Multi-process parameter cooperative control system and method of die casting machine

Abstract

The application discloses a die casting machine multi-process parameter cooperative control system and method, and relates to the technical field of casting equipment control. The system, when the mold is in a mold thin state, first completes mold thickness cooperative initialization, then cooperatively controls mold thickness and mold clamping force parameters based on the result to determine the minimum safe mold clamping force; when the mold is in a mold thin state, the mold thickness change rate under the minimum safe mold clamping force is monitored in real time, and progressive cooperative control of the mold thickness is performed; when the mold is in a mold thick state, the mold thickness and pressure parameters are cooperatively controlled according to the progressive cooperative control result to determine the optimal mold clamping force peak value; when the mold is in a mold thick state, fluctuation data of the casting forming process under the optimal mold clamping force peak value are collected, and the cooperative matching degree of the mold thickness and the fluctuation data is quantified, so that full-process multi-process parameter dynamic cooperation from mold closing initialization to forming process verification is realized.

Description

Technical Field

[0001] This invention relates to the field of casting equipment control technology, and in particular to a multi-process parameter collaborative control system and method for die casting machines. Background Technology

[0002] As global manufacturing upgrades towards lightweighting, precision, and integration, the automotive, electronics, and communications industries are demanding increasingly higher dimensional accuracy and molding stability for complex die-cast parts. As the core equipment in die-casting production, the collaborative operation of the die-casting machine's clamping system, injection system, and ejector system directly determines the precision of process parameter control, thus affecting product quality and production efficiency. The clamping system is responsible for mold closure and clamping force application, ensuring mold sealing during the molding process; the injection system handles the high-speed filling of molten metal, determining the flow and filling effect of the molten metal in the cavity; and the ejector system completes demolding and part removal after the casting solidifies, affecting production continuity.

[0003] The die-casting machine's process flow encompasses raw material melting, mold closing and locking, injection filling, pressure holding and solidification, and mold opening and part removal. First, the alloy raw material is heated to a molten state using melting equipment to ensure good fluidity of the molten metal, providing a foundation for subsequent filling. Then, the mold-locking system, through a hydraulic or servo-driven mechanism, precisely engages the moving and fixed mold plates while applying sufficient locking force to balance the cavity expansion force during injection, preventing the mold from bursting during filling and ensuring a tight seal. Next, the injection system, receiving a control signal, injects the molten metal into the mold cavity at a preset speed and pressure via the injection rod, achieving rapid filling of complex cavity structures. During the pressure holding stage, the system maintains a certain pressure to compensate for volume shrinkage during solidification, effectively preventing defects such as shrinkage cavities and porosity in the casting. After the molten metal has completely solidified, the mold-locking system unlocks, the mold-opening mechanism drives the mold to separate, and the ejector system, under servo control, ejects the casting along a preset trajectory, completing one production cycle. The equipment then proceeds to the next batch processing flow. The entire process achieves stable production of precision die-cast parts through parameter coordination and precise timing control of various systems.

[0004] In die casting production, fluctuations in process parameters can directly lead to casting defects. For example, excessively high melting temperatures can cause oxidation and shrinkage porosity, while excessively low temperatures result in incomplete filling. Too high an injection speed can cause air entrapment and internal voids, while too low a speed can cause cold shuts. Insufficient clamping force can lead to mold opening and overflow, while excessive force can accelerate mold wear. Furthermore, independent parameter control cannot handle complex scenarios involving multiple coupled parameters, making it difficult to meet the stringent standards of downstream industries for die castings. Against this backdrop, a multi-process parameter collaborative control system for die casting machines has emerged. Its core is to achieve coordinated optimization of multiple parameters by integrating sensors, industrial algorithms, and control modules.

[0005] It is worth noting that when die casting machines produce die castings, the corresponding mold must first be selected according to the specifications of the castings. At this time, the initial calibration of the mold thickness becomes the first critical process. The four states of thin mold, medium thin mold, thick mold, and medium thick mold are required to be roughly adjusted to the target thickness range by quickly moving the moving template through the motor-driven lead screw. Then, fine adjustment is performed by the data feedback from the displacement sensor. Finally, the fit is checked by the pressure sensor to ensure the initial fitting accuracy of the mold fitting surface.

[0006] During the production process, the actual thickness of the mold changes dynamically due to factors such as thermal expansion and contraction during the die-casting cycle, cumulative errors in mold installation, and cavity wear caused by long-term production. For example, the high temperature during molten metal filling causes the mold to expand, resulting in a temporary increase in thickness, while it contracts as the temperature drops during the cooling phase. If the mold adjustment mechanism cannot sense these dynamic changes in real time and adapt accordingly, it will directly cause poor sealing or excessive clamping during mold locking, affecting not only the dimensional accuracy of the casting but also potentially leading to mold damage and production interruptions. Therefore, in the multi-parameter collaborative control system, mold thickness is a key component. Its control logic involves the servo motor of the mold adjustment mechanism continuously driving the lead screw throughout the die-casting production cycle. Based on the real-time mold thickness data and clamping pressure signal from the sensors, the position of the moving platen is dynamically adjusted to always adapt to the actual thickness changes of the mold, ensuring the sealing and stability of each clamping operation. This, in turn, achieves precise parameter linkage with subsequent processes such as injection filling and pressure holding solidification.

[0007] Existing technologies first use high-frequency sensors to synchronously collect real-time parameters such as melting temperature, injection pressure, clamping force, and mold thickness. After preprocessing such as data noise reduction and timing alignment by an edge computing module, a multi-dimensional process database is constructed. Second, a parameter coupling model is established using mold flow simulation and machine learning algorithms (such as gradient boosting trees) to quantify the correlation weight between mold thickness and parameters such as clamping force and injection speed. Then, combined with casting quality feedback data, optimization parameters such as mold thickness adjustment range and injection speed curve are output. Finally, a programmable logic controller (PLC) is used to link the mold adjustment mechanism and hydraulic system to synchronously adjust the position of the moving platen, clamping force, and injection parameters, thereby achieving dynamic and coordinated control of multiple parameters.

[0008] However, in the actual dynamic working conditions of die casting production, the actual thickness deviates significantly from the preset value because the acquisition of mold thickness does not take into account the instantaneous deformation caused by the impact of molten metal during the injection filling process, as well as the elastic deformation caused by the force of the clamping mechanism during the mold closing and locking stage. This makes it impossible to meet the strict dimensional tolerance requirements of precision castings. At this time, if the influence of the molten metal filling pressure on the instantaneous deformation of the mold cavity wall during the injection filling process is not considered in time, the coordination between the mold thickness and the clamping force and injection parameters output by the clamping mechanism will be limited to the preset fixed correlation logic, making it difficult to dynamically adapt to the fluctuations in working conditions. For example, when the die casting machine enters the injection filling process after mold closing and locking, if the injection pressure changes suddenly, the small deformation of the mold cavity wall will directly cause the actual thickness of the mold to deviate. However, this dynamic change cannot be captured in time by static measurement methods, which will cause the clamping force adjustment of the clamping mechanism to lag, and ultimately lead to quality problems such as mold overflow.

[0009] Meanwhile, the response of the mold adjustment mechanism during die casting production inherently has a delay. For example, if the mold wears down due to long-term use, resulting in a reduction in thickness, the system needs to go through a complete process of thickness signal acquisition, data transmission to the control unit, and instruction calculation before driving the servo motor of the mold adjustment motor to perform the adjustment action. During this response cycle, if the die casting machine has completed multiple batches of casting production, the dimensions of consecutive batches of castings may fluctuate, making it difficult for the mold adjustment mechanism to adapt to the high-precision forming requirements of die castings. The superposition of the above-mentioned problems such as failure to perceive dynamic factors in real time, inaccurate coordinated adjustment, and delay in the response of the mold adjustment mechanism reduces the effectiveness of the coordinated control of mold thickness and other process parameters during die casting production. The mismatch between the clamping force and the actual mold thickness may exacerbate the wear of the mold cavity wall, thereby reducing the reliability of the coordinated control of process parameters of the die casting machine and further increasing the risk of equipment failure of key components such as the clamping mechanism and mold adjustment mechanism. Summary of the Invention

[0010] To address the technical problem of low effectiveness in the coordinated control of process parameters during die casting production in existing technologies, this invention provides a multi-process parameter coordinated control system and method for die casting machines. The technical solution is as follows:

[0011] On the one hand, a multi-process parameter collaborative control system for die casting machines is provided. This system includes: a mold thin state collaborative control module, which is used to perform collaborative initialization of mold thickness when the mold is in the mold thin limit / position state after the die casting machine and the mold adjustment mechanism have completed mechanical linkage and signal interaction, and to perform collaborative control of mold thickness and clamping force parameters based on the results of collaborative initialization to determine the minimum safe clamping force;

[0012] The thin-mold state collaborative control module is used to monitor the rate of change of mold thickness under the minimum safe clamping force in real time when the mold is transitioning from a thin-mold state to a thick-mold state, and to perform progressive collaborative control of the mold thickness. The thick-mold state collaborative control module is used to perform collaborative control of mold thickness and pressure parameters based on the results of progressive collaborative control when the mold is in the thick-mold limit / position state, and to determine the optimal clamping force peak value. The thick-mold state collaborative verification module is used to collect the fluctuation data of the corresponding casting forming process under the optimal clamping force peak value when the mold is in the thick-mold limit / position state, to simultaneously quantify the collaborative matching degree between the mold thickness and the fluctuation data, and to generate a collaborative verification report.

