Casting box opening efficiency intelligent optimization system and method integrated with IoT sensing network

Abstract

The invention relates to the field of intelligent manufacturing, in particular to a casting box opening efficiency intelligent optimization system and method integrated with an IoT sensor network, and the system comprises a data monitoring module, an abnormality feedback module, a data analysis module and an intelligent adjustment module, monitors and collects required data, determines a box opening hedging coefficient and a box opening instantaneous coefficient, and improves the efficiency of casting box opening. The method comprises the following steps: calculating a peeling vibration characterization value, judging whether a box opening state has an abnormal vibration tendency, and for the box opening state with the abnormal vibration tendency, calculating a box opening adjustment characteristic value in combination with a motion synchronization characterization value obtained through analysis and an impurity influence characterization value so as to judge whether the box opening state needs to be adjusted. And aiming at the box opening state needing to be adjusted, calculating an adjustment effective value, and determining a signal sending type. According to the method, internal factors and external factors causing vibration in the box opening process are analyzed, abnormal vibration tendency judgment and box opening state adjustment judgment are conducted on the box opening state, specific vibration in the box opening process is reduced, and the box opening efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing, and in particular to an intelligent optimization system and method for casting unpacking efficiency integrating an IoT sensor network. Background Technology

[0002] Casting is a fundamental process in the equipment manufacturing industry. Among them, the mold opening process is a key step that affects casting quality, production cycle, and energy consumption. Traditional casting mold opening operations mainly rely on the experience and judgment of operators. This method is affected by multiple complex factors such as casting structure, sand mold properties, ambient temperature and humidity, and pouring temperature. Fixed cooling times are difficult to adapt to dynamic and changing actual working conditions, which can easily lead to mold opening too early or too late, which is not conducive to the standardization and refined management of the production process. In recent years, single monitoring methods such as temperature sensors have been introduced to measure the temperature of the cavity or local parts of the casting to assist in mold opening decisions. However, such methods usually have problems such as sparse monitoring points, isolated data, and lack of system integration. They are difficult to comprehensively and accurately reflect the thermal state evolution process of the casting as a whole and inside. In addition, the data acquisition and decision-making processes are disconnected, failing to achieve closed-loop automatic control from perception, analysis to optimization decision-making. The level of intelligence is insufficient and cannot meet the needs of modern foundry workshops for efficient, high-quality, and flexible production.

[0003] Chinese Patent Publication No. CN120347175A discloses a casting structure and method for improving casting mold opening efficiency, as well as the resulting casting. The casting structure includes a sand core area and a casting area. The casting area has at least one lifting hole, and a lifting central passage is fixedly installed at the lifting hole. The lifting central passage has a through structure, with its left and right sides connected to the outside. The lifting central passage is filled with sand. This invention guides sand removal through the through-type lifting central passage. For large castings, it eliminates the need for comprehensive sand removal of the casting's internal cavity, significantly reducing the amount of sand removal work, thus significantly reducing the labor intensity of casting mold opening and improving production efficiency.

[0004] Chinese Patent Publication No. CN117259673A discloses a high-verrucation rate and high-strength cylinder head melting and production method. By optimizing the raw material composition and unpacking method of the cylinder head, and adding an air cooling step without sand removal during normal unpacking, the method solves the current problems of low strength and low verrucation rate of cylinder heads, improves the strength of the cylinder head, and increases the verrucation rate. It obtains a perfect cylinder head casting without changing the molding conditions or premature unpacking. The qualified mechanical properties and metallographic structure meet the requirements for the use of cylinder heads, improve the mechanical properties and casting quality of cylinder heads, and have high application value.

[0005] It is evident that the existing technology still has the following problems: On traditional casting production lines, due to the relatively outdated or low-tech nature of automated equipment, sudden vibrations often occur due to load changes when performing unpacking operations, especially at the critical moment of separating castings from sand molds. Such vibrations not only directly reduce the rhythm stability and efficiency of the unpacking process, but may also cause casting grasping failures and product collision damage. Summary of the Invention

[0006] To address this, the present invention provides an intelligent optimization system for casting mold opening efficiency that integrates an IoT sensor network. This system overcomes the problem that in traditional casting production lines, due to the relatively outdated or low-tech automated equipment, sudden vibrations often occur during mold opening operations, especially at critical moments when separating castings from sand molds, caused by sudden load changes. Such vibrations not only directly reduce the rhythm stability and efficiency of the mold opening process, but may also cause casting grasping failures and product collision damage.

[0007] To achieve the above objectives, the present invention provides an intelligent optimization system for casting unpacking efficiency integrating an IoT sensor network, comprising: The data monitoring module monitors the lifting force of the robotic arm on the sand mold and the friction between the casting and the sand mold during the lifting process. It also monitors the momentary force when the robotic arm lifts the sand mold to the separation critical position and simultaneously acquires visual inspection data of the sand mold gating and riser area to obtain robotic arm motion data and impurity distribution data. An abnormal feedback module, which is connected to the data monitoring module, is used to comprehensively analyze the interaction between the lifting force and the friction force in the time domain, determine the opening counter-shock coefficient, analyze the abrupt change characteristics of the lifting force and the correlation between the sudden force and the sudden force at the separation critical position, determine the opening instantaneous coefficient, calculate the peeling vibration characterization value, and determine whether there is an abnormal vibration tendency in the opening state. The data analysis module, which is connected to the data monitoring module and the anomaly feedback module, is used to respond to the unpacking state with abnormal vibration tendency, analyze the robotic arm motion data to determine the motion synchronization characterization value, analyze the impurity distribution data to determine the impurity influence characterization value, and calculate the unpacking adjustment characteristic value in combination with the peeling vibration characterization value to determine whether the unpacking state needs to be adjusted. The intelligent adjustment module, connected to the data analysis module, for open-box states that require adjustment, traverses the casting open-box database based on the open-box state, calculates the effective adjustment value, determines the effective adjustment tendency, issues an early warning signal for open-box states with a strong effective adjustment tendency to make adjustments, and issues a recasting signal for open-box states with a weak effective adjustment tendency.

