errors that may arise from simple superposition. For example, when the motion instability coefficient... At higher levels, even the quality of aluminum ingots... A smaller surface slip coefficient will amplify its contribution to the overall risk; similarly, when the surface slip coefficient is smaller... At higher levels, even the vibration amplitude Even if the risk is relatively small, its contribution to the overall risk will be amplified.

[0044] This application's solution first acquires raw data from multiple dimensions, including production line operating status, aluminum ingot surface condition, and the dynamic characteristics of the aluminum ingot itself, and transforms this data into standardized coefficients and indices to ensure the comparability of different types of risk factors. Subsequently, these standardized risk indicators are input into a carefully designed coupling formula. This formula not only considers the independent impact of each risk factor, but more importantly, it captures the potential nonlinear, synergistic, or amplifying effects between these factors through the square root form of a weighted sum of squares. For example, when the aluminum ingot motion instability coefficient... At higher levels, even the aluminum ingot quality index At a moderate level, its contribution to overall risk is significantly enhanced due to coupling effects; similarly, the surface slip coefficient... vibration amplitude index of aluminum ingot The combination of these factors allows for a more accurate reflection of potential slippage risks. In this way, the system can generate a comprehensive risk coupling coefficient. This coefficient can comprehensively and dynamically quantify the alignment risks currently faced by aluminum ingots. This refined risk assessment mechanism provides a more accurate and reliable basis for calculating the PLC delay trigger time for subsequent target alignment judgment, thereby effectively solving the problem of insufficient alignment accuracy caused by incomplete and inaccurate risk assessment in traditional methods, and enabling the entire alignment process to better adapt to the rapidly changing working environment of the production line.

[0045] The following is a concrete example to illustrate this. Suppose that at a certain moment, the system obtains the current instability coefficient. The surface slip coefficient is 0.3. The value is 0.4. Simultaneously, the measured vibration amplitude of the aluminum ingot is 0.5 mm. If the reference vibration amplitude is 1.0 mm, then the aluminum ingot vibration amplitude index is... The value is 0.5. The aluminum ingot weighs 25 kg. If the maximum-minimum normalized treatment mass range is set to 20 kg to 30 kg, then the aluminum ingot mass index is... The value is 0.5. Assume the risk weighting coefficient is 0.5. The value is set to 0.6. Substituting these values ​​into the formula for calculating the risk coupling coefficient yields the risk coupling coefficient. It is approximately 0.1717. This value comprehensively reflects the overall risk level of the aluminum ingot under various factors such as motion, surface condition, vibration, and quality, providing a quantitative basis for the precise adjustment of the PLC delay trigger time.

[0046] Through the above technical solution, this application can comprehensively and accurately quantify the multiple risk factors affecting alignment accuracy in aluminum ingot continuous casting production lines and their coupling effects. This enables the system to more precisely perceive and predict potential alignment deviation risks, thereby providing a more reliable input for the subsequent PLC delay trigger time calculation for target alignment judgment. In this way, the decrease in alignment accuracy caused by insufficient assessment of single or isolated risks can be effectively avoided, significantly improving the alignment robustness and production efficiency of aluminum ingot continuous casting production lines under complex operating conditions.

[0047] This application further proposes the following steps for calculating and obtaining the motion instability coefficient: Acquiring instantaneous velocity and acceleration of the current production line, as well as the angular velocity of the aluminum ingot, aims to comprehensively capture key parameters affecting the dynamic motion state of the production line and the aluminum ingot. Instantaneous velocity and acceleration characterize the overall motion trend and rate of change of the production line, while the angular velocity of the aluminum ingot reflects the possible rotation or oscillation of the ingot itself. These parameters are fundamental data for assessing motion instability. They can be acquired in real time by measuring the motion state of the production line using speed sensors (e.g., encoders, laser tachometers) and acceleration sensors (e.g., MEMS accelerometers) installed on the production line; simultaneously, the angular velocity of the aluminum ingot can be monitored and calculated in real time using vision systems (e.g., high-speed cameras with image processing algorithms) or inertial measurement units (IMUs).

[0048] The instantaneous speed of the current production line is compared with its designed maximum speed to obtain the instantaneous speed index. This step visually reflects the percentage of the current production line's operating speed relative to its design limit, thus quantifying the impact of speed on motion stability. For example, when the instantaneous speed is close to the designed maximum speed, the production line may be more prone to instability. This ratio can be achieved directly through division, such as dividing the instantaneous speed value by the preset designed maximum speed value of the production line.

