Air source regulation system and method of adjustable sand shooting mechanism
By combining a high-level collaborative strategy with a low-level precision control gas source regulation system, the problems of dual-station coupling interference and insufficient pressure response in the gas source regulation system of the core shooter were solved, achieving efficient and stable sand core production and energy consumption optimization.
Patent Information
- Application Number
- CN202610559256.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-26
- Publication Date
- 2026-07-24
AI Technical Summary
The existing gas source control system for core shooting machines suffers from problems such as dual-station coupling interference, insufficient accuracy and stability of dynamic pressure response, and high energy consumption, making it difficult to meet the needs of multi-variety and differentiated sand core production.
The gas source control system, which combines high-level collaborative strategies with low-level precision control, achieves multi-stage progressive pressure control and intelligent scheduling through a status acquisition module, operating condition identification module, strategy execution module, and pressure control module, and dynamically decouples the dual-station air intake timing and flow rate.
The independent operation of the dual-station sand-shooting mechanism was achieved, with pressure fluctuations controlled within ±1.5%, which improved production flexibility and equipment utilization, reduced energy consumption, and ensured the consistency of sand core quality and production stability.
Smart Images
Figure CN122441894A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of sand-shooting machine control technology, specifically relating to an adjustable sand-shooting mechanism air source control system and method. Background Technology
[0002] Core shooters are key equipment in modern foundry production lines. By having two stations work alternately or simultaneously, they can significantly improve the efficiency of sand core manufacturing. The sand shooting process, as its core, relies on the kinetic energy generated by the instantaneous release of compressed air to inject core sand into the core box and compact it. This process places extremely high demands on the dynamic performance of the air source pressure, requiring rapid, precise pressure establishment and stable maintenance within a very short time (typically hundreds of milliseconds). The performance of the air source control system directly determines the sand shooting quality, production cycle stability, and equipment energy consumption.
[0003] Currently, the widely used gas source control systems for core shooters mainly suffer from the following technical bottlenecks: 1. The problem of air source coupling interference between dual stations is prominent. Traditional systems often use a single pipeline to supply air to both stations. Even with independent pressure regulating valves on each branch, dynamic pressure coupling between stations cannot be eliminated. When one station performs high-flow sand blasting, a sudden drop in the main pipeline pressure will immediately cause pressure fluctuations in the other station, making it impossible for it to maintain the set process pressure. This makes it difficult for the two stations to operate independently with different process parameters, severely restricting the equipment's ability to handle the production of multiple varieties and differentiated sand cores, and also resulting in poor consistency in sand core quality.
[0004] 2. Insufficient accuracy and stability of dynamic pressure response. Sand blasting is a high-speed, transient process. Traditional systems using mechanical pressure regulating valves or ordinary proportional valves have slow responses and long pressure build-up times, failing to meet the requirements of high-cycle production. More importantly, there is a lack of effective proactive compensation for the "pressure shock drop" caused by the violent airflow at the moment the sand blasting valve opens; and there is also a lack of adaptive adjustment capability to cope with complex disturbances during sand blasting. This results in a large deviation between the actual sand blasting pressure curve and the set target, directly affecting the filling integrity and compaction uniformity of the sand core.
[0005] 3. The system has high energy consumption and low intelligence level. To ensure peak demand, the system is often in a high-pressure standby state and lacks an intelligent strategy for dynamically scheduling air source allocation based on actual working conditions, resulting in a large amount of compressed air being wasted. At the same time, the existing system relies heavily on operator experience for parameter debugging, requiring repeated trial injections and adjustments after mold changes. It cannot achieve digital management, automatic optimization, and fault prediction of process parameters, making it difficult to adapt to the development trend of intelligent manufacturing.
[0006] Therefore, the industry urgently needs a new gas source control system and method that can achieve true decoupling of dual workstations, possess ultra-fast response and high-precision pressure stabilization capabilities, and be intelligent and efficient. Summary of the Invention
[0007] In view of this, the present invention proposes an air source control system and method for an adjustable sand-shooting mechanism, which deeply integrates high-level collaborative strategies with low-level precision control, effectively improving the overall performance, production cycle stability and energy utilization efficiency of the dual-station sand-shooting mechanism.
[0008] To achieve the above objectives, the present invention provides the following technical solution: This invention provides an adjustable sand-shooting mechanism air source control system and method, comprising: The status acquisition module is used to acquire the operating status information of the two workstations. The operating status information includes the current process stage, sand shooting command signal, gas storage pressure value and pressure change trend. The working condition identification module is used to identify the current working condition mode based on the operating status information. The working condition modes include single-station independent working mode, dual-station alternating sand shooting mode and dual-station synchronous sand shooting mode. The strategy execution module is used to execute a coordinated control strategy based on the identified operating mode, which dynamically allocates the air intake timing and flow quota of each station according to the multi-station air source coupling characteristics. The pressure control module is used to independently execute multi-stage progressive pressure control for each workstation. The multi-stage progressive pressure control adaptively switches the control algorithm type and parameter tuning method according to the pressure response characteristics of the sand shooting process, so as to achieve a balance between rapid pressure tracking and disturbance suppression.
