A die-casting mold temperature intelligent following control system and method

Abstract

This invention specifically relates to an intelligent temperature tracking control system and method for die casting molds, belonging to the field of automated control technology for die casting production. It includes: a data acquisition module; a data analysis module; a comprehensive processing module; and an evaluation control module. In this invention, the data acquisition module performs comprehensive monitoring of the temperature control sensor, cooling pipes, and hydraulic guide vanes, and the data analysis module calculates the sensor influence coefficient, pipe blockage coefficient, and guide vane loss coefficient respectively. This multi-dimensional collaborative monitoring method can comprehensively capture potential anomalies in the die casting process. In cooling pipe monitoring, the use of fluorescence tracing technology and image analysis can intuitively present the internal blockage of the pipes, providing greater spatial resolution compared to traditional pressure or flow detection. This allows for early detection of equipment hazards, advancing the fault warning time, effectively reducing unplanned downtime, and improving production efficiency.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for die casting production, and in particular to an intelligent temperature following control system and method for die casting molds. Background Technology

[0002] In the die casting production process, mold temperature is one of the key factors affecting the quality and production efficiency of die castings. Excessively high mold temperatures lead to slow cooling of the molten metal, making the die castings prone to defects such as shrinkage cavities and deformation; conversely, excessively low mold temperatures reduce the fluidity of the molten metal, resulting in problems such as cold shuts and incomplete filling. Currently, die casting mold temperature control mainly relies on traditional temperature sensors and simple feedback adjustment systems.

[0003] However, existing mold temperature control systems have many problems:

[0004] On the one hand, traditional temperature control sensors may experience malfunctions such as increased measurement error and unstable signal transmission during long-term use, but they lack effective fault diagnosis and early warning mechanisms, making it difficult to accurately determine whether the sensor is abnormal.

[0005] On the other hand, existing technologies often cannot detect blockages inside cooling pipes or wear on hydraulic guide vanes in real time and accurately, leading to reduced cooling efficiency and decreased hydraulic system performance, which in turn affects the stability of mold temperature and the quality of die castings.

[0006] In addition, traditional mold temperature control systems mostly use single parameter control, which cannot comprehensively consider the synergistic effect of multiple influencing factors on mold temperature, making it difficult to achieve accurate and intelligent control of die-casting mold temperature.

[0007] Therefore, a die-casting mold temperature intelligent following control system and method are needed to address the problems mentioned above. Summary of the Invention

[0008] The purpose of this invention is to provide a die-casting mold temperature intelligent following control system and method to solve the above problems.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A die-casting mold temperature intelligent tracking control system includes:

[0011] Data acquisition module: Acquires temperature control-related parameter information during the die-casting process, including information related to temperature control sensors, cooling pipes, and hydraulic guide vanes;

[0012] Data analysis module: After analyzing the relevant information of temperature control sensor, cooling pipe, and hydraulic guide vane, the sensor influence coefficient, pipe blockage coefficient, and guide vane loss coefficient are obtained.

[0013] Integrated processing module: After comprehensively processing the sensor influence coefficient, pipeline blockage coefficient, and guide vane loss coefficient, an anomaly assessment coefficient is obtained;

[0014] Assessment and control module: Matches the corresponding anomaly level based on the anomaly assessment coefficient, and performs corresponding controls according to the anomaly level.

[0015] Preferably, the data acquisition module specifically includes:

[0016] Temperature sensor related information: including the temperature data monitored by the temperature sensor, and the temperature data related to the connection cable of the temperature sensor;

[0017] Information related to cooling pipes: including fluorescence value information of various areas inside the pipes, and water flow rate information of the pipes;

[0018] Information related to hydraulic guide vanes: including image information of the guide vanes.

[0019] Preferably, the process of obtaining the sensor influence coefficient includes:

[0020] The temperature values ​​monitored by each temperature sensor are acquired at preset time intervals, and the average value of all temperature values ​​acquired at the same time interval is calculated to obtain the average temperature.

[0021] The temperature difference value is obtained by calculating the difference between each temperature value and the average temperature value in turn.

[0022] The preset allowable fluctuation range of temperature difference value is used, and temperature difference values ​​that are not within the allowable fluctuation range are recorded as abnormal temperature difference values.