[0008] Furthermore, the anomaly feedback module determines the open-box hedging coefficient, including, Used to determine the timing data of lifting force and friction force within a preset time window; This is used to synchronously segment the lifting force time series data and the friction force time series data to obtain several lifting force sequences and friction force sequences. Calculate the root mean square value of the lifting force for each of the lifting force sequences and the root mean square value of the friction force for each of the friction force sequences. Used to calculate the average value of the ratio of the root mean square value of each lifting force to the root mean square value of each friction force; The average value of the ratio after normalization is used to determine the open-box hedging coefficient.

[0009] Furthermore, the anomaly feedback module determines the instantaneous coefficient of unpacking, including, Used to extract time series data of the lifting force within a preset time window before and after the separation critical position; Used to calculate the maximum instantaneous rate of change of the lifting force in the time series data; The product of the maximum instantaneous rate of change and the momentary force is used to determine the instantaneous coefficient for opening the box.

[0010] Furthermore, the anomaly feedback module calculates the peeling vibration characterization value, including, The first stripping factor is used to determine the ratio of the opening hedging coefficient to the benchmark opening hedging coefficient. The second stripping factor is used to determine the ratio of the instantaneous unpacking coefficient to the benchmark instantaneous unpacking coefficient. The weighted sum of the first peeling factor and the second peeling factor is used to determine the peeling vibration characterization value.

[0011] Furthermore, the anomaly feedback module determines whether there is an abnormal vibration tendency in the unpacked state, wherein, If the peeling vibration characterization value is greater than the peeling vibration characterization value threshold, it is determined that there is an abnormal vibration tendency in the unpacking state. If the peeling vibration characterization value is less than or equal to the peeling vibration characterization value threshold, it is determined that there is no abnormal vibration tendency in the unpacking state.

[0012] Furthermore, the data analysis module determines motion synchronization characterization values, including: Used to obtain the actual motion trajectory of the robotic arm and the preset standard trajectory; Used to calculate the time deviation between the actual motion trajectory and the preset standard trajectory at key phase points in each degree of freedom; This is used to normalize the time deviations mentioned above and obtain the root mean square value; The root mean square value is used to determine the motion synchronization characterization value.

[0013] Furthermore, the data analysis module determines the characterization values ​​of the impurity impact, including: Used to determine the total area of ​​impurities and the degree of impurity aggregation in the sand mold gating riser region; The area influence factor is used to determine the ratio of the total area of ​​the impurities to the total area of ​​the sand mold gating riser region. The ratio of the impurity aggregation degree to the baseline impurity aggregation degree is used to determine the aggregation influence factor; The summation of the area influence factor and the aggregation influence factor is used to determine the impurity influence characterization value.

[0014] Furthermore, the data analysis module calculates unpacking adjustment feature values ​​to determine whether the unpacking state needs to be adjusted, including: The ratio of the motion synchronization characterization value to the baseline motion synchronization characterization value is used to determine the synchronization influence factor. The impurity influence factor is used to determine the ratio of the impurity influence characterization value to the baseline impurity influence characterization value. The ratio of the peeling vibration characterization value to the benchmark peeling vibration characterization value is used to determine the peeling influence factor; The weighted sum of the synchronization influence factor, the impurity influence factor, and the stripping influence factor is used to determine the open-box adjustment characteristic value. If the unpacking adjustment feature value is greater than the unpacking adjustment feature value threshold, it is determined that the unpacking status needs to be adjusted. If the unpacking adjustment feature value is less than or equal to the unpacking adjustment feature value threshold, it is determined that no adjustment to the unpacking state is required.

[0015] Furthermore, the intelligent adjustment module calculates the effective adjustment value and determines the effective adjustment tendency, including: Used to traverse the casting unpacking database to determine several historical unpacking states that are similar to the unpacking state; Determine the unpacking completion rate corresponding to each of the historical unpacking states, and record the number of complete unpackings; The ratio of the number of complete unpackings to the number of historical unpacking states is used to determine the effective adjustment value; If the effective adjustment value is greater than the effective threshold, it is determined to be a strong effective adjustment tendency; If the effective adjustment value is less than or equal to the effective threshold, it is determined to be a weak effective adjustment tendency.

[0016] Furthermore, the intelligent optimization method for casting unpacking efficiency integrating an IoT sensor network is characterized by comprising: The lifting force of the robotic arm on the sand mold and the friction between the casting and the sand mold are monitored during the lifting process. The sudden force when the robotic arm lifts the sand mold to the separation critical position is monitored. Visual inspection data of the sand mold gating and riser area are acquired simultaneously to obtain robotic arm motion data and impurity distribution data. By comprehensively analyzing the interaction between the lifting force and the frictional force in the time domain, the unpacking counter-shock coefficient is determined. The abrupt change characteristics of the lifting force and the correlation between the sudden force at the separation critical position are analyzed to determine the unpacking instantaneous coefficient, so as to calculate the peeling vibration characterization value and determine whether there is an abnormal vibration tendency in the unpacking state. In response to an unpacking state with abnormal vibration tendency, the motion data of the robotic arm is analyzed to determine the motion synchronization characterization value, the impurity distribution data is analyzed to determine the impurity influence characterization value, and the unpacking adjustment characteristic value is calculated in combination with the peeling vibration characterization value to determine whether the unpacking state needs to be adjusted. For unpacking states that require adjustment, the casting unpacking database is traversed based on the unpacking state to calculate the effective adjustment value, determine the effective adjustment tendency, and issue an early warning signal for unpacking states with a strong effective adjustment tendency to make adjustments. A recasting signal is issued for the open-box state where there is a weak tendency for effective adjustment.