[0009] Preferably, the working condition recognition logic in the working condition recognition module is implemented by constructing a dual-station state machine; The state machine defines the discrete state set and state transition conditions for each workstation. By monitoring the state code combination and state transition timing of two workstations in real time, the corresponding working mode determination is triggered. The state set includes standby state, pre-pressure state, sand-shooting state and curing state. The state transition conditions include process completion signal, sand-shooting command signal, pressure reaching threshold signal and timeout signal. The switching rules between operating modes and the state preservation mechanism during the switching process are defined through a state machine, including: When switching from single-station independent working mode to dual-station alternating sand-shooting mode, it detects whether the other station has switched from standby state to pre-pressure state. If the switching time interval meets the preset alternation condition, the switching is completed; otherwise, the single-station mode is maintained and potential conflicts are marked. When switching from the dual-station alternating sand-shooting mode to the dual-station synchronous sand-shooting mode, the system detects whether the time difference between the state transitions of the two stations is less than the preset minimum peak-shaving threshold. If the threshold is met, a mode pre-switching warning is triggered to reserve preparation time for the synchronous sand-shooting coordination control. When switching from any mode to single-station independent working mode, the system identifies whether the non-working station has entered standby or fault state, and performs safety depressurization and air intake cut-off for that station.
[0010] Preferably, the execution content of the strategy execution module includes: The system receives the operating condition mode identifier output by the operating condition identification module and selects the corresponding control strategy branch based on the operating condition mode identifier. Among them, the resource exclusive operating condition corresponds to the single-workstation optimal control, the resource time-sharing multiplexing operating condition corresponds to the timing scheduling control, and the resource contention operating condition corresponds to the collaborative optimization control. When performing single-station optimal control, the flow restriction is lifted for active stations and rapid pressure build-up is enabled, while the air intake is cut off and low-pressure standby is maintained for inactive stations. When executing timing scheduling control, the time interval between sand-shooting commands of two workstations is calculated and compared with the peak-shaving threshold. Delay control is applied to workstations with time intervals less than the threshold to stagger their sand-shooting times. When performing collaborative optimization control, pre-pressure is initiated in advance to reserve energy, and a transfer function model based on the coupling of the two workstations' air paths is constructed to optimize the air intake flow distribution of each workstation. During the sand-shooting process, pressure imbalance is monitored in real time and dynamic flow redistribution is triggered. Generates air intake valve opening commands, pressure control mode commands, and sand-shooting trigger control commands for each workstation, and receives execution feedback for closed-loop verification.
[0011] Preferably, the multi-stage progressive pressure control executed by the pressure control module includes: Before the sand-shooting command is triggered, based on the deviation between the target pressure and the current gas storage pressure, a parameter self-tuning control algorithm is used to drive the intake valve to act quickly, so that the gas storage pressure is built up to the set value without overshoot within a preset time. At the moment the sand-shooting valve opens, the expected pressure drop caused by the high-speed gas release is calculated online based on the sand-shooting flow prediction model, and a feedforward compensation amount is generated. This feedforward compensation amount is superimposed with the PID control amount based on real-time pressure feedback to form a composite control output, so as to actively offset the sand-shooting impact disturbance. During continuous sand-shooting, a multi-objective performance index is constructed based on pressure fluctuation rate, control error integral, and valve action frequency. A parameter adaptive optimization algorithm is used to adjust the controller parameters online to achieve a balance between suppressing pressure fluctuations, ensuring sand-shooting power, and reducing valve wear.
[0012] Preferably, the sand jet flow prediction model is a gray box model with multi-physics coupling; The sand-shooting flow prediction model constructs a set of differential equations describing the transient process of sand-shooting by coupling the throttling characteristic equation of the sand-shooting valve, the gas state equation of the gas storage tank, and the empirical formula of the sand flow resistance of the core box cavity. The inputs to the sand-shooting flow prediction model include the current gas storage pressure, the sand-shooting valve opening curve, and the flow resistance coefficient associated with the core box identifier. The output is the curve of the predicted sand-shooting mass flow rate changing over time. The sand-jetting flow prediction model updates key flow resistance parameters in the model periodically by comparing the deviation between the predicted flow curve and the actual flow estimate based on pressure backpropagation.
[0013] Preferably, the sand-shooting flow prediction model constructs a set of differential equations describing the transient process of sand-shooting by coupling the following sub-models: Throttling model of sand-shooting valve based on valve orifice flow formula:
[0014] in The volumetric flow rate at the valve orifice. To match the opening degree of the sand-shooting valve The relevant flow coefficient, air density, For the pressure of the gas storage tank, The pressure inside the shot nozzle; Pressure change model of gas storage tank based on gas state equation:
[0015] in The adiabatic index, The gas constant is For temperature, For the volume of the gas storage tank, This refers to the mass flow rate exiting the gas storage tank. Core cell flow resistance model based on Darcy-Weisbach formula:
[0016] in This refers to the pressure loss of the sand flow through the core box. The flow resistance coefficient is related to sand particle size and moisture content. and Let be the equivalent flow path length and hydraulic diameter of the sand flow within the core box cavity. The velocity of the sand flow; The model's inputs include the current gas storage pressure. Sand-shooting valve opening curve Flow resistance coefficient associated with the core box The output is a curve showing the predicted mass flow rate of shot blasting over time. ; The model compares predicted flow curves. The key flow resistance parameters in the model are periodically updated using a recursive least squares method to correct the discrepancy between the actual flow rate estimate and the pressure-based back-calculation estimate. .
[0017] Preferably, the parameter adaptive optimization algorithm is implemented using a constrained model predictive control framework; In each control cycle, the parameter adaptive optimization algorithm takes the weighted minimization of the sum of squares of pressure tracking error and the sum of squares of control variable change rate within a future preset time domain as the objective function, and uses the maximum / minimum opening degree of the intake valve and the maximum change rate as constraints to solve a quadratic programming problem online. The first element of the optimal control sequence obtained by solving is used as the current control output, and the optimization is rolled over in the next cycle. The weighting coefficients of the objective function are dynamically adjusted according to the stages of the sand-shooting process: in the early stage of sand-shooting, the weight of pressure tracking is increased to achieve rapid stabilization; in the middle stage of sand-shooting, the weight of control smoothness is increased to protect the valve.
