Drawing device for detecting bonding strength of asphalt material and control method thereof

Abstract

The invention discloses a drawing device for asphalt material bonding strength detection and a control method thereof.The drawing device comprises a base, a stand column vertically arranged on the base and a cross beam horizontally installed on the stand column, and the free end of the cross beam is provided with a tension sensor; the tension sensor is used for detecting a tension signal generated in the drawing process of a tested bonding test piece, the pneumatic push rod is mounted below the tension sensor, can realize axial linear movement and is used for providing controllable tensioning force, one side of the pneumatic push rod is provided with a displacement sensor for testing the displacement of the pneumatic push rod, and the other side of the pneumatic push rod is provided with a tension sensor. The drawing head is connected to the tail end of the pneumatic push rod and used for being connected with the surface of the tested bonding test piece, and interface separation is achieved in the loading process of the pneumatic push rod. The device can realize a stable and controllable loading process, accurate force value acquisition and an efficient test flow, and is suitable for rapid evaluation of the bonding strength of the asphalt material.

Description

Technical Field

[0001] The present invention belongs to the technical field of asphalt material bonding strength detection, and in particular relates to a pulling device for asphalt material bonding strength detection and a control method thereof. Background Art

[0002] With the development of road engineering technology, the bonding performance between asphalt pavement layers has a significant impact on the mechanical properties and service life of the entire road structure. Insufficient bonding, especially between the asphalt layer and the base and surface layers, can easily lead to interlayer slippage, crack propagation, and even structural delamination, seriously affecting road performance and maintenance cycles.

[0003] Currently, testing methods for the bonding properties of asphalt materials primarily include splitting tests, shear tests, and pull-out tests. The pull-out method, due to its simplicity, clear loading direction, and intuitive ability to reflect interfacial tensile strength, has become a common method for evaluating bonding properties. However, existing pull-out testing equipment, which mostly relies on manual or static loading mechanisms, suffers from unstable loading, low data accuracy, and poor repeatability, making it difficult to meet the demands of precision testing for emerging materials.

[0004] Furthermore, with the increasing demand for rapid on-site testing, traditional pull-off equipment, with its bulky structure, cumbersome operation, and limited data transmission methods, is unable to adapt to the testing requirements of bonded specimens in complex construction environments. Therefore, a compact asphalt pull-off testing device with controllable loading and real-time data acquisition and transmission capabilities is urgently needed to improve the efficiency and accuracy of interfacial adhesion testing.

[0005] In pull-out tests, the peak tensile force at interface failure is the measured bond strength. Therefore, a smooth and reproducible stress process must be achieved under set conditions such as loading rate, peak tensile force, and holding time. Otherwise, peak recognition deviations and reduced result repeatability will occur due to force overshoot, fluctuations, or rate instability. Summary of the Invention

[0006] In response to the technical problems existing in the prior art, the present invention provides a pulling device and a control method for detecting the bonding strength of asphalt materials. The pulling device can realize a stable and controllable loading process, accurate force value collection and an efficient testing process, and is suitable for the rapid evaluation of the bonding strength of asphalt materials.

[0007] In order to solve the above-mentioned technical problems, the present invention adopts the following technical solutions: a pulling device for testing the bonding strength of asphalt materials, comprising a base, a column vertically arranged on the base, and a beam horizontally installed on the column, wherein a tension sensor is provided at the free end of the beam, and the tension sensor is used to detect the tension signal generated during the pulling process of the bonding specimen to be tested; a pneumatic push rod is installed below the tension sensor and the pneumatic push rod can realize axial linear movement, and is used to provide controllable tensioning force; a displacement sensor for testing the displacement of the pneumatic push rod is provided on one side of the pneumatic push rod; a pulling head is connected to the end of the pneumatic push rod, and is used to connect with the surface of the bonding specimen to be tested, and realize interface separation during the loading process of the pneumatic push rod; during the test, the bonding specimen to be tested is installed on the base, and the pulling head is tightly fitted to the bonding surface of the bonding specimen to be tested or fixed by bonding; the pneumatic push rod is started to drive the pulling head to apply tension to the bonding specimen to be tested, thereby realizing the bonding strength test of the asphalt material.

[0008] The present invention takes precise control of loading force as the core indicator. The host computer sets parameters such as loading speed, maximum tension and holding time in the test plan, and thereby constrains the mechanical trajectory of the drawing process.