[0017] Compared with existing technologies, this invention, by setting up a data monitoring module, an anomaly feedback module, a data analysis module, and an intelligent adjustment module, monitors and collects the required data, determines the unpacking hedging coefficient and the unpacking instantaneous coefficient, and calculates the peeling vibration characterization value to determine whether there is an abnormal vibration tendency in the unpacking state. For unpacking states with abnormal vibration tendencies, it calculates the unpacking adjustment characteristic value by combining the motion synchronization characterization value and the impurity influence characterization value obtained through analysis, to determine whether the unpacking state needs adjustment. For unpacking states that need adjustment, it calculates the effective adjustment value and determines the signal emission category. This invention analyzes the internal and external factors that cause vibration during the unpacking process, and by judging the abnormal vibration tendency and adjusting the unpacking state, it reduces the specific vibration in the unpacking process, improving unpacking efficiency and safety.

[0018] In particular, by determining the opening offset coefficient and the opening instantaneous coefficient, and calculating the peeling vibration characterization value, it can be understood that in the sand casting opening process, the ideal process state is that when a vertical lifting force is applied, the sand mold and the casting can achieve clean and complete separation along the parting surface. However, in reality, the static friction and adhesion forces existing between the sand mold and the casting interface often form a dynamic counterforce with the external lifting force, leading to separation obstruction or jamming. At this time, regular external mechanical vibration is often introduced as an auxiliary means. Through vibration excitation, the equivalent friction coefficient between the interfaces is reduced, thereby helping the casting to gradually detach under vibration assistance. However, furthermore, when the casting moves to the critical separation position under the combined action of lifting and vibration, an impact force is generated, and the regular... External mechanical vibrations can be disrupted by transient impacts, resulting in irregular composite vibrations. This can cause castings to partially detach, deviate, or become stuck in unexpected locations, leading to sand mold damage, casting tearing, or even structural fracture, resulting in unpacking failure. Based on this, this invention considers analyzing the relative dynamic state of the casting and sand mold under the coupling of internal and external forces to pre-determine abnormal vibration conditions during casting separation. It also conducts targeted analysis of unpacking states with abnormal vibration tendencies, providing data and theoretical basis for subsequent analysis of abnormal unpacking states. It is understood that if unpacking states with abnormal vibration tendencies do not require adjustment, then states without abnormal vibration tendencies do not require adjustment either, in order to reduce unnecessary downtime for adjustments, thereby improving overall unpacking efficiency and systematically reducing production risks.

[0019] In particular, for mold opening states with abnormal vibration tendencies, the synchronous motion state of the robotic arm and the distribution of impurities are analyzed to calculate the mold opening adjustment characteristic value to determine whether adjustments to the mold opening state are necessary. In practice, vibrations within the preset range during the mold opening process theoretically will not negatively affect the separation state. However, if the robotic arm performing the lifting operation has kinematic asynchrony or dynamic coupling mismatch, it will lead to local stress concentration, causing the friction state between the casting and the sand mold to change from uniform slippage to local stick-slip, resulting in increased interface separation resistance and uneven force distribution during the lifting process, thus increasing the probability of abnormal mold opening states. If the sand mold itself also contains structural or material impurities, it will also form local mechanical hard points. Hard spots not only directly increase the local friction coefficient and mechanical interlocking effect, but more importantly, they have a synergistic amplifying effect with the non-uniform force state caused by the asynchronous operation of the robotic arm. In areas where impurities accumulate, the sand mold's collapsibility decreases, forming a microscopic locking effect. The combined effect of these two factors causes the system's vibration state to rapidly become unstable, transforming into a complex abnormal vibration mode with impact and intermittency. This further amplifies the abnormal mold opening state and increases the probability of sand mold breakage, casting scratches, or separation failure. Based on this, this invention conducts a targeted analysis of mold opening states with abnormal vibration tendencies, comprehensively analyzing the internal and external factors affecting the lifting process. This provides data and a theoretical basis for subsequently determining the signal emission type, thereby improving overall mold opening efficiency and systematically reducing production risks.

[0020] In particular, by calculating the effective adjustment value to determine the necessity of adjusting the unpacking state, it is understandable that in actual practice, when an abnormal state occurs during the unpacking stage, operators can usually try to adjust the operating parameters to bring the system back to normal operating conditions and continue executing the startup sequence. However, it is understandable that there is a specific type of abnormal state where even with parameter adjustments, the conditions for continuing unpacking cannot be met. At this time, the system's state space has deviated from the adjustment range of all feasible parameter sets. For this type of unpacking state, if parameter adjustments are still made, it will only lead to resource dissipation with zero or even negative marginal utility. Based on this, the present invention considers pre-calculating the effective adjustment value to determine the effective adjustment tendency of the unpacking state, and setting different types of signals for unpacking states with different tendencies to reduce resource waste, improve overall unpacking efficiency, and systematically reduce production risks. Attached Figure Description

