Linear friction welding heat input modeling analysis method and system based on vibration parameters

Abstract

The invention discloses a linear friction welding heat input modeling analysis method and system based on vibration parameters, and the method comprises the following steps: building a time-varying displacement function of a welding interface according to the amplitude and frequency based on the simple harmonic vibration characteristic of a linear friction welding machine, and obtaining the instantaneous speed and statistical characteristic value of the welding interface; defining the instantaneous friction power of the welding interface, and introducing a non-linear friction coefficient which is constructed as a dynamic function about the instantaneous temperature of the interface and the absolute value of the instantaneous speed; based on the instantaneous friction power, introducing heat conversion efficiency and deducting plastic energy consumption for material plastic deformation, establishing a net heat power expression per unit time, and integrating time to obtain total heat input; and an equivalent thermal damping power model is constructed, the average thermal damping power is made to be equal to the average net thermal power, and an equivalent thermal damping coefficient representing the heat energy dissipation capacity of the welding system is obtained. According to the method, the process parameter optimization efficiency and the active control capability of the welding process can be improved.

Description

Technical Field

[0001] This invention belongs to the field of welding engineering and thermal analysis technology, and specifically relates to a method and system for modeling and analyzing the thermal input of linear friction welding based on vibration parameters. Background Technology

[0002] Linear friction welding (LFW) is a typical solid-state joining technology. Its welding process involves applying axial pressure to the workpiece while simultaneously applying high-frequency reciprocating linear motion along the contact surface, achieving rapid interface heating, plastic deformation, and metallurgical bonding. Compared to traditional fusion welding methods, LFW does not produce a molten pool, resulting in a dense joint structure and a small heat-affected zone, making it suitable for joining high-strength, high-hardness, or difficult-to-weld materials. In recent years, with the increasing demand for welding quality, energy efficiency control, and intelligent welding systems, LFW technology has gradually moved from the laboratory to industrial applications. However, controlling the heat input distribution during the welding process remains a technical bottleneck, stemming from the complexity and unobservability of the heat source mechanism. The welding heat source mainly originates from vibration and friction, involving multiple coupling factors, including vibration frequency, amplitude, friction coefficient, contact pressure, and material thermal properties. Changes in any of these parameters can lead to changes in interfacial heat output, thereby affecting the weld formation mechanism and connection performance.

[0003] Currently, the industry mainly obtains heat information through the following means: (1) Infrared thermal imaging, which can measure the surface temperature field, but the resolution and response speed are limited, and it cannot reflect the actual temperature of the interface; (2) Thermocouple embedding method, which can provide the temperature change of a certain point over time, but the placement of the points is limited and it is easily affected by disturbances; (3) Finite element simulation, which can be used to predict thermal processes, but requires a large amount of experimental data for calibration and has high parameter sensitivity.

[0004] Chinese invention patent application CN110008554A discloses a method for predicting and optimizing the formation of friction stir welds based on numerical simulation and deep learning. The method includes: Step 1, setting three simulation experiments as a data test set; Step 2, calculating the material flow field and temperature field distribution during welding; Step 3, calculating the fracture failure of the friction stir welding tool under different parameters, and calculating the weld formation quality and defect distribution under different parameters; Step 4, using a generative adversarial network deep learning model to traverse the weld formation results of all process parameters and welding tool structures to obtain the optimal weld formation result under the premise of ensuring reliable operation of the welding tool.

[0005] The scheme achieved good results under specific working conditions, but its heat input model mainly relies on numerical simulation results, lacks in-depth analysis of the friction heat generation mechanism, and does not adequately consider the nonlinear changes in the friction coefficient. Summary of the Invention

[0006] This invention provides a method and system for modeling and analyzing the thermal input of linear friction welding based on vibration parameters, aiming to solve the shortcomings of existing technologies in nonlinear modeling, coupling mechanism description and feedback control capabilities.