[0013] On the other hand, a method for coordinated control of multiple process parameters of a die casting machine is provided. The method includes: S1, when the mold is in the mold thin limit / position state, coordinating initialization of the mold thickness is performed, and coordinating control of the mold thickness and clamping force parameters is performed based on the result of the coordinating initialization to determine the minimum safe clamping force;

[0014] S2, When the mold is in the thin-mold state transitioning from a thin-mold state to a thick-mold state, monitor the mold thickness change rate under the minimum safe clamping force in real time and perform progressive collaborative control of the mold thickness; S3, When the mold is in the mold thickness limit / position state, perform collaborative control of mold thickness and pressure parameters based on the results of progressive collaborative control to determine the optimal clamping force peak value; S4, When the mold is in the mold thickness middle limit / position state, collect the fluctuation data of the corresponding casting forming process under the optimal clamping force peak value, simultaneously quantify the collaborative matching degree between mold thickness and fluctuation data, and generate a collaborative verification report.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0016] 1. To address issues such as parameter misalignment and mold adjustment response delay caused by static acquisition of mold thickness under dynamic working conditions, this invention implements a step-by-step control process based on the mold state: When the mold is in the thin-film limit / position state, thickness coordination initialization is completed first, followed by coordinated control of thickness and clamping force to determine the minimum safe clamping force and avoid impact damage in the initial closing stage; in the transition state of thin-film, the thickness change rate is monitored in real time and progressive coordinated control is implemented to ensure dynamic matching between clamping force growth and closing progress, solving the problem that fixed correlation logic is difficult to adapt to working condition fluctuations; in the thick-film limit / position state, thickness and pressure parameters are coordinated and adjusted based on the progressive control results to determine the optimal clamping force peak value, accurately adapting to dynamic factors such as molten metal impact and clamping force elastic deformation, avoiding thickness deviation and overflow risks caused by sudden changes in injection pressure; in the thick-film state, molding fluctuation data is collected and the coordination matching degree between thickness and fluctuation data is quantified to provide support for parameter optimization in the next batch. The entire process enables real-time perception of dynamic factors and precise adjustment of parameters, compensating for the response delay of the mold adjustment mechanism, avoiding dimensional fluctuations in continuous batches of castings, reducing mold cavity wall wear and equipment failure risks, ensuring the dimensional tolerance requirements of precision castings, and improving the reliability of process collaborative control and casting quality.

[0017] 2. By first obtaining the closed thickness of the mating surfaces of the moving and fixed mold plates under thin mold conditions and comparing it with the corresponding preset values, the initial deviation is obtained to reduce the impact of the initial thickness deviation on subsequent collaborative control. Then, the initial value is extracted by matching the thickness-minimum clamping force table, verifying the closing impact state, and monitoring hydraulic circuit pressure fluctuations. After ensuring parameter adaptation, the initialization result is output, effectively avoiding impact damage caused by thickness deviation in the early stage of mold closure. In the collaborative control stage, the thickness fluctuation and clamping force fluctuation are monitored in real time during the mold closing and clamping process. Within the preset monitoring period, the thickness fluctuation and clamping force fluctuation are not greater than the corresponding preset allowable values, confirming the minimum safe clamping force. The entire process eliminates thickness deviation through initial calibration, dynamically adjusts and adapts to fluctuations, accurately matches the actual mold state and clamping force, reduces hydraulic circuit pressure fluctuations and impact damage, avoids equipment failures caused by clamping force and thickness mismatch, ensures the stability of the mold closing and clamping process, and lays a reliable foundation for subsequent progressive collaborative control.

[0018] 3. By collecting thickness and timestamp data of the moving template bonding surface at preset acquisition intervals, and forming thickness and time series through time-series processing, the real-time thickness change rate is obtained by calculating the ratio of the thickness difference series to the time difference series and combining it with the arithmetic mean. This rate is then compared with the corresponding rate range and controlled using a PID algorithm to achieve dynamic coordination between mold adjustment speed and clamping force growth. The effect of progressive coordinated control is verified, the deviation ratio is calculated, and if it exceeds the corresponding allowable range, the control is repeated. The entire process achieves precise coordination between mold adjustment speed and clamping force growth through real-time rate monitoring and PID dynamic control, avoiding mold impact damage and molten metal overflow, suppressing thickness fluctuations, solving the problem of parameter adaptation lag under dynamic working conditions, and ensuring the stability of the pre-filling process.

[0019] 4. First, the clamping force and mold thickness after progressive collaborative control are acquired. Simultaneously, the cooling temperature of the mold cavity wall is collected, and the average value is used as a baseline. The initial injection pressure for the injection molding process is determined by combining the mold thickness with this baseline value. Simultaneously, the cooling temperature is monitored in real time. If the cooling temperature exceeds the preset allowable high temperature, the pressure is increased by a positive difference; if it is below the preset allowable low temperature, the pressure is decreased by a negative difference. If it is within the corresponding allowable low temperature / pre-allowable high temperature range, the initial value is maintained, and monitoring continues. Then, the adaptation coefficient of the three is calculated: the clamping force, mold thickness, and injection pressure after progressive control are normalized to construct a baseline vector. The real-time vector normalization vector is acquired simultaneously. Similarity is calculated using the cosine theorem to characterize trend synergy. The arithmetic mean of the absolute differences of each dimension is then taken to obtain the comprehensive deviation. After linear fusion, the adaptation coefficient is obtained. If the adaptation coefficient is not less than the preset adaptation coefficient, the current injection pressure is maintained; if it is less than the preset adaptation coefficient, it is finely adjusted according to the deviation ratio. After calibration, the corresponding stable clamping force is taken as the optimal peak value. The entire process dynamically adapts to changes in cooling temperature and the relationship between parameters, reducing casting shrinkage and deformation, while avoiding mold damage caused by overpressure, and improving the accuracy of process parameter coordination and the stability of casting quality.

[0020] 5. Based on the preset acquisition trigger conditions and monitoring nodes for casting forming under the optimal clamping force peak, before acquisition, the first-order difference sequence of historical data under the same working conditions is calculated, and its variance and extreme value distribution are analyzed to determine the dynamic sampling frequency, accurately adapting to the data fluctuation characteristics. Subsequently, the original fluctuation data of each node is captured in real time, and after Kalman filtering for noise reduction and time-series alignment, a time-series fluctuation data sequence containing mold thickness, clamping force, and cooling temperature is formed. Finally, structured extraction and standardization are performed to obtain the fluctuation data. When quantifying the degree of coordination and matching, the mold thickness fluctuation data is used as the control reference vector, and each fluctuation data is used as the response vector. The similarity is calculated using the cosine formula of the angle to quantify the trend consistency. The deviation sequence of the response vector relative to the reference vector is integrated over time to obtain the cumulative deviation, reflecting the degree of deviation. Then, the cosine similarity and the normalized complement of the cumulative deviation are weighted and fused to generate a coordination and matching index. If the index is greater than the preset coordination and matching index, it is judged as qualified and a verification report is generated; otherwise, manual intervention is prompted. The entire process uses dynamic sampling and precise quantification to comprehensively capture the fluctuation patterns of molding process parameters, accurately assess the synergistic effect of mold thickness and key parameters, provide reliable data support for the optimization of parameters in the next batch of production, effectively reduce casting defects caused by parameter fluctuations, and further improve process stability and the precision forming quality of castings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of the multi-process parameter collaborative control system for a die-casting machine provided in an embodiment of the present invention;

[0023] Figure 2 A dynamic interaction timing diagram of the four-stage collaborative control module in the die-casting process provided in an embodiment of the present invention;

[0024] Figure 3 A flowchart illustrating the progressive collaborative control of mold thickness and the verification of its effect, provided in an embodiment of the present invention.