[0018] The preferred method for constructing and solving the quadratic programming problem is as follows: Define decision variables: the current control cycle From now on, the future will continue Within each control cycle, the sequence of opening increments of the intake valves at each station is defined as the future input sequence to be optimized. ; Constructing a predictive model: Based on the dynamic characteristics of the gas storage tank and inlet valve at this workstation, a model is established to predict future input sequences. Under the influence of the gas storage pressure change trajectory The discrete state-space model or input-output model, where , To predict the length of the time domain; Constructing a multi-objective function: Objective function for a quadratic programming problem It consists of the following two weighted sums: Pressure tracking error penalty: Used to punish predictive stress Expected pressure trajectory Deviation; Controlling the incremental smoothing term: Used to penalize control increments Drastic changes to smooth valve operation; Setting process constraints: During the optimization process, the following physical constraints are imposed on the decision variables: Control amplitude constraints: Ensure the calculated valve opening It is within its physically feasible range. , These are the minimum and maximum allowable valve openings of the intake valve, respectively. Control quantity rate constraint: This directly limits the maximum variation in valve opening within a single control cycle. To protect the enforcement agency , These are the minimum and maximum permissible rates of change for the intake valve, respectively. Implement rolling optimization: in each control cycle Solve A quadratic programming problem with a target and physical constraints; obtaining the optimal solution sequence. and only its first element As the actual control increment output; in the next cycle, the above steps are repeated based on the latest state measurement value to achieve closed-loop predictive control; Adaptive weight adjustment: Dynamically adjust the weight coefficients in the objective function based on the stage characteristics of the sand blasting process. and In the initial stage of sand shooting, increase the weighting coefficient of pressure tracking error. Or reduce the weighting coefficient of the control increment. Prioritize rapid pressure establishment and tracking; during the mid-stabilization phase of sandblasting, reduce... or increase Prioritize ensuring smooth valve operation and long-term equipment lifespan.
[0019] To achieve the above objectives, the present invention also provides a method for controlling the air source of a dual-station adjustable sand-shooting mechanism, comprising: Acquire the operating status information of the two workstations, including the current process stage, sand-shooting command signal, gas storage pressure value, and pressure change trend; The current working mode is identified based on the operating status information. The working modes include single-station independent working mode, dual-station alternating sand-shooting mode and dual-station synchronous sand-shooting mode. Based on the identified operating mode, a coordinated control strategy is implemented to dynamically allocate the air intake timing and flow quota of each station based on the multi-station air source coupling characteristics. Multi-stage progressive pressure control is independently implemented for each workstation. The multi-stage progressive pressure control adaptively switches the control algorithm type and parameter tuning method according to the pressure response characteristics of the sand shooting process, so as to achieve a balance between rapid pressure tracking and disturbance suppression.
[0020] The present invention has achieved at least the following beneficial effects: 1. This invention employs an innovative operating condition identification and dynamic decoupling control strategy, combined with the physical isolation of independent micro gas storage tanks, to intelligently schedule the air intake timing and flow rate of the two workstations. This ensures that the pressure of the two workstations does not interfere with each other when performing different processes, fundamentally solving the coupling problem of traditional systems and significantly improving equipment utilization and production flexibility.
[0021] 2. This invention employs a multi-stage progressive control algorithm: during the pre-compression stage, pressure is rapidly established without overshoot; during sand injection, impact is actively compensated via model feedforward; and during the holding period, stability is optimized through model predictive control. Pressure fluctuations throughout the entire process are strictly controlled within ±1.5% of the set value, ensuring a high degree of consistency in the compactness of each sand core and significantly improving product quality.
[0022] 3. Through online model identification and multi-objective optimization algorithms, it can automatically adapt to changes in different molds, sand types, and working conditions, always maintaining optimal performance. Its intelligent scheduling and on-demand air supply strategy can significantly reduce compressed air energy consumption, while greatly reducing reliance on manual experience, achieving a balance between operational efficiency, reliability, and energy saving.
[0023] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0024] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart illustrating the steps of a method for controlling the air source of a dual-station adjustable sand-shooting mechanism according to an embodiment of the present invention. Detailed Implementation
[0025] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0026] This invention provides an adjustable sand-shooting mechanism's air source control system and method, referring to... Figure 1 ,include: The status acquisition module is used to acquire the operating status information of the two workstations. The operating status information includes the current process stage, sand shooting command signal, gas storage pressure value and pressure change trend. The working condition identification module is used to identify the current working condition mode based on the operating status information. The working condition modes include single-station independent working mode, dual-station alternating sand shooting mode and dual-station synchronous sand shooting mode. The strategy execution module is used to execute a coordinated control strategy based on the identified operating mode, which dynamically allocates the air intake timing and flow quota of each station according to the multi-station air source coupling characteristics. The pressure control module is used to independently execute multi-stage progressive pressure control for each workstation. The multi-stage progressive pressure control adaptively switches the control algorithm type and parameter tuning method according to the pressure response characteristics of the sand shooting process, so as to achieve a balance between rapid pressure tracking and disturbance suppression.
[0027] In this embodiment, the operating status information refers to the data set of real-time reverse sanding station working process and air source status, including at least: the current process stage (such as standby, mold closing, sand shooting, curing, mold opening), sand shooting command signal from the host computer, real-time air storage pressure value of each station's independent micro air storage tank, and pressure change trend obtained by differential processing of the pressure signal.