[0007] To address the aforementioned technical problems, this invention proposes a linear friction welding heat input modeling and analysis method based on vibration parameters, comprising the following steps: Based on the simple harmonic vibration characteristics of the linear friction welding machine, a displacement function of the welding interface as a function of time is established according to the amplitude and frequency, and the instantaneous velocity and statistical characteristic values ​​of the welding interface are obtained. The instantaneous frictional power of the welding interface is defined, wherein a nonlinear friction coefficient is introduced, which is constructed as a dynamic function of the instantaneous temperature and the absolute value of the instantaneous velocity of the interface. Based on the instantaneous frictional power, the heat conversion efficiency is introduced and the plastic energy consumption for the plastic deformation of the material is subtracted to establish the net heat power expression per unit time, and the total heat input is obtained by integrating over time. An equivalent thermal damping power model is constructed, and the average thermal damping power is equal to the average net heat power to obtain the equivalent thermal damping coefficient characterizing the heat dissipation capacity of the welding system.

[0008] Preferably, the method further includes the following steps: the calculated total heat input or equivalent thermal damping coefficient is input as a feedback variable into the control system of the linear friction welding machine, and the active control of the welding thermal cycle is achieved by adjusting the amplitude or frequency of the linear friction welding machine.

[0009] Preferably, the statistical characteristic value includes the squared mean of velocity, and the instantaneous velocity and the squared mean of velocity are expressed as follows:

[0010]

[0011] In the formula, Instantaneous velocity Let f be the squared average of the velocity, f be the vibration frequency, A be the amplitude, and t be the time.

[0012] Preferably, the dynamic function of the nonlinear friction coefficient is expressed as follows:

[0013] In the formula, The friction coefficient is nonlinear. The initial friction coefficient, For temperature sensitivity coefficient, For interface temperature, For speed sensitivity coefficient, Let t be the instantaneous velocity and t be the time.

[0014] Preferably, the interface temperature is linearly extrapolated by accumulated heat under low-temperature approximation conditions, and the expression is:

[0015] In the formula, The initial temperature, The temperature rise coefficient, To accumulate heat.

[0016] Preferably, the initial friction coefficient, temperature sensitivity coefficient, and velocity sensitivity coefficient are obtained by any of the following methods: Fitting based on welding experiment data; The finite element simulation data is used in combination with genetic algorithms or machine learning algorithms for training and identification.

[0017] Preferably, the expression for the net heat power per unit time is constructed as follows:

[0018] In the formula, Here, is the nonlinear friction coefficient, p is the axial pressure per unit area of ​​the weld interface, and S is the contact area. Instantaneous velocity This represents the net heat power per unit time. For heat conversion efficiency, This is for instantaneous plastic energy consumption.

[0019] Preferably, the instantaneous plastic energy dissipation is proportional to the square of the instantaneous velocity, as expressed below:

[0020] In the formula, This is the plastic work coefficient related to the rheological properties of the material.

[0021] Preferably, the average thermal damping power is defined as By setting the average thermal damping power to equal the average net heat power, the equivalent thermal damping coefficient can be obtained. The nonlinear friction coefficient is taken as an equivalent constant and higher-order plastic energy dissipation terms are ignored. The simplified calculation formula for the equivalent thermal damping coefficient is as follows:

[0022] In the formula, This is the equivalent thermal damping coefficient. For heat conversion efficiency, is the equivalent friction coefficient, p is the axial pressure per unit area of ​​the welding interface, S is the contact area, f is the vibration frequency, and A is the amplitude.

[0023] On the other hand, the present invention also proposes a linear friction welding heat input modeling and analysis system based on vibration parameters, the system being used to implement the modeling and analysis method as described in the first aspect of the present invention, comprising: The motion model construction module is used to establish the displacement function of the welding interface as a function of time based on the simple harmonic vibration characteristics of the linear friction welding machine, according to the input amplitude and frequency, and to calculate and obtain the instantaneous velocity and statistical characteristic value of the welding interface accordingly. The nonlinear friction analysis module is used to define the instantaneous friction power of the welding interface and construct a nonlinear friction coefficient model, which is configured as a dynamic function of the instantaneous temperature and the absolute value of the instantaneous velocity of the interface. The net heat power calculation module is used to establish a net heat power expression per unit time based on the instantaneous friction power, by introducing the heat conversion efficiency and deducting the plastic energy consumption for the plastic deformation of the material, and by integrating the net heat power expression in the time domain to obtain the total heat input. The equivalent damping derivation module is used to construct an equivalent thermal damping power model. By setting the average thermal damping power equal to the average net heat power, it calculates and outputs the equivalent thermal damping coefficient, which characterizes the heat dissipation capacity of the welding system.