[0021] Figure 1 A schematic diagram of the intelligent optimization system for casting unpacking efficiency with integrated IoT sensor network, as an embodiment of the invention. Figure 2 This is a logic block diagram for determining whether there is an abnormal vibration tendency in the unpacking state according to an embodiment of the invention; Figure 3 This is a logic block diagram illustrating how to determine whether the unpacking state needs to be adjusted according to an embodiment of the invention. Figure 4 A logic block diagram for determining an effective adjustment tendency in an embodiment of the invention; Figure 5 A schematic diagram illustrating the steps of a method for intelligently optimizing casting unpacking efficiency using an integrated IoT sensor network, as an embodiment of the invention. Figure 6 This is a schematic diagram of the casting structure according to an embodiment of the invention; In the diagram, 1 is the casting area and 2 is the lifting point. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] Please see Figure 1 , Figure 1 This is a schematic diagram of a casting mold opening efficiency intelligent optimization system integrating an IoT sensor network, as an embodiment of the invention. The casting mold opening efficiency intelligent optimization system integrating an IoT sensor network of the present invention includes: The data monitoring module monitors the lifting force of the robotic arm on the sand mold and the friction between the casting and the sand mold during the lifting process. It also monitors the momentary force when the robotic arm lifts the sand mold to the separation critical position and simultaneously acquires visual inspection data of the sand mold gating and riser area to obtain robotic arm motion data and impurity distribution data. An abnormal feedback module, which is connected to the data monitoring module, is used to comprehensively analyze the interaction between the lifting force and the friction force in the time domain, determine the opening counter-shock coefficient, analyze the abrupt change characteristics of the lifting force and the correlation between the sudden force and the sudden force at the separation critical position, determine the opening instantaneous coefficient, calculate the peeling vibration characterization value, and determine whether there is an abnormal vibration tendency in the opening state. The data analysis module, which is connected to the data monitoring module and the anomaly feedback module, is used to respond to the unpacking state with abnormal vibration tendency, analyze the robotic arm motion data to determine the motion synchronization characterization value, analyze the impurity distribution data to determine the impurity influence characterization value, and calculate the unpacking adjustment characteristic value in combination with the peeling vibration characterization value to determine whether the unpacking state needs to be adjusted. The intelligent adjustment module, connected to the data analysis module, for open-box states that require adjustment, traverses the casting open-box database based on the open-box state, calculates the effective adjustment value, determines the effective adjustment tendency, issues an early warning signal for open-box states with a strong effective adjustment tendency to make adjustments, and issues a recasting signal for open-box states with a weak effective adjustment tendency.

[0026] Specifically, there are no restrictions on the methods for obtaining the lifting force and friction. For example, the lifting force can be obtained by force sensors or torque sensors installed on the robotic arm drive unit or hook; the friction can be obtained by a thin-film pressure sensor array arranged on the contact surface between the casting mold and the sand mold. Of course, those skilled in the art can also obtain the lifting force and friction according to the actual situation, as long as it is reasonable, which will not be elaborated here.

[0027] Specifically, there are no restrictions on the method of obtaining the impact force. For example, it can be the impact value measured directly by a high-frequency response accelerometer at the moment of separation, or it can be the peak value of the first derivative of the lifting force-time curve at the critical separation position. Those skilled in the art can also obtain the impact force according to the actual situation, as long as it is reasonable, which will not be elaborated here.

[0028] Specifically, there are no restrictions on the method of acquiring visual inspection data. For example, it can be obtained by taking pictures or scanning the gating and riser area with equipment such as industrial cameras, 3D scanners, and laser profilometers before or during unpacking to obtain two-dimensional images, three-dimensional point cloud data, or their fused data. Of course, those skilled in the art can also acquire visual inspection data according to the actual situation, as long as it is reasonable, which will not be elaborated here.

[0029] Specifically, the exact location of the separation critical position is not limited. For example, it can be obtained through a pre-designed casting manual. Of course, those skilled in the art can also determine the method of obtaining it according to the actual situation, as long as it is reasonable. This will not be elaborated further.

[0030] Specifically, there are no restrictions on how historical adjustment data is obtained. For example, it can come from the database accumulated by the long-term operation of this system, or it can be obtained by querying the factory's production and manufacturing execution system. It can also be learned from the data of other production lines with similar processes through cloud collaboration. The authorization status of the data must be guaranteed. Of course, those skilled in the art can also use other methods to obtain the data, as long as they are reasonable. This will not be elaborated further.

[0031] Specifically, there are no restrictions on the specific form of the warning signal and the recasting signal. For example, it can be an easily identifiable buzzing sound, a flashing indicator light, or a specific pop-up window and text prompt on the control panel screen, as long as it can achieve the effect that the corresponding signal can achieve. This will not be elaborated further.

[0032] Specifically, the anomaly feedback module determines the open-box hedging coefficient, including, Used to determine the timing data of lifting force and friction force within a preset time window; This is used to synchronously segment the lifting force time series data and the friction force time series data to obtain several lifting force sequences and friction force sequences. Calculate the root mean square value of the lifting force for each of the lifting force sequences and the root mean square value of the friction force for each of the friction force sequences. Used to calculate the average value of the ratio of the root mean square value of each lifting force to the root mean square value of each friction force; The average value of the ratio after normalization is used to determine the open-box hedging coefficient.

[0033] Specifically, the exact duration of the preset time window is not limited. In practice, the preset time window is set to 3 seconds. Of course, those skilled in the art can also determine it according to the actual situation, as long as it is reasonable. This will not be elaborated further.

[0034] Specifically, there is no limit to the exact length of the segments. In practice, for ease of calculation, the time corresponding to each segment length is determined to be 1 second. Of course, those skilled in the art can also divide the length in other ways, as long as it is reasonable, which will not be elaborated here.

[0035] Understandably, normalization maps the average of the ratios to the range [0,1].

[0036] The anomaly feedback module determines the instantaneous coefficient of unpacking, including, Used to extract time series data of the lifting force within a preset time window before and after the separation critical position; Used to calculate the maximum instantaneous rate of change of the lifting force in the time series data; The product of the maximum instantaneous rate of change and the momentary force is used to determine the instantaneous coefficient for opening the box.