[0025] Figure 4 A flowchart illustrating the coordinated control of mold thickness and pressure parameters provided in an embodiment of the present invention;

[0026] Figure 5 A flowchart illustrating the quantification process for the collaborative matching between mold thickness and fluctuation data provided in this embodiment of the invention;

[0027] Figure 6This is a flowchart of a multi-process parameter collaborative control method for a die-casting machine provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0029] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0030] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0031] This invention controls the entire die-casting production cycle, which comprises four consecutive, interconnected stages: thin mold, thin mold middle stage, thick mold, and thick mold middle stage, rather than isolating each stage. Throughout the die-casting cycle, the mold state is constantly changing, and the requirements for mold thickness, clamping force, and pressure parameters differ significantly at each stage. Traditional single control logic cannot simultaneously address the multi-dimensional requirements of equipment safety and casting quality, easily leading to problems such as mold closing impact wear, air entrapment during filling, or casting deformation.

[0032] The multi-process parameter collaborative control system for die casting machines of this invention is designed around dynamic adaptation throughout the entire lifecycle. It achieves precise collaborative control at each stage through corresponding modules: from safe initialization in the thin mold stage, to dynamic transition adaptation in the middle mold stage, to precision forming control in the thick mold stage, and finally to closed-loop verification and optimization in the middle mold stage. This forms a parameter collaboration mechanism covering the entire lifecycle, effectively solving the shortcomings of traditional control modes, ensuring equipment safety, and improving casting quality. Specifically:

[0033] This invention provides a multi-process parameter collaborative control system for die-casting machines, such as... Figure 1 The diagram shown illustrates the structure of a multi-process parameter collaborative control system for a die-casting machine. This system may include:

[0034] The thin mold state collaborative control module is used to perform collaborative initialization of mold thickness when the mold is in the thin mold limit / position state after the die casting machine and mold adjustment mechanism have completed mechanical linkage and signal interaction. Based on the results of collaborative initialization, it performs collaborative control of mold thickness and clamping force parameters to determine the minimum safe clamping force, avoid damage caused by excessive impact force in the early stage of mold closure, and achieve safe adaptation during equipment startup.

[0035] The thin-mold state collaborative control module is used to monitor the rate of change of mold thickness under the minimum safe clamping force in real time when the mold is transitioning from a thin-mold state to a thick-mold state. It also performs progressive collaborative control of the mold thickness to ensure that the increase of clamping force matches the mold closing progress, avoids cooling or turbulent air entrapment problems before filling, and realizes dynamic parameter adaptation during the transition stage.

[0036] The mold thickness state collaborative control module is used to coordinate the control of mold thickness and pressure parameters based on the results of progressive collaborative control when the mold is in the mold thickness limit / position state. This is to determine the optimal clamping force peak value, ensure mold sealing, improve the coordination between cooling rate and molten metal solidification characteristics and mold thickness, reduce casting shrinkage deformation, and achieve precision adaptation in the molding stage.

[0037] The mold thickness state co-verification module is used to collect fluctuation data of the corresponding casting forming process under the optimal clamping force peak when the mold is in the mold thickness limit / position state. It simultaneously quantifies the co-matching degree between the mold thickness and the fluctuation data, generates a co-verification report, provides data support for parameter optimization of the next batch of production, and realizes closed-loop optimization of control logic.

[0038] In a specific embodiment, for example, in the die-casting production of aluminum alloy automotive parts, the mold needs to frequently switch between thin and thick states to adapt to castings of different specifications. Traditional unified parameter control often results in problems such as mold wear caused by closing impact, air entrapment during filling, or casting deformation. Moreover, the adaptation logic of thickness, clamping force, and pressure varies significantly throughout the entire process from the thin mold state to the thick mold state, and a single control mode cannot take into account multiple dimensions of indicators.

[0039] This example addresses the dynamic changes in the die-casting process across four stages: thin mold positioning / positioning, thin mold positioning / positioning, thick mold positioning / positioning, and thick mold positioning / positioning. It addresses these issues through: coordinated initialization during the thin mold positioning stage, establishing a safety baseline through mechanical linkage and signal interaction to prevent closing impacts from the outset; rate-linked control during the thin mold positioning / positioning stage, dynamically adapting to parameter changes during the mold transition process to resolve issues such as uneven cooling and air entrapment before filling; peak value optimization during the thick mold positioning stage, deeply coordinating thickness and pressure parameters to ensure matching of sealing performance and solidification characteristics, reducing shrinkage deformation; and closed-loop verification during the thick mold positioning stage, quantifying the matching degree through fluctuation data. The entire process deeply integrates mold status and process parameters. Each module is designed with specific stage adaptation needs and control objectives in mind. Through the dual guarantees of mechanical linkage and signal interaction, and the coordinated cooperation of real-time monitoring and dynamic control, precise matching of parameters and mold status at each stage is achieved, effectively improving the intelligence, precision, and stability of die-casting production.

[0040] like Figure 2The dynamic interaction timing diagram of the four-stage collaborative control module in the die casting process shown in the figure (the red marks in the figure represent the output nodes of the corresponding parameters) fully presents the timing relationship, functional division and parameter flow logic of each module: In the die casting scenario of aluminum alloy automotive parts, after startup, the mold thin state collaborative control module is triggered first. It completes the initialization preparation through mechanical linkage and signal interaction. Then, the mold thickness collaborative initialization is completed within 0.5s, and the minimum safe clamping force is calculated and output. The safety benchmark is established from the source to avoid impact damage in the early stage of mold closure.

[0041] Next, the mold thickness state collaborative control module receives this parameter and monitors the mold thickness change rate in real time for 1.2 seconds. It executes progressive collaborative control and outputs a clamping force growth adaptation curve to ensure dynamic matching between the clamping force and the mold closing progress, thus solving the problems of uneven cooling and turbulent air entrapment before filling. Subsequently, the mold thickness state collaborative control module is activated. Within 0.8 seconds, based on the previous parameters, it deeply coordinates the mold thickness and pressure to determine the optimal clamping force peak value, ensuring mold sealing, improving the compatibility between the cooling rate and the solidification characteristics of the molten metal, and reducing casting shrinkage deformation.

[0042] Finally, the mold thickness and state collaborative verification module completes the acquisition and matching quantification of fluctuation data in 1.0s, outputs a collaborative verification report, and feeds back parameter optimization suggestions to the next batch of production. This timing process, through precise phased control, overcomes the shortcomings of traditional unified parameter control in multi-state switching scenarios, effectively improving the intelligence, precision, and stability of die casting production.

[0043] Furthermore, the mold thickness is initialized collaboratively. The specific process is as follows: Under the mold thickness limit / position state, the closing thickness of the mating surface between the moving and fixed mold plates driven by the mold adjustment mechanism during the die-casting process is obtained and compared with a pre-set closing thickness to obtain the initial deviation of the mold thickness, i.e., the difference between the closing thickness of the mating surface between the moving and fixed mold plates and the pre-set closing thickness. The pre-set closing thickness is determined through a no-load calibration experiment based on the die-casting machine model, the mold design theoretical closing thickness, and the target die-casting part forming process requirements. After assembling a qualified mold, the mold adjustment mechanism is driven to ensure seamless mating of the moving and fixed mold plates. Three position sensor feedback data are collected, and the average value is taken as the reference value. Initialization calibration is performed based on the obtained initial deviation.

[0044] If the obtained initial deviation is not greater than the preset initial allowable deviation, the closed thickness of the mating surface corresponding to the current moving mold position is directly used as the benchmark for the calibrated mold thickness. The preset initial allowable deviation, combined with the mold machining tolerance (±0.05~±0.1mm), mold adjustment transmission error, and casting forming accuracy requirements, is determined to be ±0.1~±0.15mm through orthogonal experiments, balancing accuracy and efficiency. Otherwise, the operator is prompted to gradually adjust the position of the moving mold, with each adjustment step being a proportion corresponding to the initial deviation. The operator then adjusts the position based on the mold structural rigidity and the transmission accuracy of the mold adjustment mechanism. The initial deviation and on-site adjustment experience are set according to the principle of small-amplitude, multi-stage adjustment to avoid problems such as impact on the mating surface of the moving and stationary templates and overload of transmission components caused by excessive adjustment in a single step. If the initial deviation obtained after initial calibration is not greater than the preset initial allowable deviation, the closing thickness of the mating surface corresponding to the position of the moving template is used as the benchmark for the calibrated mold thickness. Otherwise, an abnormal warning for the initial thickness calibration is issued. The warning may include: Please check the transmission accuracy of the mold adjustment mechanism, the wear condition of the mold mating surface, and the validity of the position sensor signal, and suspend production to troubleshoot the fault.