[0024] Compared with the prior art, the present invention has the following technical effects: 1. The modeling and analysis method proposed in this invention establishes a quantitative relationship between vibration parameters, friction power, and heat input, successfully realizing real-time estimation of heat and assessment of heat dissipation capacity in the linear friction welding process. It not only reflects the coupling mechanism between interface motion and heat energy conversion but also possesses good scalability and parameter adaptability.

[0025] 2. The modeling and analysis method proposed in this invention introduces a thermo-velocity coupled friction model and a thermal damping mechanism. Compared with thermal analysis methods based on empirical or simplified single-factor models, this method fully considers the material softening caused by the increase in interface temperature and the dynamic influence of relative velocity changes on friction behavior. It can more accurately reflect the nonlinear physical mechanism of mechanical energy to heat energy conversion during linear friction welding, significantly reducing the error in heat input estimation. It can more accurately predict heat input trends and temperature rise levels, significantly improving the controllability and intelligence level of the welding process.

[0026] 3. The modeling and analysis method proposed in this invention simplifies the complex nonlinear thermo-mechanical coupling process into a single, quantifiable physical index by deriving the equivalent thermal damping coefficient. This coefficient establishes an analytical relationship between process parameters such as vibration frequency, amplitude, and pressure and the system's heat dissipation capacity, providing process engineers with a theoretical tool for intuitively evaluating the heat generation efficiency of welding systems and significantly lowering the threshold for process analysis.

[0027] 4. The modeling and analysis method proposed in this invention can calculate the interface heat input state in real time by collecting the motion parameters (frequency, amplitude, pressure) of the equipment. This makes it possible to embed the heat input model into the welding machine controller, thereby constructing a "predictive-feedback" closed-loop control system, realizing active control of the welding heat cycle, and effectively solving the problem of poor welding quality consistency under traditional open-loop control. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the modeling and analysis method described in this invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present application and with reference to the accompanying drawings.

[0030] Example 1 This embodiment describes a linear friction welding heat input modeling and analysis method based on vibration parameters, such as... Figure 1 As shown, it includes the following steps one through four: Step 1: Based on the simple harmonic vibration characteristics of the linear friction welding machine, establish the displacement function of the welding interface as a function of time according to the amplitude and frequency, and obtain the instantaneous velocity and statistical characteristic value of the welding interface.

[0031] This embodiment first requires mathematical modeling of the mechanical motion during linear friction welding. The core of linear friction welding lies in generating frictional heat through the high-frequency reciprocating motion of the workpiece contact surface. To achieve analytical calculation of the heat input, this embodiment idealizes the actual motion of the welding machine as a simple harmonic linear vibration model, setting the vibration direction of the welding interface as... The shaft, with the vibration center as the origin, assumes a stable vibration process and ignores waveform distortion caused by insufficient equipment rigidity. Based on the set process parameters—amplitude… (Unit: m) and vibration frequency (Unit: Hz), defining the weld interface at any given time. Relative displacement function (unit: s) for:

[0032] This formula describes the trajectory of the weld interface as it changes sinusoidally over time.

[0033] Based on kinematic principles, the displacement function Regarding time Find the first derivative to obtain the instantaneous relative velocity at the weld interface. :

[0034] Furthermore, by differentiating the instantaneous velocity, we can obtain the instantaneous acceleration. :

[0035] Instantaneous velocity It not only determines the instantaneous power of frictional heat generation, but its frequency characteristics also directly affect the fluctuation characteristics of heat flow.

[0036] To enable subsequent steps in calculating the average frictional power and deriving the equivalent thermal damping coefficient of the system, this embodiment further performs statistical analysis on the instantaneous velocity, calculating its value over one vibration cycle. ( The statistical characteristic values ​​within the range include the squared mean of velocity and the absolute mean of velocity.

[0037] Further calculate the squared average of the speeds and absolute value average The expression is as follows:

[0038]

[0039] Among them, the average square of the velocity is closely related to the kinetic energy and viscous dissipation (or plastic energy dissipation) of the system, while the average absolute value represents the average distance traveled by the welding interface per unit time, which is directly related to the calculation of friction work.

[0040] Through the above steps, this embodiment establishes a system based on fundamental process parameters. To key kinematic variables The analytical relationship laid the theoretical foundation for the subsequent introduction of nonlinear friction coefficient and calculation of net heat power.