[0049] Based on the ratio of instantaneous acceleration to reference acceleration, the instantaneous acceleration exponent of the production line is calculated using the hyperbolic tangent function. The instantaneous acceleration exponent of the production line monotonically increases with increasing instantaneous acceleration and tends to saturate. The specific calculation method is as follows: input the instantaneous acceleration of the current production line into the formula. Obtain the instantaneous acceleration index of the production line ,in, The instantaneous acceleration of the current production line. As a reference acceleration, the absolute value of the acceleration is processed using the hyperbolic tangent function (tanh), which can effectively map the acceleration value to an exponential function between 0 and 1. When the acceleration is small, A value close to 0 indicates minimal impact on stability; when the acceleration is large, A value close to 1 indicates a significant impact on stability. (Reference acceleration) As an adjustment parameter, it can be set according to the characteristics of the actual production line and the requirements for acceleration sensitivity. For example, it can be set as a certain percentage of the maximum acceleration that the production line may encounter during normal operation, or it can be calibrated through experimental data.

[0050] Based on the ratio of the current aluminum ingot's angular velocity to the reference angular velocity, the exponent of the aluminum ingot's angular velocity is calculated using an exponentially decaying complementary function. The exponent of the aluminum ingot's angular velocity increases monotonically with increasing angular velocity and tends to saturate. The specific calculation method is as follows: the current aluminum ingot's angular velocity is imported into the formula... Obtain the angular velocity index of aluminum ingot attitude ,in, The current angular velocity of the aluminum ingot. The reference angular velocity; the attitude angular velocity of the aluminum ingot is obtained through an exponential function. In the form of mapping its absolute value to an exponent between 0 and 1, the absolute value is mapped to an exponent. When the angular velocity is small, A value close to 0 indicates that the aluminum ingot is in a stable position; when the angular velocity is large, A value close to 1 indicates that the aluminum ingot's attitude is unstable. Reference angular velocity. As an adjustment parameter, it can be set according to the size and shape of the aluminum ingot and the requirements of the production line for posture stability. For example, it can be set to the minimum angular velocity value that causes alignment difficulties, or it can be adjusted by empirical value.

[0051] The instantaneous speed index and instantaneous acceleration index of the production line are weighted and summed, with the weight of the instantaneous acceleration index term modulated by the aluminum ingot attitude angular velocity index, thus amplifying the instability effect of the instantaneous acceleration of the production line. The weighted sum is then limited to obtain a motion instability coefficient between 0 and 1. The specific calculation method is as follows: the instantaneous speed index, instantaneous acceleration index, and aluminum ingot attitude angular velocity index of the production line are imported into the formula. Obtain the motion instability coefficient , Output range 0-1, At this time, it indicates low speed, no acceleration, no rotation, and the motion is completely stable. At this time, it indicates high speed and violent acceleration accompanied by strong rotation, and the motion is extremely unstable. and All are weighting coefficients with values ​​ranging from 0 to 1, and , The instantaneous speed index of the production line. The instantaneous acceleration index of the production line. This represents the angular velocity exponent of the aluminum ingot's attitude. The formula cleverly incorporates a weighting coefficient. and This allows for adjusting the relative importance of speed and acceleration to overall instability based on the characteristics of the actual production line. Specifically, the formula includes the instantaneous acceleration exponent of the production line. With the angular velocity index of aluminum ingot attitude Through the product term Coupling means that when the aluminum ingot is rotating, even the same acceleration will amplify its impact on motion instability. This aligns with actual physics, where acceleration is more likely to cause system instability during rotation. The min function limits the final result to the range of 0 to 1, ensuring... The physical meaning and comparability of this method for calculating the instability coefficient. The method makes the risk coupling coefficient The calculations can more accurately reflect the actual dynamic motion risks of the production line and aluminum ingots. When the production line operates at high speeds, with large accelerations and significant angular velocities in the aluminum ingots... The value will approach 1, thus causing the risk coupling coefficient to... This increases the delay time of the PLC for target alignment detection, thus affecting the PLC's trigger time. The calculations prompt the system to trigger earlier or later to cope with the challenges posed by higher motion instability. Conversely, when the motion state is stable, The value approaches 0, making This reduction optimizes the delay trigger time, improving the accuracy and efficiency of alignment. This refined calculation of the motion instability coefficient provides a more solid data foundation for the entire aluminum ingot alignment method, enabling the system to adapt more intelligently and robustly to the rapidly changing dynamic conditions of the production line. The following example illustrates how the motion instability coefficient can be calculated in an aluminum ingot continuous casting production line. First, the instantaneous speed of the production line is acquired in real time by a rotary encoder installed on the drive shaft, and the instantaneous acceleration is obtained by differential processing of the speed signal. Simultaneously, a laser rangefinder array is installed above the aluminum ingot. By measuring the change in distance from different points on the ingot to the sensor, and combining this with a geometric model, the attitude angular velocity of the ingot is calculated. Assume the maximum designed speed of the production line is 10 m / s. When the instantaneous speed is 5 m / s, the instantaneous speed exponent of the production line is... It can be calculated as 0.5. For instantaneous acceleration, a reference acceleration can be set. It is 2 m / s². When the instantaneous acceleration... The instantaneous acceleration index of the production line at 1 m / s² It can be calculated as 0.462. For the angular velocity of the aluminum ingot attitude, a reference angular velocity can be set. The value is 0.5 radians per second. When the aluminum ingot's attitude angular velocity... The angular velocity exponent of aluminum ingot at 0.2 radians / second It can be calculated as 0.330. This is used when calculating the instability coefficient. At that time, weighting coefficients can be set. It is 0.6. The value is 0.4. Substituting the exponent obtained above into the formula, we get... The value is 0.545. This value will then be used as the risk coupling coefficient. The calculation affects the final PLC delay trigger time.