[0028] In this embodiment, the step of identifying the current operating mode is implemented through a dual-station state machine based on a discrete event system; the state transitions of the state machine are determined by a set of guard condition functions. Triggered, in and These represent the current states of workstations A and B respectively (encoded as discrete values, such as 0: standby, 1: pre-compression, 2: sandblasting, 3: curing). Indicates external events (such as sand-shooting commands, timer timeouts); operating mode Mapping function composed of states determination.
[0029] In this embodiment, the operating mode refers to a typical working scenario summarized based on the combination of dual-station operating states, including: single-station independent operating mode, dual-station alternating sand-shooting mode, and dual-station synchronous sand-shooting mode. The purpose of identifying the operating mode is to provide a high-level strategic basis for subsequent gas source allocation and pressure control.
[0030] In this embodiment, the coordinated control strategy refers to a set of rules that dynamically determine the opening timing of the air intake valves and the allocation of flow quotas for each workstation based on the identified operating conditions and the coupling characteristics of the multi-station air sources. Its core objective is to achieve optimal overall system efficiency, that is, to maximize the utilization rate of the air source and minimize the interference between workstations while meeting the sand-shooting process requirements of each workstation.
[0031] In this embodiment, multi-stage progressive pressure control refers to dividing a single sand-shooting action cycle into continuous stages with different control objectives, and adaptively switching the most suitable control algorithm and parameters at each stage to achieve precise control throughout the entire process from rapid pressure build-up to stable maintenance.
[0032] In a preferred embodiment, the working condition pattern recognition logic in the working condition recognition module is implemented by constructing a dual-station state machine; The state machine defines the discrete state set and state transition conditions for each workstation. By monitoring the state code combination and state transition timing of two workstations in real time, the corresponding working mode determination is triggered. The state set includes standby state, pre-pressure state, sand-shooting state and curing state. The state transition conditions include process completion signal, sand-shooting command signal, pressure reaching threshold signal and timeout signal. The switching rules between operating modes and the state preservation mechanism during the switching process are defined through a state machine, including: When switching from single-station independent working mode to dual-station alternating sand-shooting mode, it detects whether the other station has switched from standby state to pre-pressure state. If the switching time interval meets the preset alternation condition, the switching is completed; otherwise, the single-station mode is maintained and potential conflicts are marked. When switching from the dual-station alternating sand-shooting mode to the dual-station synchronous sand-shooting mode, the system detects whether the time difference between the state transitions of the two stations is less than the preset minimum peak-shaving threshold. If the threshold is met, a mode pre-switching warning is triggered to reserve preparation time for the synchronous sand-shooting coordination control. When switching from any mode to single-station independent working mode, the system identifies whether the non-working station has entered standby or fault state, and performs safety depressurization and air intake cut-off for that station.
[0033] In this embodiment, the dual-station state machine is a discrete event system model used to formally describe and track the collaborative working process of two stations. A discrete set of states is defined for each station, and the determination of the overall system operating mode is triggered by monitoring the combination and transition sequence of the states of the two stations.
[0034] In this embodiment, the discrete state set defined for each workstation includes: standby state (waiting for sand-shooting command), pre-pressure state (building sand-shooting pressure), sand-shooting state (sand-shooting valve open, sand-shooting in progress), and curing state (sand-shooting completed, waiting for sand core to cure). Each state has clear entry and exit conditions.
[0035] In this embodiment, the state transition condition is an event or logical judgment that triggers the workstation to switch from one state to another, including: process completion signal from the production cycle, sand shooting command signal from the control system, signal that the pressure fed back by the pressure sensor has reached a preset threshold, and safety timer timeout signal to prevent process jamming.
[0036] In this embodiment, the operating mode switching rules and state maintenance mechanism defined by the state machine are key to ensuring smooth and conflict-free system operation. Specifically, this includes: When the system switches from a single-station independent working mode to a dual-station alternating sand-shooting mode, it needs to continuously monitor whether the other station has switched from standby to pre-pressure state. The mode switch is only completed when the time interval between the state transitions of the two stations meets the preset alternation conditions (e.g., the interval is greater than the minimum safe time); otherwise, the single-station mode will be maintained and a potential time conflict risk will be marked.
[0037] When the system switches from alternating sand-shooting mode to synchronous sand-shooting mode, it needs to detect the time difference between the two stations entering the sand-shooting state. If the time difference is less than the preset minimum peak-shaving threshold, it is determined that the sand-shooting actions of the two stations are about to overlap, and a mode pre-switching warning is immediately triggered, allowing the strategy execution module to reserve preparation time in advance for the execution of highly coupled synchronous sand-shooting coordination control.
[0038] When the system switches from any mode to a single-station independent working mode, it must actively identify whether the non-working stations have safely entered standby or fault status. Upon confirmation, immediately execute the safety depressurization procedure for that station and cut off its air intake to ensure that resources are fully concentrated on the working station.
[0039] In a preferred embodiment, the execution content of the policy execution module includes: The system receives the operating condition mode identifier output by the operating condition identification module and selects the corresponding control strategy branch based on the operating condition mode identifier. Among them, the resource exclusive operating condition corresponds to the single-workstation optimal control, the resource time-sharing multiplexing operating condition corresponds to the timing scheduling control, and the resource contention operating condition corresponds to the collaborative optimization control. When performing single-station optimal control, the flow restriction is lifted for active stations and rapid pressure build-up is enabled, while the air intake is cut off and low-pressure standby is maintained for inactive stations. When executing timing scheduling control, the time interval between sand-shooting commands of two workstations is calculated and compared with the peak-shaving threshold. Delay control is applied to workstations with time intervals less than the threshold to stagger their sand-shooting times. When performing collaborative optimization control, pre-pressure is initiated in advance to reserve energy, and a transfer function model based on the coupling of the two workstations' air paths is constructed to optimize the air intake flow distribution of each workstation. During the sand-shooting process, pressure imbalance is monitored in real time and dynamic flow redistribution is triggered. Generates air intake valve opening commands, pressure control mode commands, and sand-shooting trigger control commands for each workstation, and receives execution feedback for closed-loop verification.