[0045] The calibrated mold thickness reference is matched with the preset thickness-minimum clamping force correspondence table, and the initial value of the minimum clamping force corresponding to the mold thickness reference is extracted. The preset thickness-minimum clamping force correspondence table is constructed based on the mold material strength, cavity structure size, and die casting material characteristics through multiple sets of clamping force gradient experiments: with different mold thicknesses as the abscissa, the minimum clamping force value that ensures no impact damage to the mold and sealing of the mating surface is tested as the ordinate. A continuous correspondence table is formed by data fitting, covering the commonly used thickness range and reserving adjustment margin. Under the initial value of minimum clamping force, the preset personnel are prompted to perform impact state verification at the initial stage of mold closure. Specifically, the vibration amplitude, abnormal noise, and uniformity of mating surface are observed when the mold is closed to complete the intuitive verification, and the hydraulic circuit pressure monitoring of the die casting machine clamping system is started simultaneously.

[0046] During the impact state verification process, the pressure fluctuation status of the hydraulic circuit corresponding to the clamping system in the die-casting machine is monitored to obtain the pressure fluctuation value of the hydraulic circuit. The pressure fluctuation value represents the maximum difference between the real-time pressure of the hydraulic circuit and the standard pressure value corresponding to the initial value of the minimum clamping force. If the obtained pressure fluctuation value is not greater than the preset allowable pressure fluctuation value, it indicates that the current mold thickness and clamping force are coordinated and adapted. The calibrated mold thickness benchmark and the initial value of the minimum clamping force are used as the result of coordinated initialization. Otherwise, a coordinated initialization abnormality prompt is sent to prompt the preset personnel to adjust the initial value of the minimum clamping force. The adjustment range is set at ±5% to ±10%. This range is based on the mold's impact resistance limit, the hydraulic adjustment accuracy of the clamping system, and the process adaptation requirements. It has been verified by multiple sets of orthogonal experiments: an adjustment of less than 5% is difficult to effectively correct the problem of excessive pressure fluctuation, while an adjustment of more than 10% is likely to cause a sudden change in clamping force, leading to mold impact damage or seal failure. The preset allowable pressure fluctuation value, combined with the rated pressure of the hydraulic circuit of the clamping system and the impact resistance capacity of the mold, is determined by reliability experiments to be ±3% to ±5% of the initial value of the minimum clamping force to avoid excessive pressure fluctuation leading to clamping instability or mold damage.

[0047] In this embodiment, by accurately acquiring the closed thickness of the mold mating surface and calibrating for deviations, combined with matching the thickness with the minimum clamping force, and integrating visual verification of the impact state with monitoring of hydraulic circuit pressure fluctuations, the coordinated initialization of mold thickness and clamping force is achieved. This process effectively avoids impact damage in the initial stage of mold closure, ensures precise matching between clamping force and mold thickness, and reduces equipment failures caused by parameter mismatch. Simultaneously, through a gradual adjustment mechanism and anomaly warning function, the safety and reliability of the initialization process are ensured, laying a solid foundation for coordinated control in the subsequent transition and forming stages. This significantly improves the accuracy and stability of die casting production and reduces the defect rate of castings.

[0048] like Figure 3The flowchart shown illustrates the progressive collaborative control of mold thickness and its effectiveness verification. The collaborative control of mold thickness and clamping force parameters includes both collaborative control of mold thickness and clamping force, and verification of the effectiveness of the collaborative control. Specifically, the collaborative control of mold thickness and clamping force involves: during the clamping process of the die-casting machine, acquiring the thickness fluctuation of the current mold thickness relative to the calibrated mold thickness benchmark, and the clamping force fluctuation of the clamping force relative to the initial minimum clamping force value fed back from the hydraulic circuit. If the thickness fluctuation is not greater than the preset allowable thickness fluctuation, and the clamping force fluctuation is not greater than the preset allowable clamping force fluctuation, then the mold thickness and clamping force are considered to be stably matched, and the current clamping force parameter is maintained without further processing. The preset allowable thickness fluctuation is set based on the casting forming accuracy requirements (±0.03~±0.08mm) and the mold's thermal deformation characteristics. The preset clamping force... The allowable fluctuation is usually determined based on the maximum value of the historical clamping force during the historical clamping force control process to ensure clamping stability. If the thickness fluctuation is not greater than the preset allowable thickness fluctuation, but the clamping force fluctuation is greater than the preset allowable clamping force fluctuation, the clamping force is dynamically compensated according to the direction and amplitude of the clamping force fluctuation: when the clamping force increases, the clamping force is positively compensated at 1.2 to 1.5 times the fluctuation amplitude (i.e., the difference between the clamping force fluctuation and the preset allowable clamping force fluctuation); when the clamping force decreases, the clamping force is reversely adjusted at 0.8 to 1.0 times the fluctuation amplitude. The positive compensation / reverse adjustment amount corresponding to this clamping force is set according to the mold stiffness characteristics, the alloy filling expansion force law, and the sealing safety redundancy requirements. When the thickness increases, a coefficient of 1.2 to 1.5 times is needed to quickly offset the risk of expansion. When the thickness decreases, a coefficient of 0.8 to 1.0 times is used to avoid mold damage caused by excessive clamping force, ensuring that the clamping force and mold thickness are dynamically matched in real time, taking into account both sealing reliability and equipment operation safety.

[0049] In addition to the above situations, all other cases are judged as abnormal mold force output, triggering an audible and visual warning for abnormal mold force output, and simultaneously recording fluctuation data, the time of abnormality occurrence, and mold status, prompting operators to check for problems such as hydraulic system leakage and pressure sensor failure. The specific process for verifying the effectiveness of collaborative control is as follows: Within a preset monitoring period (10-30 seconds, set according to the die-casting machine's clamping action response speed and the mold's thermal stability time), if the pressure fluctuation value of the hydraulic circuit is not greater than the preset allowable pressure fluctuation value, and the thickness fluctuation of the moving mold plate contact surface is not greater than the preset allowable thickness fluctuation value, then the collaborative control is deemed effective. It is confirmed that the current clamping force parameter meets the minimum safe clamping force requirement, and progressive collaborative control of the mold thickness is implemented. Otherwise, the clamping force is reduced to the safety protection value (70% of the initial value of the minimum clamping force, set by the preset personnel based on the mold material's fatigue resistance limit, the safety redundancy of the clamping system's hydraulic circuit, and historical fault data statistics. This ensures that the mold contact surface does not leave the sealing range, avoids equipment malfunction due to excessively low clamping force, and reserves safe operating space for manual troubleshooting). An abnormality diagnosis report is generated and the preset personnel are prompted to conduct manual troubleshooting.

[0050] The progressive collaborative control of mold thickness includes: continuously collecting the thickness data of the moving template bonding surface of a preset group and the corresponding timestamp data at a preset sampling interval of 30 milliseconds (the specific value is set by technicians based on the response speed of the mold adjustment mechanism and the sensitivity of mold thickness changes, and can also be fine-tuned according to the actual data acquisition and response requirements in practical applications), and obtaining the thickness sequence by sorting it in time sequence. (h) i (where the thickness sample value of the i-th group is n, and n is the total number of sample groups) and the time series. (t) i (For the i-th group of timestamp data); the thickness difference sequence is obtained by calculating the difference between adjacent thickness sample values. (Δh) i =h i+1 -h i The sampling interval of the time difference sequence is fixed at 30 milliseconds. =30ms), meaning the sampling interval for each sampling group is 30 milliseconds; then, the ratio of the difference between adjacent elements in the thickness difference sequence to the fixed sampling interval is calculated to obtain the instantaneous change rate sequence. ( (where j is the instantaneous rate sequence index), and finally the arithmetic mean of the sequence is taken. As a real-time thickness change rate, it accurately characterizes the stable change trend of mold thickness, providing reliable data support for progressive collaborative control.

[0051] The preset minimum and maximum adaptation rates are retrieved to form a coordinated control rate range. The minimum and maximum adaptation rates are set based on the mold structure rigidity, cavity closing clearance requirements, die casting material fluidity, and mold adjustment mechanism transmission characteristics. They are determined through multiple sets of orthogonal experiments. Taking the molding quality of the target die casting as the core indicator, the mold closing stability, clamping force transmission uniformity, and pre-filling cooling effect are tested at different rates. The lowest rate that does not cause jamming at the mating surface, has no clamping impact, and can avoid premature cooling of the molten metal is set as the minimum adaptation rate (normally 0.02~0.05mm / s). The highest rate that does not cause overload of the mold adjustment mechanism and whose mold vibration amplitude is within the allowable range (≤0.03mm) is set as the maximum adaptation rate (normally 0.08~0.12mm / s).