[0037] Specifically, the anomaly feedback module calculates the peeling vibration characterization values, including, The first stripping factor is used to determine the ratio of the opening hedging coefficient to the benchmark opening hedging coefficient. The second stripping factor is used to determine the ratio of the instantaneous unpacking coefficient to the benchmark instantaneous unpacking coefficient. The weighted sum of the first peeling factor and the second peeling factor is used to determine the peeling vibration characterization value.

[0038] Specifically, the benchmark opening hedging coefficient is calculated in advance. Several historical opening hedging coefficients corresponding to the opening process are obtained in advance, and the average of each historical opening hedging coefficient is determined as the benchmark opening hedging coefficient.

[0039] Specifically, the benchmark unpacking instantaneous coefficient is calculated in advance. Several historical unpacking instantaneous coefficients corresponding to the unpacking process are obtained in advance, and the average of each historical unpacking instantaneous coefficient is determined as the benchmark unpacking instantaneous coefficient.

[0040] Specifically, the sum of the weight coefficients of the first stripping factor and the second stripping factor is 1. When configuring the weights, considering that multiple forces will affect the unpacking process, the weight coefficients of both the first stripping factor and the second stripping factor are set to 0.5.

[0041] Specifically, by determining the opening offset coefficient and the opening instantaneous coefficient, and calculating the peeling vibration characterization value, it can be understood that in the sand casting opening process, the ideal process state is that when a vertical lifting force is applied, the sand mold and the casting can achieve clean and complete separation along the parting surface. However, in reality, the static friction and adhesion forces existing between the sand mold and the casting interface often form a dynamic counterforce with the external lifting force, leading to separation obstruction or jamming. At this time, regular external mechanical vibration is often introduced as an auxiliary means. Through vibration excitation, the equivalent friction coefficient between the interfaces is reduced, thereby helping the casting to gradually detach under vibration assistance. However, furthermore, when the casting moves to the critical separation position under the combined action of lifting and vibration, an impact force will be generated. External mechanical vibrations can be disrupted by transient impacts, resulting in irregular composite vibrations. This can cause castings to partially detach, deviate, or become stuck at unexpected locations, leading to sand mold damage, casting tearing, or even structural fracture, resulting in unpacking failure. Based on this, this invention considers analyzing the relative dynamic state of the casting and sand mold under the coupling of internal and external forces to pre-determine abnormal vibration conditions during casting separation. It also conducts targeted analysis of unpacking states with abnormal vibration tendencies, providing data and theoretical basis for subsequent analysis of abnormal unpacking states. It is understood that if unpacking states with abnormal vibration tendencies do not require adjustment, then states without abnormal vibration tendencies do not require adjustment at all, in order to reduce unnecessary downtime for adjustments, thereby improving overall unpacking efficiency and systematically reducing production risks.

[0042] Please see Figure 2 , Figure 2 This is a logic block diagram illustrating how to determine whether there is an abnormal vibration tendency in the unpacking state, according to an embodiment of the invention. Specifically, the abnormality feedback module determines whether there is an abnormal vibration tendency in the unpacking state, wherein... If the peeling vibration characterization value is greater than the peeling vibration characterization value threshold, it is determined that there is an abnormal vibration tendency in the unpacking state. If the peeling vibration characterization value is less than or equal to the peeling vibration characterization value threshold, it is determined that there is no abnormal vibration tendency in the unpacking state.

[0043] Specifically, the peeling vibration characterization threshold represents a boundary where abnormal vibration occurs during the unpacking process. It is calculated in advance by obtaining historical peeling vibration characterization values ​​corresponding to several abnormal unpacking states in advance. The product of the mean of each historical peeling vibration characterization value and the peeling coefficient is determined as the peeling vibration characterization threshold. The peeling coefficient is selected in the range [0.8, 1.0]. In practice, considering the need to ensure the integrity of unpacking, the peeling coefficient is determined to be 0.9.

[0044] Specifically, the data analysis module determines motion synchronization characterization values, including, Used to obtain the actual motion trajectory of the robotic arm and the preset standard trajectory; Used to calculate the time deviation between the actual motion trajectory and the preset standard trajectory at key phase points in each degree of freedom; This is used to normalize the time deviations mentioned above and obtain the root mean square value; The root mean square value is used to determine the motion synchronization characterization value.

[0045] Specifically, there are no restrictions on the method of acquiring the actual motion trajectory. For example, it can be acquired by using a high-definition camera set up at a high place. Of course, those skilled in the art can also use other methods, as long as they are reasonable, which will not be elaborated here.

[0046] Specifically, there are no restrictions on how the preset standard trajectory is obtained. For example, it can be obtained from a publicly available planning manual, which will not be elaborated further.

[0047] Specifically, there is no limitation on the method of determining the critical phase point. In practice, the point corresponding to every 2 seconds during the lifting process is determined as the critical phase point. Those skilled in the art can also determine the point according to the actual situation, as long as it is reasonable. This will not be elaborated further.

[0048] Specifically, the data analysis module determines the impact of impurities on characterization values, including: Used to determine the total area of ​​impurities and the degree of impurity aggregation in the sand mold gating riser region; The area influence factor is used to determine the ratio of the total area of ​​the impurities to the total area of ​​the sand mold gating riser region. The ratio of the impurity aggregation degree to the baseline impurity aggregation degree is used to determine the aggregation influence factor; The summation of the area influence factor and the aggregation influence factor is used to determine the impurity influence characterization value.