[0040] In this embodiment, the strategy execution module first receives the operating condition mode identifier output by the operating condition identification module, and selects the corresponding control strategy branch based on the identifier. This invention treats the gas source as a contestable system resource and maps the three operating condition modes to three typical resource allocation problems: resource monopoly corresponds to single-station optimal control, resource time-sharing multiplexing corresponds to time-series scheduling control, and resource contention corresponds to collaborative optimization control.
[0041] In this embodiment, when performing single-station optimal control, the system determines that the gas source bandwidth can be exclusively occupied by one station. The strategy execution module removes all flow restrictions for the currently active station and instructs its pressure control module to enable the highest-performance fast pressure build-up algorithm; at the same time, it sends instructions to inactive stations to cut off their air intake passages and maintain their micro gas storage tanks at a low standby pressure to reduce leakage and energy consumption.
[0042] In this embodiment, when performing timing scheduling control, the core of the system is to stagger the peak demand times for gas from the two workstations. The strategy execution module calculates the actual time interval between the issuance of sand-shooting commands by the two workstations and compares it with a dynamic peak-shaving threshold. If the time interval is less than the threshold, a calculated delay control amount is applied to the workstation that issues the command later, causing its sand-shooting valve to actively postpone its opening time, thereby achieving "peak-shaving" of gas usage in the time dimension.
[0043] In this embodiment, the system faces the challenge of highly overlapping sand-shooting actions between the two stations when performing collaborative optimization control. The strategy execution module first instructs both stations to initiate pre-pressure establishment procedures in advance to store more energy before sand-shooting. More importantly, it needs to construct a transfer function model based on the air path coupling between the two stations. This model quantitatively describes the dynamic disturbance to the pressure of the other station when one station's air intake action occurs. Based on this model, the strategy execution module can optimize and calculate the optimal flow distribution ratio of the air intake valves of the two stations, rather than simply dividing it equally. During sand-shooting, the module monitors the pressure imbalance between the two stations in real time. Once a pressure difference caused by model error or disturbance is detected, a dynamic flow redistribution command is immediately triggered for compensation.
[0044] In this embodiment, the instructions ultimately generated and issued by the strategy execution module include: target opening degree instructions for the air intake valves of each station, instructions indicating which control mode the pressure control module should adopt, and trigger control instructions for the sand-shooting valve. Simultaneously, the module receives execution feedback from the pressure control module and sensors to achieve closed-loop verification and adaptive adjustment of the strategy.
[0045] In a preferred embodiment, the multi-stage progressive pressure control performed by the pressure control module includes: Before the sand-shooting command is triggered, based on the deviation between the target pressure and the current gas storage pressure, a parameter self-tuning control algorithm is used to drive the intake valve to act quickly, so that the gas storage pressure is built up to the set value without overshoot within a preset time. At the moment the sand-shooting valve opens, the expected pressure drop caused by the high-speed gas release is calculated online based on the sand-shooting flow prediction model, and a feedforward compensation amount is generated. This feedforward compensation amount is superimposed with the PID control amount based on real-time pressure feedback to form a composite control output, so as to actively offset the sand-shooting impact disturbance. During continuous sand-shooting, a multi-objective performance index is constructed based on pressure fluctuation rate, control error integral, and valve action frequency. A parameter adaptive optimization algorithm is used to adjust the controller parameters online to achieve a balance between suppressing pressure fluctuations, ensuring sand-shooting power, and reducing valve wear.
[0046] In this embodiment, during the pre-pressure establishment phase before the sand-shooting command is triggered, the control objective is "fast and accurate". The module employs a parameter self-tuning control algorithm based on the real-time deviation between the set target sand-shooting pressure and the current gas storage pressure. This algorithm can automatically adjust the controller gain according to the magnitude of the deviation, driving the intake valve to act quickly, ensuring that the gas storage pressure is stably established to the set value without overshoot within a very short preset time (e.g., within 100 milliseconds).
[0047] In this embodiment, during the dynamic compensation phase at the moment the sand-jetting valve opens, the core control principle is "impact resistance." The module, based on a sand-jetting flow prediction model, calculates the expected pressure drop curve caused by high-speed gas release in real-time online. Based on this prediction, the module generates a feedforward compensation control quantity, which is superimposed on a conventional PID control quantity based on real-time pressure feedback to form a composite control output. This "feedforward + feedback" structure can apply reverse control before the disturbance (sand-jetting impact) truly affects the controlled variable (pressure), thereby actively offsetting most of the pressure drop.
[0048] In this embodiment, during the continuous sand-shooting pressure-holding phase, the control aims for "stability and gentleness." The module constructs a multi-objective performance index based on pressure fluctuation rate, integral of control error, and valve actuation frequency. An adaptive parameter optimization algorithm is employed to continuously adjust the controller parameters online. The optimization objective is to dynamically find the optimal balance between several mutually constraining objectives, such as suppressing pressure fluctuations, ensuring sufficient sand-shooting power, reducing valve wear, and extending equipment lifespan.