[0052] If the real-time thickness change rate is within the coordinated control rate range, it is determined that the clamping force growth matches the mold closing progress, and the current mold adjustment mechanism moving speed and clamping force growth gradient are maintained. If the real-time thickness change rate is lower than the minimum adaptation rate, it is determined that the mold adjustment speed is too slow and the clamping force growth is relatively ahead. Based on the difference between the minimum adaptation rate and the real-time thickness change rate, the rate compensation amount is calculated through the PID algorithm, and the moving speed of the mold adjustment mechanism is increased according to the rate compensation amount to accelerate the position response speed of the moving mold plate. At the same time, in order to avoid mold impact caused by excessive clamping force growth during the speed increase, the clamping force growth gradient needs to be reduced by 10% simultaneously to ensure coordinated adaptation between the mold adjustment speed increase and the clamping force growth, and to prevent stress concentration on the bonding surface caused by the clamping force being ahead.

[0053] If the real-time thickness change rate is higher than the maximum adaptation rate, it is determined that the mold adjustment speed is too fast and the clamping force growth is relatively lagging. Based on the difference between the real-time thickness change rate and the maximum adaptation rate, the rate decay is calculated by the PID algorithm, and the moving speed of the mold adjustment mechanism is reduced according to the rate decay to slow down the moving platen movement rhythm and avoid mold impact damage caused by excessively fast fitting. At the same time, in order to prevent the clamping force from lagging behind after the mold adjustment speed is reduced, resulting in poor mold sealing, the clamping force growth gradient needs to be increased by 10% simultaneously so that the clamping force growth rhythm and the mold adjustment speed complement each other and avoid molten metal overflow or casting size deviation caused by insufficient clamping force.

[0054] Specifically, a certain model of cold chamber die casting machine (clamping force 8000kN) is used in conjunction with an aluminum alloy automotive steering knuckle mold (rigidity 2.5×10). 5Taking the production of high-strength, lightweight structural components (N / mm) die-cast aluminum alloy automotive steering knuckles as an example, in the calculation of the rate compensation, the PID algorithm uses the difference between the minimum adaptation rate and the real-time thickness change rate as input. The proportional coefficient (P) is set to 0.8 to quickly respond to deviations, the integral time (I) is set to 1.2s to eliminate steady-state errors caused by system inertia, and the derivative time (D) is set to 0.3s to suppress speed overshoot. Combined with the 15Hz response bandwidth of the hydraulic system and the 0.2s inertial time constant, the output is a precise rate compensation. The PID parameters for the rate decay are set to a proportional coefficient of 0.6, an integral time of 1.5s, and a derivative time of 0.2s to match the damping characteristics and inertial lag of the deceleration stage. The 10% clamping force growth gradient adjustment ratio is determined by the preset personnel based on the equipment clamping force transmission efficiency and the filling seal safety margin, which avoids parameter mismatch and prevents mold damage or seal failure caused by gradient abrupt changes, thus balancing adaptation efficiency and operational safety.

[0055] The specific values ​​mentioned above were all set by the pre-set personnel based on the actual physical characteristics of the die-casting machine and the die-casting process requirements. The PID parameters were calculated based on the hydraulic system response bandwidth, inertia time constant, and mold stiffness of the cold chamber die-casting machine. Specifically, the critical gain and critical period were first calculated using the Ziegler-Nichols empirical formula, combined with the 15Hz response bandwidth and 0.2s inertia time constant of the cold chamber die-casting machine's hydraulic system. Then, the critical gain and critical period were calculated using 2.5 × 10⁻⁶ ppm. 5 The die stiffness (N / mm) was normalized and damped, resulting in a proportional gain of 0.8, an integral time of 1.2s, and a derivative time of 0.3s. The PID parameters for rate decay were further optimized based on the damping characteristics during the deceleration phase. In actual production scenarios, fine-tuning can be performed according to the die-casting machine model, die material, or casting specifications. For example, the proportional gain can be appropriately increased for higher stiffness dies, while the gradient adjustment ratio can be reduced for thin-walled castings to further adapt to the dynamic requirements of different working conditions and ensure control accuracy and equipment safety.

[0056] After each rate calculation and control adjustment, a progressive collaborative control effect verification is performed. Specifically, the deviation ratio between the real-time thickness change rate and the optimal adaptation rate is recalculated; this is the ratio of the absolute value of the difference between the real-time thickness change rate and the optimal adaptation rate to the optimal adaptation rate. The optimal adaptation rate represents the average of the preset minimum and maximum adaptation rates. If the deviation ratio does not exceed the preset allowable deviation ratio, the progressive collaborative control effect is deemed to meet the expected requirements, and collaborative control of the mold thickness and pressure parameters is implemented. Otherwise, the above progressive collaborative control execution process is repeated. The preset allowable deviation ratio, combined with molding accuracy requirements and control response accuracy, is experimentally set to ±8% to ±12%. The process is executed a preset number of times (3 to 5 times). The calculation process for the real-time thickness change rate involves obtaining a preset number of real-time thickness change rate values ​​for a preset number of times and taking their arithmetic mean. If the arithmetic mean is between the minimum and optimal adaptation rates, the pre-filling state is confirmed to be stable, and the current clamping force and mold thickness are obtained to complete progressive collaborative control. If the arithmetic mean is not between the minimum and optimal adaptation rates, the pre-filling state is confirmed to be unstable, and a progressive collaborative control warning is triggered. The clamping force is then fine-tuned to a preset multiple of the minimum safe clamping force (1.04 times, which is based on the mold sealing redundancy requirements and the pressure adjustment accuracy of the clamping system, and is set by the preset personnel in conjunction with the actual casting requirements) to improve the rigidity and sealing of the clamping system and suppress further expansion of thickness fluctuations.

[0057] In this embodiment, by real-time monitoring of thickness and clamping force fluctuations and dynamic compensation of clamping force, combined with anomaly warning and safety protection mechanisms, mold impact damage and clamping instability are avoided at the source. Secondly, progressive collaborative control, based on a PID algorithm, dynamically adjusts the mold adjustment speed and clamping force growth gradient, precisely matching the mold closing progress and effectively solving pain points such as uneven cooling before filling, air entrapment, and overflow. Furthermore, a multi-round effect verification and mean judgment mechanism ensures the stability of the pre-filling state, laying a solid foundation for subsequent molding processes. The entire control system achieves deep binding between parameters and mold state, balancing equipment safety, process stability, and casting accuracy, significantly reducing mold wear and casting defect rates, and improving the intelligence and reliability of die casting production.

[0058] like Figure 4The flowchart shown illustrates the coordinated control process of mold thickness and pressure parameters. The specific steps include: acquiring the clamping force and mold thickness after progressive coordinated control, simultaneously collecting the cooling temperature of the mold cavity wall, and averaging the values ​​to obtain the current cooling temperature baseline; combining the current mold thickness and the current cooling temperature baseline, determining the initial value of the injection pressure in the injection circuit during the die-casting process, and monitoring the changes in cooling temperature: if the monitored cooling temperature is greater than the preset allowable high cooling temperature, the injection pressure of the injection circuit is increased based on the positive cooling temperature difference; if the monitored cooling temperature is less than the preset allowable low cooling temperature, the injection pressure of the injection circuit is decreased based on the negative cooling temperature difference.

[0059] If the monitored cooling temperature falls between the preset allowable low and high cooling temperatures, the initial injection pressure is maintained and monitoring continues. The preset allowable low and high cooling temperatures are determined through multiple process experiments based on the solidification characteristics of the die-casting material (e.g., aluminum alloy 660℃~680℃, zinc alloy 410℃~430℃), mold thermal stability, and filling fluidity requirements. This ensures the cooling temperature is within the optimal filling range of the molten metal, preventing casting sticking or overheating due to excessive temperature, or insufficient filling due to excessively low temperature. Based on the compatibility relationship between the current injection pressure, mold thickness, and clamping force, the compatibility coefficient is calculated. If the obtained compatibility coefficient is not less than the preset compatibility coefficient, the current injection pressure is maintained; otherwise, the injection pressure is gradually fine-tuned according to the adjustment ratio corresponding to the compatibility coefficient deviation. The target is the preset compatibility coefficient, and the difference between the obtained compatibility coefficient and the preset compatibility coefficient is used as the adjustment factor. A single injection pressure adjustment range of 1 / 3 to 1 / 2 is selected (this range is determined through process coupling law analysis and multiple sets of working condition experiments, and can approach the target within 1 to 2 adjustments, taking into account both efficiency and filling stability). After each adjustment, a 5 to 8 second stabilization period is maintained (to match the pressure stabilization delay of the hydraulic system and the dynamic response cycle of the molten metal). After the process state stabilizes, the adaptation coefficient is recalculated, and the process is iterated until the adaptation coefficient reaches the standard. This avoids parameter mismatch problems such as pressure sudden changes and molten metal filling disorder caused by excessive single adjustment range, and ensures smooth adaptation of the injection process. After the above injection pressure calibration process is completed, the actual value of the stable clamping force corresponding to the calibrated injection pressure is taken as the optimal clamping force peak value. The preset adaptation coefficient, combined with mold sealing requirements, injection pressure transmission efficiency and casting forming accuracy, is determined through orthogonal experiments to ensure good mold sealing, full molten metal filling and no excessive extrusion damage under the synergistic effect of the three factors.