[0023] The temperature control sensors corresponding to all abnormal temperature difference values ​​acquired within the same time interval are counted sequentially. The temperature control sensors corresponding to abnormal temperature difference values ​​in each time interval are compared, and the temperature control sensors that appear more than k times are marked and recorded as abnormal sensors.

[0024] The sensor influence coefficient was obtained after analyzing the abnormal sensors.

[0025] Preferably, the method further includes:

[0026] The thermal image of the cable of the abnormal sensor is acquired at preset time intervals, and the cable is divided into a middle area and a joint area according to the region; there are two joint areas, namely the joint areas at both ends of the cable.

[0027] The temperature of the middle area of ​​the cable is marked as the reference temperature. The difference between the temperature of the cable joint area and the reference temperature is calculated to obtain the joint temperature.

[0028] The preset allowable fluctuation range of joint temperature is defined, and joint temperatures outside the allowable fluctuation range are recorded as abnormal contact temperatures.

[0029] After obtaining the number of all abnormal contact temperatures, divide the number of reference temperatures by the number of abnormal contact temperatures, and take half of the result to obtain the abnormality level.

[0030] Sort all abnormal contact temperatures in descending order of numerical value, extract the maximum and minimum abnormal contact temperatures and their corresponding time points, and obtain the time interval between the maximum and minimum abnormal contact temperatures, which is recorded as the abnormal transition value.

[0031] The sensor influence coefficient is obtained by combining the anomaly degree and the anomaly transition value.

[0032] Preferably, the process of obtaining the pipeline blockage coefficient includes:

[0033] Food-grade fluorescent tracers are injected into the cooling pipe at preset time intervals; and ultraviolet lamps are simultaneously activated to irradiate the outer wall of the cooling pipe to obtain image information of the cooling pipe.

[0034] All cooling pipe images are sorted according to time series. By analyzing the cooling pipe images, gray values ​​are extracted from the images. A gray value threshold is preset, and gray values ​​greater than the gray value threshold are recorded as fluorescence values.

[0035] The region corresponding to the fluorescence value is recorded as the dwell region. The fluorescence value corresponding to each dwell region is obtained from all the cooling pipe images, and the maximum fluorescence value corresponding to each dwell region is extracted.

[0036] The maximum fluorescence values ​​of each residence area are sorted in descending order of numerical value, and the three largest fluorescence values ​​and their corresponding residence area centers are extracted to obtain three area center points. The three area center points are connected by straight lines to obtain a triangle model. The triangle model is projected onto the cross-section of the cooling pipe to obtain the projected triangle.

[0037] The blockage degree is obtained by calculating the area of ​​the projected triangle and dividing it by the cross-sectional area of ​​the cooling pipe.

[0038] Obtain the inlet and outlet flow velocities of the cooling pipe. Subtract the outlet flow velocity from the inlet flow velocity and divide by the inlet flow velocity to obtain the velocity drop. Calculate the average velocity drop by averaging the velocity drops obtained at each time interval.

[0039] The pipe blockage coefficient is obtained by weighting the degree of blockage and the average decrease in flow velocity.

[0040] Preferably, the process of obtaining the guide vane loss coefficient includes:

[0041] Acquire image information of the guide vane when it stops rotating;

[0042] The guide vane is divided into multiple sub-regions. Based on each sub-region of the guide vane, the image information of each corresponding sub-region is obtained, and the obtained image information of each sub-region is preprocessed.

[0043] Feature extraction is performed on each sub-region of the guide vane to extract features related to wear and corrosion, and the extracted features are marked to obtain the edge contours of the wear area and corrosion area to obtain the wear area and corrosion area. The wear area and corrosion area are summed to obtain the wear area.

[0044] Divide the eroded area by the area of ​​the corresponding sub-region to obtain the erosion ratio; after obtaining the erosion area ratio of each sub-region in turn, extract the largest erosion ratio and record it as the abnormal erosion ratio;

[0045] Obtain the overlapping area of ​​the wear area and corrosion area, and its corresponding overlapping area area; accumulate the overlapping area areas of each sub-region to obtain the total overlapping area; divide the total overlapping area by the guide vane area to obtain the overlapping ratio;

[0046] The guide vane loss coefficient is obtained by comprehensively processing the abnormal wear ratio and the overlap ratio.