[0009] In the pneumatic loading link, the compressibility of air causes the equivalent stiffness of the actuator chamber to decrease and brings about a time delay in the transmission of charging and exhaust. The flow-pressure difference relationship of the proportional valve / solenoid valve is nonlinear and accompanied by dead zone and hysteresis loop. The cylinder sealing pair has visco-Coulomb composite friction and stick-slip. At the same time, factors such as gas source pressure / temperature fluctuations, pipeline volume elasticity and micro-leakage, as well as sudden changes in the boundary conditions of the specimen during the process of crack initiation to fracture work together to make the output force prone to oscillation, overshoot and random fluctuations, and weaken the repeatability. In this context, the position PID relies on a single feedback and fixed parameters. It is difficult to maintain consistent dynamic quality under different operating points and rapidly changing load scenarios. Integral saturation and sudden changes in valve opening often occur, resulting in long constant force tracking adjustment time, large overshoot, and insufficient anti-interference performance.

[0010] The control method of the pulling device for testing the bonding strength of asphalt materials according to the present invention has the following specific steps:

[0011] The present invention constructs a model predictive control (MPC) closed-loop architecture: it consists of a pneumatic loading structure, a sensor feedback unit, a signal acquisition controller and a host computer. The controller collects tension and displacement in real time as state quantities and inputs them into the MPC, realizing predictive and optimal control of the electric proportional pressure valve, so that the force trajectory can be smoothly tracked according to the set target.

[0012] Based on the discrete state space model, the control algorithm makes a rolling prediction of future outputs and constructs a quadratic cost function based on "tracking error minimization + control increment penalty". At the same time, it suppresses sudden changes and overshoots in valve opening through constraints such as output range, input amplitude and input increment. It only executes the first optimal control quantity in each cycle and iterates with a window, thereby achieving shorter adjustment time and smaller overshoot under disturbances and model uncertainties.

[0013] As a result, the system ensures stable force and reliable data while meeting the established test parameters, effectively improving the accuracy and repeatability of bond strength testing;

[0014] (1) System Modeling

[0015] The signal acquisition controller first discretizes the state space model of the loading system and establishes the following state space model based on the dynamic response of the pneumatic cylinder under different control inputs:

[0016] x(k+1)=Ax(k)+Bu(k), y(k)=Cx(k)

[0017] Wherein, the state variable x(k) represents the loading state of the system, the control input u(k) represents the input control quantity of the electric proportional pressure valve, the output y(k) is the current tension value of the system, and the system matrices A, B, and C are obtained through experimental identification or least squares modeling.

[0018] The state vector is selected as follows:

[0019]

[0020] (2) Rolling Forecast Mechanism

[0021] In each control cycle, starting from the current state x(k), the model is used to predict the system output at the next N moments

[0022] The prediction recursion is as follows:

[0023]

[0024] The entire prediction output vector is:

[0025]

[0026] Where N is the prediction step size, M is the control step size, and M≤N;

[0027] (3) Cost function construction

[0028] The control goal is to make the predicted output trajectory as close as possible to the target trajectory y ref , while suppressing the drastic changes of the control variables, and constructing the following cost function:

[0029]

[0030] in

[0031] y ref (k+i): expected loading trajectory;

[0032] Δu(k+i)=u(k+j)-u(k+j-1): control increment;

[0033] λ: controller smoothing weight factor, used to prevent control jumps;

[0034] The optimization is transformed into a standard quadratic optimization problem (QP) form:

[0035]

[0036] (4) Constraint design

[0037] To ensure the physical feasibility and system safety of the loading process, the following constraints are introduced:

[0038] Load range limit:

[0039] Control input limit: u min ≤u(k+i)≤u max

[0040] Control increment limit: |Δu(k+i)|≤Δu max

[0041] The above inequality constraints are uniformly written into matrix form to construct G and h, and the open source solver qpOASES is called to achieve fast solution;

[0042] (5) Online optimization solution and execution

[0043] In each sampling period, the following optimization process is performed:

[0044] 1) Get the current system state x(k);

[0045] 2) Construct prediction models and inequality constraints;

[0046] 3) Construct the cost function and generate the QP problem;

[0047] 4) Use the solver to obtain the optimal control sequence U * ;

[0048] 5) Only execute the first control quantity u of the optimal sequence * (k)), for driving an electric proportional pressure valve;

[0049] 6) Repeat the above steps in the next cycle;

[0050] (6) State update and anti-disturbance mechanism

[0051] After each control cycle, the controller will update the state according to the actual load value y(k) fed back by the tension sensor:

[0052] If some of the system states are unmeasurable, a Kalman filter can be introduced for estimation;

[0053] If there is a persistent deviation, the model error can be corrected using a disturbance observer;

[0054] If the model error is large, a robust MPC control strategy can be constructed to improve the anti-disturbance capability.