[0060] Specifically, based on the adaptation relationship between the current injection pressure, mold thickness, and clamping force, the adaptation coefficient of the three is calculated. First, using the clamping force, mold thickness, and injection pressure determined after the progressive collaborative control is completed as benchmark parameters, the corresponding maximum rated values ​​for each parameter are queried (the maximum rated value of clamping force is set based on the rated load of the die-casting machine, the maximum rated value of mold thickness is determined based on the mold design limit thickness, and the maximum rated value of injection pressure is referenced to the rated output pressure of the injection system). Each benchmark parameter is divided by its corresponding maximum rated value to complete the normalization process, resulting in a normalized benchmark vector composed of the three normalized benchmark values. Simultaneously, the real-time clamping force, real-time mold thickness, and real-time injection pressure are collected and divided by their corresponding maximum rated values ​​to generate a normalized real-time vector. Next, the adaptation consistency is calculated using the cosine theorem of the vector angle: the normalized benchmark vector and the normalized real-time vector are treated as two three-dimensional vectors. By calculating the cosine value of the angle between them, the consistency of the vector direction is quantified. The closer the cosine value is to 1, the more consistent the changing trends of the real-time parameters and the benchmark parameters are.

[0061] Then, the degree of parameter deviation is calculated: the absolute difference between the real-time values ​​of clamping force, mold thickness, and injection pressure in the normalized real-time vector and the corresponding reference values ​​in the normalized reference vector is calculated. The arithmetic mean of the three absolute differences is then calculated to obtain the comprehensive absolute difference reflecting the deviation of each parameter from the reference. The smaller this value, the smaller the numerical deviation between the real-time parameter and the reference parameter. Finally, linear fusion is performed to obtain the fitting coefficient: the comprehensive absolute difference is converted into the deviation complement (i.e., 1 minus the comprehensive absolute difference), and then the deviation complement is linearly weighted with the aforementioned cosine similarity using the same fusion coefficient of 0.5 to obtain the final fitting coefficient. The fitting coefficient ranges from [0,1]. The closer the value is to 1, the better the comprehensive fitting of the real-time injection pressure, mold thickness, and clamping force in terms of consistency of change trend and degree of numerical deviation; conversely, the closer the value is to 1, the worse the fitting.

[0062] In this embodiment, the collaborative control scheme for mold thickness and pressure parameters significantly improves the stability and casting quality of die casting through multi-dimensional parameter linkage and precise quantitative adaptation. First, by dynamically determining and adjusting the injection pressure based on mold thickness and cooling temperature, the filling requirements of the molten metal are precisely matched, effectively avoiding defects such as sticking, overheating, or insufficient filling caused by abnormal temperatures. Second, by employing a normalized processing, vector similarity calculation, and deviation quantification fusion adaptation coefficient, the degree of adaptation among injection pressure, mold thickness, and clamping force is scientifically quantified, providing a precise basis for parameter adjustment. Third, through step-by-step fine-tuning and a stabilization period monitoring mechanism, parameter adjustments are ensured to be stable and controllable, avoiding mismatch problems caused by single large adjustments. This scheme breaks through the limitations of traditional independent parameter control, achieving deep collaboration and dynamic optimization of multiple parameters. It ensures both mold sealing and injection pressure transmission efficiency, as well as full molten metal filling and achieving the required casting forming accuracy, significantly reducing the casting defect rate and improving the precision and reliability of die casting production.

[0063] Furthermore, the process of collecting fluctuation data is as follows: Based on the process characteristics of the casting process under the optimal clamping force peak, data acquisition trigger conditions (such as injection start, filling end, holding pressure start, cooling completion, and other key process actions) and monitoring nodes (covering dimensions such as mold thickness, clamping force, and injection pressure) are pre-defined. To achieve accurate monitoring, fluctuation data of each monitoring node under historical working conditions are first retrieved, and the first-order difference sequence of data for each node is calculated (reflecting the change amplitude of data at adjacent times). Then, by analyzing the variance (characterizing the intensity of data fluctuation) and extreme value distribution (determining the boundary of the fluctuation range) of the first-order difference sequence, the sampling frequency of the fluctuation data is determined according to the sampling frequency mapping rules set by the personnel based on process characteristics and data processing capacity. This ensures that the sampling strategy is both adapted to the dynamic change characteristics of each monitoring node and can balance monitoring accuracy and system operating efficiency.

[0064] During real-time monitoring, when the triggering conditions are met, the raw fluctuation data of each monitoring node are captured synchronously. Kalman filtering is performed on the noise interference in the data to remove random errors and restore the true trend of the data. At the same time, the data collected from different nodes are time-series aligned (correcting the data acquisition delay based on a unified timestamp) to form a regular time-series fluctuation data sequence. Finally, the sequence is subjected to structured extraction (extracting characteristic parameters such as peak value, mean, and fluctuation amplitude) and standardization (normalizing according to the rated range of the process) to obtain fluctuation data that can accurately reflect the multi-dimensional dynamic changes in the casting process. The fluctuation data includes mold thickness fluctuation data, clamping force fluctuation data, and cooling temperature fluctuation data.

[0065] The mold thickness fluctuation data is the real-time deviation of the mating surface closure thickness relative to the preset mating surface closure thickness during the casting process. It is obtained by continuously collecting the relative position data of the moving mold plate and the fixed mold plate through a displacement sensor, and calculating the difference with the preset mating surface closure thickness. The unit is mm. The clamping force fluctuation data is the real-time fluctuation data of the output force of the clamping mechanism under the optimal clamping force peak value. It is obtained by collecting the pressure signal through the hydraulic circuit pressure sensor of the clamping system, converting it through the force value conversion formula (clamping force = hydraulic pressure × action area), and calculating the deviation with the optimal clamping force peak value. The unit is N or kN. The cooling temperature fluctuation data is the real-time fluctuation data of the cooling temperature of the mold cavity wall during the casting process. It is obtained by collecting the corresponding cooling temperature through the temperature sensor embedded in the mold cavity wall, and calculating the difference with the preset allowable high cooling temperature and preset allowable low cooling temperature. The unit is ℃.

[0066] like Figure 5 The flowchart shown quantifies the synergistic matching between mold thickness and fluctuation data. Specifically, it involves: First, defining the data vector construction rules: using a calibrated mold thickness benchmark as a reference, calculating the deviation between the measured thickness value and the benchmark value at each moment to form a continuous thickness deviation sequence, which serves as the core control dimension data. Simultaneously, other fluctuation data such as clamping force, injection pressure, and cavity temperature are organized into independent sequence data. Based on this data, two types of time-series vectors are constructed: one is the control benchmark vector, i.e., the mold thickness vector composed of mold thickness fluctuation data, serving as the core reference characterizing the thickness control target; the other is the response vector, i.e., the fluctuation data vectors corresponding to each of the other fluctuation data, reflecting the dynamic response of different dimensions to thickness control.

[0067] Next, the matching degree is calculated using the cosine formula of the vector angle: the control reference vector and each response vector are substituted into the formula to solve for the cosine value of the angle between them. This value is used to quantify the consistency of the changing trends of the two types of data. The smaller the vector angle, the closer the cosine value is to 1, indicating that the changing trend of the corresponding fluctuation data is more consistent with the mold thickness control requirements, that is, the response direction of other dimensions of data is more consistent with the control target when the thickness fluctuates; conversely, it indicates that the trend deviation is large and there is a misalignment between the response logic and the thickness control requirements.

[0068] Subsequently, the cumulative deviation is calculated to assess the degree of deviation: for each response vector, its deviation sequence relative to the control reference vector is calculated (i.e., the difference between the corresponding elements of the response vector and the control reference vector at the same moment); within a preset monitoring period (such as a complete forming cycle), the deviation sequence is integrated over time to obtain the cumulative deviation. This indicator directly reflects the cumulative deviation between the fluctuation data and the thickness control target within the monitoring period; the smaller the cumulative deviation, the higher the accuracy of the data response.