[0047] Preferably, the anomaly assessment coefficient is obtained by comprehensively processing the sensor influence coefficient, pipeline blockage coefficient, and guide vane loss coefficient. The specific process is as follows:

[0048] After normalizing the sensor influence coefficient, pipeline blockage coefficient, and guide vane loss coefficient, the sensor influence coefficient and pipeline blockage coefficient are respectively used as two legs of a right triangle. The remaining leg is then connected to form a complete right triangle. The guide vane loss coefficient is used as the height of the triangle to establish a triangular pyramid model. The volume of the triangular pyramid model is calculated and denoted as the anomaly evaluation coefficient.

[0049] Preferably, the evaluation control module specifically includes:

[0050] Three threshold ranges are preset, and each threshold range corresponds to an anomaly level. The anomaly assessment coefficient is matched with the three threshold ranges to obtain the anomaly level corresponding to the anomaly assessment coefficient. The anomaly levels include mild anomaly, moderate anomaly, and severe anomaly.

[0051] When the anomaly level is mild: the sampling frequency of the abnormal parameters is automatically increased and a real-time trend curve is generated; information related to the temperature control sensor, cooling pipes, and hydraulic guide vanes is continuously monitored.

[0052] When the anomaly level is moderate: increase the lubrication frequency of the moving parts of the mold to reduce frictional heat generation; start the auxiliary cooling equipment to force air cooling of the high-temperature areas of the mold;

[0053] When the anomaly level is severe: immediately cut off the heating source of the die-casting machine and stop the injection of molten metal; start the mold emergency cooling procedure, including fully opening the cooling water valve and starting the backup chiller; perform hydraulic system depressurization to prevent abnormal load from damaging the guide vanes; automatically generate a "Severe Anomaly Shutdown Report".

[0054] A method for intelligent temperature tracking and control of die-casting molds includes:

[0055] Data Acquisition and Analysis: Acquire temperature control-related parameter information during the die casting process, including information related to temperature control sensors, cooling pipes, and hydraulic guide vanes; and analyze the information related to temperature control sensors, cooling pipes, and hydraulic guide vanes to obtain sensor influence coefficients, pipe blockage coefficients, and guide vane loss coefficients.

[0056] Unified processing: After comprehensively processing the sensor influence coefficient, pipeline blockage coefficient, and guide vane loss coefficient, the anomaly assessment coefficient is obtained;

[0057] Anomaly Level Assessment and Control: Based on the anomaly assessment coefficient, the corresponding anomaly level is matched, and corresponding controls are implemented according to the anomaly level.

[0058] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0059] 1. This invention uses a data acquisition module to comprehensively monitor the temperature control sensor, cooling pipes, and hydraulic guide vanes, and calculates the sensor influence coefficient, pipe blockage coefficient, and guide vane loss coefficient in the data analysis module. This multi-dimensional collaborative monitoring method can comprehensively capture potential anomalies in the die-casting process. In cooling pipe monitoring, the use of fluorescence tracer technology and image analysis can intuitively present the internal blockage of the pipes, providing better spatial resolution compared to traditional pressure or flow detection. This allows for early detection of equipment hazards, advances fault warning time, effectively reduces unplanned downtime, and improves production efficiency.

[0060] 2. This invention achieves hierarchical control based on anomaly evaluation coefficients through an evaluation control module, and adopts differentiated response measures for different anomaly levels; for mild anomalies, the monitoring frequency is increased; for moderate anomalies, process parameter adjustments and auxiliary cooling are initiated; and for severe anomalies, emergency shutdown and safety protection are implemented. The hierarchical control strategy ensures that the system responds promptly to anomalies while avoiding production fluctuations caused by excessive intervention. At the same time, the system is linked with a cloud-based expert system, which can generate fault tree analysis reports and recommend maintenance solutions based on real-time anomaly data. Attached Figure Description

[0061] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0062] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0063] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0064] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0065] Please see Figure 1 As shown, the present invention provides a technical solution:

[0066] A die-casting mold temperature intelligent tracking control system includes:

[0067] Data acquisition module: Acquires temperature control-related parameter information during the die-casting process, including information related to temperature control sensors, cooling pipes, and hydraulic guide vanes;

[0068] Specifically, it includes:

[0069] Temperature sensor related information: including the temperature data monitored by the temperature sensor, and the temperature data related to the connection cable of the temperature sensor;

[0070] Information related to cooling pipes: including fluorescence value information of various areas inside the pipes, and water flow rate information of the pipes;

[0071] Information related to hydraulic guide vanes: including image information of the guide vanes;

[0072] Data analysis module: After analyzing the relevant information of temperature control sensor, cooling pipe, and hydraulic guide vane, the sensor influence coefficient, pipe blockage coefficient, and guide vane loss coefficient are obtained.