[0049] In a preferred embodiment, the sand jet flow prediction model is a multi-physics coupled gray box model. The sand-shooting flow prediction model constructs a set of differential equations describing the transient process of sand-shooting by coupling the throttling characteristic equation of the sand-shooting valve, the gas state equation of the gas storage tank, and the empirical formula of the sand flow resistance of the core box cavity. The inputs to the sand-shooting flow prediction model include the current gas storage pressure, the sand-shooting valve opening curve, and the flow resistance coefficient associated with the core box identifier. The output is the curve of the predicted sand-shooting mass flow rate changing over time. The sand-jetting flow prediction model updates key flow resistance parameters in the model periodically by comparing the deviation between the predicted flow curve and the actual flow estimate based on pressure backpropagation.
[0050] In a preferred embodiment, the sand-shooting flow prediction model constructs a set of differential equations describing the transient process of sand-shooting by coupling the following sub-models: Throttling model of sand-shooting valve based on valve orifice flow formula:
[0051] in The volumetric flow rate at the valve orifice. To match the opening degree of the sand-shooting valve The relevant flow coefficient, air density, For the pressure of the gas storage tank, The pressure inside the shot nozzle; Pressure change model of gas storage tank based on gas state equation:
[0052] in The adiabatic index, The gas constant is For temperature, For the volume of the gas storage tank, This refers to the mass flow rate exiting the gas storage tank. Core cell flow resistance model based on Darcy-Weisbach formula:
[0053] in This refers to the pressure loss of the sand flow through the core box. The flow resistance coefficient is related to sand particle size and moisture content. and Let be the equivalent flow path length and hydraulic diameter of the sand flow within the core box cavity. The velocity of the sand flow; The model's inputs include the current gas storage pressure. Sand-shooting valve opening curve Flow resistance coefficient associated with the core box The output is a curve showing the predicted mass flow rate of shot blasting over time. ; The model compares predicted flow curves. The key flow resistance parameters in the model are periodically updated using a recursive least squares method to correct the discrepancy between the actual flow rate estimate and the pressure-based back-calculation estimate. .
[0054] In a preferred embodiment, the parameter adaptive optimization algorithm is implemented using a constrained model predictive control framework; In each control cycle, the parameter adaptive optimization algorithm takes the weighted minimization of the sum of squares of pressure tracking error and the sum of squares of control variable change rate within a future preset time domain as the objective function, and uses the maximum / minimum opening degree of the intake valve and the maximum change rate as constraints to solve a quadratic programming problem online. The first element of the optimal control sequence obtained by solving is used as the current control output, and the optimization is rolled over in the next cycle. The weighting coefficients of the objective function are dynamically adjusted according to the stages of the sand-shooting process: in the early stage of sand-shooting, the weight of pressure tracking is increased to achieve rapid stabilization; in the middle stage of sand-shooting, the weight of control smoothness is increased to protect the valve.
[0055] In a preferred embodiment, the quadratic programming problem is constructed and solved as follows: Define decision variables: the current control cycle From now on, the future will continue Within each control cycle, the sequence of opening increments of the intake valves at each workstation , defined as the future input sequence to be optimized ; Constructing a predictive model: Based on the dynamic characteristics of the gas storage tank and inlet valve at this workstation, a model is established to predict future input sequences. Under the influence of the gas storage pressure change trajectory The discrete state-space model or input-output model, where , To predict the length of the time domain; Constructing a multi-objective function: Objective function for a quadratic programming problem It consists of the following two weighted sums: Pressure tracking error penalty: Used to punish predictive stress Expected pressure trajectory Deviation; Controlling the incremental smoothing term: Used to penalize control increments Drastic changes to smooth valve operation; Setting process constraints: During the optimization process, the following physical constraints are imposed on the decision variables: Control amplitude constraints: Ensure the calculated valve opening It is within its physically feasible range. , These are the minimum and maximum allowable valve openings of the intake valve, respectively. Control quantity rate constraint: This directly limits the maximum variation in valve opening within a single control cycle. To protect the enforcement agency , These are the minimum and maximum permissible rates of change for the intake valve, respectively. Implement rolling optimization: in each control cycle Solve A quadratic programming problem with a target and physical constraints; obtaining the optimal solution sequence. and only its first element As the actual control increment output; in the next cycle, the above steps are repeated based on the latest state measurement value to achieve closed-loop predictive control; Adaptive weight adjustment: Dynamically adjust the weight coefficients in the objective function based on the stage characteristics of the sand blasting process. and In the initial stage of sand shooting, increase the weighting coefficient of pressure tracking error. Or reduce the weighting coefficient of the control increment. Prioritize rapid pressure establishment and tracking; during the mid-stabilization phase of sandblasting, reduce... or increase Prioritize ensuring smooth valve operation and long-term equipment lifespan.
[0056] To achieve the above objectives, the present invention also provides a method for controlling the air source of a dual-station adjustable sand-shooting mechanism, comprising: Acquire the operating status information of the two workstations, including the current process stage, sand-shooting command signal, gas storage pressure value, and pressure change trend; The current working mode is identified based on the operating status information. The working modes include single-station independent working mode, dual-station alternating sand-shooting mode and dual-station synchronous sand-shooting mode. Based on the identified operating mode, a coordinated control strategy is implemented to dynamically allocate the air intake timing and flow quota of each station based on the multi-station air source coupling characteristics. Multi-stage progressive pressure control is independently implemented for each workstation. The multi-stage progressive pressure control adaptively switches the control algorithm type and parameter tuning method according to the pressure response characteristics of the sand shooting process, so as to achieve a balance between rapid pressure tracking and disturbance suppression.