[0052] Through the above technical solution, this application can more comprehensively and accurately assess the motion instability of aluminum ingots in a continuous casting production line. By quantifying and comprehensively calculating the instantaneous velocity, instantaneous acceleration, and aluminum ingot attitude angular velocity of the production line, it can accurately capture multiple dynamic factors affecting motion stability. This refined motion instability coefficient... The calculation method makes the subsequent risk coupling coefficient This solution more accurately reflects the potential risks faced by the production line, thus avoiding alignment errors caused by inaccurate motion state assessment. Especially when the production line is running at high speed, experiencing frequent acceleration and deceleration, or when the aluminum ingot exhibits slight oscillations, this solution can promptly identify and quantify these instabilities, providing a more reliable basis for the PLC delay trigger time for target alignment judgment. This significantly improves the accuracy and robustness of aluminum ingot alignment, effectively reducing the alignment failure rate and product defect rate during production, thereby improving production efficiency and product quality.

[0053] This application further proposes the following steps for calculating and obtaining the surface slip coefficient: The system acquires the current bottom surface temperature of the aluminum ingot, the thickness of the residual cooling water film, and the amount of release agent sprayed. The bottom surface temperature directly affects the frictional characteristics between the aluminum ingot and the mold or support surface; the thickness of the residual cooling water film determines whether a water film lubrication effect exists on the aluminum ingot surface; and the amount of release agent sprayed directly affects the coverage and lubrication effect of the release agent on the aluminum ingot surface. These parameters can be acquired in real-time or near real-time using various methods. For example, the bottom surface temperature can be measured using an infrared thermometer or a contact thermocouple; the thickness of the residual cooling water film can be measured non-contactly using a laser rangefinder or ultrasonic sensor, or estimated by analyzing the reflective properties of the water film using image recognition technology; and the amount of release agent sprayed can be obtained by monitoring the flow rate of the spraying equipment using a flow sensor, or by detecting the spray coverage using a vision system.

[0054] The current bottom surface temperature and release agent spraying amount of the aluminum ingot are compared with the maximum allowable temperature and maximum allowable release agent spraying amount of the aluminum ingot bottom surface, respectively. A min function is then used to limit the ratio to an upper limit of 1 to obtain the bottom surface temperature index and the release agent spraying amount index. This step transforms the original physical quantities into dimensionless indices for subsequent unified calculations and risk assessments. The ratio processing standardizes data with different dimensions, allowing them to vary between 0 and 1, reflecting their proportion relative to the maximum allowable value. The min function's upper limit of 1 ensures that even if the actual value exceeds the maximum allowable value, the index will not increase indefinitely, thus avoiding excessive influence of extreme cases on the overall risk assessment and maintaining the model's robustness. Based on the ratio of the residual water film thickness to the critical water film thickness, the residual water film thickness index is calculated using a square function. The residual water film thickness index increases rapidly with increasing residual water film thickness. Specifically, the calculation method involves importing the current residual water film thickness of the aluminum ingot into the formula. Obtain the residual water film thickness index of cooling water. ,in, This refers to the current thickness of the residual cooling water film on the aluminum ingot. This represents the critical water film thickness; this step specifically exponentializes the residual water film thickness in the cooling water. A squared term is used. This approach more sensitively reflects the nonlinear effect of water film thickness on slip risk; that is, when the water film thickness approaches or exceeds a critical value, the slip risk increases significantly. The upper limit of the min function's amplitude limit ratio is 1, also to prevent the exponential value from growing indefinitely when the water film thickness is too large, thus maintaining model stability. Critical water film thickness. It is a preset threshold, representing the thickness at which the water film begins to significantly affect the slip properties.