[0073] The process of obtaining the sensor influence coefficient includes:

[0074] The temperature values ​​monitored by each temperature sensor are acquired at preset time intervals, and the average value of all temperature values ​​acquired at the same time interval is calculated to obtain the average temperature.

[0075] The temperature difference value is obtained by calculating the difference between each temperature value and the average temperature value in turn.

[0076] The preset allowable fluctuation range of temperature difference value is used, and temperature difference values ​​that are not within the allowable fluctuation range are recorded as abnormal temperature difference values.

[0077] The temperature control sensors corresponding to all abnormal temperature difference values ​​acquired within the same time interval are counted sequentially. The temperature control sensors corresponding to abnormal temperature difference values ​​in each time interval are compared, and the temperature control sensors that appear more than k times are marked and recorded as abnormal sensors.

[0078] By calculating the temperature difference between the temperature value of the temperature control sensor and the average value, and filtering out abnormal temperature difference values ​​that exceed the allowable fluctuation range, the sensor with abnormal temperature monitoring can be quickly located.

[0079] For example, when a sensor continuously outputs a temperature value that deviates significantly from other sensors, the system can use a marking rule of "occurrence greater than k times" to eliminate random interference and pinpoint the truly faulty sensor (such as poor sensor probe contact, abnormal signal transmission, etc.).

[0080] To avoid misjudgment based on a single temperature value, multiple anomaly statistics over time series are used to reduce the false alarm rate and ensure the accuracy of fault location.

[0081] At preset time intervals, the infrared thermal imaging device acquires thermal images of the cable of the abnormal sensor and divides the cable into a middle area and a joint area according to the region; there are two joint areas, namely the joint areas at both ends of the cable.

[0082] The cables of abnormal sensors are divided into intermediate and connector areas. The contact condition is determined by the difference between the connector temperature and a reference temperature (e.g., connector oxidation or loosening leading to increased resistance and heat). For example, if the temperature of the connector area is significantly higher than that of the intermediate area, it can be determined that there is poor contact.

[0083] The analysis delves into the root cause of sensor anomalies at the physical layer (cable contact), rather than merely focusing on the surface of temperature data, to establish a causal link between data anomalies and hardware failures.

[0084] The temperature of the middle area of ​​the cable is marked as the reference temperature. The difference between the temperature of the cable joint area and the reference temperature is calculated to obtain the joint temperature.

[0085] The preset allowable fluctuation range of joint temperature is defined, and joint temperatures outside the allowable fluctuation range are recorded as abnormal contact temperatures.

[0086] After obtaining the number of all abnormal contact temperatures, divide the number of reference temperatures by the number of abnormal contact temperatures, and take half of the result to obtain the abnormality level.

[0087] Sort all abnormal contact temperatures in descending order of numerical value, extract the maximum and minimum abnormal contact temperatures and their corresponding time points, and obtain the time interval between the maximum and minimum abnormal contact temperatures, which is recorded as the abnormal transition value.

[0088] The sensor influence coefficient is obtained by comprehensively processing the anomaly degree and the anomaly transition value;

[0089] Label the anomaly degree and the anomaly transition value as DA and UV, respectively, and then enter them into the formula: The sensor influence coefficient ζ is obtained;

[0090] Where UV′ is the maximum permissible abnormal transition value;

[0091] a1 and a2 are the weighting factors corresponding to the anomaly degree and the anomaly transition value, respectively;

[0092] The process of obtaining the pipeline blockage coefficient includes:

[0093] Food-grade fluorescent tracer (such as Rhodamine B, concentration ≤0.01%) is injected into the cooling pipe at preset time intervals; and at the same time, ultraviolet lamps are turned on to irradiate the outer wall of the cooling pipe to obtain image information of the cooling pipe.