[0057] The beneficial effects of the above technical solution are as follows: Through the status acquisition and working condition identification module, the system can intelligently perceive the collaborative working status of the two workstations, laying the foundation for precise control; through the strategy execution module, global optimization allocation of the gas source is realized for different working conditions, fundamentally solving the problem of coupling interference between the two workstations; through the pressure control module and its embedded multi-stage progressive control algorithm, high-precision gray box prediction model, and constrained model predictive controller, rapid, stable, gentle, and adaptive precise control of the sand-shooting pressure of a single workstation is achieved. This invention deeply integrates high-level collaborative strategies with low-level precision control, effectively improving the overall performance, production cycle stability, and energy utilization efficiency of the dual-workstation sand-shooting mechanism.
[0058] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.
Claims
1. A gas source control system for an adjustable sand-shooting mechanism, characterized in that, include: The status acquisition module is used to acquire the operating status information of the two workstations. The operating status information includes the current process stage, sand shooting command signal, gas storage pressure value and pressure change trend. The working condition identification module is used to identify the current working condition mode based on the operating status information. The working condition modes include single-station independent working mode, dual-station alternating sand shooting mode and dual-station synchronous sand shooting mode. The strategy execution module is used to execute a coordinated control strategy based on the identified operating mode, which dynamically allocates the air intake timing and flow quota of each station according to the multi-station air source coupling characteristics. The pressure control module is used to independently execute multi-stage progressive pressure control for each workstation. The multi-stage progressive pressure control adaptively switches the control algorithm type and parameter tuning method according to the pressure response characteristics of the sand shooting process, so as to achieve a balance between rapid pressure tracking and disturbance suppression.
2. The air source control system for an adjustable sand-shooting mechanism according to claim 1, characterized in that, The working condition identification logic in the working condition identification module is implemented by constructing a dual-station state machine; The state machine defines the discrete state set and state transition conditions for each workstation. By monitoring the state code combination and state transition timing of two workstations in real time, the corresponding working mode determination is triggered. The state set includes standby state, pre-pressure state, sand-shooting state, and curing state. The state transition conditions include process completion signal, sand-shooting command signal, pressure reaching threshold signal, and timeout signal. The switching rules between operating modes and the state preservation mechanism during the switching process are defined through a state machine, including: When switching from single-station independent working mode to dual-station alternating sand-shooting mode, it detects whether the other station has switched from standby state to pre-pressure state. If the switching time interval meets the preset alternation condition, the switching is completed; otherwise, the single-station mode is maintained and potential conflicts are marked. When switching from the dual-station alternating sand-shooting mode to the dual-station synchronous sand-shooting mode, the system detects whether the time difference between the state transitions of the two stations is less than the preset minimum peak-shaving threshold. If the threshold is met, a mode pre-switching warning is triggered to reserve preparation time for the synchronous sand-shooting coordination control. When switching from any mode to single-station independent working mode, the system identifies whether the non-working station has entered standby or fault state, and performs safety depressurization and air intake cut-off for that station.
3. The air source control system for an adjustable sand-shooting mechanism according to claim 1, characterized in that, The execution content of the strategy execution module includes: The system receives the operating condition mode identifier output by the operating condition identification module and selects the corresponding control strategy branch based on the operating condition mode identifier. Among them, the resource exclusive operating condition corresponds to the single-workstation optimal control, the resource time-sharing multiplexing operating condition corresponds to the timing scheduling control, and the resource contention operating condition corresponds to the collaborative optimization control. When performing single-station optimal control, the flow restriction is lifted for active stations and rapid pressure build-up is enabled, while the air intake is cut off for inactive stations and low-pressure standby is maintained. When executing timing control, the time interval between sand-shooting commands of two workstations is calculated and compared with the peak-shaving threshold. Delay control is applied to workstations with time intervals less than the threshold to stagger their sand-shooting times. When performing collaborative optimization control, pre-pressure is initiated in advance to reserve energy, and a transfer function model based on the coupling of the two workstations' air paths is constructed to optimize the air intake flow distribution of each workstation. During the sand-shooting process, pressure imbalance is monitored in real time and dynamic flow redistribution is triggered. Generates air intake valve opening commands, pressure control mode commands, and sand-shooting trigger control commands for each workstation, and receives execution feedback for closed-loop verification.
4. The air source control system for an adjustable sand-shooting mechanism according to claim 1, characterized in that, The multi-stage progressive pressure control performed by the pressure control module includes: Before the sand-shooting command is triggered, based on the deviation between the target pressure and the current gas storage pressure, a parameter self-tuning control algorithm is used to drive the intake valve to act quickly, so that the gas storage pressure is built up to the set value without overshoot within a preset time. At the moment the sand-shooting valve opens, the expected pressure drop caused by the high-speed gas release is calculated online based on the sand-shooting flow prediction model, and a feedforward compensation amount is generated. This feedforward compensation amount is superimposed with the PID control amount based on real-time pressure feedback to form a composite control output, so as to actively offset the sand-shooting impact disturbance. During continuous sand-shooting, a multi-objective performance index is constructed based on pressure fluctuation rate, control error integral, and valve action frequency. A parameter adaptive optimization algorithm is used to adjust the controller parameters online to achieve a balance between suppressing pressure fluctuations, ensuring sand-shooting power, and reducing valve wear.