[0041] Step 2: Define the instantaneous frictional power of the welding interface, where a nonlinear friction coefficient is introduced. This nonlinear friction coefficient is constructed as a dynamic function of the instantaneous temperature and the absolute value of the instantaneous velocity of the interface.

[0042] After establishing the kinematic model of the welding interface, this step further constructs a frictional power model describing the heat source generation mechanism. Unlike traditional models that often treat the friction coefficient as a constant, this embodiment fully considers the dynamic changes in the interface state during linear friction welding and introduces a nonlinear friction coefficient to improve the physical realism of the heat input calculation.

[0043] In this embodiment, let the pressure per unit area of ​​the welding interface be p, the contact area be S, and the instantaneous frictional force of the welding interface be... and instantaneous frictional power They are respectively:

[0044]

[0045] To accurately reflect the tribological behavior of the weld interface during high-speed friction and heating, this embodiment uses the nonlinear friction coefficient. Build for instantaneous temperature of the interface and the absolute value of instantaneous velocity The dynamic function of friction coefficient, in this embodiment, is expressed as follows:

[0046] In the formula, The friction coefficient is nonlinear. The initial friction coefficient characterizes the basic frictional properties of the material at room temperature and under static / low-speed conditions. The temperature sensitivity coefficient characterizes the effect of interface temperature on temperature. The rate of change in the coefficient of friction due to material softening increases. For interface temperature, is the velocity sensitivity coefficient, characterizing the dynamic effect of relative sliding velocity on friction behavior (such as stick-slip effect or lubricating film formation), and t is time. To ensure the above model can be adapted to different welding materials (such as titanium alloys, nickel-based superalloys, etc.), key parameters in the formula... Calibration is required. In this embodiment, these parameters are obtained using any of the following methods: Fitting is performed based on welding experimental data; a series of variable parameter welding process experiments are carried out, and actual data such as friction force and temperature are collected. Mathematical tools such as least squares method are used to perform regression analysis on the data, so as to fit the optimal parameter set.

[0047] The finite element simulation data is used in combination with genetic algorithms or machine learning algorithms for training and identification; a finite element simulation model of the welding process is established to generate a large amount of simulation data under working conditions, and the parameters are optimized by combining genetic algorithms, or machine learning algorithms (such as neural networks) are used to train and identify a set of parameters that can characterize the frictional properties of the material.

[0048] In the friction coefficient model, the interface temperature It is a variable that changes in real time. Considering that it is difficult and there is a lag in the real-time and accurate measurement of interface temperature in actual welding control systems, this embodiment adopts an energy-temperature feedback mechanism to simplify calculations and achieve rapid prediction.

[0049] Under the low-temperature approximation conditions (i.e., the initial stage of welding or before a violent phase transformation occurs), assuming that the increase in interface temperature is proportional to the accumulated heat, it can be approximated using linear extrapolation as follows:

[0050] In the formula, The initial temperature, The temperature rise coefficient, To accumulate heat.

[0051] This step successfully established a nonlinear power model containing a closed-loop coupling mechanism of "velocity-heat-temperature-friction", providing core algorithmic support for the accurate integration of net heat power in subsequent steps.

[0052] Step 3: Based on the instantaneous frictional power, introduce the heat conversion efficiency and subtract the plastic energy consumption for material plastic deformation to establish the net heat power expression per unit time, and integrate over time to obtain the total heat input.

[0053] Having clarified the generation mechanism of frictional power, this embodiment further considers the losses and distribution during the energy conversion process and establishes a calculation model for net heat power. Since not all the mechanical work generated by friction is converted into heat energy for heating the interface, a portion of it will be consumed by the plastic flow deformation of the material at high temperatures, and the system has certain limitations in heat conversion efficiency, the total power must be corrected.

[0054] This embodiment defines net thermal power. This refers to the effective heat flow used to practically increase the welding interface temperature and maintain thermal equilibrium. Based on the law of conservation of energy, heat conversion efficiency is introduced. And plastic energy dissipation for plastic deformation of materials The constructed expression is as follows:

[0055] In the formula, Here, is the nonlinear friction coefficient, p is the axial pressure per unit area of ​​the weld interface, and S is the contact area. Instantaneous velocity This represents the net heat power per unit time. For heat conversion efficiency, This is for instantaneous plastic energy consumption.