[0094] Food-grade fluorescent tracers (such as Rhodamine B) can generate strong fluorescence signals when excited by ultraviolet light at low concentrations (≤0.01%), which can accurately mark fluid stagnation areas. This method does not contaminate the fluid medium, and the fluorescence signal has a significant difference in grayscale from the background, which facilitates the rapid location of abnormal areas (such as blockages or abnormal flow rates) through image recognition.

[0095] The real-time linkage between ultraviolet lamp irradiation and image acquisition can dynamically capture the movement trajectory of fluid in the pipe, and compared with traditional pressure detection or flow sensors, it can more intuitively present the spatial distribution and shape of blockage.

[0096] All cooling pipe images are sorted according to time series. The images are then analyzed using ImageJ software to extract grayscale values. A grayscale value threshold is preset, and grayscale values ​​greater than the threshold are recorded as fluorescence values.

[0097] The region corresponding to the fluorescence value is recorded as the dwell region. The fluorescence value corresponding to each dwell region is obtained from all the cooling pipe images, and the maximum fluorescence value corresponding to each dwell region is extracted.

[0098] The maximum fluorescence values ​​of each residence area are sorted in descending order of numerical value, and the three largest fluorescence values ​​and their corresponding residence area centers are extracted to obtain three area center points. The three area center points are connected by straight lines to obtain a triangle model. The triangle model is projected onto the cross-section of the cooling pipe to obtain the projected triangle.

[0099] The blockage degree is obtained by calculating the area of ​​the projected triangle and dividing it by the cross-sectional area of ​​the cooling pipe.

[0100] Obtain the inlet and outlet flow velocities of the cooling pipe. Subtract the outlet flow velocity from the inlet flow velocity and divide by the inlet flow velocity to obtain the velocity drop. Calculate the average velocity drop by averaging the velocity drops obtained at each time interval.

[0101] The pipe blockage coefficient is obtained by weighting the blockage degree and the average flow velocity decrease.

[0102] The pipe blockage coefficient is obtained by multiplying the blockage degree and the average velocity drop by their respective weighting factors and summing them.

[0103] The process of obtaining the guide vane loss coefficient includes:

[0104] Acquire image information of the guide vane when it stops rotating;

[0105] The guide vane is divided into multiple sub-regions. Based on each sub-region of the guide vane, the image information of each corresponding sub-region is obtained, and the obtained image information of each sub-region is preprocessed.

[0106] Dividing the guide vane into multiple sub-regions (such as by the leading edge, middle, trailing edge, or flow channel) can achieve "precise local diagnosis".

[0107] For example, wear on turbine guide vanes is often concentrated on the water-facing surface or in the high-speed water flow impact zone. Regional treatment can directly pinpoint the high-incidence areas of the fault and avoid the ambiguity of global analysis.

[0108] Feature extraction is performed on each sub-region of the guide vane to extract features related to wear and corrosion, and the extracted features are marked to obtain the edge contours of the wear area and corrosion area to obtain the wear area and corrosion area. The wear area and corrosion area are summed to obtain the wear area.

[0109] Divide the eroded area by the area of ​​the corresponding sub-region to obtain the erosion ratio; after obtaining the erosion area ratio of each sub-region in turn, extract the largest erosion ratio and record it as the abnormal erosion ratio;

[0110] Extracting the maximum abrasion ratio (abnormal abrasion ratio) can directly pinpoint the most severely damaged local location on the guide vane. For example, if the abrasion ratio of sub-region C of a water pump guide vane reaches 0.25, far exceeding the 0.08 of other regions, it indicates that a "damage hotspot" has formed in this region, which may cause water flow vibration or a sudden drop in efficiency.

[0111] Obtain the overlapping area of ​​the wear area and corrosion area, and its corresponding overlapping area area; accumulate the overlapping area areas of each sub-region to obtain the total overlapping area; divide the total overlapping area by the guide vane area to obtain the overlapping ratio;

[0112] After comprehensively processing the abnormal abrasion ratio and the overlap ratio, the guide vane loss coefficient is obtained;

[0113] After normalizing the abnormal wear ratio and overlap ratio, the abnormal wear ratio and overlap ratio are used as the major and minor semi-axes of the ellipse, respectively, to establish an elliptical model. The area of ​​the elliptical model is calculated and denoted as the guide vane loss coefficient.