[0049] Specifically, the calculation method for impurity aggregation degree is as follows: Determine the location of several impurity points; Calculate the distance between each impurity point and its nearest neighbor; The ratio of the standard deviation to the mean for each distance is determined as the degree of impurity aggregation.

[0050] Specifically, the baseline impurity aggregation degree is calculated in advance. Several historical impurity aggregation degrees corresponding to normal open-box states are obtained in advance, and the average of each historical impurity aggregation degree is determined as the baseline impurity aggregation degree.

[0051] Please see Figure 3 , Figure 3 This is a logic block diagram illustrating how to determine whether the unpacking state needs adjustment, according to an embodiment of the invention. Specifically, the data analysis module calculates unpacking adjustment feature values ​​to determine whether the unpacking state needs adjustment, including: The ratio of the motion synchronization characterization value to the baseline motion synchronization characterization value is used to determine the synchronization influence factor. The impurity influence factor is used to determine the ratio of the impurity influence characterization value to the baseline impurity influence characterization value. The ratio of the peeling vibration characterization value to the benchmark peeling vibration characterization value is used to determine the peeling influence factor; The weighted sum of the synchronization influence factor, the impurity influence factor, and the stripping influence factor is used to determine the open-box adjustment characteristic value. If the unpacking adjustment feature value is greater than the unpacking adjustment feature value threshold, it is determined that the unpacking status needs to be adjusted. If the unpacking adjustment feature value is less than or equal to the unpacking adjustment feature value threshold, it is determined that no adjustment to the unpacking state is required.

[0052] Specifically, the baseline motion synchronization characterization value is calculated in advance. Several historical motion synchronization characterization values ​​corresponding to the unopened state adjustment are obtained in advance, and the average value of each historical motion synchronization characterization value is determined as the baseline motion synchronization characterization value.

[0053] Specifically, the baseline impurity impact characterization value is calculated in advance, and several historical impurity impact characterization values ​​corresponding to the period without opening the box are obtained in advance. The average value of each historical impurity impact characterization value is determined as the baseline impurity impact characterization value.

[0054] Specifically, the baseline peeling vibration characterization value is the peeling vibration characterization value corresponding to the baseline unpacking hedging coefficient and the baseline unpacking instantaneous coefficient.

[0055] Specifically, the sum of the weight coefficients of the synchronization impact factor, impurity impact factor, and stripping impact factor is 1. In the weighting configuration, considering that external factors may have a significant impact on the opening efficiency, the weight coefficients of the synchronization impact factor and impurity impact factor are both determined to be 0.3, and the weight coefficient of the stripping impact factor is 0.2.

[0056] Specifically, the unpacking adjustment feature value threshold represents a boundary that requires adjustment of the unpacking state. It is pre-calculated by obtaining historical unpacking adjustment feature values ​​corresponding to thousands of times when the unpacking state has not been adjusted. The product of the mean of each historical unpacking adjustment feature value and the adjustment coefficient is determined as the unpacking adjustment feature value threshold. The adjustment coefficient is selected in the interval [0.8, 1.0]. In implementation, considering the need to ensure the unpacking completion rate, the adjustment coefficient is determined to be 0.9.

[0057] Specifically, for mold opening states exhibiting abnormal vibration tendencies, the synchronous motion state of the robotic arm and the distribution of impurities are analyzed to calculate mold opening adjustment characteristic values, determining whether adjustments are necessary. In practice, vibrations within a preset range during the mold opening process theoretically should not negatively impact the separation state. However, if the robotic arm performing the lifting operation exhibits kinematic asynchrony or dynamic coupling mismatch, it can lead to localized stress concentration. This causes the friction state between the casting and the sand mold to shift from uniform slippage to localized stick-slip, increasing the interface separation resistance and resulting in uneven force distribution during the lifting process. This further increases the probability of abnormal mold opening states. If the sand mold itself also contains structural or material impurities, it can also create localized mechanical hard points. These hard spots not only directly increase the local friction coefficient and mechanical interlocking effect, but more importantly, they have a synergistic amplifying effect with the non-uniform force state caused by the asynchronous operation of the robotic arm. In the impurity accumulation area, the sand mold collapse decreases, forming micro-locking. The combined effect of these two factors causes the vibration state of the system to quickly become unstable, transforming into a composite abnormal vibration mode with impact and intermittency. This further amplifies the abnormal opening state and increases the probability of sand mold breakage, casting scratches, or separation failure. Based on this, this invention conducts a targeted analysis of the opening state with abnormal vibration tendency, comprehensively analyzes the internal and external factors affecting the lifting process, and provides data and theoretical basis for subsequent determination of signal emission type, thereby improving the overall opening efficiency and systematically reducing production risks.

[0058] Please see Figure 4 , Figure 4 This is a logic block diagram illustrating the determination of an effective adjustment tendency according to an embodiment of the invention. Specifically, the intelligent adjustment module calculates the effective adjustment value and determines the effective adjustment tendency, including: Used to traverse the casting unpacking database to determine several historical unpacking states that are similar to the unpacking state; Determine the unpacking completion rate corresponding to each of the historical unpacking states, and record the number of complete unpackings; The ratio of the number of complete unpackings to the number of historical unpacking states is used to determine the effective adjustment value; If the effective adjustment value is greater than the effective threshold, it is determined to be a strong effective adjustment tendency; If the effective adjustment value is less than or equal to the effective threshold, it is determined to be a weak effective adjustment tendency.

[0059] Specifically, there are no restrictions on the source of the casting unpacking database. In practice, it can be composed of authorized historical data, or it can be an unpacking process data stream that is automatically collected and generated during historical operation, or a set of compliant data from other similar casting production lines obtained through a security interface. As long as the source is reasonable, it will not be elaborated further.