[0069] Finally, a synergy matching index is generated: first, the cumulative deviation is normalized (mapped to the [0,1] interval according to the allowable deviation range of the process), and then its normalized complement (1 - normalized cumulative deviation) is taken to characterize the deviation control effect; this complement is weighted and summed with the aforementioned cosine of the angle with equal weight (0.5 each), because the two have equal importance to the synergy matching degree, thus obtaining the synergy matching index (value range [0,1]). If the synergy matching index is greater than the preset synergy matching index, the synergy matching degree is judged to be qualified, and a synergy verification report containing information such as trend consistency and deviation accumulation is generated. The preset synergy matching index is determined through multiple sets of verification experiments, combined with the core quality requirements of the die casting process (such as casting dimensional tolerance and surface defect rate), equipment control accuracy, and historical qualified working condition data, and is conventionally set to 0.8 to 0.85 to ensure that the synergy control effect meets the requirements of casting forming accuracy and production stability; otherwise, it is judged to be unqualified, and a manual intervention prompt is immediately sent, requiring investigation of parameter adaptation problems or equipment abnormalities.

[0070] In this embodiment, firstly, by dynamically allocating sampling frequencies based on historical data and combining Kalman filtering with time-series alignment processing, efficient acquisition and noise reduction of multi-dimensional fluctuation data are achieved, providing high-quality data support for matching degree analysis. Secondly, the degree of synergistic adaptation between mold thickness and various fluctuation data is scientifically evaluated through equal-weighted fusion logic, breaking through the limitations of traditional single-indicator evaluation. Thirdly, with the addition of qualified report generation and anomaly early warning mechanisms, real-time verification and closed-loop management of the collaborative control effect are realized. This solution not only ensures the logical rationality and execution accuracy of parameter linkage, but also promptly detects and warns of parameter mismatch problems, effectively reducing quality risks such as casting dimensional deviations and surface defects, and improving the intelligence level and reliability of die-casting production.

[0071] This invention provides a method for coordinated control of multiple process parameters in a die-casting machine, such as... Figure 6The flowchart shown is for a multi-process parameter collaborative control method for a die-casting machine. The processing flow of this method may include the following steps: S1, when the mold is in the thin-mold limit / position state, perform collaborative initialization of the mold thickness, and based on the results of the collaborative initialization, perform collaborative control of the mold thickness and clamping force parameters to determine the minimum safe clamping force; S2, when the mold is in the thin-mold intermediate state transitioning from a thin-mold state to a thick-mold state, monitor the mold thickness change rate under the minimum safe clamping force in real time, and perform progressive collaborative control of the mold thickness; S3, when the mold is in the thick-mold limit / position state, perform collaborative control of the mold thickness and pressure parameters based on the results of the progressive collaborative control to determine the optimal clamping force peak value; S4, when the mold is in the thick-mold intermediate limit / position state, collect the fluctuation data of the corresponding casting forming process under the optimal clamping force peak value, synchronously quantify the collaborative matching degree between the mold thickness and the fluctuation data, and generate a collaborative verification report.

[0072] In this embodiment, the process progresses from thickness initialization and clamping force calibration in the thin mold state, to rate control during the transition phase, and then to pressure coordination and optimal clamping force determination in the thick mold state. Finally, closed-loop verification is completed through fluctuation data acquisition and matching quantification. Each stage is progressive and precisely linked. This solution effectively avoids mold damage and casting defects caused by parameter mismatch, ensures clamping stability, filling fullness, and forming accuracy, significantly reduces the casting defect rate, and improves the intelligence level, process controllability, and overall reliability of die casting production.

[0073] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0077] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0078] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-process parameter collaborative control system for a die-casting machine, characterized in that, include: The thin mold state collaborative control module is used to perform collaborative initialization of mold thickness when the mold is in the thin mold limit / position state after the die casting machine and mold adjustment mechanism have completed mechanical linkage and signal interaction. Based on the result of collaborative initialization, it performs collaborative control of mold thickness and clamping force parameters to determine the minimum safe clamping force. The thin-mold state collaborative control module is used to monitor the rate of change of mold thickness under the minimum safe clamping force in real time when the mold is transitioning from a thin-mold state to a thick-mold state, and to perform progressive collaborative control of the mold thickness. The mold thickness state collaborative control module is used to coordinate the control of mold thickness and pressure parameters based on the results of progressive collaborative control when the mold is in the mold thickness limit / position state, and to determine the optimal clamping force peak value. The mold thickness state co-verification module is used to collect fluctuation data of the corresponding casting forming process under the optimal clamping force peak when the mold is in the mold thickness limit / position state, and simultaneously quantify the co-matching degree between the mold thickness and the fluctuation data to generate a co-verification report. The progressive collaborative control of mold thickness specifically includes: The calculation process of real-time thickness change rate is as follows: In the thin-film state, according to the preset sampling interval, the thickness data of the moving template bonding surface of the preset group and the corresponding timestamp data are collected and recorded as sampling data. The time sequence sorting process is performed to obtain the thickness sequence and the time sequence. A thickness difference sequence is formed by calculating the thickness difference between two adjacent sets of sampled data in the thickness sequence. A time difference sequence is obtained based on the time sequence and a preset sampling interval. The difference between two adjacent elements in the thickness difference sequence is obtained, and combined with the preset sampling interval, a rate value is obtained to reflect the thickness change trend. The corresponding arithmetic mean is obtained as the real-time thickness change rate, and the calculation of the real-time thickness change rate is completed. The preset minimum and maximum adaptation rates are retrieved to form a collaborative control rate range; If the real-time thickness change rate is between the minimum and maximum adaptation rates, it is determined that the clamping force growth matches the mold closing progress, and the current mold adjustment mechanism moving speed and clamping force growth gradient are maintained. If the real-time thickness change rate is lower than the minimum adaptation rate, it is determined that the mold adjustment speed is too slow and the clamping force increases relatively ahead. Based on the difference between the minimum adaptation rate and the real-time thickness change rate, the rate compensation amount is calculated through the PID algorithm, and the moving speed of the mold adjustment mechanism is increased according to the rate compensation amount, while the clamping force growth gradient is adjusted down simultaneously. If the real-time thickness change rate is higher than the maximum adaptation rate, it is determined that the mold adjustment speed is too fast and the clamping force growth is relatively lagging. Based on the difference between the real-time thickness change rate and the maximum adaptation rate, the rate decay amount is calculated through the PID algorithm, and the moving speed of the mold adjustment mechanism is reduced according to the rate decay amount, while the clamping force growth gradient is adjusted upward simultaneously. After each rate calculation and control adjustment is completed, a progressive collaborative control effect verification is performed.

2. The multi-process parameter collaborative control system for die-casting machines as described in claim 1, characterized in that, The specific process for performing collaborative initialization of mold thickness is as follows: Under the mold thin limit / position state, the closing thickness of the mating surface between the moving mold plate and the fixed mold plate driven by the mold adjustment mechanism during the die casting process is obtained, and compared with the pre-set closing thickness of the mating surface to obtain the initial deviation of the mold thickness. If the initial deviation is not greater than the preset initial allowable deviation, the closed thickness of the mating surface corresponding to the current moving template position is directly used as the calibrated mold thickness benchmark. Otherwise, the preset personnel are prompted to gradually adjust the moving template position, and the step size of each adjustment is the proportion corresponding to the initial deviation. If the initial deviation obtained after initial calibration is not greater than the preset initial allowable deviation, the closing thickness of the mating surface corresponding to the moving mold position will be used as the calibrated mold thickness reference; otherwise, an abnormal warning for the initial thickness calibration will be issued. The calibrated mold thickness reference is matched with the mold's preset thickness-minimum clamping force correspondence table, and the initial value of the minimum clamping force corresponding to the mold thickness reference is extracted. At the minimum clamping force initial value, the preset personnel are prompted to perform impact state verification at the initial stage of mold closure. During the impact state verification process, the pressure fluctuation state of the hydraulic circuit corresponding to the clamping system in the die casting machine is monitored to obtain the pressure fluctuation value of the hydraulic circuit. If the obtained pressure fluctuation value is not greater than the preset allowable pressure fluctuation value, it indicates that the current mold thickness and clamping force are matched, and the calibrated mold thickness benchmark and the minimum clamping force initial value are used as the result of the coordinated initialization; otherwise, a coordinated initialization error message is sent.