[0114] Integrated processing module: After comprehensively processing the sensor influence coefficient, pipeline blockage coefficient, and guide vane loss coefficient, an anomaly assessment coefficient is obtained;

[0115] The specific process is as follows:

[0116] After normalizing the sensor influence coefficient, pipeline blockage coefficient, and guide vane loss coefficient, the sensor influence coefficient and pipeline blockage coefficient are respectively used as two legs of a right triangle. The remaining leg is connected to form a complete right triangle. The guide vane loss coefficient is used as the height of the triangle to establish a triangular pyramid model. The volume of the triangular pyramid model is calculated and denoted as the anomaly evaluation coefficient.

[0117] Assessment and control module: Matches the corresponding anomaly level based on the anomaly assessment coefficient, and performs corresponding controls according to the anomaly level;

[0118] Specifically, it includes:

[0119] Three threshold ranges are preset, and each threshold range corresponds to an anomaly level. The anomaly assessment coefficient is matched with the three threshold ranges to obtain the anomaly level corresponding to the anomaly assessment coefficient. The anomaly levels include mild anomaly, moderate anomaly, and severe anomaly.

[0120] When the anomaly level is mild: the sampling frequency of the abnormal parameters is automatically increased and a real-time trend curve is generated; information related to the temperature control sensor, cooling pipes, and hydraulic guide vanes is continuously monitored.

[0121] When the anomaly level is moderate: increase the lubrication frequency of moving parts of the mold to reduce frictional heat generation; start auxiliary cooling equipment (such as temporary fans) to force air cooling of high-temperature areas of the mold; monitor the mold temperature fluctuation range in real time, and if the actual temperature exceeds the set value by 10℃ (e.g., set at 200℃, measured at 210℃), the system will automatically trigger "temperature over-limit protection" to temporarily suspend the injection operation until the temperature drops back to a safe range; push a "moderate anomaly handling work order" to the workshop MES system, requiring engineers to arrive at the site within 2 hours to confirm, and the work order includes anomaly location data;

[0122] When the anomaly level is severe: immediately cut off the heating source of the die-casting machine (such as the electric heating tube of the mold temperature controller) and stop the injection of molten metal; start the mold emergency cooling procedure, including fully opening the cooling water valve and starting the backup chiller; perform hydraulic system depressurization (pressure drops below 0.2MPa) to prevent abnormal load from damaging the guide vanes; automatically generate a "Severe Anomaly Shutdown Report", including: the time of anomaly occurrence, the highest evaluation coefficient K value and the contribution ratio of each sub-coefficient (such as the sensor influence coefficient accounting for 45% and the pipeline blockage accounting for 35%); log all parameters (temperature, pressure, flow rate, etc.) for 30 seconds before shutdown for fault review; automatically activate the equipment protective door lock after shutdown to prevent unauthorized personnel from approaching the high-temperature mold or abnormal pipeline; if the anomaly is accompanied by high temperature (such as sensor cable short circuit and heat generation), automatically activate the workshop fire alarm system and release inert gas to suppress the fire source;

[0123] By linking with the cloud-based expert system, a fault tree analysis (FTA) report is generated based on abnormal data (such as "sensor connector temperature rises by 20℃ / min" and "pipe flow rate drops by 30%), and a recommended repair solution (such as "it is recommended to replace sensor C and its corresponding cable" and "the pipe needs chemical cleaning and mechanical unblocking") is pushed to the repair team's mobile device.

[0124] A method for intelligent temperature tracking and control of die-casting molds includes:

[0125] Data Acquisition and Analysis: Acquire temperature control-related parameter information during the die casting process, including information related to temperature control sensors, cooling pipes, and hydraulic guide vanes; and analyze the information related to temperature control sensors, cooling pipes, and hydraulic guide vanes to obtain sensor influence coefficients, pipe blockage coefficients, and guide vane loss coefficients.

[0126] Unified processing: After comprehensively processing the sensor influence coefficient, pipeline blockage coefficient, and guide vane loss coefficient, the anomaly assessment coefficient is obtained;

[0127] Anomaly Level Assessment and Control: Based on the anomaly assessment coefficient, the corresponding anomaly level is matched, and corresponding controls are implemented according to the anomaly level.

[0128] The above formulas are derived from software simulations using a large amount of data and are selected to be close to the actual values. The influence weighting factors and specific coefficient values ​​in the formulas are set by those skilled in the art based on the actual situation and can be adjusted and modified in the future.