[0056] Under the high strain rate conditions of linear friction welding, the interface metal undergoes intense plastic deformation, requiring energy consumption. This embodiment assumes that the instantaneous plastic energy consumption is related to the kinetic energy of the relative motion of the interface, i.e., proportional to the square of the instantaneous velocity. Its mathematical model is expressed as:

[0057] In the formula, The plastic work coefficient is related to the rheological properties of the material. This parameter is related to the high-temperature rheological properties of the welding material (such as yield strength and viscosity) and is used to quantify the mechanical energy loss during the material deformation process.

[0058] To achieve direct calculation from process input parameters (frequency, amplitude) to heat output, the instantaneous velocity expression from step one and the nonlinear friction coefficient expression from step two are substituted into the net heat power expression, resulting in:

[0059] The formation of welded joints depends on the cumulative effect of energy. This requires obtaining the total heat input throughout the welding process or within a specific time period. The instantaneous net heat power needs to be integrated over the time domain. The formula for calculating the total heat over the time interval [0, t] is:

[0060] In practical digital analysis systems, this integration process can be discretized and solved using numerical integration methods (such as the trapezoidal rule or Simpson's rule), thereby tracking the energy accumulation state during the welding process in real time and providing data support for subsequent thermal damping analysis and process control.

[0061] Step 4: Construct an equivalent thermal damping power model, setting the average thermal damping power to equal the average net heat power, and obtain the equivalent thermal damping coefficient that characterizes the heat dissipation capacity of the welding system.

[0062] To apply the complex nonlinear thermal power model described above to practical welding control systems (such as PID control or adaptive control), this embodiment proposes a model simplification method based on the principle of energy equivalence. By analogy between the heat generation process and the viscous damping dissipation process in mechanical vibration, a comprehensive parameter characterizing the system's heat dissipation capacity—the equivalent thermal damping coefficient—is derived.

[0063] In this embodiment, it is assumed that the heat generation behavior of the linear friction welding system can be macroscopically equivalent to a damped system proportional to the square of the velocity. The instantaneous thermal damping power is defined as follows:

[0064] in, For instantaneous thermal damping power, The equivalent thermal damping coefficient is to be determined, and its physical meaning is the magnitude of the equivalent "resistance" in converting mechanical motion into heat energy.

[0065] Combined with the average squared velocity calculated in step one The average thermal damping power is obtained by averaging the instantaneous thermal damping power over one oscillation cycle. :

[0066] Specifically, average thermal damping power Defined as By setting the average thermal damping power equal to the average net heat power, the equivalent thermal damping coefficient can be obtained as follows:

[0067] This formula shows that, It is related to the vibration frequency and amplitude The dynamic parameter is inversely proportional to the square of the value.

[0068] In practical engineering applications, nonlinear factors are typically simplified to facilitate rapid estimation and real-time control. Assuming a stable welding phase: nonlinear friction coefficient... The fluctuation is small, and it can be approximated as an equivalent constant. Ignore higher-order plastic energy dissipation terms The impact on average power. Under these simplified conditions, the average net heat power... for:

[0069] Substituting the simplified average net heat power into the above... In general expressions:

[0070] In the formula, This is the equivalent thermal damping coefficient. For heat conversion efficiency, Here, is the equivalent friction coefficient, p is the axial pressure per unit area of ​​the weld interface, S is the contact area, f is the vibration frequency, and A is the amplitude. This formula is used to measure the heat dissipation capacity of a welding system.

[0071] This simplified formula clearly reveals the parameter coupling mechanism in the linear friction welding process: the equivalent thermal damping coefficient. With axial pressure It is directly proportional to the product of vibration frequency and amplitude, and inversely proportional to the product of vibration frequency and amplitude. This means that in a control strategy that maintains a constant heat input, increasing the vibration frequency or amplitude will reduce the system's equivalent thermal damping. This parameter provides a quantitative indicator for optimizing the process parameters of the welding system and can be used as a feedback variable input into the control algorithm to achieve active regulation of the welding thermal cycle.

[0072] In one embodiment of the present invention, after obtaining the equivalent thermal damping coefficient characterizing the heat dissipation capacity of the welding system, the method further includes the following steps: the calculated total heat input or equivalent thermal damping coefficient is input as a feedback variable into the control system of the linear friction welding machine, and the active control of the welding thermal cycle is achieved by adjusting the amplitude or frequency of the linear friction welding machine.