[0055] Compared with the prior art, the present invention has the following advantages and beneficial effects: the present invention can realize a stable and controllable loading process, accurate force value collection and efficient testing process, and is suitable for rapid evaluation of the bonding strength of various road materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is the overall structural diagram of the drawing device in the present invention.

[0057] Figure 2 It is a structural schematic diagram of the drawing device in the present invention.

[0058] Figure 3 This is a control principle diagram of the pneumatic loading system in the present invention.

[0059] Figure 4 This is a control principle diagram of the signal acquisition, control unit and host computer analysis system in the present invention.

[0060] In the figure: 1-base, 2-column, 3-beam, 4-tension sensor, 5-pneumatic push rod, 6-pulling head. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] like Figure 1-2 As shown, the present invention provides a pulling device for asphalt bonding strength testing, which mainly includes the following parts:

[0063] Base 1: used to install the bonded specimen to be tested. It adopts a rigid structure as a whole, which can firmly support the entire system and ensure that the equipment does not move during the test; Column 2: vertically installed on the base 1, providing support for the beam 3, ensuring that it can adjust its height along the axis of the column; Beam 3: horizontally arranged on the column 2, one end of which is fixedly connected to the tension sensor 4 by screws to support the entire loading system; Tension sensor 4: used to detect the tension signal generated during the pulling process, located below the beam 3, and its output end is connected to the host computer for data acquisition; Pneumatic push rod 5: installed below the tension sensor 4, capable of axial linear movement, and used to provide controllable pulling force; Pulling head 6: connected to the end of the pneumatic push rod 5, used to connect with the surface of the bonded specimen to be tested (such as asphalt coating) to achieve interface separation during the loading process.

[0064] In actual work, the operator installs the prepared bonded specimen to be tested on the base, and the pulling head is tightly fitted to the bonding surface of the bonded specimen to be tested or fixed by gluing. After the host computer sets the loading parameters (such as maximum tension, loading rate, etc.), it starts the pulling instrument. The electric push rod begins to move downward, driving the pulling head to apply tension to the bonded specimen to be tested. During the entire pulling process, the tension sensor records the change data of the pulling force over time in real time, and transmits the data to the host computer in real time for processing and display. When the interface bonding of the bonded specimen to be tested fails, the tension value reaches its peak value, which is the maximum bonding strength of the bonded specimen to be tested. The test ends automatically and the test data is automatically saved.

[0065] like Figure 3 and Figure 4 As shown, it includes a pneumatic loading system, a signal acquisition and control unit, and a host computer analysis system, which are used to achieve precise control of the mechanical response of the asphalt specimen during the drawing process and to collect bonding performance data.

[0066] 1. Pneumatic loading system

[0067] like Figure 3 As shown, the pneumatic loading module includes:

[0068] Air source, providing stable compressed air for the system;

[0069] The gas source regulating unit and drying filter water separator are used to stabilize the gas source pressure and remove moisture to ensure that the gas entering the system is clean and stable;

[0070] The electric proportional pressure valve receives the analog control signal (voltage) from the controller to achieve continuous regulation of the air pressure entering the actuator cylinder;

[0071] The solenoid reversing valve is controlled by the digital signal output by the controller, thereby controlling the inlet and outlet paths of the cylinder and realizing the forward and backward movement of the cylinder piston;

[0072] The front end of the actuator cylinder (or pneumatic push rod) is connected to the pulling head, which is used to connect with the asphalt specimen to be tested;

[0073] The tension sensor is installed at the fixed end of the actuator cylinder to collect the axial tension generated during the drawing process in real time;

[0074] The displacement sensor is installed on the moving axis of the cylinder and is used to record the displacement of the drawing head.

[0075] During system operation, the user sets parameters such as the target loading rate and peak pulling force through the host computer. The controller sends an analog voltage signal to the electric proportional valve based on these parameters, adjusting the inlet pressure of the actuator cylinder to achieve continuous, adjustable pulling loading. The electromagnetic reversing valve, in conjunction with the proportional valve, switches the inlet and outlet direction of the actuator cylinder, thus extending or retracting the push rod.