[0129] The above description of the embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent follow-up control system for die temperature of die casting, characterized by, The application relates to a die-casting process temperature control abnormality evaluation method and device. The data acquisition module acquires temperature control related parameter information in the die-casting process, including temperature control sensor related information, cooling pipe related information and hydraulic guide vane related information. The data analysis module analyzes the temperature control sensor related information, the cooling pipe related information and the hydraulic guide vane related information to obtain a sensor influence coefficient, a pipe blockage coefficient and a guide vane loss coefficient. The pipe blockage coefficient acquisition process comprises the following steps: Food-grade fluorescent tracers are injected into the cooling pipe at preset time intervals, and an ultraviolet lamp is started to irradiate the outer wall of the cooling pipe to obtain image information of the cooling pipe. All the cooling pipe images are sorted in time sequence, and the gray values in the cooling pipe images are extracted by analyzing the cooling pipe images, a preset gray value threshold is set, and the gray values greater than the gray value threshold are recorded as fluorescent values. The regions corresponding to the fluorescent values are recorded as residence regions, the fluorescent values corresponding to all the residence regions are obtained from all the cooling pipe images, and the maximum fluorescent values corresponding to the residence regions are extracted. The maximum fluorescent values of the residence regions are arranged in descending order according to the numerical values, and the three largest fluorescent values and the corresponding residence region centers are extracted to obtain three region center points, the three region center points are connected by straight lines to obtain a triangular model, and the triangular model is projected onto the cross section of the cooling pipe to obtain a projection triangle. The area of the projection triangle is divided by the cross-sectional area of the cooling pipe to obtain a blockage degree. The water inlet flow rate and the water outlet flow rate of the cooling pipe are obtained, the water inlet flow rate is subtracted from the water outlet flow rate, and the result is divided by the water inlet flow rate to obtain a flow rate reduction degree. The blockage degree and the flow rate average reduction degree are weighted to obtain the pipe blockage coefficient. The sensor influence coefficient, the pipe blockage coefficient and the guide vane loss coefficient are comprehensively processed to obtain an abnormality evaluation coefficient. The evaluation control module matches the corresponding abnormality level based on the abnormality evaluation coefficient, and performs corresponding control according to the abnormality level.

2. The die-casting mold temperature intelligent following control system according to claim 1, characterized in that, The data acquisition module specifically comprises: The temperature control sensor related information comprises monitoring temperature data of the temperature control sensor and connection cable related temperature data of the temperature control sensor. The cooling pipe related information comprises fluorescent value information of each region in the pipe and pipe water inlet and outlet flow rate information. The hydraulic guide vane related information comprises image information of the guide vane.

3. The die-casting die temperature intelligent following control system according to claim 2, characterized in that, The sensor influence coefficient acquisition process comprises the following steps: Temperature values monitored by each temperature control sensor are obtained at preset time intervals, and the temperature values of all the temperature control sensors obtained at the same time interval are averaged to obtain a temperature average value. Each temperature value is subtracted from the temperature average value to obtain a temperature difference value. An allowable fluctuation range of the temperature difference value is preset, and the temperature difference values not in the allowable fluctuation range of the temperature difference value are recorded as abnormal temperature difference values. Statistically record the temperature control sensors corresponding to all abnormal temperature difference values obtained in the same time interval in sequence; compare the temperature control sensors corresponding to the abnormal temperature difference values in each time interval, mark the temperature control sensors with the number of occurrences greater than k times as abnormal sensors, and record them as abnormal sensors; And analyze the abnormal sensors to obtain the sensor influence coefficient.

4. The die-casting mold temperature intelligent following control system according to claim 3, characterized in that, Also includes: Obtain the cable thermal imaging image of the abnormal sensor at a preset time interval, and divide it into a middle region and a joint region according to the region of the cable; Wherein the joint region has two, which are the two end joint regions of the cable; Mark the temperature of the middle region of the cable as the reference temperature, and calculate the difference between the temperature of the joint region of the cable and the reference temperature to obtain the joint temperature; Pre-set the allowable fluctuation range of the joint temperature, and record the joint temperature not within the allowable fluctuation range of the joint temperature as the abnormal contact temperature; After obtaining the number of all abnormal contact temperatures in sequence, divide the number of reference temperatures by the number of abnormal contact temperatures, take half of the obtained value, and obtain the abnormality degree; Arrange all abnormal contact temperatures in descending order according to the numerical value, and extract the maximum abnormal contact temperature and the minimum abnormal contact temperature, as well as the corresponding time point, obtain the time interval between the maximum abnormal contact temperature and the minimum abnormal contact temperature, and record it as the abnormal transition value; After comprehensive processing of the abnormality degree and the abnormal transition value, the sensor influence coefficient is obtained.