5. The air source control system for an adjustable sand-shooting mechanism according to claim 4, characterized in that, The sand-jetting flow prediction model is a gray box model with multi-physics coupling; The sand-shooting flow prediction model constructs a set of differential equations describing the transient process of sand-shooting by coupling the throttling characteristic equation of the sand-shooting valve, the gas state equation of the gas storage tank, and the empirical formula of the sand flow resistance of the core box cavity. The inputs to the sand-shooting flow prediction model include the current gas storage pressure, the sand-shooting valve opening curve, and the flow resistance coefficient associated with the core box identifier. The output is the curve of the predicted sand-shooting mass flow rate changing over time. The sand-jetting flow prediction model updates key flow resistance parameters in the model periodically by comparing the deviation between the predicted flow curve and the actual flow estimate based on pressure backpropagation.
6. The air source control system for an adjustable sand-shooting mechanism according to claim 5, characterized in that, The sand-shooting flow rate prediction model constructs a set of differential equations describing the transient process of sand-shooting by coupling the following sub-models: Throttling model of sand-shooting valve based on valve orifice flow formula: ， in The volumetric flow rate at the valve orifice. To match the opening degree of the sand-shooting valve The relevant flow coefficient, air density, For the pressure of the gas storage tank, The pressure inside the shot nozzle; Pressure change model of gas storage tank based on gas state equation: ， in The adiabatic index, The gas constant is For temperature, For the volume of the gas storage tank, This refers to the mass flow rate exiting the gas storage tank. Core cell flow resistance model based on Darcy-Weisbach formula: ， in This refers to the pressure loss of the sand flow through the core box. The flow resistance coefficient is related to sand particle size and moisture content. and Let be the equivalent flow path length and hydraulic diameter of the sand flow within the core box cavity. The velocity of the sand flow; The model's inputs include the current gas storage pressure. Sand-shooting valve opening curve Flow resistance coefficient associated with the core box The output is a curve showing the predicted mass flow rate of shot blasting over time. ; The model compares predicted flow curves. The key flow resistance parameters in the model are periodically updated using a recursive least squares method to correct the discrepancy between the actual flow rate estimate and the pressure-based back-calculation estimate. .
7. The air source control system for an adjustable sand-shooting mechanism according to claim 4, characterized in that, The parameter adaptive optimization algorithm is implemented using a constrained model predictive control framework; In each control cycle, the parameter adaptive optimization algorithm takes the weighted minimization of the sum of squares of pressure tracking error and the sum of squares of control variable change rate within a future preset time domain as the objective function, and uses the maximum / minimum opening degree of the intake valve and the maximum change rate as constraints to solve a quadratic programming problem online. The first element of the optimal control sequence obtained by solving is used as the current control output, and the optimization is rolled over in the next cycle. The weighting coefficients of the objective function are dynamically adjusted according to the stages of the sand-shooting process: in the early stage of sand-shooting, the weight of pressure tracking is increased to achieve rapid stabilization; in the middle stage of sand-shooting, the weight of control smoothness is increased to protect the valve.
8. The air source control system for an adjustable sand-shooting mechanism according to claim 7, characterized in that, The quadratic programming problem is constructed and solved as follows: Define decision variables: the current control cycle From now on, the future will continue Within each control cycle, the sequence of opening increments of the intake valves at each station is defined as the future input sequence to be optimized. ; Constructing a predictive model: Based on the dynamic characteristics of the gas storage tank and inlet valve at this workstation, a model is established to predict future input sequences. Under the influence of the gas storage pressure change trajectory The discrete state-space model or input-output model, where , To predict the length of the time domain; Constructing a multi-objective function: Objective function for a quadratic programming problem It consists of the following two weighted sums: Pressure tracking error penalty: Used to punish predictive stress Expected pressure trajectory Deviation; Controlling the incremental smoothing term: Used to penalize control increments Drastic changes to smooth valve operation; Setting process constraints: During the optimization process, the following physical constraints are imposed on the decision variables: Control amplitude constraints: Ensure the calculated valve opening It is within its physically feasible range. , These are the minimum and maximum allowable valve openings of the intake valve, respectively. Control quantity rate constraint: This directly limits the maximum variation in valve opening within a single control cycle. To protect the enforcement agency , These are the minimum and maximum permissible rates of change for the intake valve, respectively. Implement rolling optimization: in each control cycle Solve A quadratic programming problem with a target and physical constraints; obtaining the optimal solution sequence. and only its first element As the actual control increment output; in the next cycle, the above steps are repeated based on the latest state measurement value to achieve closed-loop predictive control; Adaptive weight adjustment: Dynamically adjust the weight coefficients in the objective function based on the stage characteristics of the sand blasting process. and In the initial stage of sand shooting, increase the weighting coefficient of pressure tracking error. Or reduce the weighting coefficient of the control increment. Prioritize rapid pressure establishment and tracking; during the mid-stabilization phase of sandblasting, reduce... or increase Prioritize ensuring smooth valve operation and long-term equipment lifespan.
9. A method for controlling the air source of a dual-station adjustable sand-shooting mechanism, characterized in that, include: Acquire the operating status information of the two workstations, including the current process stage, sand-shooting command signal, gas storage pressure value, and pressure change trend; The current working mode is identified based on the operating status information. The working modes include single-station independent working mode, dual-station alternating sand-shooting mode and dual-station synchronous sand-shooting mode. Based on the identified operating mode, a coordinated control strategy is implemented to dynamically allocate the air intake timing and flow quota of each station based on the multi-station air source coupling characteristics. Multi-stage progressive pressure control is independently implemented for each workstation. The multi-stage progressive pressure control adaptively switches the control algorithm type and parameter tuning method according to the pressure response characteristics of the sand shooting process, so as to achieve a balance between rapid pressure tracking and disturbance suppression.