[0076] 2. Signal acquisition and control system

[0077] like Figure 2 As shown, the control core of the system is the signal acquisition controller, which is responsible for bidirectional processing of analog / digital signals. Its signal channels include:

[0078] Analog input channel A11: connects to the tension sensor to collect real-time tension signals;

[0079] Analog input channel A12: connects to the displacement sensor to collect the displacement of the pulling head;

[0080] Analog output channel A01: connected to the electric proportional pressure valve, used to output the control voltage signal to adjust the cylinder loading force;

[0081] Digital output channels D01 and D02: connected to the electromagnetic reversing valve to drive the switching of the cylinder's movement direction.

[0082] The signal acquisition controller is connected to the host computer, and the built-in test software of the host computer can realize the following functions:

[0083] Test plan parameter settings, such as loading speed, maximum tension, holding time, etc.;

[0084] Start / stop loading control;

[0085] Real-time graphical display of tension-displacement curve;

[0086] Automatically save test data and calculate mechanical indicators such as bonding strength.

[0087] The system achieves high response speed and control accuracy through pneumatic loading. The closed-loop feedback control circuit composed of sensor-controller-actuator can ensure stable force and reliable data during the pulling process. It is suitable for various asphalt bonding performance testing needs in the laboratory and on-site.

[0088] Control method:

[0089] In order to further improve the loading accuracy and control stability of the asphalt material pulling test system, this embodiment constructs a closed-loop control strategy and system implementation architecture based on model predictive control theory, which can dynamically adjust the loading force and suppress the influence of disturbances.

[0090] The system includes a pneumatic loading structure, a sensor feedback unit, a signal acquisition controller, and a host computer control module. The signal acquisition controller is used to collect analog signals from the tension sensor and displacement sensor in real time and input them into the MPC algorithm as system state variables to achieve predictive optimization control.

[0091] (1) System Modeling

[0092] The signal acquisition controller first discretizes the state space model of the loading system. Based on the dynamic response of the actuator cylinder under different control inputs (such as voltage or ventilation ratio), the following state space model is established:

[0093] x(k+1)=Ax(k)+Bu(k),y(k)=Cx(k)

[0094] Among them, the state variable x(k) represents the loading state of the system (such as the loading force F(k) and the loading speed F'(K)), the control input u(k) represents the input control quantity of the electric proportional pressure valve, and the output y(k) is the current tension value of the system. The system matrices A, B, and C can be obtained through experimental identification or least squares modeling.

[0095] In the present invention, the state vector is in the following form:

[0096]

[0097] (2) Rolling Forecast Mechanism

[0098] In each control cycle, starting from the current state x(k), the model is used to predict the system output at the next N moments

[0099] The prediction recursion is as follows:

[0100]

[0101] The entire prediction output vector is:

[0102]

[0103] Where N is the prediction step size, M is the control step size, and M≤N.

[0104] (3) Cost function construction

[0105] The control goal is to make the predicted output trajectory as close as possible to the target trajectory y ref , while suppressing the drastic changes of the control variables. Construct the following cost function:

[0106]

[0107] in

[0108] y ref (k+i): expected loading trajectory;

[0109] Δu(k+i)=u(k+j)-u(k+j-1): control increment;

[0110] λ: Controller smoothing weight factor, used to prevent control jumps.

[0111] The optimization is transformed into a standard quadratic optimization problem (QP) form:

[0112]

[0113] (4) Constraint design

[0114] To ensure the physical feasibility and system safety of the loading process, the following constraints are introduced:

[0115] Load range limit:

[0116] Control input limit: u min ≤u(k+i)≤u max

[0117] Control increment limit: |Δu(k+i)|≤Δu max

[0118] The above inequality constraints are uniformly written into matrix form to construct G and h, and the open source solver qpOASES is called to achieve fast solution.

[0119] (5) Online optimization solution and execution

[0120] In each sampling period, the following optimization process is performed:

[0121] 1) Get the current system state x(k);

[0122] 2) Construct prediction models and inequality constraints;

[0123] 3) Construct the cost function and generate the QP problem;

[0124] 4) Use the solver to obtain the optimal control sequence U * ;

[0125] 5) Only execute the first control quantity u of the optimal sequence * (k), for driving an electric proportional pressure valve;

[0126] 6) Repeat the above steps in the next cycle.