5. The die-casting die temperature intelligent following control system according to claim 1, characterized in that, The process of obtaining the guide vane loss coefficient includes: Obtain the image information of the guide vane at the time of stopping; Divide the guide vane into multiple sub-regions, obtain the image information of the guide vane corresponding to each sub-region based on each sub-region divided by the guide vane, and pre-process the obtained image information of each sub-region; Feature extraction is performed on each sub-region of the guide vane, features related to wear and corrosion are extracted, and the extracted features are marked to obtain the edge profile of the wear region and the corrosion region to obtain the wear area and the corrosion area. Sum the wear area and the corrosion area to obtain the erosion area; Divide the erosion area by the corresponding sub-region area to obtain the erosion ratio; after obtaining the erosion area ratio of each sub-region in sequence, extract the maximum erosion ratio, and record it as the abnormal erosion ratio; Obtain the overlapping region of the wear region and the corrosion region, and the corresponding overlapping region area; accumulate the overlapping region area of each sub-region to obtain the total overlapping area; divide the total overlapping area by the guide vane area to obtain the overlapping ratio; After comprehensive processing of the abnormal erosion ratio and the overlapping ratio, the guide vane loss coefficient is obtained.

6. The die-casting die temperature intelligent following control system according to claim 5, characterized in that, After comprehensive processing of the sensor influence coefficient, the pipeline blockage coefficient and the guide vane loss coefficient, the abnormal evaluation coefficient is obtained, and the specific process is as follows: After normalization processing of the sensor influence coefficient, the pipeline blockage coefficient and the guide vane loss coefficient, the sensor influence coefficient and the pipeline blockage coefficient are respectively taken as two right-angle sides of a right-angled triangle, and the remaining side is connected to form a complete right-angled triangle. Take the guide vane loss coefficient as the height of the triangle, establish a three-prism model, calculate the volume of the three-prism model, and record it as the abnormal evaluation coefficient.

7. The die-casting die temperature intelligent following control system according to claim 1, characterized in that, The evaluation control module specifically includes: The preset three groups of threshold value ranges, each of which corresponds to an abnormality level, are matched with the abnormality evaluation coefficient to obtain an abnormality level corresponding to the abnormality evaluation coefficient; wherein the abnormality level includes mild abnormality, moderate abnormality and severe abnormality; When the abnormality level is mild abnormality: automatically increase the sampling frequency of the abnormality parameter, and generate a real-time trend curve; continuously monitor the temperature control sensor related information, the cooling pipeline related information and the hydraulic guide vane related information; When the abnormality level is moderate abnormality: increase the lubrication frequency of the mold movable part to reduce the heat generated by friction; start the auxiliary cooling equipment to forcibly air cool the high temperature area of the mold; When the abnormality level is severe abnormality: immediately cut off the heating source of the die casting machine and stop the metal liquid injection; start the mold emergency cooling program including fully opening the cooling water valve and enabling the standby cooling water machine; execute the hydraulic system pressure relief to prevent the abnormal load from damaging the guide vane; automatically generate a severe abnormality shutdown report.

8. A die-casting mold temperature intelligent following control method, according to any one of the die-casting mold temperature intelligent following control system of claims 1-7, characterized in that, It includes: Data acquisition and analysis: obtain temperature control related parameter information in the die casting process, including temperature control sensor related information, cooling pipeline related information and hydraulic guide vane related information; And after analyzing the temperature control sensor related information, the cooling pipeline related information and the hydraulic guide vane related information respectively, the sensor influence coefficient, the pipeline blockage coefficient and the guide vane loss coefficient are obtained; Unified processing: after comprehensive processing of the sensor influence coefficient, the pipeline blockage coefficient and the guide vane loss coefficient, an abnormality evaluation coefficient is obtained; Abnormality level evaluation and control: based on the abnormality evaluation coefficient, the corresponding abnormality level is matched, and corresponding control is performed according to the abnormality level.

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