[0055] The residual water film thickness index of cooling water and the release agent spraying amount index are superimposed and fused to obtain the comprehensive lubrication factor. The bottom surface temperature index is then used to amplify the comprehensive lubrication factor to obtain the surface slip coefficient. The surface slip coefficient increases monotonically with the increase of the bottom surface temperature index, the residual water film thickness index of cooling water, and the release agent spraying amount index. The specific calculation method is as follows: the bottom surface temperature index, the residual water film thickness index of cooling water, and the release agent spraying amount index are imported into the formula. Obtain the surface slip coefficient , The value range is 0-1, when When, it indicates low temperature, dryness, no release agent, and no risk of slippage. At this time, it indicates high temperature, a large amount of water film, and release agent, resulting in a very high risk of slippage. This is an adjustment coefficient ranging from 0.8 to 1.2, which can be assigned a value through a preset strategy or a dynamic algorithm. This refers to the bottom surface temperature index. The residual water film thickness index is the index of cooling water. This refers to the release agent application rate index. This step involves calculating the surface slip coefficient. The core of this formula is to combine three key indices through multiplication and addition / subtraction to comprehensively assess surface slip risk. Among them, This term can be understood as the combined lubrication or slippage effect under the combined action of the water film and the release agent, taking into account the possible superposition or partial cancellation of the two effects. Bottom surface temperature index As a multiplicative factor, this indicates that temperature has a global amplifying or reducing effect on slip risk. Adjustment coefficient The overall assessment of slip risk can be fine-tuned based on the specific conditions of the actual production line to accommodate different material, process, or equipment characteristics. The upper limit of the min function's amplitude limit ratio is 1, ensuring the surface slip coefficient... It always ranges between 0 and 1, intuitively representing the degree of slippage risk.

[0056] This application's solution acquires three key parameters in real-time or near real-time: the bottom surface temperature of the aluminum ingot, the thickness of the residual cooling water film, and the amount of mold release agent sprayed. These parameters are direct physical quantities affecting the friction state between the aluminum ingot and the mold or support surface. Subsequently, to unify these physical quantities with different dimensions and quantify their risks, the bottom surface temperature and the amount of mold release agent sprayed are compared with preset maximum allowable temperature and maximum allowable spraying amount, respectively, and then limited to obtain the bottom surface temperature index and the mold release agent spraying amount index. For the thickness of the residual cooling water film, a nonlinear formula is used to convert it into a residual cooling water film thickness index, which more sensitively reflects the nonlinear influence of the film thickness on slip risk. Finally, these three indices—bottom surface temperature index, residual cooling water film thickness index, and mold release agent spraying amount index—along with an adjustable risk weighting coefficient, are substituted into a comprehensive mathematical model to calculate the surface slip coefficient. This model cleverly combines the contributions of various factors to slip risk, especially through... This method considers the interaction between water film and release agent in influencing slip risk, while the bottom surface temperature index serves as a multiplicative factor, reflecting the global impact of temperature on slip risk. In this way, the method can comprehensively and accurately quantify the slip risk on the aluminum ingot surface, providing a more refined and reliable input for subsequent calculations of the risk coupling coefficient. This makes the risk assessment of the entire aluminum ingot alignment method more accurate, ultimately improving the accuracy of alignment judgment and the stability of the production line.