[0127] (6) State update and anti-disturbance mechanism

[0128] After each control cycle, the controller will update the state according to the actual load value y(k) fed back by the tension sensor:

[0129] If some of the system states are unmeasurable, a Kalman filter can be introduced for estimation;

[0130] If there are persistent deviations, a disturbance observer can be used to correct the model error.

[0131] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A pulling device for testing the bonding strength of asphalt materials, characterized in that It includes a base, a column vertically arranged on the base and a beam horizontally installed on the column, wherein the free end of the beam is provided with a tension sensor, which is used to detect the tension signal generated during the pulling process of the bonded specimen to be tested. The pneumatic push rod is installed below the tension sensor and the pneumatic push rod can realize axial linear movement to provide controllable tensioning force. A displacement sensor for testing the displacement of the pneumatic push rod is provided on one side of the pneumatic push rod. The pulling head is connected to the end of the pneumatic push rod and is used to connect with the surface of the bonded specimen to be tested to achieve interface separation during the loading process of the pneumatic push rod. During the test, the bonded specimen to be tested is installed on the base, and the pulling head is tightly fitted to the bonding surface of the bonded specimen to be tested or fixed by bonding. The pneumatic push rod is started to drive the pulling head to apply tension to the bonded specimen to be tested, thereby realizing the bonding strength test of the asphalt material.

2. A control method for a pulling device for testing the bonding strength of asphalt materials according to claim 1, characterized in that The specific process involves building a model predictive control (MPC) closed-loop architecture, consisting of a pneumatic loading structure, a sensor feedback unit, a signal acquisition controller, and a host computer. The controller collects tension and displacement in real time as state variables and inputs them into the MPC, enabling predictive and optimal control of the electric proportional pressure valve, ensuring that the force trajectory is smoothly tracked according to the set target. Based on a discrete state-space model, the control algorithm makes a rolling prediction of future outputs and constructs a quadratic cost function using "tracking error minimization + control increment penalty." Simultaneously, constraints such as the output range, input amplitude, and input increment are used to suppress sudden changes in valve opening and overshoot. Only the first optimal control variable in each cycle is executed and iterated with a window shift. This results in shorter adjustment times and smaller overshoots under disturbances and model uncertainty. While meeting established test parameters, the system ensures stable force and reliable data, effectively improving the accuracy and repeatability of bond strength testing. (1) System Modeling The signal acquisition controller first discretizes the state space model of the loading system and establishes the following state space model based on the dynamic response of the pneumatic cylinder under different control inputs: x(k+1)=Ax(k)+Bu(k), y(k)=Cx(k) Wherein, the state variable x(k) represents the loading state of the system, the control input u(k) represents the input control quantity of the electric proportional pressure valve, the output y(k) is the current tension value of the system, and the system matrices A, B, and C are obtained through experimental identification or least squares modeling. The state vector is selected as follows: (2) Rolling Forecast Mechanism In each control cycle, starting from the current state x(k), the model is used to predict the system output at the next N moments The prediction recursion is as follows: The entire prediction output vector is: Where N is the prediction step size, M is the control step size, and M≤N; (3) Cost function construction The control goal is to make the predicted output trajectory as close as possible to the target trajectory y ref , while suppressing the drastic changes of the control variables, and constructing the following cost function: in y ref (k+i): expected loading trajectory; Δi(k+i)=u(k+j)-u(k+j-1): control increment; λ: controller smoothing weight factor, used to prevent control jumps; The optimization is transformed into a standard quadratic optimization problem (QP) form: subject to GU≤h (4) Constraint design To ensure the physical feasibility and system safety of the loading process, the following constraints are introduced: Load range limit: Control input limit: u min ≤u(k+i)≤u max Control increment limit: |Δu(k+i)|≤Δu max The above inequality constraints are uniformly written into matrix form to construct G and h, and the open source solver qpOASES is called to achieve fast solution; (5) Online optimization solution and execution In each sampling period, the following optimization process is performed: 1) Get the current system state x(k); 2) Construct prediction models and inequality constraints; 3) Construct the cost function and generate the QP problem; 4) Use the solver to obtain the optimal control sequence U * ; 5) Only execute the first control quantity u of the optimal sequence * (k)), for driving an electric proportional pressure valve; 6) Repeat the above steps in the next cycle; (6) State update and anti-disturbance mechanism After each control cycle, the controller will update the state according to the actual load value y(k) fed back by the tension sensor: If part of the system state is unmeasurable, a Kalman filter can be introduced for estimation; If there are persistent deviations, a disturbance observer can be used to correct the model error.

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