[0073] This embodiment further calculates the total heat input obtained from the above steps. Or the derived equivalent thermal damping coefficient As a key state variable, it is integrated into the control system of the linear friction welding machine to construct a heat input prediction-feedback adjustment closed loop, thereby achieving active control of welding quality.

[0074] The specific implementation process is as follows: The calculation algorithm module described in steps one through four above is embedded in the control unit of the linear friction welding machine. The system collects the welding machine's operating parameters (current frequency) in real time. Current amplitude Axial pressure ) and auxiliary data acquired through sensors (such as ambient temperature) The current cumulative heat input is calculated in real time with a sampling period of milliseconds. and real-time equivalent thermal damping coefficient .

[0075] Based on the welding procedure qualification results, preset the target control curve or threshold range: Heat input control mode: Set the target total heat input Or a target thermal power curve. For example, for heat-sensitive materials, limit the maximum heat input to prevent grain coarsening; for difficult-to-weld materials, ensure the minimum heat input is achieved to guarantee metallurgical bonding.

[0076] Damping characteristic control mode: Set target equivalent thermal damping coefficient .because This reflects the energy dissipation characteristics of the system and maintains Stability helps ensure the stability of the welding process.

[0077] The control system compares the real-time calculated value with the target value and calculates the deviation signal. It generates adjustment commands through a PID controller or fuzzy logic controller to dynamically adjust the actuator parameters (mainly amplitude) of the welding machine. and frequency The adjustment logic is as follows: Scenario 1: Adjustment when heat input is insufficient If real-time monitoring is detected A reading below the preset trajectory indicates insufficient heat generation at the interface. Based on the derivation in step three, it can be seen that thermal power is related to frequency. and amplitude Positive correlation. At this time, the control system outputs a command to increase the amplitude. Or increase the vibration frequency This increases instantaneous frictional power and accelerates heat accumulation.

[0078] Scenario 2: Parameter optimization based on equivalent thermal damping coefficient If the equivalent thermal damping coefficient is monitored in real time A mutation occurs, according to the simplified formula in step four. The control system can automatically fine-tune the increase. The product of these factors is used to reduce the equivalent damping and restore the welding process to its optimal viscoplastic rheological state.

[0079] Through the aforementioned active control steps, this embodiment overcomes the welding quality fluctuation problem caused by traditional linear friction welding relying solely on open-loop parameter settings (fixed frequency, fixed amplitude). Utilizing the mathematical model of this invention as a "soft sensor," it achieves "transparent" observation and precise control of the interface heat input, which cannot be directly measured, significantly improving the consistency of joint performance.

[0080] Example 2 This embodiment is a linear friction welding heat input modeling and analysis system based on vibration parameters. The system is used to implement the modeling and analysis method described in Embodiment 1, including: The motion model construction module is used to establish the displacement function of the welding interface as a function of time based on the simple harmonic vibration characteristics of the linear friction welding machine, according to the input amplitude and frequency, and to calculate and obtain the instantaneous velocity and statistical characteristic value of the welding interface.

[0081] The nonlinear friction analysis module is used to define the instantaneous friction power of the welding interface and construct a nonlinear friction coefficient model, which is configured as a dynamic function of the instantaneous temperature and the absolute value of the instantaneous velocity of the interface.

[0082] The net heat power calculation module is used to establish a net heat power expression per unit time based on the instantaneous friction power, by introducing the heat conversion efficiency and deducting the plastic energy consumption for the plastic deformation of the material, and to integrate the net heat power expression in the time domain to obtain the total heat input.

[0083] The equivalent damping derivation module is used to construct an equivalent thermal damping power model. By setting the average thermal damping power equal to the average net heat power, it calculates and outputs the equivalent thermal damping coefficient, which characterizes the heat dissipation capacity of the welding system.