[0057] The following is a specific example to illustrate this. In an aluminum ingot continuous casting production line, the surface slip coefficient can be calculated using the following method. First, the bottom surface temperature of the aluminum ingot is acquired in real time using an infrared thermometer installed at the bottom of the ingot; for example, the current measured bottom surface temperature is 450°C. Simultaneously, the thickness of the cooling water film on the surface of the aluminum ingot is measured using a laser rangefinder installed in the cooling area; for example, the current measured water film thickness is 0.2 mm. Furthermore, the real-time spraying rate of the release agent is obtained by monitoring the flow meter of the release agent spraying system; for example, the current spraying rate is 1.5 liters / minute. Assuming the maximum allowable temperature of the aluminum ingot bottom surface is set to 500°C, the maximum allowable release agent spraying rate is set to 2.0 liters / minute, and the critical water film thickness... Set to 0.1 mm, adjust coefficient The value is set to 1.0. Based on the above data, the bottom surface temperature index is first calculated. The value is 0.9. Next, the release agent spraying amount index is calculated. The value is 0.75. Then, the residual water film thickness index of the cooling water is calculated. The value is 1. Finally, these exponents are substituted into the surface slip coefficient. The calculation formula and the result are obtained. The value is 0.9. Through the above calculations, the surface slip coefficient under the current operating conditions can be obtained. The value is 0.9, and this coefficient will be used as input for the subsequent calculation of the risk coupling coefficient.

[0058] Through the above technical solution, this application can more comprehensively and accurately assess the surface slip risk of aluminum ingots during continuous casting. By comprehensively considering three key factors—bottom surface temperature, residual water film thickness in cooling water, and release agent spraying amount—and employing nonlinear exponentialization and coupled calculation methods, the calculated surface slip coefficient more accurately reflects the actual production situation. This avoids the limitations of single-factor assessment bias or simple linear superposition that may exist in traditional methods, thus providing a more accurate input for calculating the risk coupling coefficient. Ultimately, this refined surface slip risk assessment helps improve the accuracy and robustness of the entire aluminum ingot alignment method, effectively reducing alignment errors and production accident risks caused by surface slip, and ensuring the stable operation of the continuous casting production line and product quality.

[0059] In aluminum ingot continuous casting production lines, achieving precise alignment of aluminum ingots requires comprehensive consideration of various complex factors, including the instantaneous operating state of the production line, the surface characteristics of the aluminum ingots, vibration conditions, and the reliability of the system itself. Based on these factors, the PLC delay trigger time for alignment judgment must be dynamically calculated and adjusted. However, in actual production environments, efficiently, accurately, and stably executing these complex calculation logics and real-time control instructions to ensure the effective implementation of the alignment method remains a significant technical challenge.

[0060] In response, this application proposes an aluminum ingot alignment device for use in an aluminum ingot continuous casting production line, comprising a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, implements the steps of the aforementioned aluminum ingot alignment method for an aluminum ingot continuous casting production line.

[0061] The memory is a hardware component used to store data and instructions. Its function is to provide the processor with all the instructions and data required for execution, ensuring that the logic of the correct method can be completely and accurately saved and invoked. This memory can be implemented in various forms; for example, it can be read-only memory (ROM), such as EEPROM or flash memory, used to store fixed program code and configuration parameters; or it can be random access memory (RAM), such as DRAM or SRAM, used to store temporary data and variables during program execution.

[0062] The processor is the core unit that executes computer instructions, responsible for data processing, logical operations, and control. As the core computing and control unit, its function is to parse and execute the computer program in memory, thereby completing all the complex calculations, judgments, and control logic in the aluminum ingot alignment method, and realizing dynamic adjustment of the PLC delay trigger time. This processor can be a microcontroller (MCU), which integrates a CPU, memory, and peripheral interfaces, suitable for real-time control and embedded applications; or it can be a programmable logic controller (PLC), which directly executes industrial control logic through its built-in CPU and programming environment; or it can be an industrial PC (IPC), providing more powerful computing capabilities and a more flexible software environment, suitable for complex algorithms and data processing.

[0063] The solution in this application solidifies the aforementioned aluminum ingot alignment method used in an aluminum ingot continuous casting production line into a computer program and stores it in a memory. The processor then executes this program, forming an integrated and automated alignment device. The processor acquires real-time data from various sensors on the production line, including the instantaneous speed and acceleration of the production line, the angular velocity of the aluminum ingot, the bottom surface temperature of the aluminum ingot, the thickness of the residual water film in the cooling water, the amount of release agent sprayed, the vibration amplitude of the aluminum ingot, the mass of the aluminum ingot, the sensor signal de-jitter time, the standard deviation of multi-sensor readings, and the response time deviation of the actuator. Subsequently, according to the preset computer program in the memory, the processor calculates the motion instability coefficient, surface slip coefficient, risk coupling coefficient, and system reliability coefficient in a predetermined logical order. Finally, based on the basic delay, the calculated risk coupling coefficient, and the system reliability coefficient, the processor accurately calculates the PLC delay trigger time for target alignment judgment. Afterward, the processor outputs the calculated target delay time to the production line control system to adjust the current alignment judgment PLC delay trigger time. In this way, the device combines complex mathematical models with real-time data processing capabilities to achieve intelligent and dynamic control of the aluminum ingot alignment process, transforming theoretical methods into practical and operable automated solutions.