[0060] Specifically, the effective threshold represents a boundary at which the unpacking can be completed by adjusting the state. It is calculated in advance and obtained in advance by obtaining several historical effective values ​​of the adjustment that still cannot be completed after adjusting the state. The effective threshold is determined by the product of the mean of each historical effective value and the effective coefficient. The effective coefficient is obtained in the interval [0.8, 1.0]. In implementation, considering the reduction of unnecessary energy consumption, the effective coefficient is determined to be 0.9.

[0061] Specifically, by calculating the effective adjustment value to determine the necessity of adjusting the unpacking state, it is understood that in practice, when an abnormal state occurs during the unpacking stage, operators can usually try to adjust the operating parameters to bring the system back to normal operating conditions and continue executing the startup sequence. However, it is understood that there is a specific type of abnormal state where even with parameter adjustments, the conditions for continuing unpacking cannot be met. At this time, the system's state space has deviated from the adjustment range of all feasible parameter sets. For this type of unpacking state, if parameter adjustments are still made, it will only lead to resource dissipation with zero or even negative marginal utility. Based on this, the present invention considers pre-calculating the effective adjustment value to determine the effective adjustment tendency of the unpacking state, and setting different types of signals for unpacking states with different tendencies to reduce resource waste, improve overall unpacking efficiency, and systematically reduce production risks.

[0062] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating the steps of a method for intelligently optimizing casting unpacking efficiency using an integrated IoT sensor network, as described in an embodiment of the invention. Specifically, the method of the intelligent optimization system for casting unpacking efficiency using an integrated IoT sensor network is characterized by including: Step S1: Monitor the lifting force of the robotic arm on the sand mold and the friction between the casting and the sand mold during the lifting process. Monitor the momentary force when the robotic arm lifts the sand mold to the separation critical position. Simultaneously acquire visual inspection data of the sand mold gating and riser area to obtain robotic arm motion data and impurity distribution data. Step S2: Comprehensively analyze the interaction relationship between the lifting force and the friction force in the time domain, determine the opening counter-shock coefficient, analyze the abrupt change characteristics of the lifting force and the correlation relationship with the sudden force at the separation critical position, determine the opening instantaneous coefficient, calculate the peeling vibration characterization value, and determine whether there is an abnormal vibration tendency in the opening state. Step S3: In response to the unpacking state with abnormal vibration tendency, analyze the robotic arm motion data to determine the motion synchronization characterization value, analyze the impurity distribution data to determine the impurity influence characterization value, and calculate the unpacking adjustment characteristic value in combination with the peeling vibration characterization value to determine whether the unpacking state needs to be adjusted. Step S4: For the unpacking state that needs adjustment, traverse the casting unpacking database based on the unpacking state, calculate the effective adjustment value, determine the effective adjustment tendency, and issue a warning signal for the unpacking state with a strong effective adjustment tendency to make adjustments. A recasting signal is issued for the open-box state where there is a weak tendency for effective adjustment.

[0063] It is understandable that adjustments are not limited to a single dimension. Adjustments can be made to the lifting speed or the lifting force. Those skilled in the art can determine the appropriate adjustment based on the actual situation.

[0064] In specific embodiments, the present invention can be applied to, for example... Figure 6 The diagram shows a schematic of the casting structure. This diagram exemplifies the structure of a casting component. In this application scenario, the technical solution of the present invention can suppress sudden vibrations during the unpacking process, improve the stability and accuracy of equipment operation, and thus ensure the continuous, stable, and efficient operation of the unpacking process.

[0065] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A system for integrated IoT sensor network's casting unboxing efficiency intelligent optimization, characterized in that, The application relates to a casting opening box state abnormality detection method, which comprises the following steps: a data monitoring module is used for monitoring the pulling force of a mechanical arm on a sand mold and the friction force between a casting and the sand mold during a pulling process, monitoring the jerk of the mechanical arm when the mechanical arm pulls the sand mold to a separation critical position, synchronously acquiring visual detection data of a sand mold pouring and sprue area, obtaining mechanical arm movement data and impurity distribution data; an abnormality feedback module is connected with the data monitoring module and is used for comprehensively analyzing the interaction relationship between the pulling force and the friction force in a time domain, determining an opening box counter-shock coefficient, analyzing the correlation between the mutation characteristics of the pulling force and the jerk at the separation critical position, determining an opening box instantaneous coefficient, calculating a stripping vibration characteristic value, and judging whether the opening box state has an abnormal vibration tendency; a data analysis module is connected with the data monitoring module and the abnormality feedback module and is used for analyzing the mechanical arm movement data to determine a movement synchronization characteristic value, analyzing the impurity distribution data to determine an impurity influence characteristic value, combining the stripping vibration characteristic value to calculate an opening box adjustment characteristic value, and judging whether the opening box state needs to be adjusted in response to the opening box state having an abnormal vibration tendency; an intelligent adjustment module is connected with the data analysis module and is used for calculating an adjustment effective value, determining an effective adjustment tendency, issuing a warning signal for adjustment for the opening box state with a strong effective adjustment tendency, and issuing a recasting signal for the opening box state with a weak effective adjustment tendency.

2. The integrated IoT sensor network's casting unboxing efficiency intelligent optimization system of claim 1, wherein, The abnormality feedback module determines the opening box counter-shock coefficient, which comprises the following steps: a time sequence data of the pulling force and a time sequence data of the friction force in a preset time window are determined; the time sequence data of the pulling force and the time sequence data of the friction force are synchronously segmented to obtain a plurality of pulling force sequences and a plurality of friction force sequences; the root mean square value of each pulling force sequence and the root mean square value of each friction force sequence are respectively calculated; the average value of the ratio of each root mean square value of the pulling force to each root mean square value of the friction force is calculated; the average value of the ratio after normalization is determined as the opening box counter-shock coefficient.