3. The multi-process parameter collaborative control system for die-casting machines as described in claim 2, characterized in that, The specific process for coordinating the control of mold thickness and clamping force parameters is as follows: The first step is the coordinated control process of mold thickness and clamping force: During the clamping process of the die casting machine, the thickness fluctuation of the current mold thickness relative to the calibrated mold thickness benchmark, and the clamping force fluctuation of the clamping force relative to the minimum initial value of clamping force fed back by the hydraulic circuit are obtained. If the thickness fluctuation is not greater than the preset allowable thickness fluctuation and the clamping force fluctuation is not greater than the preset allowable clamping force fluctuation, then the mold thickness and clamping force are considered to be stably matched, and the current clamping force parameters are maintained without further processing. If the thickness fluctuation is not greater than the preset allowable thickness fluctuation, and the clamping force fluctuation is greater than the preset allowable clamping force fluctuation, then the clamping force is dynamically compensated according to the direction and amplitude of the clamping force fluctuation. In addition to the above situations, all other cases are judged as abnormal modulus output and trigger an audible and visual warning for abnormal modulus output; The second step is to verify the effectiveness of the collaborative control: within the preset monitoring period, if the pressure fluctuation value of the hydraulic circuit is not greater than the preset allowable pressure fluctuation value, and the thickness fluctuation of the moving mold plate contact surface is not greater than the preset allowable thickness fluctuation, then the collaborative control is determined to be effective, confirming that the current clamping force parameter meets the minimum safe clamping force requirement, and progressive collaborative control of the mold thickness is carried out. Otherwise, reduce the clamping force to the safety protection value, generate an abnormality diagnosis report, and prompt the designated personnel to conduct manual investigation.

4. The multi-process parameter collaborative control system for die-casting machines as described in claim 1, characterized in that, The verification of the progressive collaborative control effect specifically involves: The deviation ratio between the real-time thickness change rate and the optimal adaptation rate is recalculated, where the optimal adaptation rate represents the average of the preset minimum adaptation rate and the maximum adaptation rate. If the deviation percentage does not exceed the preset allowable deviation percentage, the progressive collaborative control effect is determined to meet the expected requirements, and collaborative control of mold thickness and pressure parameters is performed. Otherwise, the above progressive collaborative control execution process is repeated, and the calculation process of real-time thickness change rate is performed for a preset number of times to obtain the real-time thickness change rate value corresponding to the preset number of times and take the arithmetic mean. If the arithmetic mean is between the minimum and optimal fitting rates, the pre-filling state is confirmed to be stable, and the current clamping force and mold thickness are obtained to complete the progressive collaborative control. If the arithmetic mean is not between the minimum and optimal fitting rates, the pre-filling state is confirmed to be unstable, and a progressive collaborative control warning is triggered, and the clamping force is finely adjusted to a preset multiple of the minimum safe clamping force.

5. The multi-process parameter collaborative control system for die-casting machines as described in claim 4, characterized in that, The coordinated control of mold thickness and pressure parameters specifically includes: After the progressive collaborative control is completed, the clamping force and mold thickness are obtained, and the cooling temperature of the mold cavity wall is collected. The average value is then used to obtain the current cooling temperature reference value. Based on the current mold thickness and the current cooling temperature baseline, determine the initial injection pressure value of the injection circuit in the injection filling process corresponding to the die casting production process, and monitor the cooling temperature: If the detected cooling temperature is greater than the preset allowable high cooling temperature, the injection pressure of the injection circuit is increased based on the positive cooling temperature difference. If the detected cooling temperature is less than the preset allowable low cooling temperature, the injection pressure of the injection circuit is decreased based on the negative cooling temperature difference. If the detected cooling temperature is between the preset allowable low cooling temperature and the preset allowable high cooling temperature, the initial value of the injection pressure is maintained and monitoring continues. Based on the adaptation relationship between the current injection pressure, mold thickness and clamping force, the adaptation coefficient of the three is calculated. If the obtained adaptation coefficient is not less than the preset adaptation coefficient, the current injection pressure is maintained; otherwise, the injection pressure is gradually fine-tuned according to the adjustment ratio corresponding to the deviation of the adaptation coefficient. After the above injection pressure calibration process is completed, the actual value of the stable clamping force corresponding to the calibrated injection pressure is taken as the optimal clamping force peak value.

6. The multi-process parameter collaborative control system for die-casting machines as described in claim 5, characterized in that, The adaptation coefficient of the three factors—current injection pressure, mold thickness, and clamping force—is calculated based on their compatibility relationship. Based on the clamping force, mold thickness and injection pressure after progressive collaborative control, they are normalized according to the corresponding rated maximum values ​​of the process and recorded as normalized reference vectors. At the same time, the normalized real-time vectors are obtained. The cosine similarity between the normalized reference vector and the normalized real-time vector is calculated using the cosine theorem of the vector angle. Calculate the absolute difference between each dimension parameter in the normalized real-time vector and the corresponding dimension parameter in the normalized baseline vector, and then perform an arithmetic mean to obtain the comprehensive absolute difference. The cosine similarity obtained is linearly fused with the complement of the comprehensive absolute difference to obtain the matching coefficient that quantifies the degree of fit between the current injection pressure, mold thickness and clamping force.

7. The multi-process parameter collaborative control system for die-casting machines as described in claim 1, characterized in that, The process for collecting the fluctuation data is as follows: Based on the data acquisition triggering conditions and monitoring nodes that have been divided during the casting process under the optimal clamping force peak, the first-order difference sequence of the fluctuation data of each monitoring node under the same historical working conditions is calculated, and then the variance and extreme value distribution of the first-order difference sequence are analyzed to determine the dynamic sampling frequency of data in each dimension. The raw fluctuation data of each monitoring node during the synchronous acquisition process is captured in real time, and Kalman filtering and time alignment processing are performed to form a time-series fluctuation data sequence. After structured extraction and standardization processing, the fluctuation data of the casting forming process is obtained. The fluctuation data includes mold thickness fluctuation data, clamping force fluctuation data, and cooling temperature fluctuation data; The mold thickness fluctuation data is the real-time deviation data of the bonding surface closure thickness relative to the preset bonding surface closure thickness during the casting process; The clamping force fluctuation data is the real-time fluctuation data of the output force of the clamping mechanism under the optimal clamping force peak value; The cooling temperature fluctuation data refers to the real-time fluctuation data of the cooling temperature of the mold cavity wall during the casting process.

8. The multi-process parameter collaborative control system for die-casting machines as described in claim 7, characterized in that, The degree of coordination between the quantified mold thickness and the fluctuation data is specifically as follows: The mold thickness fluctuation data and each fluctuation data are used to construct time series vectors. The mold thickness fluctuation data represents the deviation sequence based on the relatively calibrated mold thickness reference. The time series vector includes a control reference vector and a response vector. The control reference vector is the mold thickness vector corresponding to the mold thickness fluctuation data, and the response vector is the fluctuation data vector corresponding to each fluctuation data. The cosine of the angle between the control reference vector and each response vector is calculated using the formula for the cosine of the vector angle. Calculate the deviation sequence of each response vector relative to the control reference vector, and perform time-cumulative integration on the deviation sequence within a preset monitoring period to obtain the cumulative deviation amount; The cosine value of the included angle and the normalized result of the cumulative deviation are fused together. That is, the cosine value of the included angle represents the trend synergy between each fluctuation data and the thickness control target, and the normalized complement of the cumulative deviation represents the deviation control effect. Based on the weighted summation logic, they are fused into a synergy matching index. If the collaboration matching index is greater than the preset collaboration matching index, the collaboration matching degree is determined to be qualified and a collaboration verification report is generated; otherwise, the collaboration matching degree is determined to be unqualified and a manual intervention prompt is sent.

9. A method for coordinated control of multiple process parameters of a die-casting machine, applied to the coordinated control system for multiple process parameters of a die-casting machine as described in any one of claims 1-8, characterized in that, Includes the following steps: S1, when the mold is in the thin mold limit / position state, perform collaborative initialization of mold thickness, and based on the result of collaborative initialization, perform collaborative control of mold thickness and clamping force parameters to determine the minimum safe clamping force; S2, when the mold is in the thin state transitioning to the thick state, monitor the mold thickness change rate under the minimum safe clamping force in real time, and perform progressive collaborative control of the mold thickness. S3, when the mold is in the mold thickness limit / position state, based on the results of progressive collaborative control, the mold thickness and pressure parameters are collaboratively controlled to determine the optimal clamping force peak value; S4. When the mold is in the mold thickness limit / position state, collect the fluctuation data of the corresponding casting forming process under the optimal clamping force peak value, and simultaneously quantify the coordination matching degree between the mold thickness and the fluctuation data to generate a coordination verification report.

Images