[0064] The following is a concrete example to illustrate this. The device's memory can be integrated flash memory within the microcontroller to store the firmware program, supplemented by a portion of SRAM for runtime data storage. The processor can be an industrial-grade microcontroller, such as STMicroelectronics' STM32 series microcontroller, or a Siemens S7 series PLC CPU module. This processor connects to various sensors (such as encoders, accelerometers, infrared thermometers, vision sensors, vibration sensors, weighing sensors, etc.) and actuators (such as PLC input modules or communication interfaces) on the production line via its digital input / output (I / O) interface or industrial Ethernet interface. The computer program can be written in C / C++, compiled, and burned into the flash memory, or written in a PLC programming language such as ladder logic / structured text and downloaded to the PLC's processor. In actual operation, the processor periodically reads data from the sensors and, based on the preset algorithm in the memory—the ingot alignment method used in the aluminum ingot continuous casting production line—calculates the motion instability coefficient, surface slip coefficient, risk coupling coefficient, and system reliability coefficient, ultimately determining the PLC delay trigger time for target alignment judgment. Subsequently, the processor sends this target delay time to the PLC in the production line control system via digital output or a communication interface, whereby the PLC adjusts the actual delay trigger to achieve precise alignment of the aluminum ingot.

[0065] Through the above technical solution, the device of this application provides a stable, efficient, and dedicated platform for implementing complex aluminum ingot alignment methods. This device ensures the accuracy and real-time performance of multi-factor calculations, thereby enabling dynamic and precise adjustment of the PLC delay trigger time, significantly improving the accuracy and reliability of aluminum ingot alignment. Furthermore, the device's automation reduces manual intervention, lowers operational errors, and improves the overall operating efficiency and product quality of the continuous casting production line, providing a solid technical guarantee for implementing refined control in high-speed, dynamically changing production environments.

[0066] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for aligning aluminum ingots in an aluminum ingot continuous casting production line, characterized in that, include: The motion instability coefficient is calculated based on the instantaneous speed and instantaneous acceleration of the production line and the angular velocity of the aluminum ingot. The surface slip coefficient is calculated based on the bottom surface temperature of the aluminum ingot, the thickness of the residual water film in the cooling water, and the amount of release agent sprayed. The risk coupling coefficient is calculated based on the motion instability coefficient, surface slip coefficient, aluminum ingot vibration amplitude, and aluminum ingot mass. The system reliability coefficient is calculated based on the sensor signal de-jitter time, the standard deviation of multi-sensor readings, and the actuator response time deviation. Based on the basic delay, risk coupling coefficient, and system reliability coefficient, the PLC delay trigger time for target alignment judgment is calculated and obtained, and the current PLC delay trigger time for alignment judgment is adjusted to the PLC delay trigger time for target alignment judgment.

2. The method for aligning aluminum ingots in a continuous casting production line according to claim 1, characterized in that, The steps for calculating and obtaining the PLC delay trigger time for target alignment judgment are as follows: Obtain the current base delay, risk coupling coefficient, and system reliability coefficient; Based on the system reliability coefficient and risk coupling coefficient, the additional delay amount is dynamically determined within the preset maximum allowable additional delay range; Add the base delay to the additional delay to obtain the PLC delay trigger time for target alignment judgment; When the system reliability coefficient is low, a larger conservative additional delay is tended to be used; when the system reliability coefficient is high, adaptive adjustment is made within the maximum allowable additional delay range according to the magnitude of the risk coupling coefficient.