3. The integrated loT sensing network's casting unboxing efficiency intelligent optimization system of claim 1, wherein, The abnormality feedback module determines the opening box instantaneous coefficient, which comprises the following steps: time sequence data of the pulling force in a preset time window before and after the separation critical position is extracted; the maximum instantaneous change rate of the pulling force in the time sequence data is calculated; the product value of the maximum instantaneous change rate and the jerk is determined as the opening box instantaneous coefficient.

4. The cast opening box efficiency intelligent optimization system of integrated loT sensor network of claim 1, wherein, The abnormality feedback module calculates the stripping vibration characteristic value, which comprises the following steps: the ratio of the opening box counter-shock coefficient to a reference opening box counter-shock coefficient is determined as a first stripping factor; the ratio of the opening box instantaneous coefficient to a reference opening box instantaneous coefficient is determined as a second stripping factor; the weighted sum value of the first stripping factor and the second stripping factor is determined as the stripping vibration characteristic value.

5. The cast opening box efficiency intelligent optimization system of integrated loT sensing network of claim 1, wherein, The abnormality feedback module judges whether the opening box state has an abnormal vibration tendency, wherein if the stripping vibration characteristic value is greater than a stripping vibration characteristic value threshold value, it is judged that the opening box state has an abnormal vibration tendency. If the peeling vibration characteristic value is less than or equal to a peeling vibration characteristic value threshold, it is determined that the unpacking state has no abnormal vibration tendency.

6. The cast opening box efficiency intelligent optimization system of integrated loT sensing network of claim 1, wherein, The data analysis module determines a motion synchronization characteristic value, including, to obtain the actual motion trajectory of the mechanical arm and the preset standard trajectory; to calculate the time deviation of the actual motion trajectory and the preset standard trajectory at the key phase point in each degree of freedom; to normalize each time deviation to obtain a root mean square value; to determine the root mean square value as the motion synchronization characteristic value.

7. The cast opening box efficiency intelligent optimization system of integrated loT sensing network of claim 1, wherein, The data analysis module determines an impurity influence characteristic value, including, to determine the total area of the impurities in the sand mold pouring and riser area and the impurity aggregation degree; to determine the ratio of the total area of the impurities to the total area of the sand mold pouring and riser area as an area influence factor; to determine the ratio of the impurity aggregation degree to the reference impurity aggregation degree as an aggregation influence factor; to determine the average value of the sum of the area influence factor and the aggregation influence factor as the impurity influence characteristic value.

8. The cast opening box efficiency intelligent optimization system of integrated loT sensing network of claim 1, wherein, The data analysis module calculates an unpacking adjustment characteristic value to determine whether the unpacking state needs to be adjusted, including, to determine the ratio of the motion synchronization characteristic value to the reference motion synchronization characteristic value as a synchronization influence factor; to determine the ratio of the impurity influence characteristic value to the reference impurity influence characteristic value as an impurity influence factor; to determine the ratio of the peeling vibration characteristic value to the reference peeling vibration characteristic value as a peeling influence factor; to determine the weighted sum of the synchronization influence factor, the impurity influence factor and the peeling influence factor as the unpacking adjustment characteristic value; If the unpacking adjustment characteristic value is greater than an unpacking adjustment characteristic value threshold, it is determined that the unpacking state needs to be adjusted; If the unpacking adjustment characteristic value is less than or equal to the unpacking adjustment characteristic value threshold, it is determined that the unpacking state does not need to be adjusted.

9. The cast opening box efficiency intelligent optimization system of integrated loT sensing network of claim 1, wherein, The intelligent adjustment module calculates an adjustment effective value to determine the effective adjustment tendency, including, to traverse the casting unpacking database to determine a number of historical unpacking states similar to the unpacking state; to determine the unpacking completion degree corresponding to each historical unpacking state and record the number of complete unpacking; to determine the ratio of the number of complete unpacking to the number of historical unpacking states as the adjustment effective value; If the adjustment effective value is greater than an effective threshold, it is determined to be a strong effective adjustment tendency; If the adjustment effective value is less than or equal to the effective threshold, it is determined to be a weak effective adjustment tendency.

10. A method for applying the foundry opening box efficiency intelligent optimization system of any one of claims 1-9, characterized in that, including, monitoring the pulling force of the mechanical arm on the sand mold and the friction force between the casting and the sand mold during the pulling process, monitoring the jerk when the mechanical arm pulls the sand mold to the separation critical position, synchronously obtaining the visual detection data of the sand mold pouring and riser area, obtaining the mechanical arm motion data and the impurity distribution data; comprehensively analyzing the interaction relationship of the pulling force and the friction force in the time domain to determine the unpacking hedging coefficient, analyzing the correlation between the mutation characteristics of the pulling force and the jerk at the separation critical position to determine the unpacking instantaneous coefficient, to calculate the peeling vibration characteristic value, and to determine whether the unpacking state has an abnormal vibration tendency; In response to the opening box state with abnormal vibration tendency, the mechanical arm motion data is analyzed to determine a motion synchronization characteristic value, the impurity distribution data is analyzed to determine an impurity influence characteristic value, and an opening box adjustment characteristic value is calculated in combination with the peeling vibration characteristic value to determine whether adjustment of the opening box state is needed; For the opening box state that needs to be adjusted, the casting opening box database is traversed based on the opening box state, an adjustment effective value is calculated, an effective adjustment tendency is determined, and for the opening box state with strong effective adjustment tendency, a warning signal is issued for adjustment; For the opening box state with weak effective adjustment tendency, a recasting signal is issued.

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