[0084] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A linear friction welding heat input modeling and analysis method based on vibration parameters, characterized in that, Includes the following steps: Based on the simple harmonic vibration characteristics of the linear friction welding machine, a displacement function of the welding interface as a function of time is established according to the amplitude and frequency, and the instantaneous velocity and statistical characteristic values ​​of the welding interface are obtained. The instantaneous frictional power of the welding interface is defined, wherein a nonlinear friction coefficient is introduced, which is constructed as a dynamic function of the instantaneous temperature and the absolute value of the instantaneous velocity of the interface. Based on the instantaneous frictional power, the heat conversion efficiency is introduced and the plastic energy consumption for the plastic deformation of the material is subtracted to establish the net heat power expression per unit time, and the total heat input is obtained by integrating over time. An equivalent thermal damping power model is constructed, and the average thermal damping power is equal to the average net heat power to obtain the equivalent thermal damping coefficient characterizing the heat dissipation capacity of the welding system.

2. The method according to claim 1, characterized in that, The method further includes the following steps: the calculated total heat input or equivalent thermal damping coefficient is input as a feedback variable into the control system of the linear friction welding machine, and the active control of the welding thermal cycle is achieved by adjusting the amplitude or frequency of the linear friction welding machine.

3. The method according to claim 1, characterized in that, The statistical characteristic value includes the squared mean of velocity, and the instantaneous velocity and the squared mean of velocity are expressed as follows: In the formula, Instantaneous velocity Let f be the squared average of the velocity, f be the vibration frequency, A be the amplitude, and t be the time.

4. The method according to claim 1, characterized in that, The dynamic function of the nonlinear friction coefficient is expressed as follows: In the formula, The friction coefficient is nonlinear. The initial friction coefficient, For temperature sensitivity coefficient, For interface temperature, For speed sensitivity coefficient, Let t be the instantaneous velocity and t be the time.

5. The method according to claim 4, characterized in that, The interface temperature, under low-temperature approximation conditions, is linearly extrapolated by accumulated heat, and the expression is: In the formula, The initial temperature, The temperature rise coefficient, To accumulate heat.

6. The method according to claim 4, characterized in that, The initial friction coefficient, temperature sensitivity coefficient, and velocity sensitivity coefficient are obtained through any of the following methods: Fitting based on welding experiment data; The finite element simulation data is used in combination with genetic algorithms or machine learning algorithms for training and identification.

7. The method according to claim 1, characterized in that, The expression for the net heat power per unit time is constructed as follows: In the formula, Here, is the nonlinear friction coefficient, p is the axial pressure per unit area of ​​the weld interface, and S is the contact area. Instantaneous velocity This represents the net heat power per unit time. For heat conversion efficiency, This is for instantaneous plastic energy consumption.

8. The method according to claim 7, characterized in that, The instantaneous plastic energy dissipation is proportional to the square of the instantaneous velocity, as expressed below: In the formula, This is the plastic work coefficient related to the rheological properties of the material.

9. The method according to claim 1, characterized in that, The average thermal damping power is defined as By setting the average thermal damping power to equal the average net heat power, the equivalent thermal damping coefficient can be obtained. The nonlinear friction coefficient is taken as an equivalent constant and higher-order plastic energy dissipation terms are ignored. The simplified calculation formula for the equivalent thermal damping coefficient is as follows: In the formula, This is the equivalent thermal damping coefficient. For heat conversion efficiency, is the equivalent friction coefficient, p is the axial pressure per unit area of ​​the welding interface, S is the contact area, f is the vibration frequency, and A is the amplitude.

10. A linear friction welding heat input modeling and analysis system based on vibration parameters, characterized in that, The system is used to implement the modeling and analysis method as described in any one of claims 1-9, including: The motion model construction module is used to establish the displacement function of the welding interface as a function of time based on the simple harmonic vibration characteristics of the linear friction welding machine, according to the input amplitude and frequency, and to calculate and obtain the instantaneous velocity and statistical characteristic value of the welding interface accordingly. The nonlinear friction analysis module is used to define the instantaneous friction power of the welding interface and construct a nonlinear friction coefficient model, which is configured as a dynamic function of the instantaneous temperature and the absolute value of the instantaneous velocity of the interface. The net heat power calculation module is used to establish a net heat power expression per unit time based on the instantaneous friction power, by introducing the heat conversion efficiency and deducting the plastic energy consumption for the plastic deformation of the material, and by integrating the net heat power expression in the time domain to obtain the total heat input. The equivalent damping derivation module is used to construct an equivalent thermal damping power model. By setting the average thermal damping power equal to the average net heat power, it calculates and outputs the equivalent thermal damping coefficient, which characterizes the heat dissipation capacity of the welding system.

Images