3. The method for aligning aluminum ingots in a continuous casting production line according to claim 2, characterized in that, The steps for calculating and obtaining the system reliability coefficient are as follows: Obtain the current sensor signal de-jitter time, the standard deviation of multi-sensor readings, and the actuator response time deviation; The sensor signal de-jitter time is normalized by a negative exponential decay function, and the signal stability factor is calculated. The signal stability factor decreases as the de-jitter time increases. The ratio of the current standard deviation of multi-sensor readings to the maximum permissible standard deviation is processed, and the complement of the ratio is taken as the multi-sensor consistency factor. The actuator response reliability factor is calculated using a linear decay function based on the ratio of the actuator response time deviation to the maximum permissible deviation. The actuator response reliability factor decreases as the deviation increases. The minimum value among the signal stability factor, multi-sensor consistency factor, and actuator response reliability factor is taken as the system reliability coefficient.

4. The method for aligning aluminum ingots in a continuous casting production line according to claim 2, characterized in that, The steps for calculating and obtaining the risk coupling coefficient are as follows: Obtain the current motion instability coefficient, surface slip coefficient, aluminum ingot vibration amplitude, and aluminum ingot mass; The current aluminum ingot vibration amplitude is compared with the reference vibration amplitude to obtain the aluminum ingot vibration amplitude index. The current aluminum ingot quality is processed by maximum-min normalization to obtain the aluminum ingot quality index; The motion instability coefficient is combined with the aluminum ingot quality index to form a motion risk component; The surface slip coefficient is combined with the aluminum ingot vibration amplitude index to form a surface risk component; A weighted sum of squares fusion operation is performed on the motion risk component and the surface risk component, and the square root of the operation result is performed to obtain a risk coupling coefficient in the range of 0 to 1. Among them, a risk coupling coefficient of 0 indicates the lowest risk of multi-factor coupling, while a risk coupling coefficient of 1 indicates the highest risk of multi-factor coupling.

5. The method for aligning aluminum ingots in a continuous casting production line according to claim 4, characterized in that, The steps for calculating and obtaining the motion instability coefficient are as follows: Obtain the instantaneous velocity and instantaneous acceleration of the current production line, as well as the angular velocity of the aluminum ingot attitude; The instantaneous speed of the current production line is compared with the maximum design speed of the production line to obtain the instantaneous speed index of the production line. Based on the ratio of instantaneous acceleration to reference acceleration, the instantaneous acceleration exponent of the production line is calculated using the hyperbolic tangent function. The instantaneous acceleration exponent of the production line increases monotonically with the increase of instantaneous acceleration and tends to saturate. Based on the ratio of the current aluminum ingot attitude angular velocity to the reference angular velocity, the aluminum ingot attitude angular velocity exponent is calculated using an exponentially decaying complementary function. The aluminum ingot attitude angular velocity exponent increases monotonically with the increase of angular velocity and tends to saturate. The instantaneous speed index and instantaneous acceleration index of the production line are weighted and summed. The weight of the instantaneous acceleration index term is modulated by the aluminum ingot attitude angular velocity index, which amplifies the instability effect of the instantaneous acceleration of the production line. The weighted sum is then subjected to amplitude limiting to obtain the motion instability coefficient between 0 and 1.

6. The method for aligning aluminum ingots in a continuous casting production line according to claim 4, characterized in that, The steps for calculating and obtaining the surface slip coefficient are as follows: Obtain the current bottom surface temperature of the aluminum ingot, the thickness of the residual water film in the cooling water, and the amount of release agent sprayed. The current bottom surface temperature of the aluminum ingot and the amount of release agent sprayed are compared with the highest allowable temperature and the maximum allowable amount of release agent sprayed on the bottom surface of the aluminum ingot, respectively. After using the min function to limit the upper limit of the ratio to 1, the bottom surface temperature index and the release agent spraying amount index are obtained. Based on the ratio of the residual water film thickness to the critical water film thickness, the residual water film thickness index is calculated using a square function. The residual water film thickness index increases rapidly with the increase of the residual water film thickness. The comprehensive lubrication factor is obtained by superimposing and fusing the residual water film thickness index of cooling water and the release agent spraying amount index. The bottom surface temperature index is then amplified to obtain the surface slip coefficient by amplifying the comprehensive lubrication factor. The surface slip coefficient increases monotonically with the increase of the bottom surface temperature index, the cooling water residual film thickness index, and the release agent spraying amount index.

7. An aluminum ingot alignment device for use in an aluminum ingot continuous casting production line, characterized in that, include: Memory, used to store computer programs; A processor, configured to, when executing a computer program, implement the steps of the aluminum ingot alignment method for an aluminum ingot continuous casting production line as described in any one of claims 1-6.

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